Research ArticleAdaptive Robust SMC-Based AGC Auxiliary Service Control forESS-Integrated PVWind Station

Xiao-Ling Su 12 Zheng-Kui Zhao12 Yang Si2 and Yong-Qing Guo2

1School of Water Resources and Electric Power Qinghai University Xining 810016 China2Qinghai Key Lab of Efficient Utilization of Clean Energy (Tus-Institute for Renewable Energy) Qinghai UniversityXining 810016 China

Correspondence should be addressed to Xiao-Ling Su suxiaolingqhueducn

Received 19 August 2020 Revised 11 September 2020 Accepted 18 September 2020 Published 7 October 2020

Academic Editor Qiang Chen

Copyright copy 2020 Xiao-Ling Su et al )is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Power source structure has developed significantly because of the increasing share of renewable energy sources (RESs) in thepower system RESs bring inevitable impacts on power system frequency voltage regulation and power system stability )econventional automatic generation control (AGC) loops which relay only on the synchronous generating units cannot meet therequirements of these new circumstances )is paper presents an ESS-integrated PVwind station topology and its controlstructure for AGC auxiliary service in order to provide existing RESs the additional functionality of AGC auxiliary service withoutchanging their control strategies conceived forMPPTmode)e shifting operationmodes and external disturbances make ESSs inan ESS-integrated PVwind station inherently nonlinear and time variable )erefore an adaptive robust sliding-mode control(ARSMC) system is proposed )e ARSMC colligates the advantages of adaptive control and SMC contains state feedback termrobust control term and adaptive compensation term )e strictly logical and rigorous proof using Lyapunov stability analysisindicates the ARSMC system is insensitive to parametric uncertainties and external disturbances meanwhile it guarantees fastresponse speed and high control precision )e case studies on NI-PXI platform validate the effectiveness of theproposed approach

1 Introduction

Power systems see more and more photovoltaic (PV) andwind generation integration Within increasing renewableenergy sources (RESs) penetration level despite the ad-vantages like environmental friendly and sustainable de-velopment they also bring problems to the utility grid [1ndash3]Adjusting power source structure brings an inevitable im-pact on power system primary frequency response due to theconventional generators reduction and consequent loss ofinertia [4] )erefore the provision of ancillary services isbecoming an increasingly challenging task to systemoperation

To deal with these issues some grid corporation releasedrelated regulation and technical standards requesting fastfrequency response from PV station and wind farm [5] Xuet al [6] proposed dynamic gain tuning control approach for

AGC with effects of wind power Wei et al [7] proposed anoptimal automatic generation controllers in a multiareainterconnected power system with utility-scale PV plants Ingeneral the typical PV and wind generation operate withmaximum power point tracking mode [8ndash10] and thecorresponding control algorithms have been developed andrefined along the years being now a mature technologyavailable in the market It is nearly impractical to requestprimary frequency response from these intermittent RESsAnd the resultant damages of reserve capacity requirementsfrom RESs are solarwind power curtailment and lowereconomic efficiency

Energy storage systems (ESSs) offer a promising capa-bility of voltage and frequency control for power systems dueto recent developments in technologies and plummetingcost [11ndash14] Research work indicates that one 10MW366MWh battery energy storage system can replace a

HindawiComplexityVolume 2020 Article ID 8879045 10 pageshttpsdoiorg10115520208879045

36MW conventional automatic generation control (AGC)units without compromising on the AGC performance ofthe system for day-to-day variations experienced in thesystem load [15] Using ESSs to add regulation capacity andimprove dynamic performance of AGC particularly at thehigh RESs penetration power systems is a feasible solution[16ndash18]

)erefore it is more practical to use commercial PVwind generation and add extra customized ESSs to provideextra functionalities namely ESS-integrated PVwind sta-tions)e ESSs can eliminate peak and filling the through forPVwind generation system equip these stations with fastfrequency response and avoid voltage fluctuations and otherpower quality issues in the main grid )ese features areimportant as prime movers are renewable energy sourceswhich are characterized by having a stochastic and inter-mittent behavior

Beside fast dynamic response the ESSs are expected tohave the characteristics of high control precision andstrong antidisturbance capacity along with the basic re-quirements like high efficiency and low output current totalharmonics distortion Many techniques have been pro-posed for ESSs to achieve those control objectives in-cluding proportional-integral-derivative control model-based control robust control and fuzzy control [19ndash24]Most likely traditional control technique only guaranteesthe desired closed-loop response at the expected operatingpoint and there are trade-offs between control like per-formances response speed static precision robustness andtracking performance [25ndash28] In addition power elec-tronic equipment and disturbance give ESSs multivariablestructure and highly coupled nonlinearity which bringsgreat challenges to conventional control techniquesHence it seems natural to explore other nonlinear controlsthat can overcome the uncertain challenges and to achievebetter compensation and global stability in all operationmodes

Sliding-mode control (SMC) [29ndash31] is one of the mosteffective nonlinear robust control strategies since it providesthe system dynamics with an invariance property to un-certainties once the system dynamics is controlled in thesliding mode [32 33] SMC has been applied to ESSs forfrequency regulation [34ndash36] power management [37]operation state [38] and voltage control [39] Morstyn et al[40] proposed a multiagent sliding-mode control for state ofcharge balancing between battery energy storage systems)e switching frequency variable or chattering is an inherentproblem of SMC and many intelligent control strategieshave been used to improve the conventional SMC [41 42]and avoid chattering Sebaaly et al [43] proposed a constantswitching frequency operation that allows chattering com-pensation Wang et al [44] proposed SMC-based ESSs toimprove the controllability of the microgrid and guaranteeseamless transition between its grid connected and islandedoperation modes and use PWM to avoid chattering prob-lems Su et al [45] developed an adaptive sliding-modecontrol with hysteresis control strategy for hybrid ESSs toeliminate the current fluctuating and improve its operatingstability

ESSs in practical ESS-integrated PVwind stations facevarious disturbances continuously and these uncertaintiesand parameter variations make accurate mathematicalmodel building challenging More seriously detectionlimitation and time delay bring more problems to thecontrol system It is very difficult to achieve outstandingresults by conventional SMC)erefore this paper proposesan adaptive robust sliding-mode control (ARSMC) system tocolligate the advantages of adaptive control and SMCeliminate the control error under various disturbances andguarantee fast response to AGC demand providing quali-tative improvements over existing AGC auxiliary service

2 Proposed ESS-Integrated PVWind Station

In this section the construction of the proposed ESS-inte-grated PVwind station is presented in Figure 1 whichincludes photovoltaic (PV) system wind generation andESSs )e ESS-integrated PVwind station is connected tothe power grid through a circuit breaker (CB) andtransformer

Note that most PVwind stations integrate to the utilitynetwork through cable or overhead line and the RESs outputpower variations aremore likely to cause voltage fluctuationsor voltage sags )ese problems may enforce RESs discon-nection from the power grid )erefore it is necessary to usethe ESSs to smooth active and reactive power and improvethe power quality

)e ESSs can flexibly importexport power fromto thegrid and compensate the power variations or reduce thepower fluctuations caused by the RESs It also can fix thestation output voltage and frequency or response to powergrid dispatching from AGC

3 AGC Auxiliary Service Control

)e control structure of ESS-integrated PVwind station-based AGC auxiliary service control is shown in Figure 2 Allgenerators in power systems operate based on daily dispatchschedule of dispatching center Meanwhile AGC monitornetwork parameters like frequency tie-line power flow andoutput power of generators calculate the area control error(ACE) according to the control scheme

Once the voltagefrequency reaches a boundary layer avoltagefrequency regulation power is produced and it isdefined as follows

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi (1)

where ΔPrequestΣ is the total amount power demand that

contains three time scales )ey are ΔPdailytimesrequestΣ

ΔPhourlytimesrequestΣ and ΔPminutestimesrequest

Σ ΔPrequestΣC is the power

demand from conventional power plant (eg frequencyregulation power plants) ΔPrequest

ΣL is the power demandfrom ESS-integrated PVwind station

Once the local energy management system (LEMS) ofthe ESS-integrated PVwind station receives the dispatchinstruction or ΔPrequest

ΣL it decomposes as

2 Complexity

ΔPrequestΣL ΔPrequest

ΣPV + ΔPrequestΣwind + ΔPrequest

ΣESSs (2)

where ΔPrequestΣPV is the power demand from PV generation

ΔPrequestΣwind is the power demand from wind generation andΔPrequestΣESSs is the power demand from ESSs )ere are three

operation modes for ESSs

Mode 1 is the local control mode whichmeans the ESSsare controlled by LEMS with the shortest communi-cation delay and most flexible and fastest responseESS-integrated PVwind station operates as a self-control unit (a) Chargedischarge based on SOC and

the station operation status for example storage sur-plus electricity to reduce solarwind power curtailment(b) Smooth RESs output power ESSs compensate thepower variations or reduce the power fluctuationscaused by PVwind generation (c) Voltagefrequencycontrol ESSs fix the ESS-integrated PVwind stationoutput voltage and frequencyMode 2 is the frequencyvoltage regulation responsemode ESSs generate power according to ΔPrequest

ΣESSs quickly responding to the director of AGCMode 3 is the dispatch curve follow mode ESSs arecontrolled to follow the dispatch curve or to com-pensate PVwind generation to decrease predictionerror

After each control cycle ESSs feedback their status in-cluding themaximum adjustable capacity and time to LEMS)en LEMS integrates all system parameters as adjustablecapacity of ESS-integrated PVwind station and feedback todispatching center

ΔPcapacityΣL ΔPcapacity

ΣPV + ΔPcapacityΣwind + ΔPcapacity

ΣESSs αj1113960 1113961 PNj1113960 1113961

(3)

where [Pj] is the rated power of each generation unit [αj] isa coefficient matrix and αj is the corresponding adjustmentcoefficient

)e AGC auxiliary service control is integrated withexisting AGC control strategies for voltagefrequency reg-ulation and power dispatching Power grid dispatchingcenter only needs to add an instruction allocation modulefor the ESS-integrated PVwind station and update its co-efficient matrix [αj] Achieve the mutual cooperation offrequency regulation resources within fewer changes in theAGC system service modules which is greatly engineeringsignificant

4 ESSs Modeling and ARSMC System

In this section the model of the ESS in PV station and theproposed ARSMC system are presented

41 ESSs Modeling )e optimization objectives of a singleESS can be summarized as follows

Figure 3 shows the circuit topology of the ESS in ESS-integrated PVwind station )e ESS consists an electricbattery and bidirectional DC-to-AC converter with induc-tor-capacitor (LC) filter are connected to the AC bus to-gether with the RESs

In this figure ua ub uc are the AC bus voltages (perphase) and ia ib ic are the AC currents (per phase) of theESSs and the convertor always works symmetricallyLa Lb Lc and Cfa Cfb Cfc are the filter inductor and ca-pacitor values respectively ra rb rc represent the equivalentseries resistor (ESR) of the converter inductor and powerline rfa rfb rfc represent the ESR of the filter capacitor

)e states of the switches of the n-th leg (n 1 2 3) canbe represented by the time-dependent variable Sn and

Grid

ESSs

CB

PV system

Wind generation

Figure 1 ESS-integrated PVwind station

AGC function module

Dispatching center

Scheduling function module

Conventional power plants

ESS-based PVwind station

PV generation

Wind generation

ESSs

Local energy

management system

ΔPΣrequest

ΔPΣLEMSrequest

ΔPΣCrequest

ΔPΣESSscapacity

ΔPΣESSsrequest

ΔPΣwindrequest

ΔPΣPVrequest

ΔPΣPVcapacity = [αPV][PNPV]

Figure 2 Control structure of ESS-integrated PVwind station-based AGC auxiliary service

Complexity 3

defined as Sn 1 if T+n is on and Tminus

n is off while Sn 0 if Tminusn is

on and T+n is off

)is switching strategy together with a small dead timegenerator is able to avoid internal shorts between the twoswitches of each bridge leg and the switches will be incomplementary states Assuming that compared to themodulation and natural frequencies the switching fre-quency is relatively high )erefore the equivalent dynamicmodel of Figure 3 is obtained as shown in Figure 4 where s isthe Laplace operator the power gain is defined askPWM (Udcutri) where utri is the amplitude of a triangularcarrier signal

)erefore the dynamic equation of the ESS during thepositive-half period can be represented as

Ldia

dt ua minus ria +

sb + sc minus 2sa

3Udc

Ldibdt

ub minus rib +sa + sc minus 2sb

3Udc

Ldic

dt uc minus ric +

sa + sb minus 2sc

3Udc

CdUdc

dt saia + sbib + scic minus idc

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(4)

And the dynamic equation of the ESSs under dq0synchronous rotating coordinate system can be representedas

ud Ldiddt

minus ωLiq + rid + sdUdc

uq Ldiq

dt+ ωLid + riq + sqUdc

dUdc

dt minus

idc

C+1C

sdid + sqiq1113872 1113873

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(5)

where ud uq and id iq are the AC voltage and current underdq0 synchronous rotating coordinate system respectivelyucond and uconq are the control signal under dq0 synchronousrotating coordinate system L is the equivalent inductor andC is the capacitor value on the convertor side r representsthe equivalent series resistor of the converter inductor and

power line Define irefd and irefq as the reference of id and iqEquation (5) is rearranged as

