Research ArticleSystem Design and Optimisation Study on a Novel CCHP SystemIntegrated with a Hybrid Energy Storage System and an ORC

Jie Ji 1 ZujunDing 1 XinXia 1 YeqinWang1HuiHuang1 ChuZhang1 TianPeng 1

Xiaolu Wang1 Muhammad Shahzad Nazir1 Yue Zhang1 Baolian Liu1 Xiaoying Jia1

Ruisheng Li2 and Yaodong Wang3

1Huaiyin Institute of Technology Huaiyin Jiangsu 223002 China2Merz Court Newcastle University Newcastle upon Tyne NE1 7RU UK3Energy Systems Department of Engineering Durham University Durham DH1 3LE UK

Correspondence should be addressed to Zujun Ding 46768424qqcom

Received 2 April 2020 Accepted 6 July 2020 Published 26 September 2020

Academic Editor Marcin Mrugalski

Copyright copy 2020 Jie Ji et al is is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

For achieving higher energy transferring efficiency from the resources to the load the Combined Cooling Heating and Power(CCHP) systems have been widely researched and applied as an efficient approach e key idea of this study is designing a novelstructure of a hybrid CCHP system and evaluating its performance In this research there is a hybrid energy storage unitenhancing the whole systemrsquos operation flexibility while supplying cooling heating and power An ORC system is integrated intothe CCHP system which takes responsibility of absorbing the low-temperature heat source for electricity generation ere are afew research studies focusing on the CCHP systemsrsquo performance with this structure In order to evaluate the integrated systemrsquosperformance investigation and optimisation work has been conducted with the approaches of experimental studies andmodellingsimulation e integrated systemrsquos configuration the model building process of several key components the optimisationmethod and the case studies are discussed and analysed in this studye design of the integrated system and the control strategyare displayed in detail Several sets of dynamic energy demand profiles are selected to evaluate the performance of the integratedsysteme simulation study of the system supplying selected scenarios of loads is conducted A comprehensive evaluation reportindicates that the systemrsquos efficiency during each study process differs while supplying different loads e results include thepower supplied by each component the energy consumed by each type of load and the efficiency improvements It is found thatthe integrated system fully satisfies the selected domestic loads and various selected scenarios of loads with high efficiencyCompared to conventional power plants or CHP systems the system efficiency enhancement comes from higher amount ofrecovery waste heat Especially the ORC system can absorb the low-temperature heat source for electricity generation Comparedto the original following electrical load (FEL) control strategy the optimisation process brings overall efficiency improvementse systemrsquos overall efficiency was increased by from 3 318 285 1711 889 and 217 in the second case studiesrough the whole study the main challenge lies within the design and the energy management of the integrated system

1 Introduction

e combined cooling heating and power CCHP systemshave the function of recovery of the waste heat to supplycooling and heat to satisfy the electrical power demandsimultaneously [1] By the other means the trigenerationsystems have advantages of high-efficiency energy trans-ferring compared to the conventional energy systems [2]

e features of high efficiency and low emission which likelycontribute significantly to reduce emissions draw a lot ofattention in the recent decades

Utilizing the recovered heat is a core technology for theCCHP systems A general point about the CCHP systemsrsquocapacity is that the power rate lower than 1MWe is regardedas small-scale applications Systems with smaller capacity arecalled small-scale or microscale CCHP systems [3] e

HindawiComplexityVolume 2020 Article ID 1278751 14 pageshttpsdoiorg10115520201278751

small-scale and microscale CCHP systems are a populartopic as the household energy demand or small commercialapplications are suitable loads for this kind of distributedenergy systems and such remote loads cannot get a high-efficiency power supply in common case [4] Adollahi dis-cusses amultiobjective optimisation on the residential small-scale CCHP system In this CCHP system the energy effi-ciency the total installation cost rate and the cost rate of theenvironment are optimised simultaneously [5] AnotherCCHP system designed for small-sale applications is pre-sented by Maraver aiming at improving the use of biomassresources and distributed generation In that research theconcepts of artificial thermal efficiency and the primaryenergy saving ratio are introduced to evaluate the designedsystem e results of the research show that a small-scaleCCHP plant based on biomass combustion is an efficientenergy generation system [6]

A multienergy products system normally needs anadvanced control system [7] e control strategy of thecomplex energy system is also an area many recent re-search studies focus on [8] e most popular and rea-sonable operation strategy for CCHP systems is followingthermal load (FTL) [9] and following electrical load (FEL)[10] A domestic CHP system which is also integrated withan electrical energy storage unit is using the FEL strategyto offer electricity and heat to the demands e finalresults show that with the support of the FEL strategy theintegrated CHP system has much higher efficiencycompared to conventional off-grid CHP systems For themost popular FTL and FEL control strategy applyingeither of the strategies often causes power surplus whichbrings an efficiency reduction [11] In a similar system adynamic programming operation strategy was developedto distribute the generated energy which brings 19 ef-ficiency improvement [12] For solving this problemintegration of energy storage units is proposed to improvepart load efficiency and operation flexibility [13] A dis-tributed power generation system (DPGS) with an electricenergy storage (EES) system has a similar idea with thecurrent studye results show that the DPGS-EES systemwas working properly to meet the demand [14]ere is anattempt of integrating the hybrid electrical energy storagewith a domestic CHP system e results show that Langpresents a CCHP system with the thermal energy storagesystem driven by a microturbine e results of the sys-temrsquos operation show that the primary energy con-sumption of that CCHP system is significantly lower thanthat of conventional separate production systems [15] Ahybrid photovoltaic-trigeneration system is presented byAmir which contains batteries in its system structure ebatteries store the electrical energy from the solar panelsand support the energy supplying of the system eoverall efficiency is demonstrated to be higher and lessemissions are generated compared to the current com-bination of centralized power plants and householdheating technologies [16] Among all the energy storagetechnologies the supercapacitors have the ability to ac-commodate a large amount of energy in a short time Ithas a similar operation principle and function with

batteries [17] When a distributed energy system has adynamic electrical load supercapacitors are suitable to beintegrated in because of its high-power density A CHPsystem combined with hybrid electrical energy storagewas presented and simulated by Chen et al In that systemthe hybrid electrical energy storage system which containslead acid batteries and supercapacitors supported thesystem to satisfy the dynamic load with limited energysource with high efficiency [18] Among all the attempts afew systems successfully combine the traditional batteriesand supercapacitors with the CCHP systems

ere is always mismatch between the energy supply andthe demand especially in the multipower products gener-ation systems [19] Lots of researchers have been working onthis topic to avoid excess output of electricity or heat [20] Inthe CCHP systems the waste heat is recovered for improvingthe systemrsquos efficiency Besides that it is well proven in actualoperational condition that there is a potential part of wasteheat which can be further extracted and transferred to usefulenergy by the ORC systems [21] e idea of combining theCCHP system with ORC has been tested by many formerresearches Zare combines a geothermal-driven CCHPsystem with the ORC system e ORC system is used toabsorb part of system internal heat for higher power output[22] InWangrsquos CCHP-ORC system the fuel consumption isreduced [23] e ORC systems have proven that part oflow-temperature heat can be transferred to available elec-tricity [23 24]

e integrated system studied in this research com-bines the trigeneration system a novel electricity storagesystem and the ORC system is new designed inte-grated system manages to meet the energy demand at highefficiency with its optimised operation strategy e aim ofthis study is to establish a feasible solution to tackle thechallenge ie to investigate and develop an integratedsystem of combined trigeneration energy storage andORC to generate multienergy products (power heat andcooling) with high efficiency to reduce the energy con-sumption and reduce the carbon emissions from thestand-alone systems is system structure shows itsnovelty for integrating the core components- a CCHPsystem a hybrid energy storage unit and an ORC systemCombining the ORC system and energy storage unit(including supercapacitors) with the trigeneration systemis the very first attempt e research focuses on buildingthis integrated system and optimisation of the systemrsquosoperation Simulation on this integrated system is builtfrom the results of the experimental tests e optimi-sation process is the main content of this article

e detailed objectives of the research are as follows

(1) Investigating the state of the art of current energysystems considering what types of the energy systemand which technologies can reach the aim of theresearch and selecting the energy system andtechnologies for the study

(2) Selecting the case study of a stand-alone user de-mand designing an integrated system which canmeet the demand and setting up computational

2 Complexity

models to simulate and evaluate the systemrsquosperformance

(3) Carrying the optimisation process on the systemrsquosoperation on several scenarios displaying the bestoptimisation of the systemrsquos performance

e main contribution of this article includes thefollowing

(1) Designing a novel structure of the hybrid CCHPsystem and evaluating its performance

(2) Investigating the control approach for the multi-object energy system and obtaining an optimisedresult

With this newly designed CCHP system integrated withhybrid energy storage units and an ORC system the systemshould have the ability to satisfy the electricity heating andcooling demand simultaneously with high efficiency

2 System Configuration

21 System Design e schematic diagram of these com-ponents is displayed in the Figure 1 where the energy-producing lines are distinguished by different colours isintegrated system combines the key components to supplymultitype of energy products

e CCHP system includes a diesel engine a generator aheat recovery system and an absorption chiller e enginedrives the generator to supply electricity e generatoroperates with the energy storage units to supply energy forthe electrical load e electricity generated from the ORC isan auxiliary electricity source for this system (electricityfrom ORC is only available when the engine is operating)e function of electricity supplying of the system is giventhe highest priority in the system design because of thefrequent changing electricity demand e heat recoverysystem collects and transfers energy from waste heat tosatisfy the heating demand and cooling demand Part of thelow-temperature heat is absorbed by the ORC system Whenthe recovery waste heat is not sufficient the electricity is usedto support the heating supply or cooling supply e ad-sorption refrigerator produces most of the cooling energyCooling demand has lower priority than the other twoenergy demands

22 Size of the System is study aims at building a micro-CCHP system and supplying according loads Specificationsabout the diesel engine and the generator are shown in theTable 1 e power rate of the engine is 88 kW and theoutput rate of the generator is 65 kW Except the generatedelectricity part of available power from the gas emissionsand coolant water of the engine is also considered as a powersource to the integrated system e theoretical full loadpeak power output of the system including electricityheating and cooling is estimated 176 kW Power demandunder 13 kW is a reasonable range for this integrated systemto supply e peak power demand of the loads selected inthis study is around 10 kW

23 Control Strategy Implementation of the operationstrategies aims at achieving a high efficiency to supply theload In the control strategies which have been conductedand tested the most popular two investigated controllingstrategies for similar systems are following the thermal load(FTL) and following the electric load (FEL) [25]e selectedload lasts for 24 hours During this period the power de-mand has an unpredictable swing according to differenthuman activities erefore the control strategy of thisintegrated system applies FEL e operation logical dia-gram is displayed in Figure 2 Supplying the electrical energydemand has the highest priority e allocation of electricitystoring and consumption depends on the power demand ofthe load when the engine is operating When the electricaldemand is fully satisfied the waste heat recovery systemsupplies energy for the thermal part and cooling part ethermal load is satisfied by the recovered waste heat ecooling energy is also supplied by the adsorption chillerwhich also extracts the recovered waste heat as energysource

Based on the amount of energy demand the integratedsystem is designed to operate as several states e electricitydemand has the highest priority when the electricity de-mand is not fully supplied and the ORC system uses therecovered heat and transfers them to electricity (when theengine is operating) When the electricity demand is fullysupplied the integrated system supplied thermal energysecondly e supplying of cooling energy has the lowestpriority When the thermal energy generated is not sufficientfor the load the energy stored in the energy storage systemsupports the generation of thermal energy

24 Optimisation Principles of the Systemrsquos Operation eoriginal strategy makes the engine to operate with a flexiblerange of load A similar optimisation process is applied inthe former research [26] e optimisation process shown inthis section aims at reaching the systemrsquos maximum effi-ciency by making the engine to work at maximum power

e engine in the system is the prime mover It transfersthe energy in the fuel into the system with certain efficiency

Pfuel middot ηengine Pengine (1)

e energy storage units contain the supercapacitors andthe batteries e power of the energy storage equals thesummation of the two componentsrsquo power

Ps PSU + PBT (2)

e equation expresses the recovery energy amountfrom the engine

PR Pfuel middot ηrecovery (3)

Part of the recovery energy is used to support the op-eration of the ORC

PORC PR(1 minus α minus β) (4)

When the generator is operating the energy using tosupply the load consists of the power of generator energy

Complexity 3

Fuel

Coolantfluid

exhaustgas heat

exchanger

Engine

ORC Coolingload

Generator

Thermalload

ElectricLoad

ACDC

Batteries

PBT

Ps

PSU

PTndashST

PR

Pengine

Pfuel

PTndashL

PCndashLPORC

PLOAD

Supercapacitor

DCAC

Heat storagetank

Energy flow joint

Electrical energy flow

Electrical energy flow

Thermal energy flow

Cooling energy flow

Figure 1 System design

Table 1 Information of the engine and generator used in this study

Engine

YANMAR TF 120MType Horizontal water-cooled 4-cycle diesel engine

Bore x stroke mm 92times 96Displacement Liters 0638Cont output kW (hp) 77 (105)Rated output kW (hp) 88 (12)Revolutions rpm 2400

Starting system Hand startCooling system Radiator

Lubricating system Fully sealed forced lubrication with a trochoid pump and hydraulic regulator valveCombustion system Direct injectionFuel consumption lith 28Fuel tank capacity Liters 11

Generator set

Capacity kVA 65kW 65

Frequency Hz Available in either 50Hz or 60HzRevolutions rpm 3000 for 50Hz3600 for 60HzNo of phases Single phasePower factor 1

Excitation system Brushless self-excitation systemDrive system V-belt derive

Voltage (standard) V 220

4 Complexity

storage system and the electric power output from the ORCDuring the systemrsquos operation the recovery energy is used tosatisfy the heat demand e thermal storage unit reservespart of the excessive energy

