Advantages of Applying Large-Scale Energy Storage for Load-Generation Balancing

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Article Advantages of Applying Large-Scale Energy Storage for Load-Generation Balancing

Dawid Chudy * and Adam Le´sniak

Institute of Electrical Power Engineering, Lodz University of Technology, Stefanowskiego Str. 18/22, PL 90-924 Lodz, Poland; [email protected] * Correspondence: [email protected]

Abstract: The continuous development of energy storage (ES) technologies and their wider utilization in modern power systems are becoming more and more visible. ES is used for a variety of applications ranging from price arbitrage, voltage and frequency regulation, reserves provision, black-starting and renewable energy sources (RESs), supporting load-generation balancing. The cost of ES technologies remains high; nevertheless, future decreases are expected. As the most proﬁtable and technically effective solutions are continuously sought, this article presents the results of the analyses which through the created unit commitment and dispatch optimization model examines the use of ES as support for load-generation balancing. The performed simulations based on various scenarios show a possibility to reduce the number of starting-up centrally dispatched generating units (CDGUs) required to satisfy the electricity demand, which results in the facilitation of load-generation balancing for transmission system operators (TSOs). The barriers that should be encountered to improving the proposed use of ES were also identiﬁed. The presented solution may be suitable for further development of renewables and, in light of strict climate and energy policies, may lead to lower utilization of large-scale power generating units required to maintain proper operation of power systems.

Citation: Chudy, D.; Le´sniak,A. Keywords: load-generation balancing; large-scale energy storage; power system services modeling; Advantages of Applying Large-Scale power system operation; power system optimization Energy Storage for Load-Generation Balancing. Energies 2021, 14, 3093. https://doi.org/10.3390/en14113093

Academic Editor: Egwu Eric Kalu 1. Introduction Nowadays, the cost of most energy storage (ES) technologies remains high, making Received: 9 March 2021 it impossible to ensure return on investment for many grid applications [1]. This claim Accepted: 21 May 2021 applies, inter alia, to battery technologies [2–4]. The profitability of a given technology and Published: 26 May 2021 application is also dependent on additional factors, such as specific market conditions and installation site [5]. On the one hand, only the most profitable and technically effective Publisher’s Note: MDPI stays neutral solutions are currently chosen by new investors, but on the other, further reduction of ES with regard to jurisdictional claims in costs can increase their profitability and scope of application [6–8]. It is therefore expected published maps and institutional affil- that new ways of ES exploiting will emerge shortly. iations. The most popular applications of ES in the power system include price arbitrage, voltage and frequency regulation, reserves provision, black-starting, renewable energy sources (RESs) supporting and load-generation balancing [9,10]. The use of ES for grid balancing was analysed mainly in the scope of distribution networks (medium voltage Copyright: © 2021 by the authors. (MV) and low voltage (LV) networks). The battery ES intended to provide balancing Licensee MDPI, Basel, Switzerland. services at MV distribution feeder was presented, inter alia, in [11,12]. The ES technologies This article is an open access article may also perform balancing services compensating renewables energy fluctuations, as distributed under the terms and shown in [13,14]. The current research covered multiple services which combine balancing conditions of the Creative Commons with reactive power compensation [15] and power loss minimization [16]. Illustrative Attribution (CC BY) license (https:// applications in LV networks cover the balancing performed by centrally located and creativecommons.org/licenses/by/ dispersed ES [17], services performed by multiple ES cooperating with smart buildings [18], 4.0/).

Energies 2021, 14, 3093. https://doi.org/10.3390/en14113093 https://www.mdpi.com/journal/energies Energies 2021, 14, x FOR PEER REVIEW 2 of 17

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and dispersed ES [17], services performed by multiple ES cooperating with smart build- andings residential [18], and photovoltaic residential photovoltaic (PV) installations (PV) [ installations19]. A high potential [19]. A high is also potential demonstrated is also bydemonstrated vehicle-to-grid by (V2G) vehicle technologies-to-grid (V2G) and utilization technologies of reused and utilization vehicle batteries of reused which vehicle lead tobatteries better performance which lead to of better renewables, performance reducing of theirrenewables curtailment, reducing and better their utilizingcurtailment excess and energybetter utilizing [20–22]. excess energy [20–22]. ToTo the the best best knowledgeknowledge of of thethe authors, authors, onlyonly aa littlelittle attentionattention hashas beenbeen paidpaid to to thethe balancingbalancing services services performed performed by by large-scale large-scale ES ES which which allow allow not not only only for thefor compensationthe compensa- oftion load-generation of load-generation ﬂuctuations fluctuations but also but for also the for reduction the reduction of the ofnumber the number of starting-up of start- centrallying-up centrally dispatched dispatch generatinged generating units (CDGUs) units (CDGUs) required required to satisfy to satisfy the peak the electricitypeak elec- consumption.tricity consumption. Consequently, Consequently, the remainder the remainder of the of article the article will focuswill focus on this on particularthis partic- ESular application. ES application. TheThe bestbest opportunitiesopportunities toto provideprovide load-balancingload-balancing servicesservices byby thethe ES ES appear appear during during signiﬁcant,significant, fast fast changes changes in in electricity electricity demand. demand. In In many many power power systems, systems, such such fast fast changes changes occuroccur inin thethe morning,morning, suchsuch asasin in the the example example presented presented inin Figure Figure1 ,1 where, where demand demand may may changechange by by about about 30% 30% over over a a few few hours hours [ 23[23,24].,24].

