oe

noniveic

Article history:

Accepted 23 November 2013Available online xxx

The subject addressed in this paper is the denition of some strategies for the design and the optimaized

highlighted the benets connected to the above described trans-formation, both in terms of costs and losses reduction and of qualityof the energy supply.

Based on the results drawn in Ref. [1], in this paper, a moredetailed technical assessment has been carried out, focusing the

EES. The latter hasiteria. The optimalution of a multi-tive functions are:

wer plants;

the power system.

rests of the distri-bution system operator who either want to preserve the lifetime ofthe installed componentsof thedistribution systemandaswell aimsat reducing operating costs while meeting sustainability criteria.

The optimization problem is solved using the NSGA-II (Nondominated-Sorting-Genetic-Algorithm II) dened in Ref. [8] andoften successfully adopted in the solution of multi-objective opti-mization problems in distribution networks [9,10].

In the following, after a brief discussion on the potential of EESin distribution network, useful for better understanding the ratio

* Corresponding author. Tel.: 39 (0)9123860205.

Contents lists availab

Ener

els

Energy 64 (2014) 648e662E-mail addresses: gaetano.zizzo@unipa.it, ing.gaetanozizzo@gmail.com (G. Zizzo).sented that, in line with the most recent trends in power systemsresearch [2e7], addresses the transformation of the electrical en-ergy generation system supplying the MV/LV (Medium Voltage/Low Voltage) distribution system in the Mediterranean island ofPantelleria from a fossil fuel-based model to a distributed andsmart renewables-based one.

In particular, a set of scenarios, characterized by the presence ofRES (renewable energy sources) available in the island, have beendened and a technical and economic analysis for these scenarioshas been carried out. The results of the simulations carried out have

systems, is affected by the design strategy of theindeed been carried out according to different crmanagement of the EES is based on the solobjective optimization problem where the objec

- the total generation cost of the traditional po- the energy losses in the grid;- the total GHG (greenhouse gas) emissions of

Such choice of the objectives reects the inteIn Ref. [1] a technical-economic feasibility study has been pre-systems. The paper studies how the optimal operation of distrib-uted energy sources in the island of Pantelleria, including EESKeywords:Electric energy storageGHG (greenhouse gas)Energy lossesIslanded system

1. Introduction0360-5442/$ e see front matter 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.energy.2013.11.065(multi-objective) optimization algorithm, the NSGA-II, minimizing the energy losses in the grid, the totalelectricity generation cost and the greenhouse gas emissions.The results obtained for different installation scenarios of the EES are presented and discussed, putting

into evidence the technical, environmental and economical benets of using EES as well as the technicalissues connected to their installation into an existing distribution network.The paper describes in details the second part of a feasibility study about the transition from a fuel-

based traditional centralized electrical system to an active and smart renewables-based electricaldistribution system.

2013 Elsevier Ltd. All rights reserved.

attention on the installation of EES (electrical energy storage)Received in revised form22 November 2013 In the paper the authors have drawn interesting conclusions through the application of an efcient MOReceived 21 May 2013 management of EES (Electrical Energy Storage) systems, for an existing islanded distribution networksupplying the Island of Pantelleria (Italy) in the Mediterranean Sea.Multi-objective optimized managementsystems in an islanded network with rendifferent design scenarios

M.G. Ippolito a, M.L. Di Silvestre a, E. Riva SanseveriaDEIM e Department of Energy, Information Engineering and Mathematical Models, Ub ENEA e Italian National Agency for New Technologies, Energy and Sustainable Econom

a r t i c l e i n f o a b s t r a c t

journal homepage: www.All rights reserved.f electrical energy storagewable energy sources under

a, G. Zizzo a,*, G. Graditi b

rsit di Palermo, Viale delle Scienze, Palermo, ItalyDevelopment, Portici, Italy

le at ScienceDirect

gy

evier .com/locate/energy

nergbehind the considered design and operation strategies, the featuresof the distribution network of the island of Pantelleria aredescribed. In the second part, three different location and sizingstrategies for the EES are proposed and then the multi-objectiveoptimization problem for the EES management is dened and theNSGA-II algorithm is briey described.

Finally, the results of the application of the optimal manage-ment strategy in the different design scenarios are presented anddiscussed and a brief description of the architecture for the controlof the EES system is presented.

2. Electrical energy storage systems

EES has different potential applications in modern active dis-tribution networks.

Active networks are characterized by a deep penetration of RES.In particular, thanks to national support policies like Feed in Tariffs,Capital subsides, Net-metering or Green Tags [11e17], and to theadvances in technology for obtaining increasing efciency of thesesources [18e20], PV (photovoltaic) and winds plants are today themost wide spread RES-based generation systems connected todistribution networks.