L _eid ud + ωLiq minus rid minus ucond minus L _irefd

L _eiq uq minus ωLid minus riq minus uconq minus L _irefq

⎧⎪⎨

⎪⎩(6)

where eid id minus irefd and eiq iq minus irefq Equation (6) isrearranged as follows

_E aI minus bU + bUc + cPI minus _Iref (7)

where E [eid middot eiq]T I [iid middot iiq]T U [uid middot uiq]TUc [ucond middot uconq]T Iref [drefd middot qrefq]T and

P 0 1

minus 1 01113890 1113891 a minus (rL) b minus (1L) c ω

According to the aforementioned discussion the ESSsare nonlinear time-variable system and there are uncer-tainties in the ESS-integrated PVwind station which arecaused by parametric variations or external disturbances)erefore equation (7) should be modified as follows

_E (a + Δa)I minus (b + Δb)U +(b + Δb)Uc +(c + Δc)PI

minus _Iref + Um

(8)

where ΔaΔb and Δc represent the system parameter var-iations and Um represents the external disturbances oruncertainties Define

W ΔaI minus ΔbU + ΔbUc + ΔcPI + Um (9)

)us equation (8) is rearranged as_E aI minus bU + bUc + cPI minus _Iref + W (10)

)e bound of the uncertainty is assumed to meet thefollowing inequality

|W|leQ (11)

where Q [QdQq]T represents the unknown positiveconstants

42ARSMCSystem )eproposed control system for ESSs isdivided into two main parts as illustrated in Figure 5 )efirst part is the primary control which produces the referencesignals Iref based on ΔPrequest

ΣESSs and the operation mode ofESSs)e second part is the ARSMC system which generatesthe control signal Ucontrol In this part the state feedbackterm gives concise sliding surface while makes full use of

UdcUtri 1sC

r

1sL

1Z

Ucontrol Ui (s)+__

I0 (s)

+_IL (s) U0 (s)

Im (s)

+

Figure 4 Equivalent dynamic model of the ESS in ESS-integratedPVwind station

ra

rbrc

LaLbLc

iaibic

T1+ T2

+ T3+

T1ndash T2

ndash T3ndash

Udc

Cfa Cfb Cfc

rfa rfb rfc

uaubuc

Figure 3 Circuit topology of the ESS in ESS-integrated PVwindstation

4 Complexity

pole assignment and state feedback Robust control termforms the structure of ESSs model Adaptive compensationterm adjusts the control law based on uncertainties ordisturbance in real time As the disturbance is unknownvariables and cannot be specified or determine as a fixedvalue introducing an adaptive strategy is a more practicalsolution

)e control objective of the ARSMC system is to makethe output power of the ESSs equal to ΔPrequest

ΣESSs Specificallyit has to enforce id iq to track its reference irefd irefq orenforce I follow its reference Iref

First define a sliding surface as equation (12) to obtain asliding motion through the entire state trajectory whileeliminate static control error

S E + 1113946 (a minus bK)Eds (12)

where S [Sd Sq]T and K [KdKq]T is the control coef-ficient matrix

Second design the control scheme as follows

Uc U1 + U2 + U3 (13)

where

U1 U + bKI

U2 bminus 1

(minus εsign(S)) + cPI + aI minus _Iref

U3 bSabs(minus bS)minus 1 1113954Q

(14)

where U1 is the state feedback term U2 is the robust controlterm and U3 is the adaptive compensation term ε is a smallpositive constant sign(S) [sign(sd) middot sign(sq)]T beingsign(middot) the sign function and abs(middot) the absolute valuefunction 1113954Q is the estimated value ofQ define the parameterdeviation as 1113957Q 1113954Q minus Q and the adaptive law as

1113954Qmiddot

abs(minus bS) (15)

Proof Sliding surface and parameters composing theadaptive law are based on the difference between thenominal nonlinear system and the uncertain nonlinearsystem and it satisfies the global Lyapunov stability con-dition Using Lyapunov stability analysis to derive the ex-istence condition of the sliding mode and setting theLyapunov function as

V S2 + 1113957Q2

2 (16)

Taking the derivative of equation (16)

_V S _S + 1113957Q 1113957Qmiddot

(17)

Taking the derivative of equation (12) along (9) andsubstituting (13) and (15) into (17) to simplify equation (17)as

_V S minus bU3 + εsign(S) + W + KE( 1113857 + 1113957Q 1113957Qmiddot

le ε middot abs(S)

(18)

)erefore _Vlt 0 when abs(S)ne 0 which ensures theasymptotically stable behavior for the sliding-mode systemon the sliding surface (12)

Once the system trajectory reaches the sliding surface ityields S _S 0

_S aI minus bU + bUc + cPI minus _Iref + W minus aE + bKE 0

(19)

Deduce the equivalent control from equation (19) as

Ueq minus bminus 1

aIref minus bU + cPI minus _Iref + W minus aE + bKE1113872 1113873

(20)

Substitute equation (20) into equation (8)_E aE minus bKE (21)

It implies that probably designed state feedback coeffi-cient K guarantees the robustness of sliding mode (21) alongwith dynamics features like rising time and maximumovershoot

5 Case Studies

A simulation platform under MATLAB environment basedon Figure 1 is developed to validate the AGC auxiliaryservice performance of the ESS-integrated PVwind stationfurthermore case studies were conducted on the NI-PXI(PCI Extensions for Instrumentation PXI) platform toverify the proposed ARSMC system as shown in Figure 6

)e key parameters of the developed model are given inTable 1 )e ESS-integrated PVwind station in Figure 1 isconnected to the grid through a 380V10 kV transformer A12MW synchronous machine in the 10 kV grid works as aconventional regulation power source responds to AGCAccording to the sliding surface (12) the control coefficientmatrix is designed to guarantee the robustness of the slidingmode show as equation (21) as well as the dynamic per-formance and stability set K [001805]

)e synchronous machine delivers 10MW active powerto the power grid )e ESSs in ESS-integrated PVwindstation deliver 100 kW active power to the power grid Setdispatch instruction from AGC ΔPrequest

Σ to 500 kW toeliminate the frequency deviation Figure 7 gives the fre-quency of this 10 kV power system with the synchronous

Adoptive compensation

term

u3

u1

Robust control term

State feedback term

Ucontrolu2

ARSMC system

Mode 3

Mode 2

Mode 1

Primary control

IrefΔPΣESSsrequest

ΔPΣESSs1request

ΔPΣESSs2request

ΔPΣESSs3request

Figure 5)e control system of the ESS in ESS-integrated PVwindstation

Complexity 5

machine working as a regulation power source response toAGC which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

500 + 0 500 kW

(22)

)en in the same scenario both the synchronous ma-chine and ESS-integrated PVwind station provide AGCauxiliary service which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

200 + 300 500 kW

(23)

ΔPrequestΣL ΔPrequest

ΣPV + ΔPrequestΣwind + ΔPrequest

ΣESSs

0 + 0 + 300 300 kW(24)

Table 1 Key parameters of ESSs and heat pumps

ESS parametersESS battery size 50 kWhDC voltage 1000VAC voltage 380VFilter capacitance 3 μFFilter inductance 15mHPower system parametersVoltage (RMS) (phase) 10 kVFrequency 50Hz

20 25 30 35 4015Time (s)

498

4985

499

4995

50

5005

501

ΔPΣCirequest

ΔPΣCirequest + ΔPΣESSs

request

Figure 7 )e 10 kV power system frequency

P (k

W)

ndash20

0

20

40

60

2 3 4 5 61Time (s)

Figure 8 Output power of the ESS (output power increases from10 kW to 40 kW)

abc

265 27 275 28 285 29 295 326Time (s)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

Figure 9 Voltage waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

Figure 6 )e NI-PXI platform

abc

265 27 275 28 285 29 295 326Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 10 Current waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

6 Complexity

In order to verify an extreme condition PV and windgeneration operate at MPPT mode and only the ESSs re-spond to AGC )e AGC auxiliary service control canimprove the existing AGC control performance with quickresponse and steady state

Figure 8 presents output power of one ESS which is10 kW at the beginning and then it goes up to 40 kW re-sponse to AGC demand Voltage and current waveforms atthe AC side are shown in Figures 9 and 10 )e output

ndash20

0

20

40

60

P (k

W)

55 6 65 7 75 85Time (s)

Figure 11 Output power of the ESS (output power falls from40 kW to 10 kW)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

605 61 615 62 625 63 635 646time (s)

abc

Figure 12 Voltage waveforms of the ESS (output power falls from40 kW to 10 kW)

abc

605 61 615 62 625 63 635 646Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 13 Current waveforms of the ESS (output power falls from40 kW to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 14 Experimental voltage waveform of the ESS (outputpower is set to 10 kW)

0

50

ndash50Time (s)

i abc (

A)

Figure 15 Experimental current waveform of the ESS (outputpower is set to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 16 Experimental voltage waveform of the ESS (outputpower is set to 40 kW)

0

i abc (

A)

100

ndash100

Time (s)

200

ndash200

Figure 17 Experimental current waveform of the ESS (outputpower is set to 40 kW)

Complexity 7

current of the ESSs does not have any inrush spikes duringthe entire transition period and there is no voltage per-turbation along the operation

Figure 11 presents the output power of one ESS which is40 kW at the beginning and then it falls from 40 kW to10 kW response to AGC instruction Its voltage and currentwaveforms at the AC side are shown in Figures 12 and 13)e output current of the ESSs does not have any inrushspikes during the entire transition period and there is novoltage perturbation along the operation

Figures 14 and 15 show the experimental waveforms ofthe ESS when its output power reference is set at 10 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

Figures 16 and 17 show the experimental waveforms ofthe ESS when its output power reference is set at 40 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

)ese results indicate smooth and stable operation of theESS-integrated PVwind station and show that the ESSsprovide PV and wind generation additional AGC auxiliaryservice functionality without changing their inner controlstrategies conceived for MPPT mode

6 Conclusions

)e AGC auxiliary service control proposed in this paper isintegrated with existing AGC control strategies Power griddispatching center only needs to add instruction allocationmodule for the ESS-integrated PVwind stations It uses ESSsto add regulation capacity and improve dynamic performanceof AGC without changing the control strategies of RESsconceived for MPPT mode As the ESSs are inherentlynonlinear and time variable the mathematical model is builtconsidering the system parameter variations and disturbancesor uncertainties )e ARSMC-based ESS control system isproposed to deal these control challenges and improve itsstability and dynamic performances )e rigorous proofprocess verifies the ARSMC strategy mathematically)e casestudies on NI-PXI platform shows the fast dynamic responseand robustness performance of the ESSs guaranteeing stableoperation of the ESS-integrated PVwind station as well asvoltage and frequency regulation capability

)e ESSs provide additional AGC auxiliary servicefunctionality without changing RES inner control strategies)e ARSMC-based ESSs is suitable for existing RESs toextend their functions and to form a ESS-integrated PVwind station )e ESSs are independent from the use ofthird-party commercial RESs units which means they donot need specific customized RESs

Data Availability

)e data used to support the findings of the study are in-cluded within the article

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work is supported by the Research on Key Technologiesof Self-Supporting Micro-Renewable Energy Network inQinghai Agricultural and Pastoral Areas No 2018-ZJ-748

References

[1] W Liu G Geng Q Jiang H Fan and J Yu ldquoModel-free fastfrequency control support with energy storage systemrdquo IEEETransactions on Power Systems vol 35 no 4 pp 3078ndash30862020

[2] Y Wang Y Xu Y Tang et al ldquoAggregated energy storage forpower system frequency control a finite-time consensusapproachrdquo IEEE Transactions on Smart Grid vol 10 no 4pp 3675ndash3686 2019

[3] F Cheng L Qu W Qiao C Wei and L Hao ldquoFault di-agnosis of wind turbine gearboxes based on DFIG statorcurrent envelope analysisrdquo IEEE Transactions on SustainableEnergy vol 10 no 3 pp 1044ndash1053 2019

[4] V Knap S K Chaudhary D-I Stroe M SwierczynskiB-I Craciun and R Teodorescu ldquoSizing of an energy storagesystem for grid inertial response and primary frequency re-serverdquo IEEE Transactions on Power Systems vol 31 no 5pp 3447ndash3456 2016

[5] X Sun X Liu S Cheng et al ldquoActual measurement andanalysis of fast frequency response capability of PV-invertersin northwest power gridrdquo Power System Technology vol 41no 9 pp 2792ndash2798 2017

[6] Y Xu F Li Z Jin and M Hassani Variani ldquoDynamic gain-tuning control (DGTC) approach for AGC with effects ofwind powerrdquo IEEE Transactions on Power Systems vol 31no 5 pp 3339ndash3348 2016

[7] Y Wei I Jayawardene and G Kumar VenayagamoorthyldquoOptimal automatic generation controllers in a multi-areainterconnected power system with utility-scale PV plantsrdquoIET Smart Grid vol 2 no 4 pp 581ndash593 2019