FigureFigure 1. 1.An An exampleexample ofof thethe dailydaily electricityelectricity demand demand and and the the operation operation of of base base and and peak peak generating generating units. Own development based on [25]. units. Own development based on [25].

TheThe fast-growing fast-growing demand demand is satisfiedis satisfied by starting-upby starting- peakup peak power power generating generating units. units. The peakThe unitspeak have units higher have highercosts and, costs unlike and, baseload unlike baseload generation, generation adjust their, adjust output their power output in apower greater in scope a greater as a scope response as a to response changes to in changes the electricity in the demand.electricity demand. TheThe starting-upstarting-up generatinggenerating unitunit injectsinjects fixedfixed portionsportions ofof energyenergy toto thethe powerpower gridgrid followingfollowing its its start-up start-up characteristic, characteristic, asas presented presented in in FigureFigure2 .2. The The fixed fixed course course of of the the characteristiccharacteristic is is related related to to the the warming-up warming-up processes processes of of plant plant installations installations and and machinery. machin- Duringery. During the start-up, the start a- givenup, a unitgiven cannot unit cannot be controlled be controlled and does and not does take activenot take participation active par- inticipation the load-generation in the load-generation balancing. balancing. The start-up The of start a power-up of generating a power generating unit lasts untilunit lasts the unituntil reaches the unit its reaches required its minimum required minimum output power, output and power from that, and point from it that can point be controlled it can be bycontrolled the power by system the power operator system [26 operato]. r [26].

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FigureFigure 2. AnAn example example of of the the start-up start-up characteristic, characteristic, where: where: tstart-up—the tstart-up—the time time point point when when the start- the startup begins;-up begins; t1–t6—characteristic t1–t6—characteristic times related times to related the warming to the up processes; warming P up1–P 3 processes;—characteristic P1– P3values—characteristic of output power values relatedof output to thepower warming related up to processes;the warming PMIN—minimum up processes; PMIN technical—minimum output technicalpower after output start-up power process; after PDIS—minimum start-up process; output PDIS power—minimum available output for the power system available operator for taking the systeminto account operator power taking reserved into account for the provisionpower reserved of the additionalfor the provision services. of Own the additional development services. based Ownon [26 development,27]. based on [26,27].

DuringDuring the the morning morning ramp ramp of of demand, demand, many many generating generating units units perform perform start start-ups-ups and dodo not provide load-generationload-generation balancing balancing services. services. Such Such a situationa situation is dangerousis dangerous from from the theperspective perspective of theof the power power system, system as, unpredictableas unpredictable changes changes in demandin demand and and emergencies emergen- ciesconstitute constitute a risk a forrisk the for stability the stability of the systemof the system operation. operation. The growing The growing number ofnumber start-ups of startis also-ups associated is also associated with higher with overall higher costs overall of the power costs systemof the power operation system as more operation expensive as moreunits are expensive committed units into are production committed [23 into,24]. production Additionally, [23,24]. the starting-up Additionally, CDGUs the require start- ingthe-up usage CDGUs of more req expensiveuire the usage heavy of fuels, more such expensive as mazuts. heavy The fuels, limitation such as of mazuts. starting-up The limitationpower generating of starting units-up requiredpower generating to satisfy theunits daily required electricity to satisfy demand the candaily be electricity therefore demandvery desirable; can be nevertheless, therefore very the majority desirable of; currentlynevertheless, performed the majority research of does currently not examine per- formedthe advantages research of does reducing not examine the number the of advantages CDGU start-ups. of reducing the number of CDGU start-Theups. utilization of ES for load-generation balancing, taking into account the number of CDGUThe utilization start-ups, of has ES been for load described,-generation e.g., inbalancing [23,24]., The taking author into presentedaccount the the number mixed- ofinteger CDGU linear start programming-ups, has been (MILP) described optimization, e.g., modelin [23,24]. with an The objective author function presented aimed the at mixedthe reduction-integer oflinear CDGU programming start-ups and (MILP) shut-downs. optimization The simulations model with performed an objective for different func- tionshares aimed of ES at in the the reduction modeled of system CDGU present start-ups the and first shut insight-downs. into theThe analysed simulations problem. per- formedIn [28], for the different use of unit shares commitment of ES in the and modeled dispatch system models present are incorporated; the first insight nevertheless, into the analythe simulationssed problem. assume In [28] a fixed, the numberuse of unit of CDGU commitment start-ups and during dispatch the simulationmodels are horizon incorporated;and are intendednevertheless, only the to simulations show general assume patterns a fixed of large-scale number of ES CDGU operation. start-ups A conceptduring theassuming simulation the replacement horizon and of are combined intended cycle only gas to turbinesshow general (CCGTs) patterns operating of large during-scale peaks ES operation.of demand A by conce large-scalept assuming ES is presented the replacement in [29]. Theof combined results indicate cycle agas significant turbines impact(CCGTs) on