Both PV and winds plants are, nevertheless, characterized byuncertainty in the production that makes them less competitive inthe electrical energy market and limit their further commercialexpansion. Indeed, the non-dispatchable nature of these sourceshas led to concerns regarding the reliability and stability of theelectrical systems hosting their installation. For this reason theDirective 2009/28/EC [21], regarding the support of the use of en-ergy from renewable sources, recognizes that there is a need tosupport the integration of energy from RES into transmission anddistribution grids and the use of EES for integrating intermittentenergy production from renewable sources. In this scenario,chemical EES have a strategic role. EES, allowing the time-decoupling between generation and utilization of electric energy,is able to promote a better penetration of RES in distributionnetworks.

Different technologies (ywheel, hydroelectric storage, etc.) aretoday available for this purpose [22e25].

In Ref. [26], the authors focus on the applicability, advantagesand disadvantages of various EES technologies for large-scaleintegration of variable RES-based generators. The work showsthat each challenge imposed by variable RES requires a different setof EES characteristics to address the issue, and that, in general,there is not an unique EES technology that consistently out-performs the others in all possible applications.

A series of important applications of EES joins classical appli-cations like emergency supply for privileged loads in hospitals, datacenters, etc. [27].

Also within isolated systems, EES has a fundamental role inmaintaining the continuity of supply in case of lack of production.In Ref. [28] a reliability study of an isolated grid integrated with EESand RES-based generators is presented. In the work the author, byadopting a Monte Carlo approach, demonstrate the economy andthe reliability improvements obtainable by combining EES and DG(distributed generators).

Another important application of EES is as rapid reserve.Together with PV or wind systems [29] or as independent units,EES systems can take part to the frequency regulation in presenceof rapid power variation. Indeed, according to the most recentItalian technical standards on the connection of active and passiveusers to MV and LV grids [30,31], DGwith nominal power from1 kW to 10 MVA are obliged to participate to voltage and fre-quency regulation, injecting reactive power according to specic

M.G. Ippolito et al. / Ecapability curves or reducing the generated active power iffrequency goes above or below specied limits. In Ref. [32] aninteresting study on the positive effects on grid stability of EESsystems is presented.

Finally EES, suitably integrated with DG and controlled, allowsthe producer to optimize the use of all the energetic sources con-nected to its system using functions like load-leveling or peak-shaving [33]. Special EES systems like Li-Ion batteries, integratedinside suitable devices can be used as active lters improving thePower Quality, protecting loads against voltage sags, harmonicdistortions, etc. or vice versa [34,35].

A critical review on the various application of EES in the net-works of the future can be found in Ref. [36].

From the scenario emerging from the literature on this issue, ageneral conclusion that can be drawn is that EES can be used withthree different purposes:

in strategic nodes of the distribution system for improving thepower quality;

together with DGs for reducing the uctuations in the produc-tion from variable RES;

together with loads for compensating rapid variations in theirrequest or for assuring the continuity of the supply.

This general conclusion is the basis for better understanding thedesign and management strategies dened in the following.

3. Description of the test MV network

The MV distribution network of the Island of Pantelleria has aradial structure supplied by a unique thermal power plant. Thenominal voltage is 10.5 kV. Currently, the Island of Pantelleria istotally dependent on external sources of energy. The supply systemis fed by diesel, but also by oil and GPL (Liqueed Petroleum Gas).The thermal generation power plant is composed of eight generatorgroups, for a total installed power of about20 MW.

From the power station, 4 MV lines spread out. The electricaldistribution system has different points where it is possible toradially counter-supply the lines, or where it is possible to create ameshed conguration. Some of these sectionalizing points arelocated inside remotely controllable substations.

With reference to the feasibility study for the integration of DGsystems based on RES, in Ref. [1] ve possible scenarios have beendened. Among these scenarios, the one considered to be the mostrealistic was selected, since it considers:

- the full exploitation of both the geothermal and waste sources;- the exclusion of the wind source, because, as already stated, ithas its maximum during the winter when the electrical energydemand is lower and it is already covered by geothermal andwaste sources;

- a limited amount of PV systems, since due to their necessaryinstallation on private properties, their exploitation depends onthe individuals will to invest on such systems.

The present study has been done considering this scenario. Inparticular are considered the DGs reported in Table 1, characterizedby the features outlined in Ref. [1].

The scheme of theMV network, with the generators indicated inTable 1, is reported in Fig. 1.

The network has 206 nodes, 150 of which are only load nodes, 2are switching nodes, 18 are generation/consumption nodes and theother are derivation nodes.

In 14 nodes are installed PV plants. These nodes are mostlyinstalled inside the urban center and, for this reason, very close to

y 64 (2014) 648e662 649the thermal power plant. The Waste-to-Energy (WtE) plant is

geographically close to the urban center, but electrically it is at theopposite being almost at the end of the fth MV line (node 206).The Geothermal plant is installed at node 79, at the end of the thirdMV line.

In maximum load condition (during summer period), simula-tions carried out on the real distribution systemwithout DGs showa high number of violations in the voltage values (in nodes that arequite far from the thermal generation plant, voltage drops exceed10% of the rated voltage).

In the same load condition, the power injections fromgeothermal and WtE plants reduce the number and the entity ofthese violations.