PLOAD PGE + PS + POE

PR middot α + PTminusSL PTminusL

PR middot β middot ηC PCminusL

(5)

When the generator is not operating the energy storageunits support the electric loads Because of lacking recoveryenergy the heating load and the cooling load are supplied bythe energy stored in the storage units

PLOAD PS

PET + PTminusSL PTminusL

PR middot β PEC

(6)

Supposing that the systemrsquos operation follows theelectric load (FEL) the optimisation of the system aims atminimizing the enginersquos operation time From calculatingthe difference between the minimum engine operation timeand the compensational engine operation time a betteroperation for the integrated system can be obtained

tminprime 1113938

te

0 Pengine (t)dt

Pengine (max)

tprime tminprime minus tmin

1113946tprime

0POE(t)dt Pengine (max) middot tprime

(7)

241 Limiting Conditions ere are a few restrictive con-ditions in the optimisation process for guaranteeing suffi-cient power for the load For ensuring the batteriesrsquo lifespanthe state of charge of the batteries is limited within the ragefrom 40 to 100 At the same time the power supplied bythe system must be equal or higher than the power demande thermal energy stored in the storage units is required tofulfill the thermal load and the cooling load e recoverythermal energy is allocated to three parts which are re-sponsible for the thermal storage heating demand andcooling demand respectively

40lt SOC(t)lt 100

PGE + PS + POE gePLOAD

PR middot α + PTminusSL gePTminusL

PR middot βgePCminusL

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(8)

e saved energy is expressed by the following equatione energy saving is

Qsaving 1113946te

0

Pengine(t)

ηenginedt minus Pengine(max)tmin (9)

3 Model Building of the System

e principle of building the components is introduced inthis section where the process of model building isexpressede evaluation work and the optimisation processare completed by the simulation models in the Matlabsoftware environment e accuracy of the build

System start

SOC of theelectrical energy

storage stayabove 30

The power demand isless than 25kW

The power demand isless than 65kW

The hybrid energystorage units operateto supply electricity

using electrical applianceto satisfying the heat and

cooling demand

The generator andthe energy storage

supplies the electrical loadtogether supplied by extra

Generator and theenergy storage units

supplied the electrical unitswaste heat is recovered for

heating and coolingdemand supplying

System alarm operatesemergency relaysstops the system

Is electrical demandfully supplied

If power demand isfully supplied

Engine drive thegenerator to chargethe hybrid energy

storage units

Waste heat is coveredto supply heat and

cooling energy demandinsufficiency part is

supported by electricalappliance

The power demand isless than 10 kW

Yes Yes Yes

No NoNo

No

YesYes

YesNoNo

Figure 2 Control strategy of the system

Complexity 5

components is guaranteed by fitting the performance of thecomponents with their experimental tests results

31 Generator of the System e electricity generation ap-plied in this project is a diesel engine Equation (16) de-scribes the mathematical model of the engine

In the equation

TdΩ EIA

E KφΩ

Td Kφ

P TdΩ times η EIA times η

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)

e engine transfers the chemical energy to mechanicalenergy and then to electricity Model for this part is built inMatlab software Using the corresponding parameters therecovered heat rate is also given in Table 2

From the simulation results of the enginersquos model theenginersquos electrical power output and the recovery heatsimulation have error less than 142 ese results indicatethat the model can simulate the operation performance ofthe engine with a high accuracy

32 Heat Recovery System e model of the waste heatrecovery module is based on the experimental results givenby a former swan researcher In the research of the biofuelmicrotrigeneration system a previous researcher Hongdong

tested the engine with 5 types of oil which are shown in theFigure 3 [27] After fitting curve with the rests results theheat recovery rate from gas oilrsquos burning is modelled

e heat recovery system is simulated as a black box usinga performance curve to fit the experimental results ere arefive operation points which are 10 25 50 75 and 100load e available waste heat and the recovered heat energyhave a linear relationship based on curve analysis and themathematical model is shown by the following equations

Pr Pengine times 0364 + 09676 (11)

Figure 4 shows the comparison of test results and thesimulation under different load conditions

Based on the control strategy of this integrated systemmost of the recovery systemrsquos operations are settled when theengine is loaded over 60 It indicates that the error of therecovery systemrsquos model is around 02 to 175

33CoolingGeneration In this integrated system the chillerpart consists of an adsorption chiller and the cooling gen-eration appliance e cooling generation is considered intwo situations 1 when the engine is working the adsorptionchiller absorbed the recovered waste heat to generate coolingenergy 2 when the engine is off the cooling is transferredfrom the electrical appliance e cooling power out isshown in the following formula

When the engine is on PCminusL PR middot β1113858 1113859 middot ηC

When the engine is off PCminusL Pengine minus PSU minus PBT minus PEminusL1113960 1113961 middot ηEminusC

⎧⎨

⎩ (12)

34HybridEnergyStorage System epower requirement ofthe integrated system is a variable during the different times ofthe day For supplying reliable energy the integrated systemcombines the hybrid energy storage system as its energy backup to make up for the disadvantages of a distributed systeme hybrid energy storage units consist of the lead acidbatteries and the supercapacitors in this research

For optimal utilization of the energy stored in the energystorage units the size of the batteries and the supercapacitorsis calculated and selected

341 Battery e batteries have high energy density It isresponsible for supplying enough electricity to load e for-mula which expresses the operation of the lead acid batteriesusing state of charge as its variable is listed as follows [27]

SOC(t + 1) SOC(t) minus SOC(t) middot σ +IBat(t) middot Δt middot ηBat

CBat(t)

(13)

In the equation σ is the self-discharge rate of the batterywhose value is set as 02 per day CBat is the capacity of thebattery

e detail parameters of the batteries and the super-capacitor are shown in Table 3

342 Supercapacitor In this study the supercapacitors areselected for satisfying dynamic changing power demandespecifications of the supercapacitor are listed in Table 4 eformula of the supercapacitor is displayed in the followingequation

PSU 12

C middot U2

12ε middot

S

dU2

ε 2W

VE2

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(14)

Here ε is the dielectric constant E is the electric fieldintensity and C is the volume of the supercapacitor

35 ORC System

351 Working Fluid Selection e ORC system integratedin is designed for low-grade heat recovery e ORC systemis driven by the waste heat absorbed from the engine Severalkey factors have the most significance on the ORCrsquos

6 Complexity

performance which are source temperature working me-dium safety issues and problem of degradation For all theworking fluids the ODP GWP R134a and R245fa are ap-propriate to be applied in a small-scale ORC [28] R123a hasozone depletion potential which leads to aggravate the globalwarming e R245fa has similar chemical and physical

properties as a good candidate for the ORC system It also haszero-ozone depletion potential low toxicity no flammabilityand applicable critical temperature and pressure ereforeR245fa is selected as the working fluid in the ORC system AnORC system contains several basic components a heat sourceevaporator turbine condenser and pump

e waste heat recovered by this system consists of heatfrom the exhaust gas and the coolant of the engine Forachieving the highest efficiency during the heat transferringprocess the working fluid is selected as R245fais workingfluid has lower ebullition temperature which has lowerevaporating temperature than water Figure 5 shows the T-Sdiagram of R245fa in anORC cyclee R245fa is pumped tothe evaporator and its entropy and temperature are slightlyincreased before entering the evaporator When the workingfluid is leaving the evaporator the entropy of R245fa in-creases compared to its state before After that the R245fagoes into the turbine and the temperature decreases whenits entropy slightly increases

352 ORC Model Building Power output of the ORCsystem is modelled in this study and some similar ORCsystem equations are also applied in the authorrsquos formerresearch [26]

Table 2 Energy output in the simulation

Load of the engine () Engine efficiency () Error () Recovery rate simulation () Error ()0 0 0 0 010 78 113 3536 007925 1636 036 3538 002550 243 041 3539 002775 2753 082 3541 0138100 285 142 3544 0137

50

Croton oilJatropha oilRapeseed oil

Sunflower oilGas oil

45

40

35

30

25

20

15

100 20 40

Load condition ()60 80 100

Pow

er (k

W)

Figure 3 Recovered exhaust gas heat for cogeneration [27]

50

45

40

35

30

25

20

15

10

Pow

er (k

W)

0

681

04

592

02

175

20 40Load condition ()

60 80 100

Simulation resultsExperimental results

Figure 4 Simulation results comparing with the experimentalresults

Table 3 Specifications of the lead acid batteries

Parameter Mean valueNominal capacity 200AhNominal voltage 12V DCDepth of discharge 40Maximum state of charge 100Minimum state of charge 40Efficiency 85ndash95Self-discharge 20dayTemperature coefficient 06degCLifetime 5 years

Table 4 Parameters of the supercapacitor

Parameter Mean valueCapacity 63 FVoltage 125V DCResistance 18mΩTemperature minus40ndash60Life span 100000 hoursEnd of life reduction of capacity 20Maximum current 150AMaximum peak current 750ASelf-discharge 5030 days

Complexity 7

Wp morc h2 minus h1( 1113857 mORC h2s minus h1( 1113857

ηp

ηp h2s minus h1

h2 minus h1

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(15)

where Wp is the pumprsquos power mORC is the mass flow rateηp is the pumprsquos efficiency and h2 h1 are the inlet and outletfluid enthalpies

Qep Qro middot ξ morc h3 minus h2( 1113857 (16)

Heat accepted by the evaporator is Qep ξ is the evap-orator effectiveness Qro is the recovered thermal energy h2are the enthalpies of the working fluid at the outlet of theevaporator

e process of the condenser is expressed by the fol-lowing equation

Qcd morc h1 minus h4( 1113857 (17)

In the equation Qcd is the heat output of the condenserand h4 is the working fluid enthalpy at the outlet of theturbine

e turbine power output is

Wt morc h3 minus h4( 1113857 morc h3 minus h2( 1113857ηt

ηt h3 minus h4

h3 minus h4s

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(18)

In the equationWt is the turbine output power and ηt isthe isentropic efficiency of the turbine

e ORC systemrsquos overall efficiency is

ηORC Wt minus Wp

Qep

h3 minus h4s( 1113857ηt minus h2s minus h1( 1113857ηminus1p

h3 minus h2

Wt QroηORCηgen

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(19)

where ηORC is the efficiency of the ORC system

e thermal dynamic module of the ORC system wasdeveloped by the researcher from the same group Moredetails can be found in this research [29] In this study ageneral efficiency module is applied to represent the ORCsystemrsquos ability of transferring low-temperature waste heat

4 Case Study

e case study includes two parts of evaluation work on thesystem performance In the first case study the system isrequired to supply multitype of energy to a dynamicchanging domestic load Simulation of supplying dynamicload with this system is performed in a former research [26]However in this article the energy supplying of thesupercapacitor is imported It has higher accuracy and morerealistic power response compared to the former researchAlso the selected domestic load includes new load from asummer day and a winter day separately In the second casethe system is supplied to two sets of designed loads (sixscenarios of load) in two seasons is article evaluates thesystemrsquos performance of supplying energy to load throughthe two case studies

41 Case Study 1- High-Resolution Domestic Load SupplyingSimulation e case study shows the optimised operationof the integrated system meeting the power demand of adomestic load in two seasons e integrated system isregulated to supply three energy products for two varyingloads for 24 hourse load targets selected in this case studyshow a seasonal difference on power demand

In both simulations the control strategy applies theoptimised FEL power allocating strategy Electricity demandgets the priority e recovery heat is the available thermalenergy collected from the system Part of the recovered heatis utilised to supply cooling load For household domesticcooling load in this case the cooling load is a refrigeratore cooling load is simulated as a pulse-shape power de-mand e optimisation for the operation is applied in thesimulation which makes the operation time of the engine tobe optimised to a minimum value

411 Summer Day Case e first simulation is a summerday load e operation results are shown in Figure 6

In Figure 7 wave A shows the power demand of thedomestic load on a summer daye simulation results showthat the peak power demand is larger than the capacity of thesystem roughout the simulations the systemrsquos capacity isexpanded by with integrating the hybrid energy storageunit Wave B shows the enginersquos operation e engine isregulated to operate only at the peak power demand periode supercapacitors respond to high-density power demandIn the wave D it only operates when the power demand andpower supplying have sudden changee wave C shows thepower supplied by batteries which support most of thepower demand when the engine is not working Part of low-temperature waste heat is utilised by the ORC is part ofthermal energy transmitted to the ORC is converted toelectricity which increases the energy utilisation efficiency

0

20

40

60

80

100

120

140

160

180

095 105 115 125 135 145 155 165 175 185

Tem

pera

ture

(degC)

Entropy (kJkgndashk)

R 245fa T-S diagram

Pump

Evaporator

Expander

Condenser

Figure 5 T-S diagram of R245fa

8 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

models to simulate and evaluate the systemrsquosperformance

(3) Carrying the optimisation process on the systemrsquosoperation on several scenarios displaying the bestoptimisation of the systemrsquos performance

e main contribution of this article includes thefollowing

(1) Designing a novel structure of the hybrid CCHPsystem and evaluating its performance

(2) Investigating the control approach for the multi-object energy system and obtaining an optimisedresult

With this newly designed CCHP system integrated withhybrid energy storage units and an ORC system the systemshould have the ability to satisfy the electricity heating andcooling demand simultaneously with high efficiency

2 System Configuration

21 System Design e schematic diagram of these com-ponents is displayed in the Figure 1 where the energy-producing lines are distinguished by different colours isintegrated system combines the key components to supplymultitype of energy products

e CCHP system includes a diesel engine a generator aheat recovery system and an absorption chiller e enginedrives the generator to supply electricity e generatoroperates with the energy storage units to supply energy forthe electrical load e electricity generated from the ORC isan auxiliary electricity source for this system (electricityfrom ORC is only available when the engine is operating)e function of electricity supplying of the system is giventhe highest priority in the system design because of thefrequent changing electricity demand e heat recoverysystem collects and transfers energy from waste heat tosatisfy the heating demand and cooling demand Part of thelow-temperature heat is absorbed by the ORC system Whenthe recovery waste heat is not sufficient the electricity is usedto support the heating supply or cooling supply e ad-sorption refrigerator produces most of the cooling energyCooling demand has lower priority than the other twoenergy demands