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the limitation of CCGT operation and carbon dioxide (CO2) reductions; nevertheless, the idea cannot be applied for other types of CDGUs as specific fixed start-up characteristics were not considered. The authors of [30] assumed dedicated large-scale ES charged by wind power plant output energy. The presented approach resulted in the reduction of peak-load CDGUs, but the outcome depends on the wind/storage ratio. Another approach is presented in [31] where the limitation of start-ups, taking into account coal-based units, is performed by distributed assets located in the MV network as a positive consequence of the load profile smoothing services. A novel approach presented within this article constitutes a development of the previously published works. Firstly, it shows the utilization of large-scale ES for the balancing purposes on the transmission grid level, oppositely to concepts presented in [11–22] where considered ES are located in distribution systems (LV and MV networks). Subsequently, an improved MILP unit commitment and dispatch model based on [28,32–35] is introduced. The model allows for analyses of different simulation scenarios, thus various operation patterns and parameters of ES can be implemented. Additionally, the model can be easily reproduced. Thirdly, the performed simulations show the specific operation of ES reducing the number of CDGU start-ups and facilitating load-generation balancing. The additional barriers in further service provision are also identified. The presented work increases the scope and the application of the findings presented in [23,24,29,30]. Finally, the conclusions of the article indicate the future field of studies related to restrictions imposed by climate and energy policies and cost-benefit analyses. The remainder of the paper is organized as follows: the second section presents the developed unit commitment and dispatch simulation model. Section3 describes the main assumptions and simulation scenarios. Section4 discusses the results of the simulations, while the last section concludes the article.

2. Simulation Model The core of the presented MILP unit commitment and dispatch simulation model is based on the methodology presented in [28,32–35]. The model reﬂects a transmission system that includes large-scale power generating units (CDGUs), large-scale ES, non-dispatchable generation (renewables, combined heat and power (CHP), import and industry) and loads. The copper plate interpretation of the power grid is implemented assuming a lack of constraints and unlimited energy transmission between connected entities. Further simulation horizon reﬂects 24 h with 1-h intervals.

2.1. Power Generating Units Centrally dispatched large-scale power generating units constitute the majority of the generating assets in the modeled system and assume the main responsibility for load- generation balancing. Two types of CDGUs can be distinguished: base-load and peak-load. The first have lower operational costs and change their output power only in a limited range, while the latter have higher costs, change their output power in a greater scope and start up and shut down in a response to significant demand changes. For the whole simulation horizon T, the output power pG out (g,t) of all CDGUs G has to be limited within their lower and upper limits denoted respectively as PG min (g) and PG max (g). Binary variables binG (g,t) reflect ON and OFF states of a generating unit (Equation (1)) [32,33].

∀g ∈ G, ∀t ∈ T: PG min (g)·binG (g,t) ≤ pG out (g,t) ≤ PG max (g)·binG (g,t) (1)

The possible variations in the output power pG out (g,t) are constrained by an increase (ramp-up) and decrease (ramp-down) limits, denoted respectively as ru (g) and rd (g) (Equation (2)) [32,33].

∀g ∈ G, ∀t ∈ [2..24]: pG out (g,t − 1) − rd (g) ≤ pG out (g,t) ≤ pG out (g,t − 1) + ru (g) (2) Energies 2021, 14, x FOR PEER REVIEW 5 of 17

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∀g ∈ G, ∀t ∈ [2..24]: pG out (g,t − 1) − rd (g) ≤ pG out (g,t) ≤ pG out (g,t − 1) + ru (g) (2)

TheThe peak peak load load CDGUs CDGUs are are committed committed into into p productionroduction when when periods periods of of higher higher con- con- sumptionsumption occur. occur. As As mentioned mentioned before before,, during during their their start start-ups-ups CDGUs CDGUs have have to to follow follow the the fixedﬁxed characteristic characteristic related related to to warming warming-up-up processes processes of of installations installations and and machinery. machinery. An An assumedassumed start start-up-up pattern pattern is is shown shown in in Figure Figure 33..

FigureFigure 3. 3. TheThe assumed assumed start start-up-up characteristic characteristic of of power power generating generating units, units, where: where: tstart tstart-up—the-up—the time time

whenwhen the the start-up start-up begins; begins; t1 –t t41—characteristic–4—characteristic times times related related to the to warming the warming up processes; up processes; P1–P2— P1characteristic-2—characteristic values of values output power of output related power to the warming related up to processes; the warming PMIN = PDIS—minimumup processes; PMIN=PDISoutput power—minimum of a given output power generatingpower of a unit.given Own power development generating basedunit. Own on [26 development–28]. based on [26–28]. To satisfy the increased demand, it is necessary to activate subsequent peak-load To satisfy the increased demand, it is necessary to activate subsequent peak-load CDGUs. The synchronization of a unit starts after time t1 from the beginning of the start-up 1 CDGUs.(tstart-up). The In the synchronization presented model of athe unit signal starts for after the synchronization time t from the sync beginning (g,t) of a of given the start-up (tstart-up). In the presented model the signal for the synchronization sync (g,t) of a CDGU g is determined by the binary variables binG (g,t) responsible for its OFF and ON givenstates CDGU (Equation g is determined (3)). Additionally, by the binary variable variables sync (g,t) binG is (g,t) limited responsible to be positive for its OFF or zeroand ON(Equation states (Equation (4)). (3)). Additionally, variable sync (g,t) is limited to be positive or zero (Equation (4)). ∀g ∈ G, ∀t ∈ [2..24]: sync (g,t) ≥ binG (g,t) − binG (g,t−1) (3) ∀g ∈ G, ∀t ∈ [2..24]: sync (g,t) ≥ binG (g,t) − binG (g,t−1) (3) ∀g ∈ G, ∀t ∈ [2..24]: sync (g,t) ≥ 0 (4) ∀g ∈ G, ∀t ∈ [2..24]: sync (g,t) ≥ 0 (4) The sync (g,t) variable is used to count the total sum of start-ups (sumstart-up) during

the simulationThe sync (g,t) period variable (Equation is used (5)). to count This sum the total is used sum in of the start following-ups (sum part start of-up the) during paper theto depictsimulation the results period that (Equation show the (5)). impact This sum of energy is used storage in the onfollowing the operation part of ofthe CDGUs paper (Section4). T G sumstart-up = ∑t=1 [∑g=1 sync (g,t)] (5)