4. Denition of the design strategies

In the aim of dening the strategies for the design and man-agement of EES systems in the existing islanded electrical network,some preliminary analysis have been made in order to identifysuitable installation nodes in the network, according to someheuristic considerations about competing objectives: achieving thehighest improvement that such plants would have brought on theone hand, and obtaining realistic conditions on the other hand,without exceeding the costs of investment hardly sustainable bythe DSO (Distribution System Operator) of the Island of Pantelleria.

For this reason, it was decided to consider scenarios inwhich thebatteries are installed only at the existing nodes of theMV network.This limits the choice of the number of equipments to be installedand of their location, since such installations require suitabletechnical spaces.

Table 1Distributed Generators in the test MV Network.

Typology Rated power[MW]

Yearly energyproduction [MWh]

Node

Geothermal plant 2.5 20,000 79PV plants 0.33 (total) 510 4, 5, 7, 8, 9, 12, 13, 32,

34, 35, 135, 144, 207Waste-to-energy

plant0.37 1600 206

M.G. Ippolito et al. / Energy 64 (2014) 648e662650Fig. 1. Simplied scheme of the MV network of the Island of Pantelleria, with the present DGs (Photovoltaic, Geothermal and Waste-to-Energy plants).

The EED consists in the optimal dispatch of the energy gener-

- the daily load prole in all the nodes of the network;- the daily electrical energy production of all the RES-basedgenerators;

- the generation costs and the GHG emissions of all the traditionaldispatchable generators;

- the EES initial SOC (state-of-charge);- the technical constraints for the thermal generator.

Considering that the simulation is done with a time-step of 1 h,the analytical formulation of the optimization problem is thefollowing.

A N-bus isolated distribution system is considered with:

- Nx load or generation nodes with xed forecasted real andreactive power demands or injections;

- NDG controllable distributed generation units.

The vector X identifying the set-points of the controllable DGunits can be expressed as:

X x1; x2;.; xh;.; x24 (1)

nergAn EES system consists, indeed, of a PCS (Power ConditioningSystem) whose aim is to process energy from the batteries and tomake it suitable for the loads [36].

The main functions of the PCS are:

- converting DC power to AC power;- maximizing power output from batteries;- stopping current ows from the batteries into the grid duringoutages to safeguard the utilitys personnel.

Inside the urban center, only a few nodes are surrounded bytechnical spaces for the installation of boxes for the EES, of MV/LVtransformers for connecting the batteries normally operating in LVto the MV network, of electronic equipment, and protectionrequired by the EES. To have an idea, in Ref. [37] a description andsome indications about the size of a large installation for EES areprovided. Outside themain urban center of the island, there is moreavailability of technical spaces.

For these reasons, after a brief economic analysis, it has beendecided that the EES systems would be installed only in four nodesof the MV network.

The size of each EES system has indeed been chosen heuristi-cally, but the total capacity installed has been kept in all scenariosequal to 5.2 MWh.

Three different design strategies have been examined:

1. compensate for the variability of power production from PVplants (PhotoVoltaic STrategy: ST-PV)

2. improvement of voltage prole (Voltage Prole STrategy: ST-VP)3. compensate for strong load course variations of large loads

(Load STrategy: ST-LO).

The rst design strategy, indicated as ST-PV, considers theinstallation and operation of batteries in combination with the PVplants for compensating the high variability in the energy pro-duction due to the uncertainty characteristic of the solar source. Inthis case, the entire available energy installed is close to the ratedPV plants production and the batteries are supposed to be installedat nodes 4, 32, 135 and 144.

The second strategy, indicated as ST-VP, considers the instal-lation of the batteries at the nodes that are the farthest from theexisting generation thermal power plant so as to improve thevoltage prole in the network, in particular in the hours ofmaximum load. In this case, batteries are supposed to be installedat nodes 3, 24, 131 and 199, namely the nodes having the greatestimportance in voltage prole regulation and showing the lowestvoltage values during maximum loading.

The third strategy, indicated as ST-LO, considers the installa-tion of the batteries in the nodes where the loads have the highestconsumption and the highest variability, with the purpose ofobtaining a full exploitation of the energy generated by RES plantsto supply the loads and of improving the total efciency of thewhole electric system. In this case, the batteries are supposed to beinstalled at nodes 2, 9, 95 and 144.

All these assumptions were made with reference to the gener-ation system with the DG units reported in Table 1 and the maingeneration thermal plant.

The effect of the distributed EES system on the managementperformance indices of the network (energy losses, GHG emissions,voltage prole) has been evaluated by comparing these indices,calculated in presence of EES and in a reference scenario with nobatteries but only hosting the distributed generation systemsdescribed in Table 1.

Fig. 2 shows the overall approach adopted in the paper. In the

M.G. Ippolito et al. / Esubsequent Sections 5 and 6 the optimal management problemated by the power plants and generated/absorbed by the EES,solving an Optimal Power Flow problem, given:formulation and the optimization algorithm solving the manage-ment problem are described.