[8] C Wei Z Zhang W Qiao and L Qu ldquoAn adaptive network-based reinforcement learning method for MPPT control ofPMSG wind energy conversion systemsrdquo IEEE Transactionson Power Electronics vol 31 no 11 pp 7837ndash7848 2016

[9] D Venkatramanan and V John ldquoDynamic modeling andanalysis of buck converter based solar PV charge controller forimproved MPPT performancerdquo IEEE Transactions on In-dustry Applications vol 55 no 6 pp 6234ndash6246 2019

[10] R B Bollipo S Mikkili and P K Bonthagorla ldquoCriticalreview on PV MPPT techniques classical intelligent andoptimisationrdquo IET Renewable Power Generation vol 14 no 9pp 1433ndash1452 2020

[11] L Meng J Zafar S K Khadem et al ldquoFast frequency re-sponse from energy storage systems-a review of grid stan-dards projects and technical issuesrdquo IEEE Transactions onSmart Grid vol 11 no 2 pp 1566ndash1581 2020

[12] X Xie Y Guo B Wang Y Dong L Mou and F XueldquoImproving AGC performance of coal-fueled thermal gen-erators using multi-MW scale BESS a practical applicationrdquoIEEE Transactions on Smart Grid vol 9 no 3 pp 1769ndash17772018

[13] W Tasnin and L C Saikia ldquoPerformance comparison ofseveral energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plantrdquo IETRenewable Power Generation vol 12 no 7 pp 761ndash772 2018

[14] B Mantar Gundogdu D T Gladwin S Nejad andD A Stone ldquoScheduling of grid-tied battery energy storage

8 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

ΔPrequestΣL ΔPrequest

ΣPV + ΔPrequestΣwind + ΔPrequest

ΣESSs (2)

where ΔPrequestΣPV is the power demand from PV generation

ΔPrequestΣwind is the power demand from wind generation andΔPrequestΣESSs is the power demand from ESSs )ere are three

operation modes for ESSs

Mode 1 is the local control mode whichmeans the ESSsare controlled by LEMS with the shortest communi-cation delay and most flexible and fastest responseESS-integrated PVwind station operates as a self-control unit (a) Chargedischarge based on SOC and

the station operation status for example storage sur-plus electricity to reduce solarwind power curtailment(b) Smooth RESs output power ESSs compensate thepower variations or reduce the power fluctuationscaused by PVwind generation (c) Voltagefrequencycontrol ESSs fix the ESS-integrated PVwind stationoutput voltage and frequencyMode 2 is the frequencyvoltage regulation responsemode ESSs generate power according to ΔPrequest

ΣESSs quickly responding to the director of AGCMode 3 is the dispatch curve follow mode ESSs arecontrolled to follow the dispatch curve or to com-pensate PVwind generation to decrease predictionerror

After each control cycle ESSs feedback their status in-cluding themaximum adjustable capacity and time to LEMS)en LEMS integrates all system parameters as adjustablecapacity of ESS-integrated PVwind station and feedback todispatching center

ΔPcapacityΣL ΔPcapacity

ΣPV + ΔPcapacityΣwind + ΔPcapacity

ΣESSs αj1113960 1113961 PNj1113960 1113961

(3)

where [Pj] is the rated power of each generation unit [αj] isa coefficient matrix and αj is the corresponding adjustmentcoefficient

)e AGC auxiliary service control is integrated withexisting AGC control strategies for voltagefrequency reg-ulation and power dispatching Power grid dispatchingcenter only needs to add an instruction allocation modulefor the ESS-integrated PVwind station and update its co-efficient matrix [αj] Achieve the mutual cooperation offrequency regulation resources within fewer changes in theAGC system service modules which is greatly engineeringsignificant

4 ESSs Modeling and ARSMC System

In this section the model of the ESS in PV station and theproposed ARSMC system are presented

41 ESSs Modeling )e optimization objectives of a singleESS can be summarized as follows

Figure 3 shows the circuit topology of the ESS in ESS-integrated PVwind station )e ESS consists an electricbattery and bidirectional DC-to-AC converter with induc-tor-capacitor (LC) filter are connected to the AC bus to-gether with the RESs

In this figure ua ub uc are the AC bus voltages (perphase) and ia ib ic are the AC currents (per phase) of theESSs and the convertor always works symmetricallyLa Lb Lc and Cfa Cfb Cfc are the filter inductor and ca-pacitor values respectively ra rb rc represent the equivalentseries resistor (ESR) of the converter inductor and powerline rfa rfb rfc represent the ESR of the filter capacitor

)e states of the switches of the n-th leg (n 1 2 3) canbe represented by the time-dependent variable Sn and

Grid

ESSs

CB

PV system

Wind generation

Figure 1 ESS-integrated PVwind station

AGC function module

Dispatching center

Scheduling function module

Conventional power plants

ESS-based PVwind station

PV generation

Wind generation

ESSs

Local energy

management system

ΔPΣrequest

ΔPΣLEMSrequest

ΔPΣCrequest

ΔPΣESSscapacity

ΔPΣESSsrequest

ΔPΣwindrequest

ΔPΣPVrequest

ΔPΣPVcapacity = [αPV][PNPV]

Figure 2 Control structure of ESS-integrated PVwind station-based AGC auxiliary service

Complexity 3

defined as Sn 1 if T+n is on and Tminus

n is off while Sn 0 if Tminusn is

on and T+n is off

)is switching strategy together with a small dead timegenerator is able to avoid internal shorts between the twoswitches of each bridge leg and the switches will be incomplementary states Assuming that compared to themodulation and natural frequencies the switching fre-quency is relatively high )erefore the equivalent dynamicmodel of Figure 3 is obtained as shown in Figure 4 where s isthe Laplace operator the power gain is defined askPWM (Udcutri) where utri is the amplitude of a triangularcarrier signal

)erefore the dynamic equation of the ESS during thepositive-half period can be represented as

Ldia

dt ua minus ria +

sb + sc minus 2sa

3Udc

Ldibdt

ub minus rib +sa + sc minus 2sb

3Udc

Ldic

dt uc minus ric +

sa + sb minus 2sc

3Udc

CdUdc

dt saia + sbib + scic minus idc

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(4)

And the dynamic equation of the ESSs under dq0synchronous rotating coordinate system can be representedas

ud Ldiddt

minus ωLiq + rid + sdUdc

uq Ldiq

dt+ ωLid + riq + sqUdc

dUdc

dt minus

idc

C+1C

sdid + sqiq1113872 1113873

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(5)

where ud uq and id iq are the AC voltage and current underdq0 synchronous rotating coordinate system respectivelyucond and uconq are the control signal under dq0 synchronousrotating coordinate system L is the equivalent inductor andC is the capacitor value on the convertor side r representsthe equivalent series resistor of the converter inductor and

power line Define irefd and irefq as the reference of id and iqEquation (5) is rearranged as

L _eid ud + ωLiq minus rid minus ucond minus L _irefd

L _eiq uq minus ωLid minus riq minus uconq minus L _irefq

⎧⎪⎨

⎪⎩(6)

where eid id minus irefd and eiq iq minus irefq Equation (6) isrearranged as follows

_E aI minus bU + bUc + cPI minus _Iref (7)

where E [eid middot eiq]T I [iid middot iiq]T U [uid middot uiq]TUc [ucond middot uconq]T Iref [drefd middot qrefq]T and

P 0 1

minus 1 01113890 1113891 a minus (rL) b minus (1L) c ω

According to the aforementioned discussion the ESSsare nonlinear time-variable system and there are uncer-tainties in the ESS-integrated PVwind station which arecaused by parametric variations or external disturbances)erefore equation (7) should be modified as follows

_E (a + Δa)I minus (b + Δb)U +(b + Δb)Uc +(c + Δc)PI

minus _Iref + Um

(8)

where ΔaΔb and Δc represent the system parameter var-iations and Um represents the external disturbances oruncertainties Define

W ΔaI minus ΔbU + ΔbUc + ΔcPI + Um (9)

)us equation (8) is rearranged as_E aI minus bU + bUc + cPI minus _Iref + W (10)

)e bound of the uncertainty is assumed to meet thefollowing inequality

|W|leQ (11)

where Q [QdQq]T represents the unknown positiveconstants

42ARSMCSystem )eproposed control system for ESSs isdivided into two main parts as illustrated in Figure 5 )efirst part is the primary control which produces the referencesignals Iref based on ΔPrequest

ΣESSs and the operation mode ofESSs)e second part is the ARSMC system which generatesthe control signal Ucontrol In this part the state feedbackterm gives concise sliding surface while makes full use of

UdcUtri 1sC

r

1sL

1Z

Ucontrol Ui (s)+__

I0 (s)

+_IL (s) U0 (s)

Im (s)

+

Figure 4 Equivalent dynamic model of the ESS in ESS-integratedPVwind station

ra

rbrc

LaLbLc

iaibic

T1+ T2

+ T3+

T1ndash T2

ndash T3ndash

Udc

Cfa Cfb Cfc

rfa rfb rfc

uaubuc

Figure 3 Circuit topology of the ESS in ESS-integrated PVwindstation

4 Complexity

pole assignment and state feedback Robust control termforms the structure of ESSs model Adaptive compensationterm adjusts the control law based on uncertainties ordisturbance in real time As the disturbance is unknownvariables and cannot be specified or determine as a fixedvalue introducing an adaptive strategy is a more practicalsolution

)e control objective of the ARSMC system is to makethe output power of the ESSs equal to ΔPrequest

ΣESSs Specificallyit has to enforce id iq to track its reference irefd irefq orenforce I follow its reference Iref

First define a sliding surface as equation (12) to obtain asliding motion through the entire state trajectory whileeliminate static control error

S E + 1113946 (a minus bK)Eds (12)

where S [Sd Sq]T and K [KdKq]T is the control coef-ficient matrix

Second design the control scheme as follows

Uc U1 + U2 + U3 (13)

where

U1 U + bKI

U2 bminus 1

(minus εsign(S)) + cPI + aI minus _Iref

U3 bSabs(minus bS)minus 1 1113954Q

(14)

where U1 is the state feedback term U2 is the robust controlterm and U3 is the adaptive compensation term ε is a smallpositive constant sign(S) [sign(sd) middot sign(sq)]T beingsign(middot) the sign function and abs(middot) the absolute valuefunction 1113954Q is the estimated value ofQ define the parameterdeviation as 1113957Q 1113954Q minus Q and the adaptive law as

1113954Qmiddot

abs(minus bS) (15)

Proof Sliding surface and parameters composing theadaptive law are based on the difference between thenominal nonlinear system and the uncertain nonlinearsystem and it satisfies the global Lyapunov stability con-dition Using Lyapunov stability analysis to derive the ex-istence condition of the sliding mode and setting theLyapunov function as

V S2 + 1113957Q2

2 (16)

Taking the derivative of equation (16)

_V S _S + 1113957Q 1113957Qmiddot

(17)

Taking the derivative of equation (12) along (9) andsubstituting (13) and (15) into (17) to simplify equation (17)as

_V S minus bU3 + εsign(S) + W + KE( 1113857 + 1113957Q 1113957Qmiddot

le ε middot abs(S)

(18)

)erefore _Vlt 0 when abs(S)ne 0 which ensures theasymptotically stable behavior for the sliding-mode systemon the sliding surface (12)

Once the system trajectory reaches the sliding surface ityields S _S 0

_S aI minus bU + bUc + cPI minus _Iref + W minus aE + bKE 0

(19)

Deduce the equivalent control from equation (19) as

Ueq minus bminus 1

aIref minus bU + cPI minus _Iref + W minus aE + bKE1113872 1113873

(20)

Substitute equation (20) into equation (8)_E aE minus bKE (21)

It implies that probably designed state feedback coeffi-cient K guarantees the robustness of sliding mode (21) alongwith dynamics features like rising time and maximumovershoot

5 Case Studies

A simulation platform under MATLAB environment basedon Figure 1 is developed to validate the AGC auxiliaryservice performance of the ESS-integrated PVwind stationfurthermore case studies were conducted on the NI-PXI(PCI Extensions for Instrumentation PXI) platform toverify the proposed ARSMC system as shown in Figure 6

)e key parameters of the developed model are given inTable 1 )e ESS-integrated PVwind station in Figure 1 isconnected to the grid through a 380V10 kV transformer A12MW synchronous machine in the 10 kV grid works as aconventional regulation power source responds to AGCAccording to the sliding surface (12) the control coefficientmatrix is designed to guarantee the robustness of the slidingmode show as equation (21) as well as the dynamic per-formance and stability set K [001805]

)e synchronous machine delivers 10MW active powerto the power grid )e ESSs in ESS-integrated PVwindstation deliver 100 kW active power to the power grid Setdispatch instruction from AGC ΔPrequest

Σ to 500 kW toeliminate the frequency deviation Figure 7 gives the fre-quency of this 10 kV power system with the synchronous

