Optimal Sitting and Sizing of Energy Storage Systems in a Smart Distribution Network Considering Network Constraints and Demand Response Program

Transcription

1 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 14 Optmal Sttng and Szng of Energy Storage Systems n a Smart Dstrbuton Network Consderng Network Constrants and Demand Response rogram Alreza Akbar-Dbavar 1, Sayyad Noavan 2,* and Kazem Zare 1 1 Faculty of Electrcal and Computer Engneerng, Unversty of Tabrz, Tabrz, Iran 2 Department of Electrcal Engneerng, Unversty of Bonab, Bonab, Iran *Correspondng autor: sayyad.noavan@bonabu.ac.r Manuscrpt receved 06 August, 2018; Revsed 23 September, 2018; accepted 13 October, aper no. JEMT Demand response program (DR) and energy storage systems (ESSs) are two man tools for load management n smart grds. Tey can make dstrbuton networks more relable wtout costly upgrades for substaton constructon or lnes renforcement. Ts work proposes an optmzaton framework of sttng and szng of ESSs n a smart dstrbuton network n te presence of DR and consderng renewable energy sources (RESs) effects and network constrants. Te proposed obectve functon ncludes two terms: 1) mnmzng te total nvestment costs of ESSs; 2) mnmzng te cost of actve losses and te power purcased from te upstream grd and desel generators. DR can reduce operaton costs by sftng some loads from ours wt g demand to ours wt lower demand and so can reduce network losses and elp n peak load savng process. In order to solve te proposed optmzaton problem, a mxed-nteger non-lnear programmng (MINL) model s constructed and solved usng DICOT solver by GAMS optmzaton software. A modfed 33-bus dstrbuton network s consdered, and te results of tree dfferent cases are compared. Fnally, t can be noted tat total cost of te network n case 2, wt optmal ESSs allocaton, s reduced by 4.9% compared wt te base case, wle ts reducton s about 20% n case 3 wt consderng DR besde ESSs allocaton. Keywords: Optmal allocaton, Energy storage systems, Demand response program, Load management, Smart dstrbuton network. ttp://dx.do.org/ /emt Nomenclature Sets, Index of buses t Index of tme Index of years Set of buses wt wnd turbnes Set of buses wt desel generators Set of buses wt V sources Set of buses wt ESS Varables sub Actve power mported from te upstream grd at t, tme t n year sub Q Reactve power mported from te upstream grd t, at tme t n year loss Actve power losses due to flowng actve power t, troug lne - at tme t n year c d ower carged or dscarged by ESS at tme t,t, and year DG ower produced by te desel generators at bus t at tme t n year u t y,, t z,, t wnd t V t TOU t D,, t S,, t,,, t, Q,, t, Bnary condton of desel generator unts at bus at tme t n year A bnary varable wc s 1 f te unt s started up; vce versa s zero A bnary varable wc s 1 f te unt s sut down; vce versa s zero ower produced by te wnd turbne at bus at tme t n year ower produced by te V system at bus at tme t n year Amount of addtonal or reduced load at bus at tme t n year (.e. load sftng) Mnmum load at bus suppled at tme t n year, wc sould be meet (consderng sfted load) Apparent power excanged between buses and at tme t n year Actve power excanged between buses and at tme t n year Reactve power excanged between buses and at tme t n year