5. Formulation of the multi-objective optimization problem

With reference to a simulation time horizon of 24 h and in theconsidered above described installation scenarios, the problem ofthe management of the EES systems is that of the identication ofthe set-points of the distributed generators and of the distributedEES, in order to minimize the energy losses in the network, the totalgeneration costs and the GHG emissions, that are the objectivefunctions of the optimization problem.

The problem exposed is an EED (Environmental and EconomicalDispatch) non- linear multi-objective optimization problem withconstraints and has, in general, various solutions. Each solution ischaracterized by different values of the objective functions.

Fig. 2. Overall approach adopted in the paper.

y 64 (2014) 648e662 651where the hth element, related to the hth hour of the day, is:

xh

wiin

ob

A.

erg652The typical constraints are:a)

Pgj;min Pg;hj P

gj;MAX

Qgj;min Qg;hj Q

gj;MAX

(6)

where:- Pg;hj ; P

gj;min and P

gj;MAX respectively represent the active

production at the hth hour and the minimum andmaximum limits of active power at the jth DG unit;

- Qg;hj ;Qgj;min and Q

gj;MAX respectively represent the reactive

production at the hth and the minimum and maximumlimits of the reactive power at the jth DG unit.

b) power transfer limits in the network branches;c) maximum and minimum limits for the voltage prole in the

nodes;d) limits on the power exchanged by each EES system;C. GHG emissionsThe third objective function O3 is the sum of the GHG emis-

sions of the traditional generators. Being Ehm;i the quantity oftons of gas produced during the hth hour by the ith generationunit, the objective function is formulated as it follows:

O3X X24h1

XNbi1

Ehm;i (5)

D. ConstraintsThe second objective function O2 is the sum of the daily en-ergy losses in all the branches of the MV network. Being Nb thenumber of branches of the network and assumed that the cur-rent in each branch is constant in the elementary time intervaland equal to Ihi , the objective function is formulated as it follows:

O2X X24h1

XNbi1

3$Ri$Ihi $t (4)terval (1 h). Under these hypotheses, the objective function isformulated as it follows:

O1X X24h1

XNDGi1

CPi$Pg;hi $t (3)

B. Energy losses1It is the sum of all the generation cost CPi (V/kW) of the NDGcontrollable distributed generator in the 24 h. The active powerPg;hi , of the ith generator or EES and the active power requestedby loads, is considered not variable in the elementary time in-Pg;h1 ; P

g;h2 ;.; P

g;hNDG

;Qg;h1 ;Qg;h2 ;.;Q

g;hNDG

(2)

thPg;hi andQg;hi , the active he active and reactive power generated

the hth hour by the ith generator or EES system.The purpose of the optimization problem is to minimize thejective functions described below.

Total generation costThe rst objective functionO is the total daily generation cost.

M.G. Ippolito et al. / Ene) active and reactive power balancing between generators andloads.The approach proposed for the management of the batteries isvery simple and easy to apply to isolated MV networks. These arecharacterized by the need of addressing specic technical issuesthat are more constraining as compared to the case of grid-connected systems. As described above, being the approachapplied to a real case, it was possible to separately consider thedesign and the management issues, on the basis of practical con-siderations. In the literature many formulations of the design andmanagement problems can be found, but only a few of them rely ona realistic test case. For example the method proposed in Ref. [38]gives a very complete formulation of the design problemalthough it does not account for the volumes of the componentsthat, in an existing network, are often to be limited due to the sizeof the available space.

Finally, in presence of more complex scenarios other approachescan be followed, as those proposed in Refs. [39,40] where the EESmanagement is connected to the uncertainty of the electricitymarket. In particular in Ref. [39] the authors consider the optimalmanagement of a micro-grid in the Northwestern European elec-tricity market (Belgium, France, Germany and the Netherlands). Inthe different examined scenarios also the presence of lead-acidbatteries is considered.

In Ref. [40] the authors propose an original scheduling approachfor optimal dispatch of electrical EES systems. The optimal dispatchis based on fuzzy rules and does not use forecasts since it repairsthe past history according to the real time data on the electricalenergy cost, renewable energy production and load. When thesystem detects a worsening of performances, the fuzzy logic rule-based control system self-adapts its membership functions usingan economic indicator.

6. The NSGA-II optimization algorithm

The optimization problem has been solved using the Nondominated Sorting Genetic Algorithm II, NSGA-II [2]. The algorithmis based on the concept of non-dominance, that is one of the basicconcepts in multi-objective optimization. Although it was proposeda few years ago, the algorithm still proves to be quite efcientespecially in the eld of power distribution operation and planningas some recent papers [41e51] prove. At the cost of a largercomputational burden, it allows the possibility to include robust-ness considerations which is quite benecial in presence of thelarge uncertainties associated with the presence of RES [52].

6.1. The concept of non dominance in MO optimization

For a problem having multiple objective functions to be mini-mized (fj, j 1,.,m with m > 1) any two multidimensional solu-tions x1 and x2 of the optimization problem can have one or twopossibilities: one dominates the other or none dominates the other.A solution x1 is said to dominate the other solution x2, if both thefollowing conditions are true:

the solution x1 is no worse than x2 in all objectives. This meansthat fj(x1) fj(x2), for all j 1..m;

the solution x1 is strictly better than x2 in at least one objective.This means that fj*(x1)

there exist no solution y, in a small neighborhood, which dominatesevery member in the set PA.