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After the synchronization, a given CDGU begins to inject its output energy into the power system. During the initial phase the amount of energy is consistent with the start-up characteristic (Figure3). From the moment of reaching the output power equal to PMIN = PDIS, a unit is operating according to its optimal pattern or it is modiﬁed to provide ancillary services (ASs) depending on the requirements of the power system operator. The model assumes that CDGUs do not provide any additional services during the simulations.

2.2. Energy Storage Large-scale ES, thanks to its bulk parameters and fast response time, can provide a load-generation balancing service next to the CDGUs. The chosen ES model described in [34,35], by specifying required parameters, allows for the implementation of any type of ES. Equation (6) presents the minimum (EES min) and the maximum (EES max) limits of ES capacity EES (t) for the whole simulation horizon T.

∀t ∈ T: EES min ≤ EES (t) ≤ EES max (6)

Operating power constraints (Equations (7) and (8)) reﬂects maximum charge power (PES char max) and maximum discharge power (PES dischar max) of ES. They also contain binary variables binES (t) responsible for switching between charging, discharging and idle modes of ES. ∀t ∈ T: PES char (t) ≤ binES (t)·PES char max (7)

∀t ∈ T: PES dischar (t) ≤ (1 − binES (t))·PES dischar max (8)

Modeled ES maintains energy EES (t) through charge and discharge cycles according to Equation 9. The charge cycle is defined by charge power PES char (t), charge time tES char and charge efficiency ηES char when the discharge cycle by discharge power PES dischar (t), discharge time tES dischar and discharge efficiency ηES dischar.

∀t ∈ [2..24]: EES (t) = EES (t − 1) + PES char (t)·tES char·ηES char − PES dischar (t)·tES dischar/ηES dischar (9)

2.3. Non-Dispatchable Generation and Loads Non-dispatchable generation cannot be controlled by the system operator and therefore does not take part in load-generation balancing. It covers renewables, CHP, import and industry and is modeled by a fixed profile. The presented analysis focuses on the large-scale ES, and therefore all loads are assumed to be passive. The demand to be covered by all power generating units is also modeled by a fixed pattern. The assumed fixed profiles related to the non-dispatchable generation and loads are presented in the following section (Section3).

2.4. Global Constraint A global constraint given by Equation (10) ensures that the total demand Dem (t) and the energy required for ES charging (EES char (t)) are balanced with total generation consisting of the energy generated by all CDGUs G (Eout (g,t)), energy injected by starting- up CDGUs after synchronization (Estart-up (g,t)), energy discharged by ES (EES dischar (t)) and output energy from all non-dispatchable assets (Enon-dis (t)) [28].

G ∀t ∈ T: Dem (t) + EES char (t) = ∑g = 1 (Eout (g,t) + Estart-up (g,t)) + EES dischar (t) + Enon-dis (t) (10)

2.5. Objective Functions The objective function reﬂects the unit commitment and dispatch aimed at minimization of the overall costs of the power system operation. In the ﬁrst step of further simulations, the load-generation balancing is provided only by CDGUs and the objective Energies 2021, 14, x FOR PEER REVIEW 7 of 17

∀t ∈ T: Dem (t) + EES char (t) = ∑g = 1G (Eout (g,t) + Estart-up (g,t)) + EES dischar (t) + Enon-dis (t) (10)

2.5. Objective Functions Energies 2021, 14, 3093 7 of 17 The objective function reflects the unit commitment and dispatch aimed at minimization of the overall costs of the power system operation. In the first step of further simulations, the load-generation balancing is provided only by CDGUs and the objective functionfunction isis given given by by Equation Equation (11). (11). TheThe costscosts ofofCDGUs CDGUsare are represented represented by by the the cost cost of of energyenergy injected injected into into the the power power system system during during their their normal normal operation operation (CG (C(g,t))G (g,t)) and and during dur- start-upsing start- (CupsSU ((g,t)).CSU (g,t)).

obj = min {∑Tt = 1T [∑gG = 1G (CG (g,t) + CSU (g,t))]} obj = min {∑t=1 [∑g=1 (CG (g,t) + CSU (g,t))]} (11)(11) InIn the the subsequent subsequent simulations simulations ES also ES also takes take parts inpart load-generation in load-generation balancing, balancing, there- therefore the objective function takes into account ES costs (CES (t)) related to the accu- fore the objective function takes into account ES costs (CES (t)) related to the accumulation andmulation transformation and transformation of the energy of which the energy is further which injected is further by them injected into the by modeled them systeminto the andmodeled is expressed system as and follows is expressed (Equation as follows (12): (Equation (12):

T G obj = min {∑T t = 1 [∑gG = 1 (CG (g,t) + CSU (g,t)) + CES (t)]} (12) obj = min {∑t=1 [∑g=1 (CG (g,t) + CSU (g,t)) + CES (t)]} (12)

3.3. Assumptions Assumptions 3.1. Electricity Demand 3.1. Electricity Demand The simulation model adopts the power system created to satisfy an electricity demand The simulation model adopts the power system created to satisfy an electricity de- with peak consumption equal to about 22 GW. The assumed load (demand) proﬁle is mand with peak consumption equal to about 22 GW. The assumed load (demand) profile presented in Figure4. is presented in Figure 4.