Adoptive compensation

term

u3

u1

Robust control term

State feedback term

Ucontrolu2

ARSMC system

Mode 3

Mode 2

Mode 1

Primary control

IrefΔPΣESSsrequest

ΔPΣESSs1request

ΔPΣESSs2request

ΔPΣESSs3request

Figure 5)e control system of the ESS in ESS-integrated PVwindstation

Complexity 5

machine working as a regulation power source response toAGC which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

500 + 0 500 kW

(22)

)en in the same scenario both the synchronous ma-chine and ESS-integrated PVwind station provide AGCauxiliary service which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

200 + 300 500 kW

(23)

ΔPrequestΣL ΔPrequest

ΣPV + ΔPrequestΣwind + ΔPrequest

ΣESSs

0 + 0 + 300 300 kW(24)

Table 1 Key parameters of ESSs and heat pumps

ESS parametersESS battery size 50 kWhDC voltage 1000VAC voltage 380VFilter capacitance 3 μFFilter inductance 15mHPower system parametersVoltage (RMS) (phase) 10 kVFrequency 50Hz

20 25 30 35 4015Time (s)

498

4985

499

4995

50

5005

501

ΔPΣCirequest

ΔPΣCirequest + ΔPΣESSs

request

Figure 7 )e 10 kV power system frequency

P (k

W)

ndash20

0

20

40

60

2 3 4 5 61Time (s)

Figure 8 Output power of the ESS (output power increases from10 kW to 40 kW)

abc

265 27 275 28 285 29 295 326Time (s)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

Figure 9 Voltage waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

Figure 6 )e NI-PXI platform

abc

265 27 275 28 285 29 295 326Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 10 Current waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

6 Complexity

In order to verify an extreme condition PV and windgeneration operate at MPPT mode and only the ESSs re-spond to AGC )e AGC auxiliary service control canimprove the existing AGC control performance with quickresponse and steady state

Figure 8 presents output power of one ESS which is10 kW at the beginning and then it goes up to 40 kW re-sponse to AGC demand Voltage and current waveforms atthe AC side are shown in Figures 9 and 10 )e output

ndash20

0

20

40

60

P (k

W)

55 6 65 7 75 85Time (s)

Figure 11 Output power of the ESS (output power falls from40 kW to 10 kW)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

605 61 615 62 625 63 635 646time (s)

abc

Figure 12 Voltage waveforms of the ESS (output power falls from40 kW to 10 kW)

abc

605 61 615 62 625 63 635 646Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 13 Current waveforms of the ESS (output power falls from40 kW to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 14 Experimental voltage waveform of the ESS (outputpower is set to 10 kW)

0

50

ndash50Time (s)

i abc (

A)

Figure 15 Experimental current waveform of the ESS (outputpower is set to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 16 Experimental voltage waveform of the ESS (outputpower is set to 40 kW)

0

i abc (

A)

100

ndash100

Time (s)

200

ndash200

Figure 17 Experimental current waveform of the ESS (outputpower is set to 40 kW)

Complexity 7

current of the ESSs does not have any inrush spikes duringthe entire transition period and there is no voltage per-turbation along the operation

Figure 11 presents the output power of one ESS which is40 kW at the beginning and then it falls from 40 kW to10 kW response to AGC instruction Its voltage and currentwaveforms at the AC side are shown in Figures 12 and 13)e output current of the ESSs does not have any inrushspikes during the entire transition period and there is novoltage perturbation along the operation

Figures 14 and 15 show the experimental waveforms ofthe ESS when its output power reference is set at 10 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

Figures 16 and 17 show the experimental waveforms ofthe ESS when its output power reference is set at 40 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

)ese results indicate smooth and stable operation of theESS-integrated PVwind station and show that the ESSsprovide PV and wind generation additional AGC auxiliaryservice functionality without changing their inner controlstrategies conceived for MPPT mode

6 Conclusions

)e AGC auxiliary service control proposed in this paper isintegrated with existing AGC control strategies Power griddispatching center only needs to add instruction allocationmodule for the ESS-integrated PVwind stations It uses ESSsto add regulation capacity and improve dynamic performanceof AGC without changing the control strategies of RESsconceived for MPPT mode As the ESSs are inherentlynonlinear and time variable the mathematical model is builtconsidering the system parameter variations and disturbancesor uncertainties )e ARSMC-based ESS control system isproposed to deal these control challenges and improve itsstability and dynamic performances )e rigorous proofprocess verifies the ARSMC strategy mathematically)e casestudies on NI-PXI platform shows the fast dynamic responseand robustness performance of the ESSs guaranteeing stableoperation of the ESS-integrated PVwind station as well asvoltage and frequency regulation capability

)e ESSs provide additional AGC auxiliary servicefunctionality without changing RES inner control strategies)e ARSMC-based ESSs is suitable for existing RESs toextend their functions and to form a ESS-integrated PVwind station )e ESSs are independent from the use ofthird-party commercial RESs units which means they donot need specific customized RESs

Data Availability

)e data used to support the findings of the study are in-cluded within the article

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work is supported by the Research on Key Technologiesof Self-Supporting Micro-Renewable Energy Network inQinghai Agricultural and Pastoral Areas No 2018-ZJ-748

References

[1] W Liu G Geng Q Jiang H Fan and J Yu ldquoModel-free fastfrequency control support with energy storage systemrdquo IEEETransactions on Power Systems vol 35 no 4 pp 3078ndash30862020

[2] Y Wang Y Xu Y Tang et al ldquoAggregated energy storage forpower system frequency control a finite-time consensusapproachrdquo IEEE Transactions on Smart Grid vol 10 no 4pp 3675ndash3686 2019

[3] F Cheng L Qu W Qiao C Wei and L Hao ldquoFault di-agnosis of wind turbine gearboxes based on DFIG statorcurrent envelope analysisrdquo IEEE Transactions on SustainableEnergy vol 10 no 3 pp 1044ndash1053 2019

[4] V Knap S K Chaudhary D-I Stroe M SwierczynskiB-I Craciun and R Teodorescu ldquoSizing of an energy storagesystem for grid inertial response and primary frequency re-serverdquo IEEE Transactions on Power Systems vol 31 no 5pp 3447ndash3456 2016

[5] X Sun X Liu S Cheng et al ldquoActual measurement andanalysis of fast frequency response capability of PV-invertersin northwest power gridrdquo Power System Technology vol 41no 9 pp 2792ndash2798 2017

[6] Y Xu F Li Z Jin and M Hassani Variani ldquoDynamic gain-tuning control (DGTC) approach for AGC with effects ofwind powerrdquo IEEE Transactions on Power Systems vol 31no 5 pp 3339ndash3348 2016

[7] Y Wei I Jayawardene and G Kumar VenayagamoorthyldquoOptimal automatic generation controllers in a multi-areainterconnected power system with utility-scale PV plantsrdquoIET Smart Grid vol 2 no 4 pp 581ndash593 2019

[8] C Wei Z Zhang W Qiao and L Qu ldquoAn adaptive network-based reinforcement learning method for MPPT control ofPMSG wind energy conversion systemsrdquo IEEE Transactionson Power Electronics vol 31 no 11 pp 7837ndash7848 2016

[9] D Venkatramanan and V John ldquoDynamic modeling andanalysis of buck converter based solar PV charge controller forimproved MPPT performancerdquo IEEE Transactions on In-dustry Applications vol 55 no 6 pp 6234ndash6246 2019

[10] R B Bollipo S Mikkili and P K Bonthagorla ldquoCriticalreview on PV MPPT techniques classical intelligent andoptimisationrdquo IET Renewable Power Generation vol 14 no 9pp 1433ndash1452 2020

[11] L Meng J Zafar S K Khadem et al ldquoFast frequency re-sponse from energy storage systems-a review of grid stan-dards projects and technical issuesrdquo IEEE Transactions onSmart Grid vol 11 no 2 pp 1566ndash1581 2020

[12] X Xie Y Guo B Wang Y Dong L Mou and F XueldquoImproving AGC performance of coal-fueled thermal gen-erators using multi-MW scale BESS a practical applicationrdquoIEEE Transactions on Smart Grid vol 9 no 3 pp 1769ndash17772018

[13] W Tasnin and L C Saikia ldquoPerformance comparison ofseveral energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plantrdquo IETRenewable Power Generation vol 12 no 7 pp 761ndash772 2018

[14] B Mantar Gundogdu D T Gladwin S Nejad andD A Stone ldquoScheduling of grid-tied battery energy storage

8 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

defined as Sn 1 if T+n is on and Tminus

n is off while Sn 0 if Tminusn is

on and T+n is off

)is switching strategy together with a small dead timegenerator is able to avoid internal shorts between the twoswitches of each bridge leg and the switches will be incomplementary states Assuming that compared to themodulation and natural frequencies the switching fre-quency is relatively high )erefore the equivalent dynamicmodel of Figure 3 is obtained as shown in Figure 4 where s isthe Laplace operator the power gain is defined askPWM (Udcutri) where utri is the amplitude of a triangularcarrier signal

)erefore the dynamic equation of the ESS during thepositive-half period can be represented as

Ldia

dt ua minus ria +

sb + sc minus 2sa

3Udc

Ldibdt

ub minus rib +sa + sc minus 2sb

3Udc

Ldic

dt uc minus ric +

sa + sb minus 2sc

3Udc

CdUdc

dt saia + sbib + scic minus idc

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(4)

And the dynamic equation of the ESSs under dq0synchronous rotating coordinate system can be representedas

ud Ldiddt

minus ωLiq + rid + sdUdc

uq Ldiq

dt+ ωLid + riq + sqUdc

dUdc

dt minus

idc

C+1C

sdid + sqiq1113872 1113873

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(5)

where ud uq and id iq are the AC voltage and current underdq0 synchronous rotating coordinate system respectivelyucond and uconq are the control signal under dq0 synchronousrotating coordinate system L is the equivalent inductor andC is the capacitor value on the convertor side r representsthe equivalent series resistor of the converter inductor and

power line Define irefd and irefq as the reference of id and iqEquation (5) is rearranged as

L _eid ud + ωLiq minus rid minus ucond minus L _irefd

L _eiq uq minus ωLid minus riq minus uconq minus L _irefq

⎧⎪⎨

⎪⎩(6)

where eid id minus irefd and eiq iq minus irefq Equation (6) isrearranged as follows

_E aI minus bU + bUc + cPI minus _Iref (7)

where E [eid middot eiq]T I [iid middot iiq]T U [uid middot uiq]TUc [ucond middot uconq]T Iref [drefd middot qrefq]T and

P 0 1

minus 1 01113890 1113891 a minus (rL) b minus (1L) c ω

According to the aforementioned discussion the ESSsare nonlinear time-variable system and there are uncer-tainties in the ESS-integrated PVwind station which arecaused by parametric variations or external disturbances)erefore equation (7) should be modified as follows

_E (a + Δa)I minus (b + Δb)U +(b + Δb)Uc +(c + Δc)PI

minus _Iref + Um

(8)

where ΔaΔb and Δc represent the system parameter var-iations and Um represents the external disturbances oruncertainties Define

W ΔaI minus ΔbU + ΔbUc + ΔcPI + Um (9)

)us equation (8) is rearranged as_E aI minus bU + bUc + cPI minus _Iref + W (10)

)e bound of the uncertainty is assumed to meet thefollowing inequality

|W|leQ (11)

where Q [QdQq]T represents the unknown positiveconstants

42ARSMCSystem )eproposed control system for ESSs isdivided into two main parts as illustrated in Figure 5 )efirst part is the primary control which produces the referencesignals Iref based on ΔPrequest

ΣESSs and the operation mode ofESSs)e second part is the ARSMC system which generatesthe control signal Ucontrol In this part the state feedbackterm gives concise sliding surface while makes full use of

UdcUtri 1sC

r

1sL

1Z

Ucontrol Ui (s)+__

I0 (s)

+_IL (s) U0 (s)

Im (s)

+

Figure 4 Equivalent dynamic model of the ESS in ESS-integratedPVwind station

ra

rbrc

LaLbLc

iaibic

T1+ T2

+ T3+

T1ndash T2

ndash T3ndash

Udc

Cfa Cfb Cfc

rfa rfb rfc

uaubuc

Figure 3 Circuit topology of the ESS in ESS-integrated PVwindstation

4 Complexity

pole assignment and state feedback Robust control termforms the structure of ESSs model Adaptive compensationterm adjusts the control law based on uncertainties ordisturbance in real time As the disturbance is unknownvariables and cannot be specified or determine as a fixedvalue introducing an adaptive strategy is a more practicalsolution

)e control objective of the ARSMC system is to makethe output power of the ESSs equal to ΔPrequest

ΣESSs Specificallyit has to enforce id iq to track its reference irefd irefq orenforce I follow its reference Iref

First define a sliding surface as equation (12) to obtain asliding motion through the entire state trajectory whileeliminate static control error