2 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 15 V,, t,, t SOC,, t Cess ess arameters ower system S, sub Q sub Z, G,, load DR t load Q,, t DR Voltage ampltude at bus at tme t n year Voltage angle at bus at tme t n year State of carge of ESS at bus at tme t n year Nomnal ESS capacty nstalled at bus Nomnal ESS power nstalled at bus Maxmum capacty of apparent power flow at lne - Rated actve power of upstream grd s transformer Rated reactve power of upstream grd s transformer Lne mpedance ampltude (between buses and ) Lne conductance ampltude (between buses and ) Lne mpedance angle (between buses and ) Actve load at bus at tme t n year Reactve load at bus at tme t n year ercentage of customers wo partcpated n DR Renewable resources (V and Wnd) cap V Rated power of V nstalled at bus TA t wnd VC I VC O V R V wndt Tme avalablty of Vs at tme t Te mum rate of te wnd turbne at bus Te cut-n speed of wnd turbnes Te cut-out speed of wnd turbnes Te rated velocty of wnd turbnes Wnd velocty at tme t ESS parameters SOC Mnmum SOC of ESS mn A constant value related to te mnmum capacty of SOC A constant value related to power carge and dscarge of ESS Carge or dscarge effcency of ESS Desel generator ag, bg, cg Desel generators cost coeffcents Maxmum lmt of power producton by te DG desel generators mn Te mnmum lmt of power producton by te DG desel generators UR Ramp up rate of desel generators unt at bus DR Ramp down rate of desel generators unt at bus UT Mnmal up tme of desel generators unt at bus Te mnmal downtme of desel generators unt DT at bus Economc parameters Cost Te nvestment cost of ESSs capacty Cost Cost up In. C In. Te nvestment cost of ESSs power Te cost related to buyng energy from te upstream grd Cost DG Cost loss OC Te cost related to buyng energy from dspatcable DG unts Loss payment cost Operatonal cost ESS nvestment cost Energy prce at tme t n year EIC t, CF p q 1. Introducton Cost factor n year Inflaton rate Interest rate Te subect of actve dstrbuton system (ADS) or smart grd (SG) s used to refer to some knds of ntellgent electrcal networks tat consst of dfferent types of energy generaton resources (ncludng RESs), communcaton nfrastructures, smart meters, actve or dspatcable loads and responsble demands, varous types of ESS tecnologes and plug-n electrcal vecles (EVs) [1-3]. ESSs are ntroduced as complementary devces n te SGs to manage te uncertanty of RESs output and mprove power mbalance, power qualty, relablty, and stablty. Tere s varous knd of ESSs may be used n te networks accordng to ter caracterstcs [4]. Te uncertan nature of RESs may lead to 1) devaton of voltage profle, 2) ncreasng of lnes current over te lmts, 3) dsturbance of local voltage and 4) power producton wt g uncertanty wc reduces system s relablty and ncreases te outages. Te man dea bend storage s tat tese devces at te off-peak tmes (wen te generaton s more tan demand) appear as passve loads and absorb excess power ust lke an actve power cavty so by consumng extra power becomes overvoltage barrer; on te contrary, t can free up te stored power n a g-capacty mode at peak tmes. Ref. [5] represents te effects of ESS ntegraton on power system flexblty and rampng capablty wt consderng wnd generaton unts. In deregulated power markets, te partcpaton of ESS unts n energy or ancllary servces can be valuable for te prvate owners of suc devces [6]. Reslence enancement s aceved troug optmal management of battery ESSs n [7]. Moreover, DRs are oter essental load management tools n SGs ntroduced recently n power systems and ave attracted muc attenton. DRs are appled to sft a specfed amount of load at peak tmes to te oter off-peak tmes n order to flatten te load curve or curtal unnecessary loads [8]. DRs can elp to moderate prce spkes n te power market [9]. DRs also elp n peak savng processes tat can postpone gly cost nvestments for te constructon of power plants or lnes renforcement [10]. Ultmately, tey are complement tools for RESs to encounter wt uncertantes and reduce te dscarded power producton [11]. Dfferent types