FigureFigure 4. 4.The The assumed assumed ﬁxed fixed load load (demand) (demand) proﬁle. profile. Data Data source source [ 25[25].].

TheThe assumed assumed demand demand profile profile represents represents an an exemplary exemplary daily daily load load from from the the operation operation ofof the the real real power power system system [25 [25].]. Despite Despite other other analyses analy inses the in literaturethe literature that takethat intotake account into ac- thecount annual the profile annual because profile theybecause focus they on economic focus on assessments, economic assessments, only a one-day only load a one profile-day wasload adopted profile was in the adopted paper. inThe the paper. choice The of this choice representative of this representative day was consideredday was consid- and motivatedered and motivated by the significant by the significant change in thechange demand in the during demand the during morning the hours morning visible hours in Figurevisible4 .in The Figure load 4. covers The load the wholecovers considered the whole considered system. system. TheThe non-dispatchable non-dispatchable generation, generation such, such as renewables, as renewables, CHP, CHP, import import and industry, and industry also , takesalso parttakes in part the coveringin the cove ofring the totalof the demand. total demand. Their assumed Their assumed fixed profile fixed is profile presented is pre- in Figuresented5. in Figure 5. Due to the fixed character of the non-dispatchable generation, further optimization covers only the demand to be satisfied by CDGUs (the electricity consumption to be covered by non-renewable generation was deducted from the total demand profile). The resulting profile is denoted as the remaining demand and presented in Figure6.

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Figure 5. The assumed fixed profile of non-dispatchable generation. Data source [36].

Due to the fixed character of the non-dispatchable generation, further optimization covers only the demand to be satisfied by CDGUs (the electricity consumption to be covered by non-renewable generation was deducted from the total demand profile). The Figure 5. The assumed fixed profile of non-dispatchable generation. Data source [36]. Figureresulting 5. The profile assumed is denoted ﬁxed proﬁle as the of non-dispatchableremaining demand generation. and presented Data source in Figure [36]. 6. Due to the fixed character of the non-dispatchable generation, further optimization covers only the demand to be satisfied by CDGUs (the electricity consumption to be covered by non-renewable generation was deducted from the total demand profile). The resulting profile is denoted as the remaining demand and presented in Figure 6.

Figure 6. The remaining demand profile assumed for further simulations. Figure 6. The remaining demand proﬁle assumed for further simulations. 3.2.3.2. Power Power Generating Generating Units Units TheThe load-generation load-generation balancing balancing of of the the modeled modeled power power system system is is based based on on 45 45 CDGUs CDGUs differentiatedFiguredifferentiated 6. The remaining according according demand to to technical technical profile parametersassumed parameters for andfurther and operating operating simulations. costs. costs. The The main main technical technical andand economic economic parameters parameters are are presented presented in in Table Table1. 1. 3.2. Power Generating Units Table 1. Parameters of the model—centrally dispatch power generating units (CDGUs). Cost The load-generation balancing of the modeled power system is based on 45 CDGUs parameters based on [27,37]. differentiated according to technical parameters and operating costs. The main technical and economicRated parametersMinimum are presented in Table 1. Number Maximum Operating Start-Up Output Output Type of Units Ramp Costs/MWh Costs/MWh Power Power High High 25 200 MW 80 MW 120 MW/h Peak-load (100%) (160%) Medium 15 400 MW 160 MW 200 MW/h Base-load 1 - (60%) 5 1000 MW 400 MW 200 MW/h Base-load 2 Low (30%) -

Operating costs/MWh cover the overall costs of electricity production by a given type of unit. The peak-load generating units are characterized by the highest costs, denoted as

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100%, which also constitutes the reference for costs of other CDGUs and ES. For base-load units generating costs are lower and are equal respectively to 60% and 30% of the peak- load generating costs. Start-up costs/MWh are borne by peak-load units committed into production during periods of high electricity consumption. Those costs are equal to 160% of normal operating costs due to the speciﬁc character of start-up processes described in previous sections. Table2 presents parameters related to the assumed start-up characteristic presented previously in Figure3.

Table 2. Parameters of the modelled start-up characteristic.

Parameter Value

Output power P1 0.5 PMIN [MW] Output power P2 0.7 PMIN [MW] Output power PMIN According to Table1 [MW] Time interval t1 2 h Time interval t2 1 h Time interval t3 1 h Time interval t4 1 h

3.3. Energy Storage The parameters of the ES are related to the further simulation scenarios and will be specified in the following subsection. For all ES, the round-trip efficiency is assumed at 81% (90% charge efficiency and 90% discharge efficiency) and operating costs/MWh as 140% of peak-load generating costs [38]. The assumed parameters can be modified according to the chosen ES technology.

3.4. Simulation Scenarios The simulation scenarios assume different shares of ES in the modeled power system to analyse their impact on the power system operation, especially in terms of the limitation of the number of CDGU start-ups. The set of the main simulation scenarios is presented in Table3.