PA is a global Pareto-optimal set, if there exist no solution in thesearch space, which dominates every member in the set PA.

From the above discussion, it is possible to point out that thereare two goals that a multi-criterion optimization algorithm mustachieve:

to guide the search towards the global Pareto-optimal region; to maintain population diversity in the Pareto-optimal front.

6.2. The NSGA-II algorithmThe crowded comparison operator (n) guides the selection

process at the various stages of the algorithm towards a uniformlyspread out Pareto-optimal front. Every individual i in the popula-

0

i

i-1

i+1

f

CUBOID

2

f1

1

Fig. 3. Crowding distance measure.

nergy 64 (2014) 648e662 653Here I[i].m refers to the mth objective function value of the ithindividual in the set I. It must be observed that the crowding dis-NSGA-II divides the population in fronts of non-dominated so-lutions so that the search can be addressed towards interestingareas of the search space, where the global Pareto-optimal region ispresumably located. NSGA-II varies from the original NSGA in threemain things:

1) it is more computationally efcient, since the ranking of solu-tions is performed with a O((m s)$Np2) algorithm, instead ofO((m s)$Np3), where m is the number of objectives, s is thenumber of constraints and Np is the population size;

2) it signicantly prevents the loss of good solutions once theyhave been found This property is named elitism;

3) it does not need any parameter specication.

A Binary Tournament Selection operator is used to select theoffspring population, whereas crossover and mutation operatorsremain as usual.

Before selection is performed, the population is ranked on thebasis of an individuals non-domination level and, to allow thediversication, a crowding factor is calculated for each solution.

To get an estimate of the density of solutions surrounding aparticular point in the population, the average distance of the twopoints on either side of this point along each of the objectives isconsidered. In this paper, this measure has been normalized. Thisquantity idistance serves as an estimate of the size of the largestcuboid enclosing the point i without including any other point inthe population (crowding distance). In Fig. 3, the crowding distanceof the ith solution in its front (marked with solid circles) is theaverage side-length of the cuboid (shownwith a dashed box) in theobjectives space.

The following algorithm, written in Pascal language, is used tocalculate the crowding:PA is a locally optimal Pareto set, if for every member x of PA,

M.G. Ippolito et al. / Etance is normalized along each direction of the objectives space.tion is given two attributes.

1) Non-domination rank in the objectives space directions (i.rank)2) Local crowding distance in the objectives space directions

(i.distance)

A partial order n can be dened as:

i nj Ifi:rank < j:rankori:rank < j:rankandi:distance > j:distanceThat is, between two solutions with differing non-domination

ranks the point with the lower rank is preferred (the front whichis closer to the axes origin). Otherwise, if both points belong to thesame front then the point which is located in a region with lessernumber of points (the size of the cuboid inclosing it is larger) ispreferred. The mutation operator applies small power injection inthe grid from the distributed generation units, within the imposedconstraints. The crossover operator is applied taking into accountthe history of the charge/discharge process of each EES system.

In Fig. 4, one cycle of the NSGA-II procedure is represented. Ptand Pt1 are the sets of solutions at iteration t and t1, while P0t is apartially ordered (n) set of solutions.

7. Results

The study has been done considering the network in its radialconguration with all the boundary tie-switches in open position.Fig. 4. A cycle of the NSGA-II algorithm.

In its current conguration, the network is indeed provided with aset of tie-switches in order to counter-supply the loads from othermain lines spreading out from the main power generation point.Therefore opening all the tie-switches keeps the network in radialconguration.

The operation of the EES systems has been determined underthe conservative hypothesis of the maximum loading day of theyear. This condition refers to the summer period, in particular to themonth of August when, due to the high ow of tourists, electricconsumptions rise and consequently power losses, GHG emissionsand voltage drops rise too (Fig. 5).

In Fig. 6 the average daily total power demand of the Island ofPantelleria in a typical working day of the month of August isrepresented.

generation prole of the whole PV park in Table 1 in a typical day inthe month of August is represented.

For the three design strategies described in Section 4, threedifferent Pareto Optimal sets of solutions of the optimal manage-ment problem have been found using a simulation tool

Fig. 5. Monthly electric energy consumption of the Island of Pantelleria (data 2011).6.72

6.746.76

6.786.8

6.824.905

4.914.915

4.924.925

95.4

95.45

95.5

95.55

95.6

95.65

95.7

95.75

Daily energy losses [MWh]

GHG

emiss

ion[t

on

]

costs[k]

Fig. 8. Optimal Pareto set of solutions for the ST-PV strategy.

95.45

95.5

95.55

95.6

95.65

95.7

95.75

GHG

em

issio

n[to

n]

Fig. 9. Optimal Pareto set of solutions for the ST-VP strategy.