of DR are ntroduced and analyzed by paper [12]. One of te most used DR s Tme of Use (TOU) based program, n wc only some percentage of load would be curtaled at peak ours and wll be sfted to off-peak tmes n order to make sure tat te generaton wll be economcal at tat tmes [13]. For sum up, ESS and DR utlzaton n power systems can aceve more benefts, suc as peak load management, relablty enancement, pase balancng, RES curtalment reducton, uncertanty management, etc. In order to aceve te mentoned benefts and gven te g captal cost of ESSs and for consderng economcally vable, an optmal allocaton wc ncludes sttng and szng of ESSs n mcrogrds or smart dstrbuton networks s a crucal decson. In [14], optmal power flow (OF) calculaton s mentoned as a fundamental optmzaton tool for te operaton and plannng of SGs and power systems. So n ts work, an ACOF s proposed to create an optmzaton framework, n wc optmal locaton and sze of ESSs as well as optmal scedulng of ESS, DG unts and power

3 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 16 necton from te upstream grd, wt and wtout DR consderaton would be determned Lterature revew Te man goal n te ESS optmal plannng s to answer te followng questons: 1) How many storage unts are needed to be nstalled n te grd? 2) Were are te best locatons for tese unts to be nstalled, or wc buses are adequate? 3) How muc s te optmal capacty and power rate of tese unts? 4) How sould tese unts be operated to be proftable? To answer tese questons, some lteratures nvestgate te optmal sze and ste of ESS unts by eurstc algortms suc as, genetc algortm (GA) [15], partcle swarm optmzaton (SO) [16], bat algortm (BA) [17], cuckoo searc algortm (CKA) [18] and etc. Also, te combnaton of tese metods referred to ybrd metods, are wdely used by te researcers. A fuzzy expert system n combnaton of te genetc algortm s used n [19], wc seeks to fnd te optmal sze of storages n a mcrogrd. A revew on storage allocaton metods and models s provded by [20], and also anoter group of algortms for ESS plannng s descrbed n ts paper, wc s reputed to analytcal metods. Tese metods are based on statstcal and storcal analyses and do not use a specfc model. Tey are currently used only for optmal szng of ESS. On te oter and, matematcal metods ave attracted enormous attenton n te power system operaton and plannng feld, especally n ESS allocaton plannng. In ts regard, OF problem [21], dynamc programmng [22] and stocastc programmng [23] are some examples tat can be found n lteratures wc am to fnd te best locaton and szng of ESS unts, smultaneously. A classcal forward-backward load flow approac, wc s desgned for radal dstrbuton systems, s used n [24], for locaton determnaton of ESS unts n 12 and 33 bus systems wt V arrays. In [25], te allocaton s done n a two-step process; n te frst step, usng te Clusterng and Senstvty Analyss algortm (CSA), te canddate buses are specfed for te ESSs placement. At te second step, OF s used to determne te sze of tem. aper [26] proposes stocastc mxed-nteger programmng for optmal szng and locatng of ESSs n a large scale system, were te obectve s to mnmze te sum of te expected operatng cost and te nvestment cost of ESSs. In [27], te carge and dscarge power curves are used for ESS szng. Concernng loss reducton n a dstrbuton network, battery storage allocaton s nvestgated n [28]. Te autors n [29], effort to mnmze te operatng costs of a mcrogrd, wt a novel energy management tecnque, ncludng economc strateges for te operaton of system and szng of battery storage. In many lteratures, te topc of ESS szng and sttng as been nvestgated by consderng dfferent RESs and DGs [30-32]. Optmal ESS szng as been