Table 3. Description of the main simulation scenarios, where ES—energy storage.

Scenario Description Reference scenario No ES operation Scenario 1 ES: 250 MW/750 MWh Scenario 2 ES: 500 MW/1500 MWh Scenario 3 ES: 1000 MW/3000 MWh Scenario 4 ES: 2000 MW/6000 MWh Energies 2021, 14, x FOR PEER REVIEW 10 of 17

Table 3. Description of the main simulation scenarios, where ES—energy storage.

Scenario Description Reference scenario No ES operation Scenario 1 ES: 250 MW/750 MWh Scenario 2 ES: 500 MW/1500 MWh Energies 2021, 14, 3093 10 of 17 Scenario 3 ES: 1000 MW/3000 MWh Scenario 4 ES: 2000 MW/6000 MWh

4. Simulation and Results 4. Simulation and Results The graphical presentation of the simulations’ results covers the fulﬁllment of the The graphical presentation of the simulations’ results covers the fulfillment of the demand by the different generating units and the ES (Figure7) and energy stored in the ES demand by the different generating units and the ES (Figure 7) and energy stored in the (Figure8). ES (Figure 8).

Figure 7. Cont.

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FigureFigure 7. 7.Results—distribution Results—distribution of of generation, generation, start-up start-up energy, energy, and and energy energy storage storage (ES) (ES) operation. operation. Figure 7. Results—distribution of generation, start-up energy, and energy storage (ES) operation.

Figure 8. Results—energy stored in the energy storage (ES) depending on the simulation scenario. Figure 8. Results—energy stored in the energy storage (ES) depending on the simulation scenario. Figure 8. Results—energy stored in the energy storage (ES) depending on the simulation scenario. The reference scenario assumes the operation of the modeled power system without the The ESThe influence. reference reference scenario Thescenario energy assumes assumes injected the the operation byoperation the CDGUs of of the the modeled duringmodeled start power power-ups system system is additionally without without thehighlighted.the ES ES inﬂuence. influence. Scena The Therios energy1 energy–4 assume injected injected the byuse by the theof the CDGUs CDGUs ES in duringdifferent during start-ups start shares-ups according is is additionally additionally to the highlighted.datahighlighted. presented ScenariosScena in Tablerios 1–41 2.–4 assumeassume thethe useuse ofof thethe ESES inin differentdifferent shares shares according according to to the the datadata presented Thepresented fast-growing in in Table Table 2demand. 2. (especially during the morning ramp) has to be covered by additionalTheThe fast-growing fast- growingCDGUs demand committeddemand (especially (especially into production during during the as the morningalready morning committed ramp) ramp) has has tounits beto coveredarebe coverednot able by additionaltoby respondadditional CDGUs to CDGUssuch committed rapid committed changes. into into productionThe production results asshow already as alreadythat committedES committeddischarging units units replaces are are not no able expen-t able to respondsiveto respond start to-up such to energy such rapid rapid and changes. canchanges. reduce The The results the results number show show thatof CDGUsthat ES discharging ES necessarydischarging replaces to replacescover expensive the expen- daily start-updemand.sive start energy -Theup energyamount and can and of reduce startcan -reduce theup numberenergy the numberis of subsequently CDGUs of CDGUs necessary limited necessary to coverby the theto growing cover daily demand.the sh aredaily of TheES.demand. amountThe ES The discharging of amount start-up of energy takes start -place isup subsequently energy only duringis subsequently limited the periods by thelimited growingof CDGU by the share start growing- ofups ES. and sh Theare does ES of dischargingES. The ES discharging takes place only takes during place theonly periods during of the CDGU periods start-ups of CDGU and start does-ups not and occur does in

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thenot remaining occur in the periods, remaining even periods, during peaks even during of demand, peaks because of demand, high costsbecause do nothigh allow costs ES do tonot compete allow ES with to compete other generating with other units. generating units. FigureFigure8 8shows shows the the utilization utilization of of the the ES ES expressed expressed as as a a percentage percentage relation relation between between energyenergy stored stored and and full full available available capacity capacity and and indicates indicates that that ES capacityES capacity is not is fullynot fully utilized uti- inlized all scenarios.in all scenarios. The The best best results results were were obtained obtained in Scenarioin Scenario 3 where3 where ES ES capacity capacity was was exploitedexploited atat 80%.80%. LowerLower ESES parametersparameters andand theirtheir furtherfurther increaseincrease resultedresulted inin signiﬁcantlysignificantly lowerlower utilization. utilization. This This indicates indicates that that the the parameters parameters of ESof ES should should be properlybe properly sized. sized. The The ES capacityES capacity may may not benot fully be fully utilized utilized and thusand thus cause cause a lack a oflack proﬁtability of profitability for undersized for undersize andd oversizedand oversized installations. installations. The problem The problem is especially is especially visible in visible the case in theof oversized case of oversized ES—the powerES—the system power can system absorb can a certainabsorb capacitya certain andcapacity its excess and its remains excess idle. remains idle. The number of start-ups for the main simulations is presented in Figure9. The number of start-ups for the main simulations is presented in Figure 9.

Figure 9. The number of start-ups for main simulations. Figure 9. The number of start-ups for main simulations.