M.G. Ippolito et al. / Energy 64 (2014) 648e662654The hourly production from PV generators has been found withreference to the irradiation data in Ref. [53]. In Fig. 7, the dailyFig. 6. Average hourly electrical power demand for a working summer day (data 2011).

Fig. 7. Average hourly PV production in a typical day of the month of August.6.726.74

6.766.78

6.86.82

4.9054.91

4.9154.92

4.925

95.4

Daily energy losses [MWh] costs[k]6.116.12

6.136.14

6.156.16

4.874.875

4.88

4.885

94.6

94.65

94.7

94.75

94.8

Daily energy losses [MWh]

GHG

em

issio

n[ton

]

costs[k]

Fig. 10. Optimal Pareto set of solutions for the ST-LO strategy.

In Fig. 8e10 are respectively reported the Optimal Pareto set ofsolutions for the objective functions O1 (cost), O2 (DE) and O3 (GHGemissions), dened in Section 5. Fig. 11 shows the comparison ofthe Optimal Pareto set of solutions for the three strategies.

In Table 2, the mean values of the same objective functions arereported for the reference scenario (scenario with DG units and noEES). In this latter case, the values have been found by solving theoptimization problem without storage.

From an analysis of the gures and the table reported above iteasy to conclude that the presence of the EES produces in all theexamined cases:

- a reduction in the order of the 10% of the total generation cost;- a reduction in the order of the 17% of the GHG emissions withrespect to the case without storage;

- a rise of the energy losses;

The best results correspond to the ST-LO design scenario, whilstthe worst results are found when the ST-PV is applied.

The best operation strategies of the DG units and of the EES areshown in the following, taking for each of the three sizing scenario,within the Pareto Optimal sets of solutions, the solution corre-sponding to the minimum generation cost and the one corre-sponding to the minimum GHG emissions.

In Figs. 12e23, the hourly electrical energy production of thedispatchable generators and the daily prole of the state of chargeof the EES, are represented for each solution and for each scenario

66.2

6.46.6

6.84.85

4.9

4.95

94.6

94.8

95

95.2

95.4

95.6

95.8

96

costs[k]Daily energy losses [MWh]

GHG

emiss

ion[t

on]

Fig. 11. Optimal Pareto set of solutions comparison for the three strategies.

Table 2Mean values of the performance indexes of the network (objective functions) in thebase scenario.

Daily total generationcost [kV]

Daily energylosses [MWh]

Daily GHGemissions [ton]

5.40 4.50 115

M.G. Ippolito et al. / Energy 64 (2014) 648e662 655implemented at DEIM (Department of Energy, Information Engi-neering andMathematics) and based on a code written on purpose,varying size and location of the EES systems. Each Pareto front ismade of 200 individuals in 400 generations, crossover and muta-tion probabilities are 0.5 and 0.1 respectively. The results have been

averaged over a sample of Nr 30 runs which is considered to be ameaningful sample.

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12

Fig. 12. Hourly electrical power production of the dispatchabl

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11 12

[MW

h]

Fig. 13. Daily prole of the state of charge (SOC) of the bcorresponding to a given strategy.

7.1. Scenario ST-PV

In Table 3, the sizes of the four groups of batteries in the nodes 4,32, 135 and 144 for the Scenario 1 are reported.

In the following gures, the hourly electrical power production ofthe dispatchable generators and the daily prole of the state ofcharge of the groups of batteries for the Scenario ST-PV,

13 14 15 16 17 18 19 20 21 22 23 24

e generators (ST-PV: minimum generation cost solution).

13 14 15 16 17 18 19 20 21 22 23 24h

Node_4Node_32Node_135Node_144atteries (ST-PV: minimum generation cost solution).

the minimum GHG emissions respectively.

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Fig. 14. Hourly electrical power production of the dispatchable generators (ST-PV: minimum GHG emission solution.

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Node_4Node_32Node_135Node_144

the b

M.G. Ippolito et al. / Energy 64 (2014) 648e662656corresponding to the minimum generation cost (Figs. 12 and 13) andto the minimum GHG emissions (Figs. 14 and 15), are represented.

7.2. Scenario ST-VP

In Table 4 are reported the sizes of the four groups of batteries inthe nodes 3, 24, 131 and 199 for the for the Scenario ST-VP.

Fig. 15. Daily prole of the state of charge (SOC) ofThe following gures, 16 to 19, represent the hourly electricalenergy production of the dispatchable generators and the daily

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12h

Fig. 16. Hourly electrical power production of the dispatchabl

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11 12

Fig. 17. Daily prole of the state of charge (SOC) of the b7.3. Scenario ST-LOprole of the state of charge of the groups of batteries for theScenario 2, corresponding to the minimum generation cost and to

atteries (ST-PV: minimum GHG emission solution).In Table 5 are reported the sizes of the four groups of batteries inthe nodes 2, 9, 95 and 144 for the for the Scenario ST-LO.

13 14 15 16 17 18 19 20 21 22 23 24

e generators (ST-VP: minimum generation cost solution).