22 Size of the System is study aims at building a micro-CCHP system and supplying according loads Specificationsabout the diesel engine and the generator are shown in theTable 1 e power rate of the engine is 88 kW and theoutput rate of the generator is 65 kW Except the generatedelectricity part of available power from the gas emissionsand coolant water of the engine is also considered as a powersource to the integrated system e theoretical full loadpeak power output of the system including electricityheating and cooling is estimated 176 kW Power demandunder 13 kW is a reasonable range for this integrated systemto supply e peak power demand of the loads selected inthis study is around 10 kW

23 Control Strategy Implementation of the operationstrategies aims at achieving a high efficiency to supply theload In the control strategies which have been conductedand tested the most popular two investigated controllingstrategies for similar systems are following the thermal load(FTL) and following the electric load (FEL) [25]e selectedload lasts for 24 hours During this period the power de-mand has an unpredictable swing according to differenthuman activities erefore the control strategy of thisintegrated system applies FEL e operation logical dia-gram is displayed in Figure 2 Supplying the electrical energydemand has the highest priority e allocation of electricitystoring and consumption depends on the power demand ofthe load when the engine is operating When the electricaldemand is fully satisfied the waste heat recovery systemsupplies energy for the thermal part and cooling part ethermal load is satisfied by the recovered waste heat ecooling energy is also supplied by the adsorption chillerwhich also extracts the recovered waste heat as energysource

Based on the amount of energy demand the integratedsystem is designed to operate as several states e electricitydemand has the highest priority when the electricity de-mand is not fully supplied and the ORC system uses therecovered heat and transfers them to electricity (when theengine is operating) When the electricity demand is fullysupplied the integrated system supplied thermal energysecondly e supplying of cooling energy has the lowestpriority When the thermal energy generated is not sufficientfor the load the energy stored in the energy storage systemsupports the generation of thermal energy

24 Optimisation Principles of the Systemrsquos Operation eoriginal strategy makes the engine to operate with a flexiblerange of load A similar optimisation process is applied inthe former research [26] e optimisation process shown inthis section aims at reaching the systemrsquos maximum effi-ciency by making the engine to work at maximum power

e engine in the system is the prime mover It transfersthe energy in the fuel into the system with certain efficiency

Pfuel middot ηengine Pengine (1)

e energy storage units contain the supercapacitors andthe batteries e power of the energy storage equals thesummation of the two componentsrsquo power

Ps PSU + PBT (2)

e equation expresses the recovery energy amountfrom the engine

PR Pfuel middot ηrecovery (3)

Part of the recovery energy is used to support the op-eration of the ORC

PORC PR(1 minus α minus β) (4)

When the generator is operating the energy using tosupply the load consists of the power of generator energy

Complexity 3

Fuel

Coolantfluid

exhaustgas heat

exchanger

Engine

ORC Coolingload

Generator

Thermalload

ElectricLoad

ACDC

Batteries

PBT

Ps

PSU

PTndashST

PR

Pengine

Pfuel

PTndashL

PCndashLPORC

PLOAD

Supercapacitor

DCAC

Heat storagetank

Energy flow joint

Electrical energy flow

Electrical energy flow

Thermal energy flow

Cooling energy flow

Figure 1 System design

Table 1 Information of the engine and generator used in this study

Engine

YANMAR TF 120MType Horizontal water-cooled 4-cycle diesel engine

Bore x stroke mm 92times 96Displacement Liters 0638Cont output kW (hp) 77 (105)Rated output kW (hp) 88 (12)Revolutions rpm 2400

Starting system Hand startCooling system Radiator

Lubricating system Fully sealed forced lubrication with a trochoid pump and hydraulic regulator valveCombustion system Direct injectionFuel consumption lith 28Fuel tank capacity Liters 11

Generator set

Capacity kVA 65kW 65

Frequency Hz Available in either 50Hz or 60HzRevolutions rpm 3000 for 50Hz3600 for 60HzNo of phases Single phasePower factor 1

Excitation system Brushless self-excitation systemDrive system V-belt derive

Voltage (standard) V 220

4 Complexity

storage system and the electric power output from the ORCDuring the systemrsquos operation the recovery energy is used tosatisfy the heat demand e thermal storage unit reservespart of the excessive energy

PLOAD PGE + PS + POE

PR middot α + PTminusSL PTminusL

PR middot β middot ηC PCminusL

(5)

When the generator is not operating the energy storageunits support the electric loads Because of lacking recoveryenergy the heating load and the cooling load are supplied bythe energy stored in the storage units

PLOAD PS

PET + PTminusSL PTminusL

PR middot β PEC

(6)

Supposing that the systemrsquos operation follows theelectric load (FEL) the optimisation of the system aims atminimizing the enginersquos operation time From calculatingthe difference between the minimum engine operation timeand the compensational engine operation time a betteroperation for the integrated system can be obtained

tminprime 1113938

te

0 Pengine (t)dt

Pengine (max)

tprime tminprime minus tmin

1113946tprime

0POE(t)dt Pengine (max) middot tprime

(7)

241 Limiting Conditions ere are a few restrictive con-ditions in the optimisation process for guaranteeing suffi-cient power for the load For ensuring the batteriesrsquo lifespanthe state of charge of the batteries is limited within the ragefrom 40 to 100 At the same time the power supplied bythe system must be equal or higher than the power demande thermal energy stored in the storage units is required tofulfill the thermal load and the cooling load e recoverythermal energy is allocated to three parts which are re-sponsible for the thermal storage heating demand andcooling demand respectively

40lt SOC(t)lt 100

PGE + PS + POE gePLOAD

PR middot α + PTminusSL gePTminusL

PR middot βgePCminusL

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(8)

e saved energy is expressed by the following equatione energy saving is

Qsaving 1113946te

0

Pengine(t)

ηenginedt minus Pengine(max)tmin (9)

3 Model Building of the System

e principle of building the components is introduced inthis section where the process of model building isexpressede evaluation work and the optimisation processare completed by the simulation models in the Matlabsoftware environment e accuracy of the build

System start

SOC of theelectrical energy

storage stayabove 30

The power demand isless than 25kW

The power demand isless than 65kW

The hybrid energystorage units operateto supply electricity

using electrical applianceto satisfying the heat and

cooling demand

The generator andthe energy storage

supplies the electrical loadtogether supplied by extra

Generator and theenergy storage units

supplied the electrical unitswaste heat is recovered for

heating and coolingdemand supplying

System alarm operatesemergency relaysstops the system

Is electrical demandfully supplied

If power demand isfully supplied

Engine drive thegenerator to chargethe hybrid energy

storage units

Waste heat is coveredto supply heat and

cooling energy demandinsufficiency part is

supported by electricalappliance

The power demand isless than 10 kW

Yes Yes Yes

No NoNo

No

YesYes

YesNoNo

Figure 2 Control strategy of the system

Complexity 5

components is guaranteed by fitting the performance of thecomponents with their experimental tests results

31 Generator of the System e electricity generation ap-plied in this project is a diesel engine Equation (16) de-scribes the mathematical model of the engine

In the equation

TdΩ EIA

E KφΩ

Td Kφ

P TdΩ times η EIA times η

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)

e engine transfers the chemical energy to mechanicalenergy and then to electricity Model for this part is built inMatlab software Using the corresponding parameters therecovered heat rate is also given in Table 2

From the simulation results of the enginersquos model theenginersquos electrical power output and the recovery heatsimulation have error less than 142 ese results indicatethat the model can simulate the operation performance ofthe engine with a high accuracy

32 Heat Recovery System e model of the waste heatrecovery module is based on the experimental results givenby a former swan researcher In the research of the biofuelmicrotrigeneration system a previous researcher Hongdong

tested the engine with 5 types of oil which are shown in theFigure 3 [27] After fitting curve with the rests results theheat recovery rate from gas oilrsquos burning is modelled

e heat recovery system is simulated as a black box usinga performance curve to fit the experimental results ere arefive operation points which are 10 25 50 75 and 100load e available waste heat and the recovered heat energyhave a linear relationship based on curve analysis and themathematical model is shown by the following equations

Pr Pengine times 0364 + 09676 (11)

Figure 4 shows the comparison of test results and thesimulation under different load conditions

Based on the control strategy of this integrated systemmost of the recovery systemrsquos operations are settled when theengine is loaded over 60 It indicates that the error of therecovery systemrsquos model is around 02 to 175

33CoolingGeneration In this integrated system the chillerpart consists of an adsorption chiller and the cooling gen-eration appliance e cooling generation is considered intwo situations 1 when the engine is working the adsorptionchiller absorbed the recovered waste heat to generate coolingenergy 2 when the engine is off the cooling is transferredfrom the electrical appliance e cooling power out isshown in the following formula

When the engine is on PCminusL PR middot β1113858 1113859 middot ηC

When the engine is off PCminusL Pengine minus PSU minus PBT minus PEminusL1113960 1113961 middot ηEminusC

⎧⎨

⎩ (12)

34HybridEnergyStorage System epower requirement ofthe integrated system is a variable during the different times ofthe day For supplying reliable energy the integrated systemcombines the hybrid energy storage system as its energy backup to make up for the disadvantages of a distributed systeme hybrid energy storage units consist of the lead acidbatteries and the supercapacitors in this research

For optimal utilization of the energy stored in the energystorage units the size of the batteries and the supercapacitorsis calculated and selected

341 Battery e batteries have high energy density It isresponsible for supplying enough electricity to load e for-mula which expresses the operation of the lead acid batteriesusing state of charge as its variable is listed as follows [27]

SOC(t + 1) SOC(t) minus SOC(t) middot σ +IBat(t) middot Δt middot ηBat

CBat(t)

(13)

In the equation σ is the self-discharge rate of the batterywhose value is set as 02 per day CBat is the capacity of thebattery

e detail parameters of the batteries and the super-capacitor are shown in Table 3

342 Supercapacitor In this study the supercapacitors areselected for satisfying dynamic changing power demandespecifications of the supercapacitor are listed in Table 4 eformula of the supercapacitor is displayed in the followingequation

PSU 12

C middot U2

12ε middot

S

dU2

ε 2W

VE2

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(14)

Here ε is the dielectric constant E is the electric fieldintensity and C is the volume of the supercapacitor

35 ORC System

351 Working Fluid Selection e ORC system integratedin is designed for low-grade heat recovery e ORC systemis driven by the waste heat absorbed from the engine Severalkey factors have the most significance on the ORCrsquos

6 Complexity

performance which are source temperature working me-dium safety issues and problem of degradation For all theworking fluids the ODP GWP R134a and R245fa are ap-propriate to be applied in a small-scale ORC [28] R123a hasozone depletion potential which leads to aggravate the globalwarming e R245fa has similar chemical and physical

properties as a good candidate for the ORC system It also haszero-ozone depletion potential low toxicity no flammabilityand applicable critical temperature and pressure ereforeR245fa is selected as the working fluid in the ORC system AnORC system contains several basic components a heat sourceevaporator turbine condenser and pump

e waste heat recovered by this system consists of heatfrom the exhaust gas and the coolant of the engine Forachieving the highest efficiency during the heat transferringprocess the working fluid is selected as R245fais workingfluid has lower ebullition temperature which has lowerevaporating temperature than water Figure 5 shows the T-Sdiagram of R245fa in anORC cyclee R245fa is pumped tothe evaporator and its entropy and temperature are slightlyincreased before entering the evaporator When the workingfluid is leaving the evaporator the entropy of R245fa in-creases compared to its state before After that the R245fagoes into the turbine and the temperature decreases whenits entropy slightly increases

352 ORC Model Building Power output of the ORCsystem is modelled in this study and some similar ORCsystem equations are also applied in the authorrsquos formerresearch [26]

Table 2 Energy output in the simulation

Load of the engine () Engine efficiency () Error () Recovery rate simulation () Error ()0 0 0 0 010 78 113 3536 007925 1636 036 3538 002550 243 041 3539 002775 2753 082 3541 0138100 285 142 3544 0137

50

Croton oilJatropha oilRapeseed oil

Sunflower oilGas oil

45

40

35

30

25

20

15

100 20 40

Load condition ()60 80 100

Pow

er (k

W)

Figure 3 Recovered exhaust gas heat for cogeneration [27]

50

45

40

35

30

25

20

15

10

Pow

er (k

W)

0

681

04

592

02

175

20 40Load condition ()

60 80 100

Simulation resultsExperimental results

Figure 4 Simulation results comparing with the experimentalresults

Table 3 Specifications of the lead acid batteries

Parameter Mean valueNominal capacity 200AhNominal voltage 12V DCDepth of discharge 40Maximum state of charge 100Minimum state of charge 40Efficiency 85ndash95Self-discharge 20dayTemperature coefficient 06degCLifetime 5 years

Table 4 Parameters of the supercapacitor

Parameter Mean valueCapacity 63 FVoltage 125V DCResistance 18mΩTemperature minus40ndash60Life span 100000 hoursEnd of life reduction of capacity 20Maximum current 150AMaximum peak current 750ASelf-discharge 5030 days

Complexity 7

Wp morc h2 minus h1( 1113857 mORC h2s minus h1( 1113857

ηp

ηp h2s minus h1

h2 minus h1

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(15)

where Wp is the pumprsquos power mORC is the mass flow rateηp is the pumprsquos efficiency and h2 h1 are the inlet and outletfluid enthalpies

Qep Qro middot ξ morc h3 minus h2( 1113857 (16)

Heat accepted by the evaporator is Qep ξ is the evap-orator effectiveness Qro is the recovered thermal energy h2are the enthalpies of the working fluid at the outlet of theevaporator

e process of the condenser is expressed by the fol-lowing equation

Qcd morc h1 minus h4( 1113857 (17)