nvestgated troug an OF and wt consderng varous uncertantes by [33]. A ybrd SO-OF based algortm s proposed by [34]. Concernng te goals of optmzaton, operatng cost mnmzaton and relablty enancement are te most consdered goals n te papers, e.g., [19] or [27]. Altoug tere are a large varety of publcatons n te ESS allocaton topc, tere are fewer references consder te optmal DR and ESS plannng or operaton, smultaneously. In [35], te mpacts of optmal scedulng of DR as well as ESS unts ave been consdered on procurement problem. aper [36], proposes a stocastc optmzaton framework for energy management of a renewable-based solated rural mcrogrd. In order to ave a balance between demand and generaton, a pumped-storage unt and a DR ave been used n ts work. Also, te obectve of [37] s to mnmze te dstrbuton losses payments wt optmal ESS and DR scedulng. Te man assumpton bend all of tese papers s tat ESSs capactes are known and te obectve s optmal scedulng of tese unts or demand-sde management tools utlzaton. In order to ave a more obvous comparson between ts work and oter related works, some of tem are mentoned n Table 1. Table 1. Comparson between some of te related works Ref. [19] [34] [38] [39] [40] [41] [42] [43] [44] Ts work Szng Sttng DR consderaton Network constrants consderaton RESs type Wnd Wnd Wnd Wnd Wnd Wnd Wnd V- Wnd Dspatcable DG unts Loss payment consderaton Metod Fuzzy expert system OF Fuzzy expert system HAWE system analyze SO Hybrd Tabu searc/so Analytcal Algortm GA OF OF As Table 1 sows, tere are lots of lteratures n te feld of ESS plannng, wt varous assumptons, goals, metods, and constrants. A maorty of papers provde ust one of te szng or sttng of ESS unts wtout consderng a network as a test bed. It sould be noted, wle te ESSs ave postve effects on power qualty or relablty of te system, also tey can ncrease te power losses by drawng more current from te DG unts or te utlty, especally at te offpeak tmes, so t sould be addressed well n te optmzaton framework. Wle most of te abovementoned papers dd not consder ts mportant subect. Also, DR consderaton, as well as ESS plannng, s a new topc, wc tere are not so many works n ts feld, as t can be concluded from Table 1. Moreover, te V system generaton s consdered ere, wle oters dd not consder t. OF problem s sutable for grd-connected devces suc as ESSs because t can afflate all parameters of a network suc as voltage, loss, etc. togeter n a coerent scene Contrbutons In ts paper, an optmzaton framework s proposed to obtan te optmal ste and sze of ESSs n a smart dstrbuton network wle DR and network constrants are consdered. V systems and wnd turbnes n ts work are ncluded as renewable-based generaton unts, and desel generators are consdered as non-renewable based energy generators. Te proposed optmzaton model mnmzes operaton and losses costs as well as total ESS nvestment cost. Tme-of-use based DR as been consdered as well as te ESS optmal sttng and szng problem n te proposed paper. Fnally, te contrbutons of te

4 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 17 presented paper can be summarzed as follows. 1. Optmal ESS szng and sttng are done by consderng te effects of TOU based DR. 2. Te obtaned ESS capactes are forced to be an nteger, so tat be a feasble soluton n practce. 3. Dfferent types of dstrbuted generators are consdered. 4. ower losses mnmzaton s consdered as one term of obectve functon aper organzaton Te organzaton of ts paper s as follow: In Secton 2, te optmal power flow s descrbed and te problem formulaton s presented. Secton 3 provdes a case study. Secton 4 presents te obtaned results and comparsons. Fnally, te paper s concluded n Secton 5, and te future works are also expressed. 