13 14 15 16 17 18 19 20 21 22 23 24

Node_4Node_32Node_135Node_144

atteries (ST-VP: minimum generation cost solution).

02

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Fig. 18. Hourly electrical power production of the dispatchable generators (ST-VP: minimum GHG emission solution).

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Node_4Node_32Node_135Node_144

the b

M.G. Ippolito et al. / Energy 64 (2014) 648e662 657The following gures, 20 to 23, represent the hourly electricalenergy production of the dispatchable generators and the dailyprole of the state of charge of the groups of batteries for the

Fig. 19. Daily prole of the state of charge (SOC) ofScenario ST-LO, corresponding to theminimum generation cost andto the minimum GHG emissions respectively.

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12

Fig. 20. Hourly electrical power production of the dispatchabl

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11 12

Fig. 21. Daily prole of the state of charge (SOC) of the b8. Discussion

Simulations show that batteries have a notable potential to affect

atteries (ST-VP: minimum GHG emission solution).the operation of distribution systems and that their operation ischaracterized by nomore than three charge/discharge cycles per day.

13 14 15 16 17 18 19 20 21 22 23 24

e generators (ST-LO: minimum generation cost solution).

13 14 15 16 17 18 19 20 21 22 23 24

Node_4Node_32Node_135Node_144

atteries (ST-LO: minimum generation cost solution).

02

4

6

8

1 2 3 4 5 6 7 8 9 10 11 12

Fig. 22. Hourly electrical power production of the dispatchab

12

the

M.G. Ippolito et al. / Energy 64 (2014) 648e662658Minimum loss and minimum GHG solutions are very similarwith reference both to the size and to the operation of the batteries.

In all cases energy losses in the network are higher in presenceof the EES.

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11

Fig. 23. Daily prole of the state of charge (SOC) ofThis result can be explained considering that the inclusion ofEES in systems not designed considering their presence and innodes that are far from the main generators cause supplementaryenergy ows in the network branches due to the charging/dis-charging processes. The corresponding current gives rise to addi-tional energy losses with respect to the reference scenario withoutEES.

The effect of this phenomenon is increased by two factors:

the quadratic dependence of the energy losses from the current; the radial structure of the network with cable or overhead lineshaving sections decreasing as the distance from the centralthermal power plant increases.

Table 3Capacity of the batteries. ST-PV.

Node Capacity [MWh]

4 0.9332 0.75135 1.18144 2.43

Table 4Capacity of the batteries. ST-VP.

Node Capacity [MWh]

3 1.924 1.16131 1.16199 0.95The existing network of the Island of Pantelleria has not beendesigned considering the possible presence of DG units and EES.Having assumed that the batteries are installed in nodes that arealso very distant from the existing thermal power plant, high en-

13 14 15 16 17 18 19 20 21 22 23 24

le generators (ST-LO: minimum GHG emission solution).

13 14 15 16 17 18 19 20 21 22 23 24

Node_4Node_32Node_135Node_144

batteries (ST-LO: minimum GHG emission solution).ergy ows have been imposed on inadequate branches.Energy losses can be reduced increasing the section of the

branches of the MV network in the neighborhood of the nodeswhere batteries and DG units are installed andmoving from a radialoperation to a meshed operation of the network.

Indeed, as it has been demonstrated in Ref. [1], as the tie-switches of the MV network are closed, both the voltage dropsand the energy losses decrease.

Anyway, even if the energy losses rise, GHG emission and gen-eration cost decrease, and therefore this increment does notsignicantly affect the other efciency indexes of the system.

In Figs. 24e26 the voltage proles obtained after the installationof the batteries and for the three strategies are compared with thevoltage prole in the reference scenario without EES. Voltages referto the maximum load hour of a heavily loaded summer day and,respectively, to the minimum cost solution in Fig. 24, to the mini-mum GHG emissions solution in Fig. 25 and to the minimum en-ergy losses solution in Fig. 26.

In Table 6 are reported the maximum percentage voltage dropsfor the three solutions and the three scenarios, compared with themaximum voltage drops in the reference scenario.

Table 5Capacity of the batteries. ST-LO.

Node Capacity [MWh]

2 3.29 0.815 0.5144 0.65

Fig. 24. Voltage proles during the maximum load hour e Minimum cost solution.

Fig. 25. Voltage proles during the maximum load hour e Minimum GHG emissions solution.

Fig. 26. Voltage proles during the maximum load hour e Minimum energy losses solution.

M.G. Ippolito et al. / Energy 64 (2014) 648e662 659

In Table 7 is reported, for each case, the number of nodes of thenetwork where the percentage voltage drop exceeds the 7%.

The most evident effects of the batteries on the voltage proleare in the ST-VP case. This results are in line with the same de-nition of ST-VP.

In the ST-PV case only the line in which voltage drops are thehighest takes advantage from the installation of the batteries. In theother lines voltage drops rise but, on the whole, the voltage proleis more regular than in absence of storage and no violations on themaximum voltage drop (7%) are present in any node.

In the ST-LO case no signicant effects on the voltage prole canbe observed, even if, in general the voltage prole improve and thenumber of violations on the maximum voltage drop is reduced.