TheThe growinggrowing shareshare ofof ESES inin the the modeled modeled powerpower systemsystem resultedresulted inin aa lower lower number number ofof CDGUCDGU start-ups.start-ups. ThisThis positivepositive effecteffect cancan facilitatefacilitate load-generationload-generation balancingbalancing asas fixedfixed start-upsstart-ups areare limitedlimited andand moremore regulation regulation abilities abilities can can be be controlled controlled by by the the power power system system operator.operator. However, However, after after exceeding exceeding the the ES ES share share of of 1000 1000 MW MW and and 3000 3000 MWh, MWh, furtherfurther reductionreduction of of start-ups start-ups does does not not occur—a occur— certaina certain number number of CDGUsof CDGUs is required is required to maintain to main- thetain operation the operation of the of powerthe power system system after afte totalr total discharging discharging of of ES ES capabilities. capabilities. It It should should bebe alsoalso noticednoticed thatthat thethe firstfirst significantsignificant reductionreduction ofof thethe CDGUCDGU start-upsstart-ups requiredrequired onlyonly 250250 MW/750MW/750 MWh MWh ES ES capabilities capabilities (Scenario (Scenario 1 1with with three three fewer fewer start start-ups-ups comparing comparing to the to thereference reference scenario) scenario) while while the the subsequent subsequent significant significant reduction reduction occurs with with four-times four-times moremore ESES capabilitiescapabilities (Scenario(Scenario 33 with with 1000 1000 MW/3000 MW/3000 MWhMWh ESES andand fivefive fewerfewer start-ups start-ups comparingcomparing toto thethe Scenario Scenario 1). 1). ScenariosScenarios 1–41–4 assumedassumed fourfour examplesexamples ofof ESES characterizedcharacterized byby thethe powerpower toto capacity capacity ratioratio equal equal to to 1/3. 1/3. Additional Additional simulations simulations analyse analy these theimpact impact of different of different range range of ES rated of ES parametersrated parameters (in terms (in of terms power of andpower capacity) and capacity) on the number on the ofnumber CDGU of start-ups CDGU start to verify-ups ifto otherverify power if other to power capacity to ratioscapacity may ratios have may a greater have a impact greater on impact the analysed on the analy problem.sed prob- The resultslem. The are results presented are presented in Figure 10 in. Figure 10.

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FigureFigureFigure 10. 10.10. Impact ImpactImpact of ofof energy energyenergy storage storagestorage (ES) (ES)(ES) rated ratedrated parameters parametersparameters on onon the thethe number numbernumber of ofof CDGUs CDGUsCDGUs start-ups. startstart--ups.ups.

TheTheThe above-presented above above--presentedpresented results results results take take take into into into account account account ES with ES ES with witha power aa powerpower to capacity to to capacity capacity ratio bigger ratio ratio thanbiggerbigger 1/6 thanthan and 1/61/6 smaller andand smallersmaller than 1. thanthan 1.1. ItItIt can c canan be be be seen seen seen that that that increasing increasing increasing ES ES EScapacity capacity capacity has has has a minor a a minor minor impact impact impact on the on on number the the number number of start- of of ups,startstart- especially-upsups,, especiallyespecially in cases inin casescases of large ofof largelarge ES (for ESES example(for(for exampleexample in the inin casethethe casecase of ES ofof withESES withwith power powerpower equal equalequal to 1800toto 18001800 MW MWMW and andand 2000 20002000 MW). MW).MW). It may ItIt maymay indicate indicateindicate that thatthat the thethe high highhigh cost costcost of of ESof ESES operation operationoperation constitutes constitutesconstitutes a barrieraa barrierbarrier for forfor further furtherfurther reduction reductionreduction of CDGU ofof CDGUCDGU start-ups startstart- because-upsups becausebecause ES is ES notES is ableis notnot to ableable exploit toto exploitexploit the whole thethe availablewholewhole availableavailable capacity. capacity.capacity. The strongly TheThe stronglystrongly reduced reducedreduced capacity capacitycapacity results inresultsresults the greater inin thethe numbergreatergreater numbernumber of CDGU ofof start-upsCDGUCDGU startstart which--upsups is whichwhich visible isis for visiblevisible small forfor ES. smallsmall ES.ES. TheTheThe lastlastlast additionaladditionaladditional simulationsimulationsimulation presentsprespresentsents thethethe operation operationoperation ofofof the thethe modeled modeledmodeled power powerpower system systemsystem assumingassumingassuming aa significantly signiﬁcantlysignificantly lowerlower lower costcost cost ofof of ESES ES operation.operation. operation. TheThe The costcost cost isis assumedassumed is assumed toto bebe to lowerlower be lower thanthan thanthethe costscosts the costs ofof thethe of mostmost the most expensiveexpensive expensive peakpeak peak powerpower power generatinggenerating generating unitsunits units (75%(75% (75% ofof thethe of peakpeak the peak-load--loadload gen-gen- generatingeratingerating costs).costs). costs). TheThe The 20002000 2000 MW/6000MW/6000 MW/6000 MMWhWh MWh ESES was ESwas was takentaken taken intointo into consideration.consideration. consideration. TheThe The resultsresults results areare arepresentedpresented presented inin FigureFigure in Figuresss 1111 and11and and 12.12. 12 .

FigureFigureFigure 11. 11.11. Results ResultsResults of ofof the thethe additional additionaladditional simulation—operation simulationsimulation——operationoperation of ofof the thethe modeled modeledmodeled system systemsystem assuming assumingassuming lower lowerlower costscostscosts of ofof energy energyenergy storage storagestorage (ES). (ES).(ES).