2. roblem formulaton In ts paper, an optmzaton model as been proposed for optmal sttng and szng of ESS n a smart dstrbuton network n te presence of DR. For devces connected to te network, locatng ncludes, varous analyses of te effects of equpment on te network, so matematcal metods lke power flow or optmal power flow are recommended Optmal power flow problem OF metods are useful n actve dstrbuton systems for te purpose of plannng or operatng. OF metods nclude nonlnear equatons and constrants, wc make tem non-convex problems. In transmsson systems, a lnear approxmaton of OF can be used accurately, wc s called DCOF; but n dstrbuton networks, te use of ts approxmaton wll be naccurate due to te large proporton of R/X of lnes; accordng to [45], usng an ACOF s unavodable. Te man dffculty wt ACOF s te dmenson of te grd, and so n some cases, a mult-perod power flow sould be solved. Tere are two general approaces to deal wt ts problem; one s to use te meta-eurstc metods and te second s usng convex relaxatons of OF problem suc as sem-defnte programmng (SD) or second-order cone programmng (SOC) wc convexfy te feasble regon enclosed by power flow equatons [46, 47]. Bot of SOC and SD can be used for radal networks [46, 47]. For large-scale power systems, e.g., dstrbuton network, SOC would perform muc faster tan SD. Tere s no guarantee wt eurstc metods to gve a global optmum soluton and also mpose a eavy computatonal load on large scale systems wt numerous constrants [45]. So n a dstrbuton system wt many connectons, brances and loads, tese metods are not sutable, and te use of convex relaxaton s superor. In ts regard lteratures [45] and [48] solve ESS allocaton problem n a dstrbuton system and transmsson system, respectvely, usng SOC relaxaton for te OF problem. Snce te dmenson of te studed dstrbuton system n ts paper, s not so large and a lmted tme orzon s nvestgated, also by consderng te fact tat te tme of computaton s not so crtcal for plannng problems, tere s no need to use SD or SOC relaxatons n ts work, because despte ter acceleraton, tey create a gap between te orgnal and relaxed model [49] Obectve Functon Equaton (1) s te proposed obectve functon and ncludes ESS nvestment cost (EIC) and operaton cost (OC) wc are presented n equatons (2) and (3), respectvely. mn OF = EIC + OC (1) Cost 5 = 1 ( In. C In. ) (2) EIC = Cess Cost + ess Cost OC = Cost up + Cost loss + Cost (3) DG 5 24 sub up = t, t, = 1 t= 1 (4) Cost ( CF 365 ) 5 24 loss loss = t, t, = 1 t= 1 (5) Cost ( CF 365 ) DG = ( CF 365 ( a ( ) + b + c )) DG 2 DG, t g, t, g, t, g (1 + p) CF = = 1,2,3,4,5 (1 + q) Total operatonal costs ncludng te cost of purcased power for demand procurement, power losses cost, and operatonal cost of desel generators are presented by equatons (4), (5) and (6), respectvely. It s assumed tat te nvestments accomplsed durng te frst years and so dd not be multpled by 365. Equaton (7) defnes te CF factor, wc s needed for economc calculatons,.e., net present value calculaton ACF Constrants = 0.5 G ( V + V loss 2 2 t,,,, t,, t, V 2V V cos( )), t, 2 t,, t, =, Z, Q, t,, t,, t,, t, cos( ) V, t, V, t, cos(, t,, t, +, ),,, t, Z V, 2 t,, t, =, Z, sn( ) V, t, V, t, sn(, t,, t, +, ),,, t, Z, = sub wnd DG V load DR c/ d t,, t,, t,, t,, t,, t, N 2 bus t, t,, t,,, t,, t, +, = 1 Z, Z, (6) (7) (8) (9) (10) V V V ( cos( ) cos( )) (11),, t, Q sub load t,, t, V V V ( sn( ) sn( )) (12) N 2 bus t, t,, t,,, t,, t, +, = 1 Z, Z,,, t, Q = S, S,, t, S,,,, t, (13) sub sub t, sub, t, (14) sub Qsub Qt, Qsub, t, (15)