In the equation Qcd is the heat output of the condenserand h4 is the working fluid enthalpy at the outlet of theturbine

e turbine power output is

Wt morc h3 minus h4( 1113857 morc h3 minus h2( 1113857ηt

ηt h3 minus h4

h3 minus h4s

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(18)

In the equationWt is the turbine output power and ηt isthe isentropic efficiency of the turbine

e ORC systemrsquos overall efficiency is

ηORC Wt minus Wp

Qep

h3 minus h4s( 1113857ηt minus h2s minus h1( 1113857ηminus1p

h3 minus h2

Wt QroηORCηgen

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(19)

where ηORC is the efficiency of the ORC system

e thermal dynamic module of the ORC system wasdeveloped by the researcher from the same group Moredetails can be found in this research [29] In this study ageneral efficiency module is applied to represent the ORCsystemrsquos ability of transferring low-temperature waste heat

4 Case Study

e case study includes two parts of evaluation work on thesystem performance In the first case study the system isrequired to supply multitype of energy to a dynamicchanging domestic load Simulation of supplying dynamicload with this system is performed in a former research [26]However in this article the energy supplying of thesupercapacitor is imported It has higher accuracy and morerealistic power response compared to the former researchAlso the selected domestic load includes new load from asummer day and a winter day separately In the second casethe system is supplied to two sets of designed loads (sixscenarios of load) in two seasons is article evaluates thesystemrsquos performance of supplying energy to load throughthe two case studies

41 Case Study 1- High-Resolution Domestic Load SupplyingSimulation e case study shows the optimised operationof the integrated system meeting the power demand of adomestic load in two seasons e integrated system isregulated to supply three energy products for two varyingloads for 24 hourse load targets selected in this case studyshow a seasonal difference on power demand

In both simulations the control strategy applies theoptimised FEL power allocating strategy Electricity demandgets the priority e recovery heat is the available thermalenergy collected from the system Part of the recovered heatis utilised to supply cooling load For household domesticcooling load in this case the cooling load is a refrigeratore cooling load is simulated as a pulse-shape power de-mand e optimisation for the operation is applied in thesimulation which makes the operation time of the engine tobe optimised to a minimum value

411 Summer Day Case e first simulation is a summerday load e operation results are shown in Figure 6

In Figure 7 wave A shows the power demand of thedomestic load on a summer daye simulation results showthat the peak power demand is larger than the capacity of thesystem roughout the simulations the systemrsquos capacity isexpanded by with integrating the hybrid energy storageunit Wave B shows the enginersquos operation e engine isregulated to operate only at the peak power demand periode supercapacitors respond to high-density power demandIn the wave D it only operates when the power demand andpower supplying have sudden changee wave C shows thepower supplied by batteries which support most of thepower demand when the engine is not working Part of low-temperature waste heat is utilised by the ORC is part ofthermal energy transmitted to the ORC is converted toelectricity which increases the energy utilisation efficiency

0

20

40

60

80

100

120

140

160

180

095 105 115 125 135 145 155 165 175 185

Tem

pera

ture

(degC)

Entropy (kJkgndashk)

R 245fa T-S diagram

Pump

Evaporator

Expander

Condenser

Figure 5 T-S diagram of R245fa

8 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

Fuel

Coolantfluid

exhaustgas heat

exchanger

Engine

ORC Coolingload

Generator

Thermalload

ElectricLoad

ACDC

Batteries

PBT

Ps

PSU

PTndashST

PR

Pengine

Pfuel

PTndashL

PCndashLPORC

PLOAD

Supercapacitor

DCAC

Heat storagetank

Energy flow joint

Electrical energy flow

Electrical energy flow

Thermal energy flow

Cooling energy flow

Figure 1 System design

Table 1 Information of the engine and generator used in this study

Engine

YANMAR TF 120MType Horizontal water-cooled 4-cycle diesel engine

Bore x stroke mm 92times 96Displacement Liters 0638Cont output kW (hp) 77 (105)Rated output kW (hp) 88 (12)Revolutions rpm 2400

Starting system Hand startCooling system Radiator

Lubricating system Fully sealed forced lubrication with a trochoid pump and hydraulic regulator valveCombustion system Direct injectionFuel consumption lith 28Fuel tank capacity Liters 11

Generator set

Capacity kVA 65kW 65

Frequency Hz Available in either 50Hz or 60HzRevolutions rpm 3000 for 50Hz3600 for 60HzNo of phases Single phasePower factor 1

Excitation system Brushless self-excitation systemDrive system V-belt derive

Voltage (standard) V 220

4 Complexity

storage system and the electric power output from the ORCDuring the systemrsquos operation the recovery energy is used tosatisfy the heat demand e thermal storage unit reservespart of the excessive energy

PLOAD PGE + PS + POE

PR middot α + PTminusSL PTminusL

PR middot β middot ηC PCminusL

(5)

When the generator is not operating the energy storageunits support the electric loads Because of lacking recoveryenergy the heating load and the cooling load are supplied bythe energy stored in the storage units

PLOAD PS

PET + PTminusSL PTminusL

PR middot β PEC

(6)

Supposing that the systemrsquos operation follows theelectric load (FEL) the optimisation of the system aims atminimizing the enginersquos operation time From calculatingthe difference between the minimum engine operation timeand the compensational engine operation time a betteroperation for the integrated system can be obtained

tminprime 1113938

te

0 Pengine (t)dt

Pengine (max)

tprime tminprime minus tmin

1113946tprime

0POE(t)dt Pengine (max) middot tprime

(7)

241 Limiting Conditions ere are a few restrictive con-ditions in the optimisation process for guaranteeing suffi-cient power for the load For ensuring the batteriesrsquo lifespanthe state of charge of the batteries is limited within the ragefrom 40 to 100 At the same time the power supplied bythe system must be equal or higher than the power demande thermal energy stored in the storage units is required tofulfill the thermal load and the cooling load e recoverythermal energy is allocated to three parts which are re-sponsible for the thermal storage heating demand andcooling demand respectively

40lt SOC(t)lt 100

PGE + PS + POE gePLOAD

PR middot α + PTminusSL gePTminusL

PR middot βgePCminusL

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(8)

e saved energy is expressed by the following equatione energy saving is

Qsaving 1113946te

0

Pengine(t)

ηenginedt minus Pengine(max)tmin (9)

3 Model Building of the System

e principle of building the components is introduced inthis section where the process of model building isexpressede evaluation work and the optimisation processare completed by the simulation models in the Matlabsoftware environment e accuracy of the build

System start

SOC of theelectrical energy

storage stayabove 30

The power demand isless than 25kW

The power demand isless than 65kW

The hybrid energystorage units operateto supply electricity

using electrical applianceto satisfying the heat and

cooling demand

The generator andthe energy storage

supplies the electrical loadtogether supplied by extra

Generator and theenergy storage units

supplied the electrical unitswaste heat is recovered for

heating and coolingdemand supplying

System alarm operatesemergency relaysstops the system

Is electrical demandfully supplied

If power demand isfully supplied

Engine drive thegenerator to chargethe hybrid energy

storage units

Waste heat is coveredto supply heat and

cooling energy demandinsufficiency part is

supported by electricalappliance

The power demand isless than 10 kW

Yes Yes Yes

No NoNo

No

YesYes

YesNoNo

Figure 2 Control strategy of the system

Complexity 5

components is guaranteed by fitting the performance of thecomponents with their experimental tests results

31 Generator of the System e electricity generation ap-plied in this project is a diesel engine Equation (16) de-scribes the mathematical model of the engine

In the equation

TdΩ EIA

E KφΩ

Td Kφ

P TdΩ times η EIA times η

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)

e engine transfers the chemical energy to mechanicalenergy and then to electricity Model for this part is built inMatlab software Using the corresponding parameters therecovered heat rate is also given in Table 2

From the simulation results of the enginersquos model theenginersquos electrical power output and the recovery heatsimulation have error less than 142 ese results indicatethat the model can simulate the operation performance ofthe engine with a high accuracy

32 Heat Recovery System e model of the waste heatrecovery module is based on the experimental results givenby a former swan researcher In the research of the biofuelmicrotrigeneration system a previous researcher Hongdong

tested the engine with 5 types of oil which are shown in theFigure 3 [27] After fitting curve with the rests results theheat recovery rate from gas oilrsquos burning is modelled

e heat recovery system is simulated as a black box usinga performance curve to fit the experimental results ere arefive operation points which are 10 25 50 75 and 100load e available waste heat and the recovered heat energyhave a linear relationship based on curve analysis and themathematical model is shown by the following equations

Pr Pengine times 0364 + 09676 (11)

Figure 4 shows the comparison of test results and thesimulation under different load conditions

Based on the control strategy of this integrated systemmost of the recovery systemrsquos operations are settled when theengine is loaded over 60 It indicates that the error of therecovery systemrsquos model is around 02 to 175

33CoolingGeneration In this integrated system the chillerpart consists of an adsorption chiller and the cooling gen-eration appliance e cooling generation is considered intwo situations 1 when the engine is working the adsorptionchiller absorbed the recovered waste heat to generate coolingenergy 2 when the engine is off the cooling is transferredfrom the electrical appliance e cooling power out isshown in the following formula

When the engine is on PCminusL PR middot β1113858 1113859 middot ηC

When the engine is off PCminusL Pengine minus PSU minus PBT minus PEminusL1113960 1113961 middot ηEminusC

⎧⎨

⎩ (12)

34HybridEnergyStorage System epower requirement ofthe integrated system is a variable during the different times ofthe day For supplying reliable energy the integrated systemcombines the hybrid energy storage system as its energy backup to make up for the disadvantages of a distributed systeme hybrid energy storage units consist of the lead acidbatteries and the supercapacitors in this research

For optimal utilization of the energy stored in the energystorage units the size of the batteries and the supercapacitorsis calculated and selected

341 Battery e batteries have high energy density It isresponsible for supplying enough electricity to load e for-mula which expresses the operation of the lead acid batteriesusing state of charge as its variable is listed as follows [27]

SOC(t + 1) SOC(t) minus SOC(t) middot σ +IBat(t) middot Δt middot ηBat

CBat(t)

(13)

In the equation σ is the self-discharge rate of the batterywhose value is set as 02 per day CBat is the capacity of thebattery

e detail parameters of the batteries and the super-capacitor are shown in Table 3

342 Supercapacitor In this study the supercapacitors areselected for satisfying dynamic changing power demandespecifications of the supercapacitor are listed in Table 4 eformula of the supercapacitor is displayed in the followingequation

PSU 12

C middot U2

12ε middot

S

dU2

ε 2W

VE2

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(14)

Here ε is the dielectric constant E is the electric fieldintensity and C is the volume of the supercapacitor

35 ORC System

351 Working Fluid Selection e ORC system integratedin is designed for low-grade heat recovery e ORC systemis driven by the waste heat absorbed from the engine Severalkey factors have the most significance on the ORCrsquos

6 Complexity

performance which are source temperature working me-dium safety issues and problem of degradation For all theworking fluids the ODP GWP R134a and R245fa are ap-propriate to be applied in a small-scale ORC [28] R123a hasozone depletion potential which leads to aggravate the globalwarming e R245fa has similar chemical and physical

properties as a good candidate for the ORC system It also haszero-ozone depletion potential low toxicity no flammabilityand applicable critical temperature and pressure ereforeR245fa is selected as the working fluid in the ORC system AnORC system contains several basic components a heat sourceevaporator turbine condenser and pump

e waste heat recovered by this system consists of heatfrom the exhaust gas and the coolant of the engine Forachieving the highest efficiency during the heat transferringprocess the working fluid is selected as R245fais workingfluid has lower ebullition temperature which has lowerevaporating temperature than water Figure 5 shows the T-Sdiagram of R245fa in anORC cyclee R245fa is pumped tothe evaporator and its entropy and temperature are slightlyincreased before entering the evaporator When the workingfluid is leaving the evaporator the entropy of R245fa in-creases compared to its state before After that the R245fagoes into the turbine and the temperature decreases whenits entropy slightly increases

352 ORC Model Building Power output of the ORCsystem is modelled in this study and some similar ORCsystem equations are also applied in the authorrsquos formerresearch [26]

Table 2 Energy output in the simulation

Load of the engine () Engine efficiency () Error () Recovery rate simulation () Error ()0 0 0 0 010 78 113 3536 007925 1636 036 3538 002550 243 041 3539 002775 2753 082 3541 0138100 285 142 3544 0137

50

Croton oilJatropha oilRapeseed oil

Sunflower oilGas oil

45

40

35

30

25

20

15

100 20 40

Load condition ()60 80 100

Pow

er (k

W)

Figure 3 Recovered exhaust gas heat for cogeneration [27]

50

45

40

35

30

25

20

15

10

Pow

er (k

W)

0

681

04

592

02

175

20 40Load condition ()

60 80 100

Simulation resultsExperimental results

Figure 4 Simulation results comparing with the experimentalresults

Table 3 Specifications of the lead acid batteries

Parameter Mean valueNominal capacity 200AhNominal voltage 12V DCDepth of discharge 40Maximum state of charge 100Minimum state of charge 40Efficiency 85ndash95Self-discharge 20dayTemperature coefficient 06degCLifetime 5 years

Table 4 Parameters of the supercapacitor

Parameter Mean valueCapacity 63 FVoltage 125V DCResistance 18mΩTemperature minus40ndash60Life span 100000 hoursEnd of life reduction of capacity 20Maximum current 150AMaximum peak current 750ASelf-discharge 5030 days

Complexity 7

Wp morc h2 minus h1( 1113857 mORC h2s minus h1( 1113857

ηp

ηp h2s minus h1

h2 minus h1

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(15)

where Wp is the pumprsquos power mORC is the mass flow rateηp is the pumprsquos efficiency and h2 h1 are the inlet and outletfluid enthalpies

Qep Qro middot ξ morc h3 minus h2( 1113857 (16)