S E + 1113946 (a minus bK)Eds (12)

where S [Sd Sq]T and K [KdKq]T is the control coef-ficient matrix

Second design the control scheme as follows

Uc U1 + U2 + U3 (13)

where

U1 U + bKI

U2 bminus 1

(minus εsign(S)) + cPI + aI minus _Iref

U3 bSabs(minus bS)minus 1 1113954Q

(14)

where U1 is the state feedback term U2 is the robust controlterm and U3 is the adaptive compensation term ε is a smallpositive constant sign(S) [sign(sd) middot sign(sq)]T beingsign(middot) the sign function and abs(middot) the absolute valuefunction 1113954Q is the estimated value ofQ define the parameterdeviation as 1113957Q 1113954Q minus Q and the adaptive law as

1113954Qmiddot

abs(minus bS) (15)

Proof Sliding surface and parameters composing theadaptive law are based on the difference between thenominal nonlinear system and the uncertain nonlinearsystem and it satisfies the global Lyapunov stability con-dition Using Lyapunov stability analysis to derive the ex-istence condition of the sliding mode and setting theLyapunov function as

V S2 + 1113957Q2

2 (16)

Taking the derivative of equation (16)

_V S _S + 1113957Q 1113957Qmiddot

(17)

Taking the derivative of equation (12) along (9) andsubstituting (13) and (15) into (17) to simplify equation (17)as

_V S minus bU3 + εsign(S) + W + KE( 1113857 + 1113957Q 1113957Qmiddot

le ε middot abs(S)

(18)

)erefore _Vlt 0 when abs(S)ne 0 which ensures theasymptotically stable behavior for the sliding-mode systemon the sliding surface (12)

Once the system trajectory reaches the sliding surface ityields S _S 0

_S aI minus bU + bUc + cPI minus _Iref + W minus aE + bKE 0

(19)

Deduce the equivalent control from equation (19) as

Ueq minus bminus 1

aIref minus bU + cPI minus _Iref + W minus aE + bKE1113872 1113873

(20)

Substitute equation (20) into equation (8)_E aE minus bKE (21)

It implies that probably designed state feedback coeffi-cient K guarantees the robustness of sliding mode (21) alongwith dynamics features like rising time and maximumovershoot

5 Case Studies

A simulation platform under MATLAB environment basedon Figure 1 is developed to validate the AGC auxiliaryservice performance of the ESS-integrated PVwind stationfurthermore case studies were conducted on the NI-PXI(PCI Extensions for Instrumentation PXI) platform toverify the proposed ARSMC system as shown in Figure 6

)e key parameters of the developed model are given inTable 1 )e ESS-integrated PVwind station in Figure 1 isconnected to the grid through a 380V10 kV transformer A12MW synchronous machine in the 10 kV grid works as aconventional regulation power source responds to AGCAccording to the sliding surface (12) the control coefficientmatrix is designed to guarantee the robustness of the slidingmode show as equation (21) as well as the dynamic per-formance and stability set K [001805]

)e synchronous machine delivers 10MW active powerto the power grid )e ESSs in ESS-integrated PVwindstation deliver 100 kW active power to the power grid Setdispatch instruction from AGC ΔPrequest

Σ to 500 kW toeliminate the frequency deviation Figure 7 gives the fre-quency of this 10 kV power system with the synchronous

Adoptive compensation

term

u3

u1

Robust control term

State feedback term

Ucontrolu2

ARSMC system

Mode 3

Mode 2

Mode 1

Primary control

IrefΔPΣESSsrequest

ΔPΣESSs1request

ΔPΣESSs2request

ΔPΣESSs3request

Figure 5)e control system of the ESS in ESS-integrated PVwindstation

Complexity 5

machine working as a regulation power source response toAGC which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

500 + 0 500 kW

(22)

)en in the same scenario both the synchronous ma-chine and ESS-integrated PVwind station provide AGCauxiliary service which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

200 + 300 500 kW

(23)

ΔPrequestΣL ΔPrequest

ΣPV + ΔPrequestΣwind + ΔPrequest

ΣESSs

0 + 0 + 300 300 kW(24)

Table 1 Key parameters of ESSs and heat pumps

ESS parametersESS battery size 50 kWhDC voltage 1000VAC voltage 380VFilter capacitance 3 μFFilter inductance 15mHPower system parametersVoltage (RMS) (phase) 10 kVFrequency 50Hz

20 25 30 35 4015Time (s)

498

4985

499

4995

50

5005

501

ΔPΣCirequest

ΔPΣCirequest + ΔPΣESSs

request

Figure 7 )e 10 kV power system frequency

P (k

W)

ndash20

0

20

40

60

2 3 4 5 61Time (s)

Figure 8 Output power of the ESS (output power increases from10 kW to 40 kW)

abc

265 27 275 28 285 29 295 326Time (s)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

Figure 9 Voltage waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

Figure 6 )e NI-PXI platform

abc

265 27 275 28 285 29 295 326Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 10 Current waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

6 Complexity

In order to verify an extreme condition PV and windgeneration operate at MPPT mode and only the ESSs re-spond to AGC )e AGC auxiliary service control canimprove the existing AGC control performance with quickresponse and steady state

Figure 8 presents output power of one ESS which is10 kW at the beginning and then it goes up to 40 kW re-sponse to AGC demand Voltage and current waveforms atthe AC side are shown in Figures 9 and 10 )e output

ndash20

0

20

40

60

P (k

W)

55 6 65 7 75 85Time (s)

Figure 11 Output power of the ESS (output power falls from40 kW to 10 kW)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

605 61 615 62 625 63 635 646time (s)

abc

Figure 12 Voltage waveforms of the ESS (output power falls from40 kW to 10 kW)

abc

605 61 615 62 625 63 635 646Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 13 Current waveforms of the ESS (output power falls from40 kW to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 14 Experimental voltage waveform of the ESS (outputpower is set to 10 kW)

0

50

ndash50Time (s)

i abc (

A)

Figure 15 Experimental current waveform of the ESS (outputpower is set to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 16 Experimental voltage waveform of the ESS (outputpower is set to 40 kW)

0

i abc (

A)

100

ndash100

Time (s)

200

ndash200

Figure 17 Experimental current waveform of the ESS (outputpower is set to 40 kW)

Complexity 7

current of the ESSs does not have any inrush spikes duringthe entire transition period and there is no voltage per-turbation along the operation

Figure 11 presents the output power of one ESS which is40 kW at the beginning and then it falls from 40 kW to10 kW response to AGC instruction Its voltage and currentwaveforms at the AC side are shown in Figures 12 and 13)e output current of the ESSs does not have any inrushspikes during the entire transition period and there is novoltage perturbation along the operation

Figures 14 and 15 show the experimental waveforms ofthe ESS when its output power reference is set at 10 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

Figures 16 and 17 show the experimental waveforms ofthe ESS when its output power reference is set at 40 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

)ese results indicate smooth and stable operation of theESS-integrated PVwind station and show that the ESSsprovide PV and wind generation additional AGC auxiliaryservice functionality without changing their inner controlstrategies conceived for MPPT mode

6 Conclusions

)e AGC auxiliary service control proposed in this paper isintegrated with existing AGC control strategies Power griddispatching center only needs to add instruction allocationmodule for the ESS-integrated PVwind stations It uses ESSsto add regulation capacity and improve dynamic performanceof AGC without changing the control strategies of RESsconceived for MPPT mode As the ESSs are inherentlynonlinear and time variable the mathematical model is builtconsidering the system parameter variations and disturbancesor uncertainties )e ARSMC-based ESS control system isproposed to deal these control challenges and improve itsstability and dynamic performances )e rigorous proofprocess verifies the ARSMC strategy mathematically)e casestudies on NI-PXI platform shows the fast dynamic responseand robustness performance of the ESSs guaranteeing stableoperation of the ESS-integrated PVwind station as well asvoltage and frequency regulation capability

)e ESSs provide additional AGC auxiliary servicefunctionality without changing RES inner control strategies)e ARSMC-based ESSs is suitable for existing RESs toextend their functions and to form a ESS-integrated PVwind station )e ESSs are independent from the use ofthird-party commercial RESs units which means they donot need specific customized RESs

Data Availability

)e data used to support the findings of the study are in-cluded within the article

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work is supported by the Research on Key Technologiesof Self-Supporting Micro-Renewable Energy Network inQinghai Agricultural and Pastoral Areas No 2018-ZJ-748

References

[1] W Liu G Geng Q Jiang H Fan and J Yu ldquoModel-free fastfrequency control support with energy storage systemrdquo IEEETransactions on Power Systems vol 35 no 4 pp 3078ndash30862020

[2] Y Wang Y Xu Y Tang et al ldquoAggregated energy storage forpower system frequency control a finite-time consensusapproachrdquo IEEE Transactions on Smart Grid vol 10 no 4pp 3675ndash3686 2019

[3] F Cheng L Qu W Qiao C Wei and L Hao ldquoFault di-agnosis of wind turbine gearboxes based on DFIG statorcurrent envelope analysisrdquo IEEE Transactions on SustainableEnergy vol 10 no 3 pp 1044ndash1053 2019

[4] V Knap S K Chaudhary D-I Stroe M SwierczynskiB-I Craciun and R Teodorescu ldquoSizing of an energy storagesystem for grid inertial response and primary frequency re-serverdquo IEEE Transactions on Power Systems vol 31 no 5pp 3447ndash3456 2016

[5] X Sun X Liu S Cheng et al ldquoActual measurement andanalysis of fast frequency response capability of PV-invertersin northwest power gridrdquo Power System Technology vol 41no 9 pp 2792ndash2798 2017

[6] Y Xu F Li Z Jin and M Hassani Variani ldquoDynamic gain-tuning control (DGTC) approach for AGC with effects ofwind powerrdquo IEEE Transactions on Power Systems vol 31no 5 pp 3339ndash3348 2016

[7] Y Wei I Jayawardene and G Kumar VenayagamoorthyldquoOptimal automatic generation controllers in a multi-areainterconnected power system with utility-scale PV plantsrdquoIET Smart Grid vol 2 no 4 pp 581ndash593 2019

[8] C Wei Z Zhang W Qiao and L Qu ldquoAn adaptive network-based reinforcement learning method for MPPT control ofPMSG wind energy conversion systemsrdquo IEEE Transactionson Power Electronics vol 31 no 11 pp 7837ndash7848 2016

[9] D Venkatramanan and V John ldquoDynamic modeling andanalysis of buck converter based solar PV charge controller forimproved MPPT performancerdquo IEEE Transactions on In-dustry Applications vol 55 no 6 pp 6234ndash6246 2019

[10] R B Bollipo S Mikkili and P K Bonthagorla ldquoCriticalreview on PV MPPT techniques classical intelligent andoptimisationrdquo IET Renewable Power Generation vol 14 no 9pp 1433ndash1452 2020

[11] L Meng J Zafar S K Khadem et al ldquoFast frequency re-sponse from energy storage systems-a review of grid stan-dards projects and technical issuesrdquo IEEE Transactions onSmart Grid vol 11 no 2 pp 1566ndash1581 2020

[12] X Xie Y Guo B Wang Y Dong L Mou and F XueldquoImproving AGC performance of coal-fueled thermal gen-erators using multi-MW scale BESS a practical applicationrdquoIEEE Transactions on Smart Grid vol 9 no 3 pp 1769ndash17772018

[13] W Tasnin and L C Saikia ldquoPerformance comparison ofseveral energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plantrdquo IETRenewable Power Generation vol 12 no 7 pp 761ndash772 2018

[14] B Mantar Gundogdu D T Gladwin S Nejad andD A Stone ldquoScheduling of grid-tied battery energy storage

8 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

pole assignment and state feedback Robust control termforms the structure of ESSs model Adaptive compensationterm adjusts the control law based on uncertainties ordisturbance in real time As the disturbance is unknownvariables and cannot be specified or determine as a fixedvalue introducing an adaptive strategy is a more practicalsolution

)e control objective of the ARSMC system is to makethe output power of the ESSs equal to ΔPrequest

ΣESSs Specificallyit has to enforce id iq to track its reference irefd irefq orenforce I follow its reference Iref

First define a sliding surface as equation (12) to obtain asliding motion through the entire state trajectory whileeliminate static control error

S E + 1113946 (a minus bK)Eds (12)

where S [Sd Sq]T and K [KdKq]T is the control coef-ficient matrix

Second design the control scheme as follows

Uc U1 + U2 + U3 (13)

where

U1 U + bKI

U2 bminus 1

(minus εsign(S)) + cPI + aI minus _Iref

U3 bSabs(minus bS)minus 1 1113954Q

(14)

where U1 is the state feedback term U2 is the robust controlterm and U3 is the adaptive compensation term ε is a smallpositive constant sign(S) [sign(sd) middot sign(sq)]T beingsign(middot) the sign function and abs(middot) the absolute valuefunction 1113954Q is the estimated value ofQ define the parameterdeviation as 1113957Q 1113954Q minus Q and the adaptive law as

1113954Qmiddot

abs(minus bS) (15)

Proof Sliding surface and parameters composing theadaptive law are based on the difference between thenominal nonlinear system and the uncertain nonlinearsystem and it satisfies the global Lyapunov stability con-dition Using Lyapunov stability analysis to derive the ex-istence condition of the sliding mode and setting theLyapunov function as

V S2 + 1113957Q2

2 (16)

Taking the derivative of equation (16)