5 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue V 1.0,, t, (16) p. u. p. u. t rad rad t,, t, (17) 2 2 Equaton (8) denotes te actve power losses n te network; (9) and (10) sow te excanged power between te connected buses at eac tme; (11) and (12) sow te power balance at eac tme for actve and reactve power, respectvely. Equatons (13)-(17) defne lmts on network varables Dstrbuted Generaton cap 0 V TA,, t, (18) V t t 0 Vwnd V t C I V wnd V t C I wnd wnd V C I Vwnd V t R V t R V = C I wnd V R Vwnd V t C O 0 Vwnd V t C O,, t, mn DG DG t t DG t (19) u,,,, u,,,, t, (20) DG DG, t,, t 1, UR (1 y ) + y,, t, mn, t, DG, t, DG DG, t 1,, t, DR (1 z ) + z,, t, t + UT 1 mn, t, DG, t, (21) (22) u, t, UT y, t,,, t, (23) k= t t + DT 1 (1 u, t, ) DT z, t,,, t, (24) k= t y,, z,, = u,, u, 1,,, t, (25) t t t t y,, + z,, 1,, t, (26) t t Equaton (18) lmts te V producton to be avalable ust n sunny ours of a day. Equaton (19) models te wnd turbnes producton and (20) descrbes te power lmt on desel generators, wle (21)-(22) sow te ramp rates of desel generators, and (23)- (24) model te mnmum up/down tme constrants of desel generators. Furtermore, (25)-(26) sow te constrants on commtment states of unts. Te start-up cost of desel generators assumed to be neglgble ESS Constrants SOC = SOC + t t (27) cd /, t,, t 1,, t,,,, 1, 24 SOC, 1, = SOC, 24, = SOCmn = C,, (28) t= t= ess SOCmn SOC, t, Cess,, t, (29) C C t (30) cd / ess,,,,, t ess Equaton (27) sows te state of carge (SOC) for te ESSs at eac tme, from (28), te amount of state of carge at te begnnng and te end of tme orzon (24-ours) sould be a constant value. cd / Equaton (29), lmts te amount of SOC for eac ESS. In (30),,, t s a decson varable wc sows te cargng or dscargng values of eac one of ESSs; wen an ESS s n cargng statue, ts sgn s negatve (acts as a load), and wen ESS s n dscargng statue, ts sgn would be postve (acts as an actve power generator). So tere s no need to add anoter equaton to sow tat cargng and dscargng processes sould not occur smultaneously and ust by addng one varable ts concept s realzed., and are constant values wc equal to 1, 0.5 and 0.4, respectvely DR Constrants load DR,, = D,, TOU,,,, t, (31) t t t load DR, t,, t, t= 1 t= 1 D =,, (32) (33) DR D, t, TOU, t, DR D, t,,, t, Equaton (31) s used to sow te concept of TOU, wc TOU sows te amount of sfted load at eac our. Based on te t defnton of te equaton, te sgn of TOU at eac bus t and at a tme and vce versa. It can be concluded from (32) tat te base load tat needs to be met s a constant for eac day and ust some percent of t, s transferred from peak perods to off-peak perods. In smple words, te ncrease and decrease of load sould be equal durng te day for eac bus. It sould be noted tat te mum amount of load ncrease/decrease,.e. DR, of te base load s equal to 20% n te presented paper n (33). t wll be postve f te loads ave been transferred to tme All of te equatons and constrants are modeled n te GAMS software envronment, wc a standard NL model used for dong an ACF on te dstrbuton network. Due to te exstence of te DG unts, tere are bnary varables, and te full model s solved as a MINL model [50]. 3. Case study In ts paper to avod eavy computatonal load, a 24-our tme orzon s consdered and assumed tat te loads n eac bus are te average consumpton for one year. Tese 24 ours wll be a representatve of te full year and a fve years plannng s consdered. Te annual nterest and nflaton rates are selected 0.2 and 0.1, respectvely. It s assumed tat actve and reactve power consumpton data for eac bus, are te average values for one-year consumpton. So by multplyng tem by 365, te annual consumpton wll be obtaned. Also snce te problem s a locaton-tme problem, t s requred to make a correspondence between eac our and te demand