Finally, the minimum generation cost solution and the mini-mum GHG emission solution are characterized by voltage prolesvery similar.

Table 6Maximum percentage voltage drop in the network.

Solution Referencescenario

ST-PV ST-VP ST-LO

Minimum generation cost 8.46% 5.11% 2.77% 7.67%Minimum GHG emissions 8.46% 5.11% 2.70% 7.61%Minimum energy losses 8.44% 5.10% 2.77% 7.54%

Table 7Number of nodes of the network where the voltage drops is higher than 7%.

Solution Referencescenario

ST-PV ST-VP ST-LO

Minimum generation cost 23 0 0 16Minimum GHG emissions 23 0 0 16Minimum energy losses 23 0 0 15

Fig. 27. Simplied scheme of the MV network of the Island of Pante

M.G. Ippolito et al. / Energy 64 (2014) 648e662660lleria, with the DGs and the EES systems for the ST-VP scenario.

objective functions, for the three different examined scenarios (ST-

anag

nerg9. The control network architecture for optimal management

The simulations have shown that the most promising designstrategy for EES installations in the considered system is the ST-VP.This result is not surprising since the optimal management hasbeen carried out minimizing power losses and this quantity isstrictly related to voltage drops limitation. In this paragraph, thecontrol architecture for the proposed ST-VP layout for EES and therelevant control architecture is briey described. The island alreadyhas a control architecture but with a limited number of devices and,as it is, it would not allow the complete implementation of theproposed optimized strategy.

The communication network can be realized employing a mixedwireless system based on radio waves (main channel) and GSM(back-up channel). This technology for the physical layer has beenchosen for two reasons:

- the existing tele-control infrastructure for secondary sub-stations is based on wireless technology;

- the distances between different devices is quite large.

The radio waves system uses UHF (Ultra High Frequency) re-peaters with omnidirectional antenna installed on MontagnaGrande, indicated with a red (in web version) circle in Fig. 28,serving also the Remote Terminal Units at secondary substationsand at the different energy resources (DGs and ESS (ESS)).

Fig. 28. Control system layout for the optimal m

M.G. Ippolito et al. / EThe system comprises:

- a MGCC (MicroGrid Central Controller) to be installed at theDiesel Power Plant;

- about 140 Measurement Units for real and reactive power andvoltages data at all secondary substations;

- three SC (Source ControllersSC) at the diesel generator, at thegeothermal generator and at the Waste-to-Energy node;

- four BMS (Battery Management Systems) at the ESS;- some SAS (State Acquisition Systems) and some remote controlunits for tie-switches control;

- some diagnostic units in communication with the MGCC unit.

The radio based serial interface employs the Master-SlaveModbus Rtu Standard.

10. Conclusion and future works

Even if the analysis carried out through the solution of anoptimization problem is limited to the minimization of only threePV, ST-VP and ST-LO), the results obtained demonstrate that it is notpossible to individuate only one optimal solution but a set ofnumerous equivalent solutions.

In the ST-LO scenario, the minimum reduction of energy lossesbut the maximum reduction of generation cost and GHG emissionis obtained. At the same time, this strategy is not able to givesensible improvements to the voltage prole.

In the ST-PV scenario, the voltage drop signicantly reduces inthe most disadvantaged nodes but, on the contrary, it rises in theother nodes. However, in the complex, the mean voltage prole isgenerally improved. Moreover it appears the less convenientamong the three examined strategies.

Finally, the ST-VP scenario appears as the most suitable for theoperation of the network, with signicant advantageous effects onthe voltage prole and intermediate values for the objectivefunctions.

The scheme of the MV network, with the distributed generatorsand the batteries for the ST-VP scenario is reported in Fig. 27.

Future works on the topic will address the following issues:

the identication of the branches whose section should beincreased in order to reduce energy losses;

the identication of the most suitable EES technology to beinstalled in the Island of Pantelleria.ement system (only DG units are represented).y 64 (2014) 648e662 661Acknowledgments

The study presented has been realized by a collaboration be-tween ENEA, the Italian National Agency for New Technologies,Energy and Sustainable Economic Development, and the DEIM(Department of Energy, Information Engineering andMathematics)of the University of Palermo within the project Advanced EnergyStorage Systems nanced by the Italian Minister for the EconomicDevelopment, through the program RdS (Research on the ElectricSystem), thanks to the data provided by S.MED.E. PANTELLERIAS.p.a., the society that manages the production and distribution ofthe electric energy in the island of Pantelleria.

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Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sour ...1 Introduction2 Electrical energy storage systems3 Description of the test MV network4 Definition of the design strategies5 Formulation of the multi-objective optimization problem6 The NSGA-II optimization algorithm6.1 The concept of non dominance in MO optimization6.2 The NSGA-II algorithm

7 Results7.1 Scenario ST-PV7.2 Scenario ST-VP7.3 Scenario ST-LO

8 Discussion9 The control network architecture for optimal management10 Conclusion and future worksAcknowledgmentsReferences