_V S _S + 1113957Q 1113957Qmiddot

(17)

Taking the derivative of equation (12) along (9) andsubstituting (13) and (15) into (17) to simplify equation (17)as

_V S minus bU3 + εsign(S) + W + KE( 1113857 + 1113957Q 1113957Qmiddot

le ε middot abs(S)

(18)

)erefore _Vlt 0 when abs(S)ne 0 which ensures theasymptotically stable behavior for the sliding-mode systemon the sliding surface (12)

Once the system trajectory reaches the sliding surface ityields S _S 0

_S aI minus bU + bUc + cPI minus _Iref + W minus aE + bKE 0

(19)

Deduce the equivalent control from equation (19) as

Ueq minus bminus 1

aIref minus bU + cPI minus _Iref + W minus aE + bKE1113872 1113873

(20)

Substitute equation (20) into equation (8)_E aE minus bKE (21)

It implies that probably designed state feedback coeffi-cient K guarantees the robustness of sliding mode (21) alongwith dynamics features like rising time and maximumovershoot

5 Case Studies

A simulation platform under MATLAB environment basedon Figure 1 is developed to validate the AGC auxiliaryservice performance of the ESS-integrated PVwind stationfurthermore case studies were conducted on the NI-PXI(PCI Extensions for Instrumentation PXI) platform toverify the proposed ARSMC system as shown in Figure 6

)e key parameters of the developed model are given inTable 1 )e ESS-integrated PVwind station in Figure 1 isconnected to the grid through a 380V10 kV transformer A12MW synchronous machine in the 10 kV grid works as aconventional regulation power source responds to AGCAccording to the sliding surface (12) the control coefficientmatrix is designed to guarantee the robustness of the slidingmode show as equation (21) as well as the dynamic per-formance and stability set K [001805]

)e synchronous machine delivers 10MW active powerto the power grid )e ESSs in ESS-integrated PVwindstation deliver 100 kW active power to the power grid Setdispatch instruction from AGC ΔPrequest

Σ to 500 kW toeliminate the frequency deviation Figure 7 gives the fre-quency of this 10 kV power system with the synchronous

Adoptive compensation

term

u3

u1

Robust control term

State feedback term

Ucontrolu2

ARSMC system

Mode 3

Mode 2

Mode 1

Primary control

IrefΔPΣESSsrequest

ΔPΣESSs1request

ΔPΣESSs2request

ΔPΣESSs3request

Figure 5)e control system of the ESS in ESS-integrated PVwindstation

Complexity 5

machine working as a regulation power source response toAGC which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

500 + 0 500 kW

(22)

)en in the same scenario both the synchronous ma-chine and ESS-integrated PVwind station provide AGCauxiliary service which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

200 + 300 500 kW

(23)

ΔPrequestΣL ΔPrequest

ΣPV + ΔPrequestΣwind + ΔPrequest

ΣESSs

0 + 0 + 300 300 kW(24)

Table 1 Key parameters of ESSs and heat pumps

ESS parametersESS battery size 50 kWhDC voltage 1000VAC voltage 380VFilter capacitance 3 μFFilter inductance 15mHPower system parametersVoltage (RMS) (phase) 10 kVFrequency 50Hz

20 25 30 35 4015Time (s)

498

4985

499

4995

50

5005

501

ΔPΣCirequest

ΔPΣCirequest + ΔPΣESSs

request

Figure 7 )e 10 kV power system frequency

P (k

W)

ndash20

0

20

40

60

2 3 4 5 61Time (s)

Figure 8 Output power of the ESS (output power increases from10 kW to 40 kW)

abc

265 27 275 28 285 29 295 326Time (s)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

Figure 9 Voltage waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

Figure 6 )e NI-PXI platform

abc

265 27 275 28 285 29 295 326Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 10 Current waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

6 Complexity

In order to verify an extreme condition PV and windgeneration operate at MPPT mode and only the ESSs re-spond to AGC )e AGC auxiliary service control canimprove the existing AGC control performance with quickresponse and steady state

Figure 8 presents output power of one ESS which is10 kW at the beginning and then it goes up to 40 kW re-sponse to AGC demand Voltage and current waveforms atthe AC side are shown in Figures 9 and 10 )e output

ndash20

0

20

40

60

P (k

W)

55 6 65 7 75 85Time (s)

Figure 11 Output power of the ESS (output power falls from40 kW to 10 kW)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

605 61 615 62 625 63 635 646time (s)

abc

Figure 12 Voltage waveforms of the ESS (output power falls from40 kW to 10 kW)

abc

605 61 615 62 625 63 635 646Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 13 Current waveforms of the ESS (output power falls from40 kW to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 14 Experimental voltage waveform of the ESS (outputpower is set to 10 kW)

0

50

ndash50Time (s)

i abc (

A)

Figure 15 Experimental current waveform of the ESS (outputpower is set to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 16 Experimental voltage waveform of the ESS (outputpower is set to 40 kW)

0

i abc (

A)

100

ndash100

Time (s)

200

ndash200

Figure 17 Experimental current waveform of the ESS (outputpower is set to 40 kW)

Complexity 7

current of the ESSs does not have any inrush spikes duringthe entire transition period and there is no voltage per-turbation along the operation

Figure 11 presents the output power of one ESS which is40 kW at the beginning and then it falls from 40 kW to10 kW response to AGC instruction Its voltage and currentwaveforms at the AC side are shown in Figures 12 and 13)e output current of the ESSs does not have any inrushspikes during the entire transition period and there is novoltage perturbation along the operation

Figures 14 and 15 show the experimental waveforms ofthe ESS when its output power reference is set at 10 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

Figures 16 and 17 show the experimental waveforms ofthe ESS when its output power reference is set at 40 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

)ese results indicate smooth and stable operation of theESS-integrated PVwind station and show that the ESSsprovide PV and wind generation additional AGC auxiliaryservice functionality without changing their inner controlstrategies conceived for MPPT mode

6 Conclusions

)e AGC auxiliary service control proposed in this paper isintegrated with existing AGC control strategies Power griddispatching center only needs to add instruction allocationmodule for the ESS-integrated PVwind stations It uses ESSsto add regulation capacity and improve dynamic performanceof AGC without changing the control strategies of RESsconceived for MPPT mode As the ESSs are inherentlynonlinear and time variable the mathematical model is builtconsidering the system parameter variations and disturbancesor uncertainties )e ARSMC-based ESS control system isproposed to deal these control challenges and improve itsstability and dynamic performances )e rigorous proofprocess verifies the ARSMC strategy mathematically)e casestudies on NI-PXI platform shows the fast dynamic responseand robustness performance of the ESSs guaranteeing stableoperation of the ESS-integrated PVwind station as well asvoltage and frequency regulation capability

)e ESSs provide additional AGC auxiliary servicefunctionality without changing RES inner control strategies)e ARSMC-based ESSs is suitable for existing RESs toextend their functions and to form a ESS-integrated PVwind station )e ESSs are independent from the use ofthird-party commercial RESs units which means they donot need specific customized RESs

Data Availability

)e data used to support the findings of the study are in-cluded within the article

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work is supported by the Research on Key Technologiesof Self-Supporting Micro-Renewable Energy Network inQinghai Agricultural and Pastoral Areas No 2018-ZJ-748

References

[1] W Liu G Geng Q Jiang H Fan and J Yu ldquoModel-free fastfrequency control support with energy storage systemrdquo IEEETransactions on Power Systems vol 35 no 4 pp 3078ndash30862020

[2] Y Wang Y Xu Y Tang et al ldquoAggregated energy storage forpower system frequency control a finite-time consensusapproachrdquo IEEE Transactions on Smart Grid vol 10 no 4pp 3675ndash3686 2019

[3] F Cheng L Qu W Qiao C Wei and L Hao ldquoFault di-agnosis of wind turbine gearboxes based on DFIG statorcurrent envelope analysisrdquo IEEE Transactions on SustainableEnergy vol 10 no 3 pp 1044ndash1053 2019

[4] V Knap S K Chaudhary D-I Stroe M SwierczynskiB-I Craciun and R Teodorescu ldquoSizing of an energy storagesystem for grid inertial response and primary frequency re-serverdquo IEEE Transactions on Power Systems vol 31 no 5pp 3447ndash3456 2016

[5] X Sun X Liu S Cheng et al ldquoActual measurement andanalysis of fast frequency response capability of PV-invertersin northwest power gridrdquo Power System Technology vol 41no 9 pp 2792ndash2798 2017

[6] Y Xu F Li Z Jin and M Hassani Variani ldquoDynamic gain-tuning control (DGTC) approach for AGC with effects ofwind powerrdquo IEEE Transactions on Power Systems vol 31no 5 pp 3339ndash3348 2016

[7] Y Wei I Jayawardene and G Kumar VenayagamoorthyldquoOptimal automatic generation controllers in a multi-areainterconnected power system with utility-scale PV plantsrdquoIET Smart Grid vol 2 no 4 pp 581ndash593 2019

[8] C Wei Z Zhang W Qiao and L Qu ldquoAn adaptive network-based reinforcement learning method for MPPT control ofPMSG wind energy conversion systemsrdquo IEEE Transactionson Power Electronics vol 31 no 11 pp 7837ndash7848 2016

[9] D Venkatramanan and V John ldquoDynamic modeling andanalysis of buck converter based solar PV charge controller forimproved MPPT performancerdquo IEEE Transactions on In-dustry Applications vol 55 no 6 pp 6234ndash6246 2019

[10] R B Bollipo S Mikkili and P K Bonthagorla ldquoCriticalreview on PV MPPT techniques classical intelligent andoptimisationrdquo IET Renewable Power Generation vol 14 no 9pp 1433ndash1452 2020

[11] L Meng J Zafar S K Khadem et al ldquoFast frequency re-sponse from energy storage systems-a review of grid stan-dards projects and technical issuesrdquo IEEE Transactions onSmart Grid vol 11 no 2 pp 1566ndash1581 2020

[12] X Xie Y Guo B Wang Y Dong L Mou and F XueldquoImproving AGC performance of coal-fueled thermal gen-erators using multi-MW scale BESS a practical applicationrdquoIEEE Transactions on Smart Grid vol 9 no 3 pp 1769ndash17772018

[13] W Tasnin and L C Saikia ldquoPerformance comparison ofseveral energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plantrdquo IETRenewable Power Generation vol 12 no 7 pp 761ndash772 2018

[14] B Mantar Gundogdu D T Gladwin S Nejad andD A Stone ldquoScheduling of grid-tied battery energy storage

8 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

machine working as a regulation power source response toAGC which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

500 + 0 500 kW

(22)

)en in the same scenario both the synchronous ma-chine and ESS-integrated PVwind station provide AGCauxiliary service which means

ΔPrequestΣ 1113944

i123middotmiddotmiddot

ΔPrequestΣCi + 1113944

i123middotmiddotmiddot

ΔPrequestΣLi

200 + 300 500 kW

(23)

ΔPrequestΣL ΔPrequest

ΣPV + ΔPrequestΣwind + ΔPrequest

ΣESSs

0 + 0 + 300 300 kW(24)

Table 1 Key parameters of ESSs and heat pumps

ESS parametersESS battery size 50 kWhDC voltage 1000VAC voltage 380VFilter capacitance 3 μFFilter inductance 15mHPower system parametersVoltage (RMS) (phase) 10 kVFrequency 50Hz

20 25 30 35 4015Time (s)

498

4985

499

4995

50

5005

501

ΔPΣCirequest

ΔPΣCirequest + ΔPΣESSs

request

Figure 7 )e 10 kV power system frequency

P (k

W)

ndash20

0

20

40

60

2 3 4 5 61Time (s)

Figure 8 Output power of the ESS (output power increases from10 kW to 40 kW)

abc

265 27 275 28 285 29 295 326Time (s)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

Figure 9 Voltage waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

Figure 6 )e NI-PXI platform

abc

265 27 275 28 285 29 295 326Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 10 Current waveforms of the ESS (output power increasesfrom 10 kW to 40 kW)

6 Complexity

In order to verify an extreme condition PV and windgeneration operate at MPPT mode and only the ESSs re-spond to AGC )e AGC auxiliary service control canimprove the existing AGC control performance with quickresponse and steady state

Figure 8 presents output power of one ESS which is10 kW at the beginning and then it goes up to 40 kW re-sponse to AGC demand Voltage and current waveforms atthe AC side are shown in Figures 9 and 10 )e output

ndash20

0

20

40

60

P (k

W)

55 6 65 7 75 85Time (s)

Figure 11 Output power of the ESS (output power falls from40 kW to 10 kW)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

605 61 615 62 625 63 635 646time (s)

abc

Figure 12 Voltage waveforms of the ESS (output power falls from40 kW to 10 kW)

abc

605 61 615 62 625 63 635 646Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 13 Current waveforms of the ESS (output power falls from40 kW to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 14 Experimental voltage waveform of the ESS (outputpower is set to 10 kW)

0

50

ndash50Time (s)

i abc (

A)

Figure 15 Experimental current waveform of the ESS (outputpower is set to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 16 Experimental voltage waveform of the ESS (outputpower is set to 40 kW)