Heat accepted by the evaporator is Qep ξ is the evap-orator effectiveness Qro is the recovered thermal energy h2are the enthalpies of the working fluid at the outlet of theevaporator

e process of the condenser is expressed by the fol-lowing equation

Qcd morc h1 minus h4( 1113857 (17)

In the equation Qcd is the heat output of the condenserand h4 is the working fluid enthalpy at the outlet of theturbine

e turbine power output is

Wt morc h3 minus h4( 1113857 morc h3 minus h2( 1113857ηt

ηt h3 minus h4

h3 minus h4s

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(18)

In the equationWt is the turbine output power and ηt isthe isentropic efficiency of the turbine

e ORC systemrsquos overall efficiency is

ηORC Wt minus Wp

Qep

h3 minus h4s( 1113857ηt minus h2s minus h1( 1113857ηminus1p

h3 minus h2

Wt QroηORCηgen

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(19)

where ηORC is the efficiency of the ORC system

e thermal dynamic module of the ORC system wasdeveloped by the researcher from the same group Moredetails can be found in this research [29] In this study ageneral efficiency module is applied to represent the ORCsystemrsquos ability of transferring low-temperature waste heat

4 Case Study

e case study includes two parts of evaluation work on thesystem performance In the first case study the system isrequired to supply multitype of energy to a dynamicchanging domestic load Simulation of supplying dynamicload with this system is performed in a former research [26]However in this article the energy supplying of thesupercapacitor is imported It has higher accuracy and morerealistic power response compared to the former researchAlso the selected domestic load includes new load from asummer day and a winter day separately In the second casethe system is supplied to two sets of designed loads (sixscenarios of load) in two seasons is article evaluates thesystemrsquos performance of supplying energy to load throughthe two case studies

41 Case Study 1- High-Resolution Domestic Load SupplyingSimulation e case study shows the optimised operationof the integrated system meeting the power demand of adomestic load in two seasons e integrated system isregulated to supply three energy products for two varyingloads for 24 hourse load targets selected in this case studyshow a seasonal difference on power demand

In both simulations the control strategy applies theoptimised FEL power allocating strategy Electricity demandgets the priority e recovery heat is the available thermalenergy collected from the system Part of the recovered heatis utilised to supply cooling load For household domesticcooling load in this case the cooling load is a refrigeratore cooling load is simulated as a pulse-shape power de-mand e optimisation for the operation is applied in thesimulation which makes the operation time of the engine tobe optimised to a minimum value

411 Summer Day Case e first simulation is a summerday load e operation results are shown in Figure 6

In Figure 7 wave A shows the power demand of thedomestic load on a summer daye simulation results showthat the peak power demand is larger than the capacity of thesystem roughout the simulations the systemrsquos capacity isexpanded by with integrating the hybrid energy storageunit Wave B shows the enginersquos operation e engine isregulated to operate only at the peak power demand periode supercapacitors respond to high-density power demandIn the wave D it only operates when the power demand andpower supplying have sudden changee wave C shows thepower supplied by batteries which support most of thepower demand when the engine is not working Part of low-temperature waste heat is utilised by the ORC is part ofthermal energy transmitted to the ORC is converted toelectricity which increases the energy utilisation efficiency

0

20

40

60

80

100

120

140

160

180

095 105 115 125 135 145 155 165 175 185

Tem

pera

ture

(degC)

Entropy (kJkgndashk)

R 245fa T-S diagram

Pump

Evaporator

Expander

Condenser

Figure 5 T-S diagram of R245fa

8 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

storage system and the electric power output from the ORCDuring the systemrsquos operation the recovery energy is used tosatisfy the heat demand e thermal storage unit reservespart of the excessive energy

PLOAD PGE + PS + POE

PR middot α + PTminusSL PTminusL

PR middot β middot ηC PCminusL

(5)

When the generator is not operating the energy storageunits support the electric loads Because of lacking recoveryenergy the heating load and the cooling load are supplied bythe energy stored in the storage units

PLOAD PS

PET + PTminusSL PTminusL

PR middot β PEC

(6)

Supposing that the systemrsquos operation follows theelectric load (FEL) the optimisation of the system aims atminimizing the enginersquos operation time From calculatingthe difference between the minimum engine operation timeand the compensational engine operation time a betteroperation for the integrated system can be obtained

tminprime 1113938

te

0 Pengine (t)dt

Pengine (max)

tprime tminprime minus tmin

1113946tprime

0POE(t)dt Pengine (max) middot tprime

(7)

241 Limiting Conditions ere are a few restrictive con-ditions in the optimisation process for guaranteeing suffi-cient power for the load For ensuring the batteriesrsquo lifespanthe state of charge of the batteries is limited within the ragefrom 40 to 100 At the same time the power supplied bythe system must be equal or higher than the power demande thermal energy stored in the storage units is required tofulfill the thermal load and the cooling load e recoverythermal energy is allocated to three parts which are re-sponsible for the thermal storage heating demand andcooling demand respectively

40lt SOC(t)lt 100

PGE + PS + POE gePLOAD

PR middot α + PTminusSL gePTminusL

PR middot βgePCminusL

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(8)

e saved energy is expressed by the following equatione energy saving is

Qsaving 1113946te

0

Pengine(t)

ηenginedt minus Pengine(max)tmin (9)

3 Model Building of the System

e principle of building the components is introduced inthis section where the process of model building isexpressede evaluation work and the optimisation processare completed by the simulation models in the Matlabsoftware environment e accuracy of the build

System start

SOC of theelectrical energy

storage stayabove 30

The power demand isless than 25kW

The power demand isless than 65kW

The hybrid energystorage units operateto supply electricity

using electrical applianceto satisfying the heat and

cooling demand

The generator andthe energy storage

supplies the electrical loadtogether supplied by extra

Generator and theenergy storage units

supplied the electrical unitswaste heat is recovered for

heating and coolingdemand supplying

System alarm operatesemergency relaysstops the system

Is electrical demandfully supplied

If power demand isfully supplied

Engine drive thegenerator to chargethe hybrid energy

storage units

Waste heat is coveredto supply heat and

cooling energy demandinsufficiency part is

supported by electricalappliance

The power demand isless than 10 kW

Yes Yes Yes

No NoNo

No

YesYes

YesNoNo

Figure 2 Control strategy of the system

Complexity 5

components is guaranteed by fitting the performance of thecomponents with their experimental tests results

31 Generator of the System e electricity generation ap-plied in this project is a diesel engine Equation (16) de-scribes the mathematical model of the engine

In the equation

TdΩ EIA

E KφΩ

Td Kφ

P TdΩ times η EIA times η

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)

e engine transfers the chemical energy to mechanicalenergy and then to electricity Model for this part is built inMatlab software Using the corresponding parameters therecovered heat rate is also given in Table 2

From the simulation results of the enginersquos model theenginersquos electrical power output and the recovery heatsimulation have error less than 142 ese results indicatethat the model can simulate the operation performance ofthe engine with a high accuracy

32 Heat Recovery System e model of the waste heatrecovery module is based on the experimental results givenby a former swan researcher In the research of the biofuelmicrotrigeneration system a previous researcher Hongdong

tested the engine with 5 types of oil which are shown in theFigure 3 [27] After fitting curve with the rests results theheat recovery rate from gas oilrsquos burning is modelled

e heat recovery system is simulated as a black box usinga performance curve to fit the experimental results ere arefive operation points which are 10 25 50 75 and 100load e available waste heat and the recovered heat energyhave a linear relationship based on curve analysis and themathematical model is shown by the following equations

Pr Pengine times 0364 + 09676 (11)

Figure 4 shows the comparison of test results and thesimulation under different load conditions

Based on the control strategy of this integrated systemmost of the recovery systemrsquos operations are settled when theengine is loaded over 60 It indicates that the error of therecovery systemrsquos model is around 02 to 175

33CoolingGeneration In this integrated system the chillerpart consists of an adsorption chiller and the cooling gen-eration appliance e cooling generation is considered intwo situations 1 when the engine is working the adsorptionchiller absorbed the recovered waste heat to generate coolingenergy 2 when the engine is off the cooling is transferredfrom the electrical appliance e cooling power out isshown in the following formula

When the engine is on PCminusL PR middot β1113858 1113859 middot ηC

When the engine is off PCminusL Pengine minus PSU minus PBT minus PEminusL1113960 1113961 middot ηEminusC

⎧⎨

⎩ (12)

34HybridEnergyStorage System epower requirement ofthe integrated system is a variable during the different times ofthe day For supplying reliable energy the integrated systemcombines the hybrid energy storage system as its energy backup to make up for the disadvantages of a distributed systeme hybrid energy storage units consist of the lead acidbatteries and the supercapacitors in this research

For optimal utilization of the energy stored in the energystorage units the size of the batteries and the supercapacitorsis calculated and selected

341 Battery e batteries have high energy density It isresponsible for supplying enough electricity to load e for-mula which expresses the operation of the lead acid batteriesusing state of charge as its variable is listed as follows [27]

SOC(t + 1) SOC(t) minus SOC(t) middot σ +IBat(t) middot Δt middot ηBat

CBat(t)

(13)

In the equation σ is the self-discharge rate of the batterywhose value is set as 02 per day CBat is the capacity of thebattery

e detail parameters of the batteries and the super-capacitor are shown in Table 3

342 Supercapacitor In this study the supercapacitors areselected for satisfying dynamic changing power demandespecifications of the supercapacitor are listed in Table 4 eformula of the supercapacitor is displayed in the followingequation

PSU 12

C middot U2

12ε middot

S

dU2

ε 2W

VE2

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(14)

Here ε is the dielectric constant E is the electric fieldintensity and C is the volume of the supercapacitor

35 ORC System

351 Working Fluid Selection e ORC system integratedin is designed for low-grade heat recovery e ORC systemis driven by the waste heat absorbed from the engine Severalkey factors have the most significance on the ORCrsquos

6 Complexity

performance which are source temperature working me-dium safety issues and problem of degradation For all theworking fluids the ODP GWP R134a and R245fa are ap-propriate to be applied in a small-scale ORC [28] R123a hasozone depletion potential which leads to aggravate the globalwarming e R245fa has similar chemical and physical

properties as a good candidate for the ORC system It also haszero-ozone depletion potential low toxicity no flammabilityand applicable critical temperature and pressure ereforeR245fa is selected as the working fluid in the ORC system AnORC system contains several basic components a heat sourceevaporator turbine condenser and pump

e waste heat recovered by this system consists of heatfrom the exhaust gas and the coolant of the engine Forachieving the highest efficiency during the heat transferringprocess the working fluid is selected as R245fais workingfluid has lower ebullition temperature which has lowerevaporating temperature than water Figure 5 shows the T-Sdiagram of R245fa in anORC cyclee R245fa is pumped tothe evaporator and its entropy and temperature are slightlyincreased before entering the evaporator When the workingfluid is leaving the evaporator the entropy of R245fa in-creases compared to its state before After that the R245fagoes into the turbine and the temperature decreases whenits entropy slightly increases

352 ORC Model Building Power output of the ORCsystem is modelled in this study and some similar ORCsystem equations are also applied in the authorrsquos formerresearch [26]

Table 2 Energy output in the simulation

Load of the engine () Engine efficiency () Error () Recovery rate simulation () Error ()0 0 0 0 010 78 113 3536 007925 1636 036 3538 002550 243 041 3539 002775 2753 082 3541 0138100 285 142 3544 0137

50

Croton oilJatropha oilRapeseed oil

Sunflower oilGas oil

45

40

35

30

25

20

15

100 20 40

Load condition ()60 80 100

Pow

er (k

W)

Figure 3 Recovered exhaust gas heat for cogeneration [27]

50

45

40

35

30

25

20

15

10

Pow

er (k

W)

0

681

04

592

02

175

20 40Load condition ()

60 80 100

Simulation resultsExperimental results

Figure 4 Simulation results comparing with the experimentalresults

Table 3 Specifications of the lead acid batteries

Parameter Mean valueNominal capacity 200AhNominal voltage 12V DCDepth of discharge 40Maximum state of charge 100Minimum state of charge 40Efficiency 85ndash95Self-discharge 20dayTemperature coefficient 06degCLifetime 5 years

Table 4 Parameters of the supercapacitor

Parameter Mean valueCapacity 63 FVoltage 125V DCResistance 18mΩTemperature minus40ndash60Life span 100000 hoursEnd of life reduction of capacity 20Maximum current 150AMaximum peak current 750ASelf-discharge 5030 days

Complexity 7

Wp morc h2 minus h1( 1113857 mORC h2s minus h1( 1113857

ηp

ηp h2s minus h1

h2 minus h1

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(15)

where Wp is the pumprsquos power mORC is the mass flow rateηp is the pumprsquos efficiency and h2 h1 are the inlet and outletfluid enthalpies

Qep Qro middot ξ morc h3 minus h2( 1113857 (16)

Heat accepted by the evaporator is Qep ξ is the evap-orator effectiveness Qro is the recovered thermal energy h2are the enthalpies of the working fluid at the outlet of theevaporator

e process of the condenser is expressed by the fol-lowing equation

Qcd morc h1 minus h4( 1113857 (17)

In the equation Qcd is the heat output of the condenserand h4 is the working fluid enthalpy at the outlet of theturbine

e turbine power output is

Wt morc h3 minus h4( 1113857 morc h3 minus h2( 1113857ηt

ηt h3 minus h4

h3 minus h4s

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(18)

In the equationWt is the turbine output power and ηt isthe isentropic efficiency of the turbine

e ORC systemrsquos overall efficiency is

ηORC Wt minus Wp

Qep

h3 minus h4s( 1113857ηt minus h2s minus h1( 1113857ηminus1p

h3 minus h2

Wt QroηORCηgen

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(19)

where ηORC is the efficiency of the ORC system

e thermal dynamic module of the ORC system wasdeveloped by the researcher from the same group Moredetails can be found in this research [29] In this study ageneral efficiency module is applied to represent the ORCsystemrsquos ability of transferring low-temperature waste heat