for eac bus. To make ts correspondence, loads n eac bus would be multpled by a constant wc models real loadng condtons. Te mum number of ESS unts n ts system consdered to be fve unts. Here te nvestment cost of ESSs s supposed 400,000 $/MW/year and 11,000 $/MW/year, as referred n [43]. Also, some ESS tecnologes nvestment costs can be found n te paper [51]. A modfed 33-bus dstrbuton network, wt lnes nformaton and load demands, as descrbed n [24] and [43], respectvely, s used n ts work wt a nomnal voltage of Vbase=12.66KV, wc s depcted n Fg. 1. It sould be noted tat tere s a 5% ncrease n power consumpton per year. It s assumed tat mum capacty wc can be allocated n te network s 15 MW. Te medum voltage feeder s assumed to be a 100 MVA transformer. Hourly energy prces wc wll be pad by Dstrbuton t

6 Wnd speed (mle/our) TA Tme (Hour) Wnd speed (mle/our) TA Tme (Hour) Wnd speed (mle/our) TA Tme (Hour) Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 19 System Operator (DSO) to buy energy from power plants are also provded n [43]. It s assumed n ts network, fve V systems, four desel generators, and tree wnd turbnes do exst, wc are located and szed randomly. Table 3. Tme-dependent nformaton Fg. 1. Sngle dagram of IEEE 33 bus network [24]. Locaton of tese unts s delneated n Fg. 1, and te nformaton of tese power generators are expressed n Table 2 for te 33-bus system. Table 2. Dstrbuted generators nformaton nstalled on 33-bus network V anels Wnd Turbnes Desel Generators Locaton (Bus) Capacty (kw) Informaton about tme avalablty of V systems (te ourly generaton factors, referred to TA) and wnd speed at eac our exst n Table 3. Desel generators cost functon coeffcents are te same as mentoned n [43] Results To glgt te tecncal and fnancal benefts of ESS and DR on te network operaton and plannng, tree study cases ave been evaluated, and te results are compared. Case 1: Base case, wtout ESS and DR Case 2: Optmal sttng and szng of ESS wtout DR effects Case 3: Optmal sttng and szng of ESS wt consderng DR 4.1. Case 1: wtout ESS and DR Ts case s te same as a smple optmal power flow n a dstrbuton network; wle, te obectve functon ncludes te summaton of costs of te drawn power from te upstream grd and te total cost of losses n dstrbuton lnes. In ts case, DR effects and te sttng and szng of ESSs are neglected. Total operatng cost ncludng te costs of purcased power and desel generators as well as power losses s $ 110,180, Case 2: Optmal sttng and szng of ESS wtout consderng DR As t was rased, n ts paper, te szng and sttng of ESSs smultaneously wll be done, usng an ACOF algortm. Fgure 2 sows te calculated sze of storage unts for eac bus ncludng nomnal capacty. rortzed buses for storage placement are also detected. Te total capacty of ESS wc s needed to be nstalled s 15 MW. Te total cost ncludng te costs of purcased power, desel generators and also power losses as well as costs related to ESSs n fve years, s $ 105,130,000. Table 4 sows te detaled results and obtaned costs for eac case. Te net reducton n costs s 1,010,000 $/year Case 3: Optmal sttng and szng of ESS wt consderng DR Ts case sows te effects of consderng a DR on te ESS allocaton procedure. Fgure 3 sows te calculated sze of storage unts for eac bus ncludng nomnal capacty. rortzed buses for storage placement, are also detected. Te total capacty of ESS wc s needed to be nstalled, s 15 MW. Total cost ncludng te costs of purcased power, desel generators and also power losses as well as costs related to ESSs for fve years s $ 89,151,100. Table 4 sows te detaled cost results obtaned for eac case. Te net reducton n costs s $ 4,206,000, wc s sown n te last column of Table 4.