0

i abc (

A)

100

ndash100

Time (s)

200

ndash200

Figure 17 Experimental current waveform of the ESS (outputpower is set to 40 kW)

Complexity 7

current of the ESSs does not have any inrush spikes duringthe entire transition period and there is no voltage per-turbation along the operation

Figure 11 presents the output power of one ESS which is40 kW at the beginning and then it falls from 40 kW to10 kW response to AGC instruction Its voltage and currentwaveforms at the AC side are shown in Figures 12 and 13)e output current of the ESSs does not have any inrushspikes during the entire transition period and there is novoltage perturbation along the operation

Figures 14 and 15 show the experimental waveforms ofthe ESS when its output power reference is set at 10 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

Figures 16 and 17 show the experimental waveforms ofthe ESS when its output power reference is set at 40 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

)ese results indicate smooth and stable operation of theESS-integrated PVwind station and show that the ESSsprovide PV and wind generation additional AGC auxiliaryservice functionality without changing their inner controlstrategies conceived for MPPT mode

6 Conclusions

)e AGC auxiliary service control proposed in this paper isintegrated with existing AGC control strategies Power griddispatching center only needs to add instruction allocationmodule for the ESS-integrated PVwind stations It uses ESSsto add regulation capacity and improve dynamic performanceof AGC without changing the control strategies of RESsconceived for MPPT mode As the ESSs are inherentlynonlinear and time variable the mathematical model is builtconsidering the system parameter variations and disturbancesor uncertainties )e ARSMC-based ESS control system isproposed to deal these control challenges and improve itsstability and dynamic performances )e rigorous proofprocess verifies the ARSMC strategy mathematically)e casestudies on NI-PXI platform shows the fast dynamic responseand robustness performance of the ESSs guaranteeing stableoperation of the ESS-integrated PVwind station as well asvoltage and frequency regulation capability

)e ESSs provide additional AGC auxiliary servicefunctionality without changing RES inner control strategies)e ARSMC-based ESSs is suitable for existing RESs toextend their functions and to form a ESS-integrated PVwind station )e ESSs are independent from the use ofthird-party commercial RESs units which means they donot need specific customized RESs

Data Availability

)e data used to support the findings of the study are in-cluded within the article

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work is supported by the Research on Key Technologiesof Self-Supporting Micro-Renewable Energy Network inQinghai Agricultural and Pastoral Areas No 2018-ZJ-748

References

[1] W Liu G Geng Q Jiang H Fan and J Yu ldquoModel-free fastfrequency control support with energy storage systemrdquo IEEETransactions on Power Systems vol 35 no 4 pp 3078ndash30862020

[2] Y Wang Y Xu Y Tang et al ldquoAggregated energy storage forpower system frequency control a finite-time consensusapproachrdquo IEEE Transactions on Smart Grid vol 10 no 4pp 3675ndash3686 2019

[3] F Cheng L Qu W Qiao C Wei and L Hao ldquoFault di-agnosis of wind turbine gearboxes based on DFIG statorcurrent envelope analysisrdquo IEEE Transactions on SustainableEnergy vol 10 no 3 pp 1044ndash1053 2019

[4] V Knap S K Chaudhary D-I Stroe M SwierczynskiB-I Craciun and R Teodorescu ldquoSizing of an energy storagesystem for grid inertial response and primary frequency re-serverdquo IEEE Transactions on Power Systems vol 31 no 5pp 3447ndash3456 2016

[5] X Sun X Liu S Cheng et al ldquoActual measurement andanalysis of fast frequency response capability of PV-invertersin northwest power gridrdquo Power System Technology vol 41no 9 pp 2792ndash2798 2017

[6] Y Xu F Li Z Jin and M Hassani Variani ldquoDynamic gain-tuning control (DGTC) approach for AGC with effects ofwind powerrdquo IEEE Transactions on Power Systems vol 31no 5 pp 3339ndash3348 2016

[7] Y Wei I Jayawardene and G Kumar VenayagamoorthyldquoOptimal automatic generation controllers in a multi-areainterconnected power system with utility-scale PV plantsrdquoIET Smart Grid vol 2 no 4 pp 581ndash593 2019

[8] C Wei Z Zhang W Qiao and L Qu ldquoAn adaptive network-based reinforcement learning method for MPPT control ofPMSG wind energy conversion systemsrdquo IEEE Transactionson Power Electronics vol 31 no 11 pp 7837ndash7848 2016

[9] D Venkatramanan and V John ldquoDynamic modeling andanalysis of buck converter based solar PV charge controller forimproved MPPT performancerdquo IEEE Transactions on In-dustry Applications vol 55 no 6 pp 6234ndash6246 2019

[10] R B Bollipo S Mikkili and P K Bonthagorla ldquoCriticalreview on PV MPPT techniques classical intelligent andoptimisationrdquo IET Renewable Power Generation vol 14 no 9pp 1433ndash1452 2020

[11] L Meng J Zafar S K Khadem et al ldquoFast frequency re-sponse from energy storage systems-a review of grid stan-dards projects and technical issuesrdquo IEEE Transactions onSmart Grid vol 11 no 2 pp 1566ndash1581 2020

[12] X Xie Y Guo B Wang Y Dong L Mou and F XueldquoImproving AGC performance of coal-fueled thermal gen-erators using multi-MW scale BESS a practical applicationrdquoIEEE Transactions on Smart Grid vol 9 no 3 pp 1769ndash17772018

[13] W Tasnin and L C Saikia ldquoPerformance comparison ofseveral energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plantrdquo IETRenewable Power Generation vol 12 no 7 pp 761ndash772 2018

[14] B Mantar Gundogdu D T Gladwin S Nejad andD A Stone ldquoScheduling of grid-tied battery energy storage

8 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

In order to verify an extreme condition PV and windgeneration operate at MPPT mode and only the ESSs re-spond to AGC )e AGC auxiliary service control canimprove the existing AGC control performance with quickresponse and steady state

Figure 8 presents output power of one ESS which is10 kW at the beginning and then it goes up to 40 kW re-sponse to AGC demand Voltage and current waveforms atthe AC side are shown in Figures 9 and 10 )e output

ndash20

0

20

40

60

P (k

W)

55 6 65 7 75 85Time (s)

Figure 11 Output power of the ESS (output power falls from40 kW to 10 kW)

ndash600

ndash400

ndash200

0

200

400

600

u abc

(V)

605 61 615 62 625 63 635 646time (s)

abc

Figure 12 Voltage waveforms of the ESS (output power falls from40 kW to 10 kW)

abc

605 61 615 62 625 63 635 646Time (s)

ndash100

ndash50

0

50

100

i abc (

A)

Figure 13 Current waveforms of the ESS (output power falls from40 kW to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 14 Experimental voltage waveform of the ESS (outputpower is set to 10 kW)

0

50

ndash50Time (s)

i abc (

A)

Figure 15 Experimental current waveform of the ESS (outputpower is set to 10 kW)

0

300

ndash300

600

ndash600Time (s)

u abc

(V)

Figure 16 Experimental voltage waveform of the ESS (outputpower is set to 40 kW)

0

i abc (

A)

100

ndash100

Time (s)

200

ndash200

Figure 17 Experimental current waveform of the ESS (outputpower is set to 40 kW)

Complexity 7

current of the ESSs does not have any inrush spikes duringthe entire transition period and there is no voltage per-turbation along the operation

Figure 11 presents the output power of one ESS which is40 kW at the beginning and then it falls from 40 kW to10 kW response to AGC instruction Its voltage and currentwaveforms at the AC side are shown in Figures 12 and 13)e output current of the ESSs does not have any inrushspikes during the entire transition period and there is novoltage perturbation along the operation

Figures 14 and 15 show the experimental waveforms ofthe ESS when its output power reference is set at 10 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

Figures 16 and 17 show the experimental waveforms ofthe ESS when its output power reference is set at 40 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

)ese results indicate smooth and stable operation of theESS-integrated PVwind station and show that the ESSsprovide PV and wind generation additional AGC auxiliaryservice functionality without changing their inner controlstrategies conceived for MPPT mode

6 Conclusions

)e AGC auxiliary service control proposed in this paper isintegrated with existing AGC control strategies Power griddispatching center only needs to add instruction allocationmodule for the ESS-integrated PVwind stations It uses ESSsto add regulation capacity and improve dynamic performanceof AGC without changing the control strategies of RESsconceived for MPPT mode As the ESSs are inherentlynonlinear and time variable the mathematical model is builtconsidering the system parameter variations and disturbancesor uncertainties )e ARSMC-based ESS control system isproposed to deal these control challenges and improve itsstability and dynamic performances )e rigorous proofprocess verifies the ARSMC strategy mathematically)e casestudies on NI-PXI platform shows the fast dynamic responseand robustness performance of the ESSs guaranteeing stableoperation of the ESS-integrated PVwind station as well asvoltage and frequency regulation capability

)e ESSs provide additional AGC auxiliary servicefunctionality without changing RES inner control strategies)e ARSMC-based ESSs is suitable for existing RESs toextend their functions and to form a ESS-integrated PVwind station )e ESSs are independent from the use ofthird-party commercial RESs units which means they donot need specific customized RESs

Data Availability

)e data used to support the findings of the study are in-cluded within the article

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work is supported by the Research on Key Technologiesof Self-Supporting Micro-Renewable Energy Network inQinghai Agricultural and Pastoral Areas No 2018-ZJ-748

References

[1] W Liu G Geng Q Jiang H Fan and J Yu ldquoModel-free fastfrequency control support with energy storage systemrdquo IEEETransactions on Power Systems vol 35 no 4 pp 3078ndash30862020

[2] Y Wang Y Xu Y Tang et al ldquoAggregated energy storage forpower system frequency control a finite-time consensusapproachrdquo IEEE Transactions on Smart Grid vol 10 no 4pp 3675ndash3686 2019

[3] F Cheng L Qu W Qiao C Wei and L Hao ldquoFault di-agnosis of wind turbine gearboxes based on DFIG statorcurrent envelope analysisrdquo IEEE Transactions on SustainableEnergy vol 10 no 3 pp 1044ndash1053 2019

[4] V Knap S K Chaudhary D-I Stroe M SwierczynskiB-I Craciun and R Teodorescu ldquoSizing of an energy storagesystem for grid inertial response and primary frequency re-serverdquo IEEE Transactions on Power Systems vol 31 no 5pp 3447ndash3456 2016

[5] X Sun X Liu S Cheng et al ldquoActual measurement andanalysis of fast frequency response capability of PV-invertersin northwest power gridrdquo Power System Technology vol 41no 9 pp 2792ndash2798 2017

[6] Y Xu F Li Z Jin and M Hassani Variani ldquoDynamic gain-tuning control (DGTC) approach for AGC with effects ofwind powerrdquo IEEE Transactions on Power Systems vol 31no 5 pp 3339ndash3348 2016

[7] Y Wei I Jayawardene and G Kumar VenayagamoorthyldquoOptimal automatic generation controllers in a multi-areainterconnected power system with utility-scale PV plantsrdquoIET Smart Grid vol 2 no 4 pp 581ndash593 2019

[8] C Wei Z Zhang W Qiao and L Qu ldquoAn adaptive network-based reinforcement learning method for MPPT control ofPMSG wind energy conversion systemsrdquo IEEE Transactionson Power Electronics vol 31 no 11 pp 7837ndash7848 2016

[9] D Venkatramanan and V John ldquoDynamic modeling andanalysis of buck converter based solar PV charge controller forimproved MPPT performancerdquo IEEE Transactions on In-dustry Applications vol 55 no 6 pp 6234ndash6246 2019

[10] R B Bollipo S Mikkili and P K Bonthagorla ldquoCriticalreview on PV MPPT techniques classical intelligent andoptimisationrdquo IET Renewable Power Generation vol 14 no 9pp 1433ndash1452 2020

[11] L Meng J Zafar S K Khadem et al ldquoFast frequency re-sponse from energy storage systems-a review of grid stan-dards projects and technical issuesrdquo IEEE Transactions onSmart Grid vol 11 no 2 pp 1566ndash1581 2020

[12] X Xie Y Guo B Wang Y Dong L Mou and F XueldquoImproving AGC performance of coal-fueled thermal gen-erators using multi-MW scale BESS a practical applicationrdquoIEEE Transactions on Smart Grid vol 9 no 3 pp 1769ndash17772018

[13] W Tasnin and L C Saikia ldquoPerformance comparison ofseveral energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plantrdquo IETRenewable Power Generation vol 12 no 7 pp 761ndash772 2018

[14] B Mantar Gundogdu D T Gladwin S Nejad andD A Stone ldquoScheduling of grid-tied battery energy storage

8 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

current of the ESSs does not have any inrush spikes duringthe entire transition period and there is no voltage per-turbation along the operation

Figure 11 presents the output power of one ESS which is40 kW at the beginning and then it falls from 40 kW to10 kW response to AGC instruction Its voltage and currentwaveforms at the AC side are shown in Figures 12 and 13)e output current of the ESSs does not have any inrushspikes during the entire transition period and there is novoltage perturbation along the operation