4 Case Study

e case study includes two parts of evaluation work on thesystem performance In the first case study the system isrequired to supply multitype of energy to a dynamicchanging domestic load Simulation of supplying dynamicload with this system is performed in a former research [26]However in this article the energy supplying of thesupercapacitor is imported It has higher accuracy and morerealistic power response compared to the former researchAlso the selected domestic load includes new load from asummer day and a winter day separately In the second casethe system is supplied to two sets of designed loads (sixscenarios of load) in two seasons is article evaluates thesystemrsquos performance of supplying energy to load throughthe two case studies

41 Case Study 1- High-Resolution Domestic Load SupplyingSimulation e case study shows the optimised operationof the integrated system meeting the power demand of adomestic load in two seasons e integrated system isregulated to supply three energy products for two varyingloads for 24 hourse load targets selected in this case studyshow a seasonal difference on power demand

In both simulations the control strategy applies theoptimised FEL power allocating strategy Electricity demandgets the priority e recovery heat is the available thermalenergy collected from the system Part of the recovered heatis utilised to supply cooling load For household domesticcooling load in this case the cooling load is a refrigeratore cooling load is simulated as a pulse-shape power de-mand e optimisation for the operation is applied in thesimulation which makes the operation time of the engine tobe optimised to a minimum value

411 Summer Day Case e first simulation is a summerday load e operation results are shown in Figure 6

In Figure 7 wave A shows the power demand of thedomestic load on a summer daye simulation results showthat the peak power demand is larger than the capacity of thesystem roughout the simulations the systemrsquos capacity isexpanded by with integrating the hybrid energy storageunit Wave B shows the enginersquos operation e engine isregulated to operate only at the peak power demand periode supercapacitors respond to high-density power demandIn the wave D it only operates when the power demand andpower supplying have sudden changee wave C shows thepower supplied by batteries which support most of thepower demand when the engine is not working Part of low-temperature waste heat is utilised by the ORC is part ofthermal energy transmitted to the ORC is converted toelectricity which increases the energy utilisation efficiency

0

20

40

60

80

100

120

140

160

180

095 105 115 125 135 145 155 165 175 185

Tem

pera

ture

(degC)

Entropy (kJkgndashk)

R 245fa T-S diagram

Pump

Evaporator

Expander

Condenser

Figure 5 T-S diagram of R245fa

8 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

components is guaranteed by fitting the performance of thecomponents with their experimental tests results

31 Generator of the System e electricity generation ap-plied in this project is a diesel engine Equation (16) de-scribes the mathematical model of the engine

In the equation

TdΩ EIA

E KφΩ

Td Kφ

P TdΩ times η EIA times η

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)

e engine transfers the chemical energy to mechanicalenergy and then to electricity Model for this part is built inMatlab software Using the corresponding parameters therecovered heat rate is also given in Table 2

From the simulation results of the enginersquos model theenginersquos electrical power output and the recovery heatsimulation have error less than 142 ese results indicatethat the model can simulate the operation performance ofthe engine with a high accuracy

32 Heat Recovery System e model of the waste heatrecovery module is based on the experimental results givenby a former swan researcher In the research of the biofuelmicrotrigeneration system a previous researcher Hongdong

tested the engine with 5 types of oil which are shown in theFigure 3 [27] After fitting curve with the rests results theheat recovery rate from gas oilrsquos burning is modelled

e heat recovery system is simulated as a black box usinga performance curve to fit the experimental results ere arefive operation points which are 10 25 50 75 and 100load e available waste heat and the recovered heat energyhave a linear relationship based on curve analysis and themathematical model is shown by the following equations

Pr Pengine times 0364 + 09676 (11)

Figure 4 shows the comparison of test results and thesimulation under different load conditions

Based on the control strategy of this integrated systemmost of the recovery systemrsquos operations are settled when theengine is loaded over 60 It indicates that the error of therecovery systemrsquos model is around 02 to 175

33CoolingGeneration In this integrated system the chillerpart consists of an adsorption chiller and the cooling gen-eration appliance e cooling generation is considered intwo situations 1 when the engine is working the adsorptionchiller absorbed the recovered waste heat to generate coolingenergy 2 when the engine is off the cooling is transferredfrom the electrical appliance e cooling power out isshown in the following formula

When the engine is on PCminusL PR middot β1113858 1113859 middot ηC

When the engine is off PCminusL Pengine minus PSU minus PBT minus PEminusL1113960 1113961 middot ηEminusC

⎧⎨

⎩ (12)

34HybridEnergyStorage System epower requirement ofthe integrated system is a variable during the different times ofthe day For supplying reliable energy the integrated systemcombines the hybrid energy storage system as its energy backup to make up for the disadvantages of a distributed systeme hybrid energy storage units consist of the lead acidbatteries and the supercapacitors in this research

For optimal utilization of the energy stored in the energystorage units the size of the batteries and the supercapacitorsis calculated and selected

341 Battery e batteries have high energy density It isresponsible for supplying enough electricity to load e for-mula which expresses the operation of the lead acid batteriesusing state of charge as its variable is listed as follows [27]

SOC(t + 1) SOC(t) minus SOC(t) middot σ +IBat(t) middot Δt middot ηBat

CBat(t)

(13)

In the equation σ is the self-discharge rate of the batterywhose value is set as 02 per day CBat is the capacity of thebattery

e detail parameters of the batteries and the super-capacitor are shown in Table 3

342 Supercapacitor In this study the supercapacitors areselected for satisfying dynamic changing power demandespecifications of the supercapacitor are listed in Table 4 eformula of the supercapacitor is displayed in the followingequation

PSU 12

C middot U2

12ε middot

S

dU2

ε 2W

VE2

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(14)

Here ε is the dielectric constant E is the electric fieldintensity and C is the volume of the supercapacitor

35 ORC System

351 Working Fluid Selection e ORC system integratedin is designed for low-grade heat recovery e ORC systemis driven by the waste heat absorbed from the engine Severalkey factors have the most significance on the ORCrsquos

6 Complexity

performance which are source temperature working me-dium safety issues and problem of degradation For all theworking fluids the ODP GWP R134a and R245fa are ap-propriate to be applied in a small-scale ORC [28] R123a hasozone depletion potential which leads to aggravate the globalwarming e R245fa has similar chemical and physical

properties as a good candidate for the ORC system It also haszero-ozone depletion potential low toxicity no flammabilityand applicable critical temperature and pressure ereforeR245fa is selected as the working fluid in the ORC system AnORC system contains several basic components a heat sourceevaporator turbine condenser and pump

e waste heat recovered by this system consists of heatfrom the exhaust gas and the coolant of the engine Forachieving the highest efficiency during the heat transferringprocess the working fluid is selected as R245fais workingfluid has lower ebullition temperature which has lowerevaporating temperature than water Figure 5 shows the T-Sdiagram of R245fa in anORC cyclee R245fa is pumped tothe evaporator and its entropy and temperature are slightlyincreased before entering the evaporator When the workingfluid is leaving the evaporator the entropy of R245fa in-creases compared to its state before After that the R245fagoes into the turbine and the temperature decreases whenits entropy slightly increases

352 ORC Model Building Power output of the ORCsystem is modelled in this study and some similar ORCsystem equations are also applied in the authorrsquos formerresearch [26]

Table 2 Energy output in the simulation

Load of the engine () Engine efficiency () Error () Recovery rate simulation () Error ()0 0 0 0 010 78 113 3536 007925 1636 036 3538 002550 243 041 3539 002775 2753 082 3541 0138100 285 142 3544 0137

50

Croton oilJatropha oilRapeseed oil

Sunflower oilGas oil

45

40

35

30

25

20

15

100 20 40

Load condition ()60 80 100

Pow

er (k

W)

Figure 3 Recovered exhaust gas heat for cogeneration [27]

50

45

40

35

30

25

20

15

10

Pow

er (k

W)

0

681

04

592

02

175

20 40Load condition ()

60 80 100

Simulation resultsExperimental results

Figure 4 Simulation results comparing with the experimentalresults

Table 3 Specifications of the lead acid batteries

Parameter Mean valueNominal capacity 200AhNominal voltage 12V DCDepth of discharge 40Maximum state of charge 100Minimum state of charge 40Efficiency 85ndash95Self-discharge 20dayTemperature coefficient 06degCLifetime 5 years

Table 4 Parameters of the supercapacitor

Parameter Mean valueCapacity 63 FVoltage 125V DCResistance 18mΩTemperature minus40ndash60Life span 100000 hoursEnd of life reduction of capacity 20Maximum current 150AMaximum peak current 750ASelf-discharge 5030 days

Complexity 7

Wp morc h2 minus h1( 1113857 mORC h2s minus h1( 1113857

ηp

ηp h2s minus h1

h2 minus h1

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(15)

where Wp is the pumprsquos power mORC is the mass flow rateηp is the pumprsquos efficiency and h2 h1 are the inlet and outletfluid enthalpies

Qep Qro middot ξ morc h3 minus h2( 1113857 (16)

Heat accepted by the evaporator is Qep ξ is the evap-orator effectiveness Qro is the recovered thermal energy h2are the enthalpies of the working fluid at the outlet of theevaporator

e process of the condenser is expressed by the fol-lowing equation

Qcd morc h1 minus h4( 1113857 (17)

In the equation Qcd is the heat output of the condenserand h4 is the working fluid enthalpy at the outlet of theturbine

e turbine power output is

Wt morc h3 minus h4( 1113857 morc h3 minus h2( 1113857ηt

ηt h3 minus h4

h3 minus h4s

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(18)

In the equationWt is the turbine output power and ηt isthe isentropic efficiency of the turbine

e ORC systemrsquos overall efficiency is

ηORC Wt minus Wp

Qep

h3 minus h4s( 1113857ηt minus h2s minus h1( 1113857ηminus1p

h3 minus h2

Wt QroηORCηgen

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(19)

where ηORC is the efficiency of the ORC system

e thermal dynamic module of the ORC system wasdeveloped by the researcher from the same group Moredetails can be found in this research [29] In this study ageneral efficiency module is applied to represent the ORCsystemrsquos ability of transferring low-temperature waste heat

4 Case Study

e case study includes two parts of evaluation work on thesystem performance In the first case study the system isrequired to supply multitype of energy to a dynamicchanging domestic load Simulation of supplying dynamicload with this system is performed in a former research [26]However in this article the energy supplying of thesupercapacitor is imported It has higher accuracy and morerealistic power response compared to the former researchAlso the selected domestic load includes new load from asummer day and a winter day separately In the second casethe system is supplied to two sets of designed loads (sixscenarios of load) in two seasons is article evaluates thesystemrsquos performance of supplying energy to load throughthe two case studies

41 Case Study 1- High-Resolution Domestic Load SupplyingSimulation e case study shows the optimised operationof the integrated system meeting the power demand of adomestic load in two seasons e integrated system isregulated to supply three energy products for two varyingloads for 24 hourse load targets selected in this case studyshow a seasonal difference on power demand

In both simulations the control strategy applies theoptimised FEL power allocating strategy Electricity demandgets the priority e recovery heat is the available thermalenergy collected from the system Part of the recovered heatis utilised to supply cooling load For household domesticcooling load in this case the cooling load is a refrigeratore cooling load is simulated as a pulse-shape power de-mand e optimisation for the operation is applied in thesimulation which makes the operation time of the engine tobe optimised to a minimum value

411 Summer Day Case e first simulation is a summerday load e operation results are shown in Figure 6

In Figure 7 wave A shows the power demand of thedomestic load on a summer daye simulation results showthat the peak power demand is larger than the capacity of thesystem roughout the simulations the systemrsquos capacity isexpanded by with integrating the hybrid energy storageunit Wave B shows the enginersquos operation e engine isregulated to operate only at the peak power demand periode supercapacitors respond to high-density power demandIn the wave D it only operates when the power demand andpower supplying have sudden changee wave C shows thepower supplied by batteries which support most of thepower demand when the engine is not working Part of low-temperature waste heat is utilised by the ORC is part ofthermal energy transmitted to the ORC is converted toelectricity which increases the energy utilisation efficiency

0

20

40

60

80

100

120

140

160

180

095 105 115 125 135 145 155 165 175 185

Tem

pera

ture

(degC)

Entropy (kJkgndashk)

R 245fa T-S diagram

Pump

Evaporator

Expander

Condenser

Figure 5 T-S diagram of R245fa

8 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

performance which are source temperature working me-dium safety issues and problem of degradation For all theworking fluids the ODP GWP R134a and R245fa are ap-propriate to be applied in a small-scale ORC [28] R123a hasozone depletion potential which leads to aggravate the globalwarming e R245fa has similar chemical and physical

properties as a good candidate for the ORC system It also haszero-ozone depletion potential low toxicity no flammabilityand applicable critical temperature and pressure ereforeR245fa is selected as the working fluid in the ORC system AnORC system contains several basic components a heat sourceevaporator turbine condenser and pump

e waste heat recovered by this system consists of heatfrom the exhaust gas and the coolant of the engine Forachieving the highest efficiency during the heat transferringprocess the working fluid is selected as R245fais workingfluid has lower ebullition temperature which has lowerevaporating temperature than water Figure 5 shows the T-Sdiagram of R245fa in anORC cyclee R245fa is pumped tothe evaporator and its entropy and temperature are slightlyincreased before entering the evaporator When the workingfluid is leaving the evaporator the entropy of R245fa in-creases compared to its state before After that the R245fagoes into the turbine and the temperature decreases whenits entropy slightly increases

352 ORC Model Building Power output of the ORCsystem is modelled in this study and some similar ORCsystem equations are also applied in the authorrsquos formerresearch [26]

Table 2 Energy output in the simulation

Load of the engine () Engine efficiency () Error () Recovery rate simulation () Error ()0 0 0 0 010 78 113 3536 007925 1636 036 3538 002550 243 041 3539 002775 2753 082 3541 0138100 285 142 3544 0137