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FigureFigure 12. 12.Results Results of of the the additional additional simulation—energy simulationsimulation—energy stored stored in in the the energy energy storage storage (ES) (ES) assuming assuming lower costs of ES. lowerlower costs costs of of ES. ES.

TheThe lowerlower costscosts ofof ESES allow allow for for their their peak peak shaving shaving operation operation and,and,,as as a a result, result, the the furtherfurtherfurther reduction reductionreduction of ofof CDGU CDGUCDGU start-ups startstart--ups as as demand demand to to be be covered covered by by CDGUs CDGUs is is lower. lower. The The dischargingdischarging takes takes place place also also during during the the afternoon afternoon and and the the evening evening peaks. peaks. The The ES ES capacity capacity ininin this thisthis case casecase is isis better betterbetter utilized utilizedutilized and andand used usedused to toto more more than than 90%. 90%. ForFor the the presented presented simulation, simulation,, numbernumber of of start-ups start--ups is is equal equal to to 19 19 and and is is smaller smaller by by 3 3 when when comparing comparing to to the the best best results results obtainedobtained in in Scenarios Scenarios 3 3 and and 4 4 (Figure (Figure 13 13).).

Figure 13. The biggest possible reductions of start-ups in the modeled system taking into account FigureFigure 13. 13.The The biggest biggest possible possible reductions reductions of of start-ups start-ups in in the the modeled modeled system system taking taking into into account account the different costs of ES operation. thethe different different costs costs of of ES ES operation. operation.

TheThe subsequent subsequent reduction reduction of of the the number number of of CDGUs CDGUs necessary necessary to to cover cover the the daily dailyly electricityelectricity demanddemand maymay bring bring additional additional beneﬁts benefits related related to to fulﬁllment fulfillment of of the the provisions provisions includedincludedincluded in inin climate climateclimate and andand energy energyenergy policies. policies.policies. Nowadays, Nowadays,Nowadays, generation generationgeneration from fromfrom conventional conventionalconventional units unitsunits suchsuch asas fossilfossilfossil and and gasgas gas powerpower power plantsplants plants isis is burdenedburdened burdened withwith with additionaladditional additional costscosts costs relatedrelated related toto i.e.i.e. to,, i.e., Eu- Europeanropeanropean UnionUnion Union EmissionsEmissions Emissions TradingTrading Trading SystemSystem System (EU(EU (EU ETS)ETS) ETS) [39][39] [39] and and provisionsprovisionsprovisions imposedimposedimposed by byby the thethe CleanClean Energy Energy for for All All Europeans Europeans Package Package issued issued by by the the European European Commission Commission [ 40 [40].]. It It shouldshould be be noted noted that that in in many many power power systems systems conventional conventional generation generation represents represents a majority a ma- ofjorityjority the centrally of of the the centrally centrally dispatched dispatched dispatched assets able assets assets to ableprovide able to to provide provide load-generation load load--generation balancing balancing services ser- to thevices power to the system power operators, system and operators their limitation,, and their may limitation bring additional may bring advantages. additional The ad-

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impact of the proposed use of large-scale ES on the fulﬁllment of the provisions given by the climate and energy policies should be therefore analysed in further research.

5. Conclusions The article presents the use of large-scale energy storage (ES) for the provision of load-generation balancing services allowing for the reduction of the number of centrally dispatched generating units (CDGUs) necessary to cover the daily electricity demand. The analysis based on the mixed-integer linear programming (MILP) optimization model and different simulation scenarios show the advantages of the proposed solution and identify barriers that should be faced to increase the proposed use of ES. The reduction of CDGU start-ups results in more regulation abilities in periods of growing demand and therefore causes an increase in the power system’s operation security and consequently may allow for a better adaptation of variable generation such as renewables. Further advantages cover the limitation of more expensive peak power generating units to be committed into production and the limitation of additional spending related to expensive start-up processes. Nevertheless, the scope of the proposed service is limited due to the high costs of ES operation. The increased share of ES in the modeled system, both in terms of rated power as well as rated capacity, does not improve the scope of the service. The performed simulations also showed that subsequent significant reductions of the CDGU start-ups require several times more ES capabilities. The parameters of ES should be properly sized. The ES capacity may not be fully utilized and thus cause a lack of profitability for undersized and oversized installations. The further limitation of CDGU start-ups will be followed by a decrease in ES overall costs, which may occur shortly. This will also bring better utilization of the available ES rated parameters. The subsequent benefits and precise impact of the proposed solutions on the implementation of the requirements given by the climate and energy policies should be analysed in further research. In particular, the European Union Emissions Trading System (EU ETS) and provisions imposed by the Clean Energy for all Europeans Package should be analysed. The cost-benefit analysis of the presented problem involving long-duration profiles (for a month, season or year) should be also performed to identify required earnings and the profitability of this ES application. In addition, the identification of the appropriate rated parameters of the ES providing the proposed service may be a challenge.

Author Contributions: D.C. and A.L. contributed equally to this work and all of its stages and elements. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors would like to thank Professor Władysław Mielczarski (Institute of Electrical Power Engineering, Lodz University of Technology) for helpful suggestions and discussions which improve and strengthen the quality of this article. The authors would also like to express the gratitude to the FICO® corporation for programming support and the provision of academic licenses for Xpress Optimization Suite to the Institute of Electrical Power Engineering at the Lodz University of Technology. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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