Figures 14 and 15 show the experimental waveforms ofthe ESS when its output power reference is set at 10 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

Figures 16 and 17 show the experimental waveforms ofthe ESS when its output power reference is set at 40 kWFigure 14 is the voltage waveform of the ESS at the AC sideand Figure 15 is the current waveform of phase A

)ese results indicate smooth and stable operation of theESS-integrated PVwind station and show that the ESSsprovide PV and wind generation additional AGC auxiliaryservice functionality without changing their inner controlstrategies conceived for MPPT mode

6 Conclusions

)e AGC auxiliary service control proposed in this paper isintegrated with existing AGC control strategies Power griddispatching center only needs to add instruction allocationmodule for the ESS-integrated PVwind stations It uses ESSsto add regulation capacity and improve dynamic performanceof AGC without changing the control strategies of RESsconceived for MPPT mode As the ESSs are inherentlynonlinear and time variable the mathematical model is builtconsidering the system parameter variations and disturbancesor uncertainties )e ARSMC-based ESS control system isproposed to deal these control challenges and improve itsstability and dynamic performances )e rigorous proofprocess verifies the ARSMC strategy mathematically)e casestudies on NI-PXI platform shows the fast dynamic responseand robustness performance of the ESSs guaranteeing stableoperation of the ESS-integrated PVwind station as well asvoltage and frequency regulation capability

)e ESSs provide additional AGC auxiliary servicefunctionality without changing RES inner control strategies)e ARSMC-based ESSs is suitable for existing RESs toextend their functions and to form a ESS-integrated PVwind station )e ESSs are independent from the use ofthird-party commercial RESs units which means they donot need specific customized RESs

Data Availability

)e data used to support the findings of the study are in-cluded within the article

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work is supported by the Research on Key Technologiesof Self-Supporting Micro-Renewable Energy Network inQinghai Agricultural and Pastoral Areas No 2018-ZJ-748

References

[1] W Liu G Geng Q Jiang H Fan and J Yu ldquoModel-free fastfrequency control support with energy storage systemrdquo IEEETransactions on Power Systems vol 35 no 4 pp 3078ndash30862020

[2] Y Wang Y Xu Y Tang et al ldquoAggregated energy storage forpower system frequency control a finite-time consensusapproachrdquo IEEE Transactions on Smart Grid vol 10 no 4pp 3675ndash3686 2019

[3] F Cheng L Qu W Qiao C Wei and L Hao ldquoFault di-agnosis of wind turbine gearboxes based on DFIG statorcurrent envelope analysisrdquo IEEE Transactions on SustainableEnergy vol 10 no 3 pp 1044ndash1053 2019

[4] V Knap S K Chaudhary D-I Stroe M SwierczynskiB-I Craciun and R Teodorescu ldquoSizing of an energy storagesystem for grid inertial response and primary frequency re-serverdquo IEEE Transactions on Power Systems vol 31 no 5pp 3447ndash3456 2016

[5] X Sun X Liu S Cheng et al ldquoActual measurement andanalysis of fast frequency response capability of PV-invertersin northwest power gridrdquo Power System Technology vol 41no 9 pp 2792ndash2798 2017

[6] Y Xu F Li Z Jin and M Hassani Variani ldquoDynamic gain-tuning control (DGTC) approach for AGC with effects ofwind powerrdquo IEEE Transactions on Power Systems vol 31no 5 pp 3339ndash3348 2016

[7] Y Wei I Jayawardene and G Kumar VenayagamoorthyldquoOptimal automatic generation controllers in a multi-areainterconnected power system with utility-scale PV plantsrdquoIET Smart Grid vol 2 no 4 pp 581ndash593 2019

[8] C Wei Z Zhang W Qiao and L Qu ldquoAn adaptive network-based reinforcement learning method for MPPT control ofPMSG wind energy conversion systemsrdquo IEEE Transactionson Power Electronics vol 31 no 11 pp 7837ndash7848 2016

[9] D Venkatramanan and V John ldquoDynamic modeling andanalysis of buck converter based solar PV charge controller forimproved MPPT performancerdquo IEEE Transactions on In-dustry Applications vol 55 no 6 pp 6234ndash6246 2019

[10] R B Bollipo S Mikkili and P K Bonthagorla ldquoCriticalreview on PV MPPT techniques classical intelligent andoptimisationrdquo IET Renewable Power Generation vol 14 no 9pp 1433ndash1452 2020

[11] L Meng J Zafar S K Khadem et al ldquoFast frequency re-sponse from energy storage systems-a review of grid stan-dards projects and technical issuesrdquo IEEE Transactions onSmart Grid vol 11 no 2 pp 1566ndash1581 2020

[12] X Xie Y Guo B Wang Y Dong L Mou and F XueldquoImproving AGC performance of coal-fueled thermal gen-erators using multi-MW scale BESS a practical applicationrdquoIEEE Transactions on Smart Grid vol 9 no 3 pp 1769ndash17772018

[13] W Tasnin and L C Saikia ldquoPerformance comparison ofseveral energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plantrdquo IETRenewable Power Generation vol 12 no 7 pp 761ndash772 2018

[14] B Mantar Gundogdu D T Gladwin S Nejad andD A Stone ldquoScheduling of grid-tied battery energy storage

8 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

system participating in frequency response services and en-ergy arbitragerdquo IET Generation Transmission amp Distributionvol 13 no 14 pp 2930ndash2941 2019

[15] Y Cheng M Tabrizi M Sahni A Povedano and D NicholsldquoDynamic available AGC based approach for enhancingutility scale energy storage performancerdquo IEEE Transactionson Smart Grid vol 5 no 2 pp 1070ndash1078 2014

[16] K Doenges I Egido L Sigrist E Lobato Miguelez andL Rouco ldquoImproving AGC performance in power systemswith regulation response accuracy margins using batteryenergy storage system (BESS)rdquo IEEE Transactions on PowerSystems vol 35 no 4 pp 2816ndash2825 2020

[17] F Zhang Z Hu K Meng L Ding and Z Dong ldquoHESS sizingmethodology for an existing thermal generator for the pro-motion of AGC response abilityrdquo IEEE Transactions onSustainable Energy vol 11 no 2 pp 608ndash617 2020

[18] Y Wang C Wan Z Zhou K Zhang and A BotterudldquoImproving deployment availability of energy storage withdata-driven AGC signal modelsrdquo IEEE Transactions on PowerSystems vol 33 no 4 pp 4207ndash4217 2018

[19] P )ounthong A Luksanasakul P Koseeyaporn andB Davat ldquoIntelligent model-based control of a standalonephotovoltaicfuel cell power plant with supercapacitor energystoragerdquo IEEE Transactions on Sustainable Energy vol 4no 1 pp 240ndash249 2013

[20] M Datta and T Senjyu ldquoFuzzy control of distributed PVinvertersenergy storage systemselectric vehicles for fre-quency regulation in a large power systemrdquo IEEE Transactionson Smart Grid vol 4 no 1 pp 479ndash488 2013

[21] A Moeini I Kamwa Z Gallehdari and A GhazanfarildquoOptimal robust primary frequency response control forbattery energy storage systemsrdquo in Proceedings of the 2019IEEE Power amp Energy Society General Meeting (PESGM)pp 1ndash5 Atlanta GA USA August 2019

[22] S Zhang Y Mishra and M Shahidehpour ldquoFuzzy-logicbased frequency controller for wind farms augmented withenergy storage systemsrdquo IEEE Transactions on Power Systemsvol 31 no 2 pp 1595ndash1603 2016

[23] C Wei M Benosman and T Kim ldquoOnline parameteridentification for state of power prediction of lithium-ionbatteries in electric vehicles using extremum seekingrdquo In-ternational Journal of Control Automation and Systemsvol 17 no 11 pp 2906ndash2916 2019

[24] Q Chen H Shi and M Sun ldquoEcho state network-basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2020

[25] Q Chen H Shi and M Sun ldquoEcho state network basedbackstepping adaptive iterative learning control for strict-feedback systems an error-tracking approachrdquo IEEE Trans-actions on Cybernetics vol 50 no 7 pp 3009ndash3022 2019

[26] Q Chen S Xie M Sun and X He ldquoAdaptive nonsingularfixed-time attitude stabilization of uncertain spacecraftrdquo IEEETransactions on Aerospace and Electronic Systems vol 54no 6 pp 2937ndash2950 2018

[27] Q Chen X Yu M Sun C Wu and Z Fu ldquoAdaptive re-petitive learning control of PMSM servo systems withbounded nonparametric uncertainties theory and experi-mentsrdquo IEEE Transactions on Industrial Electronics 2020

[28] J Na Y Li Y Huang G Gao and Q Chen ldquoOutput feedbackcontrol of uncertain hydraulic servo systemsrdquo IEEE Trans-actions on Industrial Electronics vol 67 no 1 pp 490ndash5002020

[29] V Utkin ldquoVariable Structure system with sliding modesrdquoIEEE Transactions on Automatic and Control vol 22 no 2pp 212ndash222 1979

[30] V Utkin I Guldner and J X Shi Sliding Mode Control inElectromechanical System Taylor and Francis London UK1999

[31] S Wang L Tao Q Chen J Na and X Ren ldquoUSDE-basedsliding mode control for servo mechanisms with unknownsystem dynamicsrdquo IEEEASME Transactions onMechatronicsvol 25 no 2 pp 1056ndash1066 2020

[32] D Xu Q Liu W Yan and W Yang ldquoAdaptive terminalsliding mode control for hybrid energy storage systems of fuelcell battery and supercapacitorrdquo IEEE Access vol 7pp 29295ndash29303 2019

[33] R Zhang and B Hredzak ldquoNonlinear sliding mode anddistributed control of battery energy storage and photovoltaicsystems in AC microgrids with communication delaysrdquo IEEETransactions on Industrial Informatics vol 15 no 9pp 5149ndash5160 2019

[34] V Patel D Guha and S Purwar ldquoFrequency regulation of anislanded microgrid using integral sliding mode controlrdquo inProceedings of the 2019 8th International Conference on PowerSystems (ICPS) pp 1ndash6 Jaipur India December 2019

[35] Y Mi X He X Hao et al ldquoFrequency control strategy ofmulti-area hybrid power system based on frequency divisionand sliding mode algorithmrdquo IET Generation Transmission ampDistribution vol 13 no 7 pp 1145ndash1152 2019

[36] Z Afshar N T Bazargani and S M T Bathaee ldquoVirtualsynchronous generator for frequency response improving andpower damping in microgrids using adaptive sliding modecontrolrdquo in Proceedings of the 2018 International Conferenceand Exposition on Electrical and Power Engineering (EPE)pp 199ndash204 Iasi Romania October 2018

[37] C Swetha N S Jayalakshmi K M Bhargavi andP B Nempu ldquoControl strategies for power management ofPVbattery system with electric vehiclerdquo in Proceedings of the2019 IEEE International Conference on Distributed Comput-ing VLSI Electrical Circuits and Robotics (DISCOVER)pp 1ndash6 Manipal India August 2019

[38] I Kim ldquoA technique for estimating the state of health oflithium batteries through a dual-sliding-mode observerrdquoIEEE Transactions on Power Electronics vol 25 no 4pp 1013ndash1022 2010

[39] H Delavari and S Naderian ldquoBackstepping fractional slidingmode voltage control of an islanded microgridrdquo IET Gen-eration Transmission amp Distribution vol 13 no 12pp 2464ndash2473 2019

[40] T Morstyn A V Savkin B Hredzak and V G AgelidisldquoMulti-agent sliding mode control for state of charge bal-ancing between battery energy storage systems distributed in aDCmicrogridrdquo IEEE Transactions on Smart Grid vol 9 no 5pp 4735ndash4743 2018

[41] M B Delghavi S Shoja-Majidabad and A Yazdani ldquoFrac-tional-order sliding-mode control of islanded distributedenergy resource systemsrdquo IEEE Transactions on SustainableEnergy vol 7 no 4 pp 1482ndash1491 2016

[42] M I Ghiasi M A Golkar and A Hajizadeh ldquoLyapunovbased-distributed fuzzy-sliding mode control for buildingintegrated-DC microgrid with plug-in electric vehiclerdquo IEEEAccess vol 5 pp 7746ndash7752 2017

[43] F Sebaaly H Vahedi H Y Kanaan N Moubayed and K Al-Haddad ldquoSliding mode fixed frequency current controllerdesign for grid-connected NPC inverterrdquo IEEE Journal of

Complexity 9

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity

Emerging and Selected Topics in Power Electronics vol 4 no 4pp 1397ndash1405 2016

[44] B Wang J Xu R-J Wai and B Cao ldquoAdaptive sliding-modewith hysteresis control strategy for simple multimode hybridenergy storage system in electric vehiclesrdquo IEEE Transactionson Industrial Electronics vol 64 no 2 pp 1404ndash1414 2017

[45] X Su M Han J Guerrero and H Sun ldquoMicrogrid stabilitycontroller based on adaptive robust total SMCrdquo Energiesvol 8 no 3 pp 1784ndash1801 2015

10 Complexity