50

Croton oilJatropha oilRapeseed oil

Sunflower oilGas oil

45

40

35

30

25

20

15

100 20 40

Load condition ()60 80 100

Pow

er (k

W)

Figure 3 Recovered exhaust gas heat for cogeneration [27]

50

45

40

35

30

25

20

15

10

Pow

er (k

W)

0

681

04

592

02

175

20 40Load condition ()

60 80 100

Simulation resultsExperimental results

Figure 4 Simulation results comparing with the experimentalresults

Table 3 Specifications of the lead acid batteries

Parameter Mean valueNominal capacity 200AhNominal voltage 12V DCDepth of discharge 40Maximum state of charge 100Minimum state of charge 40Efficiency 85ndash95Self-discharge 20dayTemperature coefficient 06degCLifetime 5 years

Table 4 Parameters of the supercapacitor

Parameter Mean valueCapacity 63 FVoltage 125V DCResistance 18mΩTemperature minus40ndash60Life span 100000 hoursEnd of life reduction of capacity 20Maximum current 150AMaximum peak current 750ASelf-discharge 5030 days

Complexity 7

Wp morc h2 minus h1( 1113857 mORC h2s minus h1( 1113857

ηp

ηp h2s minus h1

h2 minus h1

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(15)

where Wp is the pumprsquos power mORC is the mass flow rateηp is the pumprsquos efficiency and h2 h1 are the inlet and outletfluid enthalpies

Qep Qro middot ξ morc h3 minus h2( 1113857 (16)

Heat accepted by the evaporator is Qep ξ is the evap-orator effectiveness Qro is the recovered thermal energy h2are the enthalpies of the working fluid at the outlet of theevaporator

e process of the condenser is expressed by the fol-lowing equation

Qcd morc h1 minus h4( 1113857 (17)

In the equation Qcd is the heat output of the condenserand h4 is the working fluid enthalpy at the outlet of theturbine

e turbine power output is

Wt morc h3 minus h4( 1113857 morc h3 minus h2( 1113857ηt

ηt h3 minus h4

h3 minus h4s

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(18)

In the equationWt is the turbine output power and ηt isthe isentropic efficiency of the turbine

e ORC systemrsquos overall efficiency is

ηORC Wt minus Wp

Qep

h3 minus h4s( 1113857ηt minus h2s minus h1( 1113857ηminus1p

h3 minus h2

Wt QroηORCηgen

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(19)

where ηORC is the efficiency of the ORC system

e thermal dynamic module of the ORC system wasdeveloped by the researcher from the same group Moredetails can be found in this research [29] In this study ageneral efficiency module is applied to represent the ORCsystemrsquos ability of transferring low-temperature waste heat

4 Case Study

e case study includes two parts of evaluation work on thesystem performance In the first case study the system isrequired to supply multitype of energy to a dynamicchanging domestic load Simulation of supplying dynamicload with this system is performed in a former research [26]However in this article the energy supplying of thesupercapacitor is imported It has higher accuracy and morerealistic power response compared to the former researchAlso the selected domestic load includes new load from asummer day and a winter day separately In the second casethe system is supplied to two sets of designed loads (sixscenarios of load) in two seasons is article evaluates thesystemrsquos performance of supplying energy to load throughthe two case studies

41 Case Study 1- High-Resolution Domestic Load SupplyingSimulation e case study shows the optimised operationof the integrated system meeting the power demand of adomestic load in two seasons e integrated system isregulated to supply three energy products for two varyingloads for 24 hourse load targets selected in this case studyshow a seasonal difference on power demand

In both simulations the control strategy applies theoptimised FEL power allocating strategy Electricity demandgets the priority e recovery heat is the available thermalenergy collected from the system Part of the recovered heatis utilised to supply cooling load For household domesticcooling load in this case the cooling load is a refrigeratore cooling load is simulated as a pulse-shape power de-mand e optimisation for the operation is applied in thesimulation which makes the operation time of the engine tobe optimised to a minimum value

411 Summer Day Case e first simulation is a summerday load e operation results are shown in Figure 6

In Figure 7 wave A shows the power demand of thedomestic load on a summer daye simulation results showthat the peak power demand is larger than the capacity of thesystem roughout the simulations the systemrsquos capacity isexpanded by with integrating the hybrid energy storageunit Wave B shows the enginersquos operation e engine isregulated to operate only at the peak power demand periode supercapacitors respond to high-density power demandIn the wave D it only operates when the power demand andpower supplying have sudden changee wave C shows thepower supplied by batteries which support most of thepower demand when the engine is not working Part of low-temperature waste heat is utilised by the ORC is part ofthermal energy transmitted to the ORC is converted toelectricity which increases the energy utilisation efficiency

0

20

40

60

80

100

120

140

160

180

095 105 115 125 135 145 155 165 175 185

Tem

pera

ture

(degC)

Entropy (kJkgndashk)

R 245fa T-S diagram

Pump

Evaporator

Expander

Condenser

Figure 5 T-S diagram of R245fa

8 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

Wp morc h2 minus h1( 1113857 mORC h2s minus h1( 1113857

ηp

ηp h2s minus h1

h2 minus h1

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(15)

where Wp is the pumprsquos power mORC is the mass flow rateηp is the pumprsquos efficiency and h2 h1 are the inlet and outletfluid enthalpies

Qep Qro middot ξ morc h3 minus h2( 1113857 (16)

Heat accepted by the evaporator is Qep ξ is the evap-orator effectiveness Qro is the recovered thermal energy h2are the enthalpies of the working fluid at the outlet of theevaporator

e process of the condenser is expressed by the fol-lowing equation

Qcd morc h1 minus h4( 1113857 (17)

In the equation Qcd is the heat output of the condenserand h4 is the working fluid enthalpy at the outlet of theturbine

e turbine power output is

Wt morc h3 minus h4( 1113857 morc h3 minus h2( 1113857ηt

ηt h3 minus h4

h3 minus h4s

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(18)

In the equationWt is the turbine output power and ηt isthe isentropic efficiency of the turbine

e ORC systemrsquos overall efficiency is

ηORC Wt minus Wp

Qep

h3 minus h4s( 1113857ηt minus h2s minus h1( 1113857ηminus1p

h3 minus h2

Wt QroηORCηgen

⎧⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

(19)

where ηORC is the efficiency of the ORC system

e thermal dynamic module of the ORC system wasdeveloped by the researcher from the same group Moredetails can be found in this research [29] In this study ageneral efficiency module is applied to represent the ORCsystemrsquos ability of transferring low-temperature waste heat

4 Case Study

e case study includes two parts of evaluation work on thesystem performance In the first case study the system isrequired to supply multitype of energy to a dynamicchanging domestic load Simulation of supplying dynamicload with this system is performed in a former research [26]However in this article the energy supplying of thesupercapacitor is imported It has higher accuracy and morerealistic power response compared to the former researchAlso the selected domestic load includes new load from asummer day and a winter day separately In the second casethe system is supplied to two sets of designed loads (sixscenarios of load) in two seasons is article evaluates thesystemrsquos performance of supplying energy to load throughthe two case studies

41 Case Study 1- High-Resolution Domestic Load SupplyingSimulation e case study shows the optimised operationof the integrated system meeting the power demand of adomestic load in two seasons e integrated system isregulated to supply three energy products for two varyingloads for 24 hourse load targets selected in this case studyshow a seasonal difference on power demand

In both simulations the control strategy applies theoptimised FEL power allocating strategy Electricity demandgets the priority e recovery heat is the available thermalenergy collected from the system Part of the recovered heatis utilised to supply cooling load For household domesticcooling load in this case the cooling load is a refrigeratore cooling load is simulated as a pulse-shape power de-mand e optimisation for the operation is applied in thesimulation which makes the operation time of the engine tobe optimised to a minimum value

411 Summer Day Case e first simulation is a summerday load e operation results are shown in Figure 6

In Figure 7 wave A shows the power demand of thedomestic load on a summer daye simulation results showthat the peak power demand is larger than the capacity of thesystem roughout the simulations the systemrsquos capacity isexpanded by with integrating the hybrid energy storageunit Wave B shows the enginersquos operation e engine isregulated to operate only at the peak power demand periode supercapacitors respond to high-density power demandIn the wave D it only operates when the power demand andpower supplying have sudden changee wave C shows thepower supplied by batteries which support most of thepower demand when the engine is not working Part of low-temperature waste heat is utilised by the ORC is part ofthermal energy transmitted to the ORC is converted toelectricity which increases the energy utilisation efficiency

0

20

40

60

80

100

120

140

160

180

095 105 115 125 135 145 155 165 175 185

Tem

pera

ture

(degC)

Entropy (kJkgndashk)

R 245fa T-S diagram

Pump

Evaporator

Expander

Condenser

Figure 5 T-S diagram of R245fa

8 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

During the systemrsquos operation when the engine is on theORC system utilizes the low-temperature waste heat andtransfers them to available electricity Based on the calcu-lation of the results the ORC contributes 23 electricitywhich brings a 237 increment to the electrical efficiency

e recovered waste heat is used to supply the thermaldemand e specific thermal demand lacks data supporterefore the heat consumption is an estimated value based onthe governmentrsquos data and the householdrsquos energy consumptionpattern [30] e thermal energy generated is 46 kWh and it isfully consumed to supply the thermal power demand Most ofthe waste heat is recovered for heat demande thermal energycould be either stored in a heat tank or consumed directly to theload e system is flexible to deal with the thermal energy esimulation results show that 41 of available energy generatedby the system is consumed for heat demand

An absorption chiller is integrated in the system forsatisfying cooling demand e simulation results show thatthe cooling energy demand of the selected domestic load is432 kWh e cooling load is satisfied with 2576 recov-ered heating energy

412 Winter Load In the simulation the power source ofthe dynamic load consists of the engine and the waste heatrecovery system Wave A shows the power demand inFigure 7 e peak power demand cannot be satisfied by theengine alone e hybrid energy storage unit operates as anauxiliary power supplier In the simulation energy storageunits supplement the energy gap when the peak energydemand is larger than the rate power of engine From the Cand D chart in Figure 7 large amount of electrical energy

8000600040002000

0

Pow

erde

man

d (W

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000

ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

0908070605SO

C (lowast

100

)

0 2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(e)

Figure 6 Five key parametersrsquo values are recorded during the operation of the integrated system with label A B C D and E ey arerespectively power demand engine out batteries operation supercapacitors operation and batteriesrsquo state of charge

Complexity 9

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

was supplied by the batteries e supercapacitors offeredcertain amount of energy in short time to fulfill the dynamicenergy demand Batteriesrsquo state of charge started from 80and ends at 75 when the simulation ends

e ORC system in this simulation produces 038 kWhwith the heat source recovered from the engine e op-eration of the ORC system makes the trigeneration to havehigher conversion rate on the waste heat energy eelectricity supplied by the ORC system takes up 24 of thetotal dynamic energy demand

When the recovery heat is used to generate theelectricity the supplying of the heating demand is af-fected In this case as the efficiency of the recovery heat ishigher than that of the ORC system using certainamount of the recovery heat for electricity generation isunder the condition that the heat demand is sufficientlysupplied

e heat demand is fulfilled by the recovered waste heate energy consumption is an estimated value which is in areasonable range e winter day load consumes 57 kWh ofheat Most of the waste heat is used for thermal demand Partof low-temperature waste heat is transmitted to the ORC forelectricity conversion

e cooling energy consumption is 062 kWh in this casewhich is supplied with 27 recovered heat

413 Summary of the Two Simulations Information of thesimulation results is displayed in the Table 5 In the twosimulations the energy demands are similar which are1292 kWh and 1598 kWh respectivelye energy demand ofthe target residential household has larger energy demand on awinter day than on a summer day e engine is designed tooperate at a shortest time to save energye integrated system

600040002000

0

Pow

erde

man

d (W

)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(a)

8000600040002000

0Engi

ne o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(b)

5000

ndash5000ndash10000

0

Batte

ry o

utpu

tPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(c)

20001000

0ndash1000ndash2000Su

perc

apac

itor

outp

utPo

wer

(W)

2000 4000 6000 8000Time (5s)

10000 12000 14000 16000

(d)

09085

08075

072000 4000 6000 8000

Time (5s)10000 12000 14000 16000

SOC

(lowast10

0)

(e)

Figure 7 High-resolution simulation of the electrical energy supplying (a) Electrical energy demand (b) engine operation period (c)batteries chargingdischarging (d) supercapacitor chargingdischarging and (e) the tracking of batteriesrsquo SOC

10 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

satisfies the energy demand in both cases with the engine onlyoperating at peak times During the long off-peak time theenergy storage units supply the energy demand to the loadeORC system transferred part of the waste heat energy toelectricity Limited by the heat source the ORC system onlygenerates a small amount of energy in these two cases Al-though the ORC system enhances the system energy con-version ratio based on the results of the simulations stable heatsources are necessary to have high economic efficiency eelectrical energy storage units are important in the simulationfor expanding the systemrsquos capacity and enhancing the systemrsquosstability With the energy storage units the trigenerationsystem has the ability to supply load larger than the enginersquosrate power

e amount of energy supplied by the engine was893 kWh in summer and 11 kWh in winter At the sametime the energy storage system only supplied 368 kWh insummer and 459 kWh in winter It indicates that the energystorage system operates as auxiliary supplied in the electricpart of the trigeneration system Combined with the energystorage system the energy supplying has little waste ecalculation results from the generator output and energyconsumption and the efficiency enhancements of the systemare 4700 and 3970 in this case study In the energystorage system the lead batteries have high energy capacitycompared to the supercapacitors e maximum error of thetwo sets of simulation is 242 and 215 e averagesimulation errors are controlled in 185 and 179 in twosets respectively

42 Case Study 2ndash the Optimisation Process on SeveralScenarios Aiming at a comprehensive evaluation on thesystemrsquos performance various scenarios of load aredesigned for the systemrsquos performance assessment ecooling heating and electricity loads on typical summerand winter days under different conditions are given inthe Figures 8 and 9 In the simulation the loads aredesigned to two groups e first groups are loads forapplications on typical summer days e second groupsare loads designed for applications on typical winter daysUnlike in domestic summer load and winter load morecomplex requirements on the heating cooling andelectricity are selected in this case study e energy

consumption in the scenarios come from the coolingheating and electricity loads profiles for industrialcommercial and residential areas e system simulatedin this research has the power rate same as that in casestudy 1 Using the system to supply a wide range of loadsgives a comprehensive evaluation on the systemrsquosperformance

421 Optimisation Results of Case Study 2 Table 6 showsthe simulation results of the systemrsquos optimised opera-tion Simulation results from the 6 scenarios give acomprehensive evaluation on the systemrsquo performanceScenario one scenario two and scenario three simulatethe systemrsquos operation on a summer day e other threeloads are designed for energy supplying in a winter daye later three simulations investigate the systemrsquosperformance on a winter day During the systemrsquos sim-ulation the ORC system supplied about 10 of theelectric energy demand In the different scenarios theenergy storage units improve the systemrsquos capacity sig-nificantly e minimum value was in scenario 5 (415)and the maximum value is in scenario 6ndash6415 After theoptimisation process the systemrsquos engine operated withless time and the systemrsquos overall efficiency is improvedin each scenario Based on the optimisation results theengine is controlled to operate with full power rate withminimum time to satisfy the load

Different from the previous simulation of the tri-generation system short time of the enginersquos operationwith full power rate improves the electric efficiency of thesystem e optimisation process converts more energy toelectricity from the fuel and the higher electricity effi-ciency contributes to the system overall efficiency In thefirst three scenarios the overall efficiency is lower thanthat in the last three scenarios e load on a typicalsummer day needs more cooling energy which is the weakpart of the system e cooling energy in this system istransferred from the recovery heat It limited the coolingenergy conversion efficiency which is the fundamentalreason why the systemrsquos efficiency is low in the first threescenarios In the last three scenarios the system generatedmore heating and electricity which make the overall ef-ficiency higher

Table 5 Key parameters displaying in the two set of simulations

Summer day Winter dayElectrical energy demand 1292 kWh 1598 kWhEnergy supplied by the engine 893 kWh 11 kWhEnergy supplied by energy storage units 368 kWh 459 kWhElectricity generated by the ORC system 0307 kWh 038 kWhCooling energy demand 432 kWh 062 kWhHeating demandlowast 05 kWh 57 kWhSystem capacity enhancement 11 920Energy consumed by the integrated system 274 kWh 325 kWhEnergy consumed by the separate system 517 kWh 5392 kWhOverall system efficiency 3570 4270Efficiency enhancement compared to the separate system 4700 3970lowastPart of thermal demand is supplied by electrical energy that is why the thermal demand is smaller than normal residential cases

Complexity 11

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

5 Conclusions

is study describes a detailed study on the optimisation ofthe operation of the integrated CCHP system

In the first case study the proposed system supplied thecomplex power demand with the overall efficiency 3570

and 4270 ere is over 40 efficiency enhancementcompared to conventional separate production systemsBesides that in the simulations the optimisation processsaves 361 kWh and 186 kWh in the summer case load andwinter case load respectively compared to the originalcontrol strategy applied

121110

9876543210

5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20 5 10 15Time (h)

CoolingHeatingElectricity

20

Pow

er d

eman

d (k

W)

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 1 Scenario 2 Scenario 312

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Figure 8 System energy supply information of scenarios 1ndash3 (designed for summer load)

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 4

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 5

5 10 15Time (h)

CoolingHeatingElectricity

20

12

10

8

6

4

2

0

Pow

er d

eman

d (k

W)

Scenario 6

5 10 15Time (h)

CoolingHeatingElectricity

20

Figure 9 System energy supply information of scenarios 4ndash6 (designed for winter load)

Table 6 Optimisation results summary table

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6Electrical energy demand (kWh) 5892 9347 7221 4949 11314 40461Heat demand (kWh) 1444 0 1635 10122 43 17377Cooling demand (kWh) 12342 5546 8713 2756 0 0Electrical energy generated by the engine (kWh) 6887 52 6149 624 5873 7495Energy supplied by electrical energy storage (kWh) 2004 243 3748 2704 468 1261Energy supplied by the heating storage unit (kwh) 3852 106 099 4731 946 0Energy supplied by the ORC system (kWh) 448 337 4 406 356 488System capacity enhancement () 63 4701 6461 5510 4150 6415Engine operation time (h) 1055 8 946 96 841 1153Energy consumption amount (kWh) 19679 14893 1757 17828 15613 21423Overall efficiency () 3349 3613 3492 4951 437 5653Efficiency enhancement () 3 318 285 1711 898 2235

12 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

In the second case study several designed scenarios ofloads are the power demand in the evaluation process of thesystem e loads show a random energy consumption ontypical summer days and on typical winter days e overallefficiency varies from 3349 to 5653 in the 6 scenariose simulation of supplying the designed load and pro-cessing the optimisation on the system shows that thesystemrsquos energy performance is enhanced from 3 to2235

is study shows that the idea of this integrated system isan efficient solution of multienergy products generationAlthough this structure of the energy system has the abilityof handling the dynamic power supplying with flexibilitymore energy source is suggested in the future research

51 Future Work of Ais Project

(1) As an off-grid energy system the wind turbine andthe photovoltaic panel have great potential to supplygreen energy to the new designed CCHP systemMore energy sources are looking forward to beingintegrated in

(2) A more detailed thermal dynamic analysis about thesystemrsquos thermal energy supplying will be carried onin the future work

(3) e control strategy of the system has potential to beimproved In the future work the intelligent algo-rithm will be added in for more flexible and efficientenergy distribution

Nomenclature

Pfuel Fuel consumption (primary energy consumption)of the trigeneration system

Pengine Power output of the engine generatorPs Power output of the energy storage systemPSU Power output of the supercapacitorPBT Power output of batteriesPLOAD Electrical power demand (load)PR Power output of the recovery systemPTminusST Power output of the thermal storage systemPET Power consumption of transferring from

electricity to thermal energyPEC Power consumption of cooling generated by

electricityPOE Power generated from the ORC systemPOT Rate of low level heat from the ORCPORC Power output of the ORC systemPCminusL Cooling loadP Electrical output powerSOC Batteriesrsquo state of chargete At the end of operation timeCS Batteriesrsquo capacityWp Power of the pumpξ Evaporator effectivenessQep Heat obtained by the evaporatorQro Recovered thermal energyE Generated Electromotive Force (EMF) in volts

φ Air gap flux per pole in webersΩ Angular velocity in radians per secondTd Developed torque in Newton-metersTd Armature current in amperesK Constant for the given machineη Electrical efficiency of the engineηC Efficiency of recovery heat transferring to cooling

energyηp Efficiency of the pumpηengine Efficiency of the engineηrecovery Efficiency of the recovery processηORC Efficiency of the ORCα Ratio of recovery heat transferring to thermal load

supplyingβ Ratio of recovery heat transferring to cooling

supplyingσ Self-discharge rate of the batteryηBat Electricity efficiency of the batteryε Dielectric constantE e electric field intensityC Volume of the supercapacitortprime OptimisedmORC Organic fluid mass flow rateWt Turbine output power

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors gratefully acknowledge the support by EPSRCGlobal Secure (EPK0046891) and National Natural ScienceFoundation of China (NSFC) (No51709121)

References

[1] S Yang M Liu and F Fang State-of-the-Art of CombinedCooling Heating and Power (CCHP) Systems John Wiley ampSons Ltd Hoboken NJ USA 2017

[2] L Grazia ldquoAn innovative trigeneration system using thebiogas as renewable energyrdquo Chinese Journal of ChemicalEngineering vol 26 no 5 pp 1179ndash1191 2018

[3] M Ebrahimi and A Keshavarz ldquoClimate impact on the primemover size and design of a CCHP system for the residentialbuildingrdquo Energy amp Buildings vol 54 pp 283ndash289 2012

[4] S Sanaye and A Sarrafi ldquoOptimization of combined coolingheating and power generation by a solar systemrdquo RenewableEnergy vol 80 pp 699ndash712 2015

[5] G Abdollahi andMMeratizaman ldquoMulti-objective approachin thermoenvironomic optimization of a small-scale dis-tributed CCHP system with risk analysisrdquo Energy andBuildings vol 43 no 11 pp 3144ndash3153 2011

[6] D Maraver ldquoAssessment of CCHP systems based on biomasscombustion for small-scale applications through a review of

Complexity 13

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity

the technology and analysis of energy efficiency parametersrdquoApplied Energy vol 102 pp 1303ndash1313 2013

[7] F Fang Q HWang and Y Shi ldquoA novel optimal operationalstrategy for the CCHP system based on two operating modesrdquoIEEE Transactions on Power Systems vol 27 no 2pp 1032ndash1041 2011

[8] W Liu ldquoHourly operation strategy of a CCHP system withGSHP and thermal energy storage (TES) under variable loadsa case studyrdquo Energy amp Buildings vol 93 pp 143ndash153 2015

[9] Y Li ldquoAn optimal planning method for CCHP systems basedon operation simulationrdquo in Proceedings of the 2018 IEEEInternational Conference on Smart Energy Grid Engineering(SEGE) IEEE Oshawa Canada August 2018

[10] G Yang C Y Zheng and X Q Zhai ldquoInfluence analysis ofbuilding energy demands on the optimal design and per-formance of CCHP system by using statistical analysisrdquoEnergy amp Buildings vol 153 pp 297ndash316 2017

[11] C Yang ldquoDesign and simulation of gas turbine-based CCHPcombined with solar and compressed air energy storage in ahotel buildingrdquo Energy amp Buildings vol 153 pp 412ndash4202017

[12] X-P Chen Y-D Wang and W U L Qin-Mu ldquoA bio-fuelpower generation system with hybrid energy storage under adynamic programming operation strategyrdquo IEEE Accessvol 7 pp 64966ndash64977 2019

[13] L Urbanucci F DrsquoEttorre and D Testi ldquoA comprehensivemethodology for the integrated optimal sizing and operationof cogeneration systems with thermal energy storagerdquo En-ergies vol 12 no 5 p 875 2019

[14] Y Wang F Ronilaya X Chen and A P Roskilly ldquoModellingand simulation of a distributed power generation system withenergy storage to meet dynamic household electricity de-mandrdquo Applied Aermal Engineering vol 50 no 1pp 523ndash535 2013

[15] W Lang ldquoFeasibility analysis of CCHP system with thermalenergy storage driven by micro turbinerdquo Energy Procediavol 105 pp 2396ndash2402 2017

[16] A H Nosrat L G Swan and J M Pearce ldquoImprovedperformance of hybrid photovoltaic-trigeneration systemsover photovoltaic-cogen systems including effects of batterystoragerdquo Energy vol 49 pp 366ndash374 2013

[17] J I Libich ldquoSupercapacitors properties and applicationsrdquoJournal of Energy Storage vol 17 pp 224ndash227 2018

[18] X P Chen N Hewitt Z T Li Q M Wu X Yuan andT Roskilly ldquoDynamic programming for optimal operation ofa biofuel micro CHP-HES systemrdquo Applied Energy vol 208pp 132ndash141 2017

[19] F Fang ldquoComplementary configuration and operation of aCCHP-ORC systemrdquo Energy vol 46 no 1 pp 211ndash220 2012

[20] A Mohammadi and M Mehrpooya ldquoEnergy and exergyanalyses of a combined desalination and CCHP system drivenby geothermal energyrdquo AppliedAermal Engineering vol 116pp 685ndash694 2017

[21] A Sadreddini ldquoExergy analysis and optimization of a CCHPsystem composed of compressed air energy storage systemand ORC cyclerdquo Energy Conversion amp Management vol 157pp 111ndash122 2018

[22] V Zare and H Rostamnejad Takleh ldquoNovel geothermaldriven CCHP systems integrating ejector transcritical CO2and Rankine cycles thermodynamic modeling and para-metric studyrdquo Energy Conversion and Management vol 205Article ID 112396 2020

[23] S Wang and Z Fu ldquoermodynamic and economic analysisof solar assisted CCHP-ORC system with DME as fuelrdquo

Energy Conversion and Management vol 186 pp 535ndash5452019

[24] Y Zhu W Li and G Sun ldquoermodynamic analysis ofevaporation temperature glide of zeotropic mixtures on theORC-CCHP system integrated with ejector and heat pumprdquoEnergy Procedia vol 158 pp 1632ndash1639 2019

[25] P J Mago N Fumo and L M Chamra ldquoPerformanceanalysis of CCHP and CHP systems operating following thethermal and electric loadrdquo International Journal of EnergyResearch vol 33 no 9 pp 852ndash864 2009

[26] J Ji C He Z Xiao et al ldquoSimulation study of an ORC systemdriven by the waste heat recovered from a trigenerationsystemrdquo Energy Procedia vol 105 pp 5040ndash5047 2017

[27] Y Hong Dong W Yao Dang W Da T Roskilly H S Chenand C Q Tan ldquoPerformance of a micro-cogeneration systemrunning with preheated raw vegetable oilsrdquo Applied Me-chanics and Materials vol 453 p 6 2013

[28] E Galloni G Fontana and S Staccone ldquoDesign and ex-perimental analysis of a mini ORC (organic Rankine cycle)power plant based on R245fa working fluidrdquo Energy vol 90pp 768ndash775 2015

[29] L Luo Y Wang H Chen X Zhang and T Roskilly ldquoORCunits driven by engine waste heat-a simulation studyrdquo EnergyProcedia vol 142 pp 1022ndash1027 2017

[30] F Tsang ldquoWhat works in changing energy-using behavioursin the homerdquo Final Report A Rapid Evidence AssessmentLondon UK 2012

14 Complexity