7 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 20 Fg. 2. ESS optmal nomnal capacty and power for case 2 Fg. 3. ESS optmal nomnal capacty and power for case 3 Cases 1. Base case (Wtout ESS and DR) 2. Wt only ESS optmally szed and placed (cost of ESS constructon added) Table 4. Obectve functon values Investment costs ($) Operatonal costs ($/year) ercentage of reducton of operatonal cost (%) ESS optmzaton wt consderng DR (ncentve and constructon costs added) In te followng, te tecncal effects of ESS and DR are also consdered n a fve-year orzon, and te results are depcted n fgures 4-9. In te legends, Y sows te t year of operaton. As can be seen n Fg. 4, n off-peak ours wt low energy prces, te power consumpton n case 3, s more tan te base case. Ts s because of exstng DR and ESSs n te network. In contrast at peak ours, te consumpton s reduced wc s economcal. Te loads are sfted from peak ours to te off-peak ours usng ESS tme-sftng ablty and TOU based DR. Also, te load demand for te 5t year s ger tan te oters, because of annually ncreasng n power consumpton, wc as been addressed prevously. In case 3 compared to case 2, DR utlzaton can make load reducton an average of 5 MW for all years. Also, ESS unts ave tme-sftng capablty, but loss reducton assumpton probts large cargng at off-peak tmes and dscargng at peak tmes. Wle n DRs, some percentage of te load s assumed to be curtalable, and by ts mean, absolute of operatng cost reducton s more tan loss payment ncrement. As can be seen n Fg. 5, for off-peak ours te mported actve power n cases 2 and 3, s more tan te consumpton of base case and ts s caused by te sfted loads or ESSs cargng processes. In contrast at peak tmes, te purcased actve power from te upstream grd wt g prce s decreased sgnfcantly compared to case 1; even ts mported power reaces to zero for case 3 at peak tmes wt approprate management of ESSs and DR. Ts s occurred because of te capablty of ESSs n storng of exceed renewable produced power at off-peak tmes (especally by Vs) and releasng t back to te grd at peak tmes and also by te load sftng actons of DR. From Fg. 6, t can be concluded tat te purcasng power from dspatcable DG unts as lttle dfferences among te cases, and ts lttle dfference s also caused by load ncrement at eac year. Because of exstng renewable generatons n te network wt neglgble operaton cost, t s economcal to buy energy from desel generators, only wen tere s a power lack. Te man dfference between cases 2/3 and case 1 s ESS power requests at off-peak tmes. Fgure 7 sows te amount of total actve power losses n te system. As t was mentoned before, one of te terms n te obectve functon s te actve power losses payments, and te proposed optmzaton seeks to mnmze t. Wt ts regard, t can be seen n ts fgure, due to te g-power consumpton at off-peak tmes, te actve losses are ger tan te on-peak tmes, and ts s economcal because te energy prce s usually lower at off-peak tmes. Also, case 3 as te least actve losses compared to cases 1 and 2. Te actve loss mostly depends on te lne's current, and te currents are related to te demands of eac year, so te annual ncrement of losses s logcal. It s decded to depct one bus voltage profle, wc s a far end bus. Fgure 8 sows te voltage profle of te 17 t bus for all of te mentoned study cases. As can be seen, for off-peak tmes, case 3 as te least voltage compared to te oters. Ts s because of load sftng to tose ours wt low energy prces and cargng of ESS unts; owever, at peak tmes, te voltage level of ts bus s te gest at case 3. Te voltage level s decreasng as te load consumpton ncreases every year. It sould be noted tat n ts paper, te voltage profle mprovement s not consdered n te obectve functon. One can extend te proposed model to aceve ger voltage profles n te network.

8 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 21 Fgure 9 depcts te carged and dscarged power of ESS unts for bot cases. It s wort mentonng tat te optmal scedulng of cargng or dscargng of ESS unts s obtaned troug te optmzaton runnng process. In ts fgure, f an ESS unt draws power from te grd (cargng), ten t appears wt a negatve sgn and vce versa. In case 2, tere are four canddate buses for ESS nstallaton. As all of te curves sow, for eac year, at off-peak tmes, te ESS s forced to be carged and ten at te peak tmes, ts stored energy s released. Because te plannng n ts work s consdered to be statc plannng, and te requred capacty s nvested at te frst year, te curves of power cargng/dscargng for te frst year are dfferent from te rest years, and ter cargng/dscargng curves are overlapped. Also, te nstalled capactes ave nfluenced te rates of carged/dscarged powers. Fg. 4. Total demand consumpton n cases wt/out DR Fg. 5. Imported actve power from te upstream grd for tree cases

9 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 22 Fg. 6. Te total provded actve power by desel generators Fg. 7. Total actve power losses Fg. 8. Voltage profle of bus No. 17

10 Journal of Energy Management and Tecnology (JEMT) Vol. 3, Issue 2 23 (a) Case 2 (b) Case 3 Fg. 9. Carge and dscarge of nstalled ESS unts n cases 2 and 3 5. Concluson Ts paper proposed an optmzaton framework to obtan te optmal capacty and locaton of ESS unts n a smart dstrbuton network wt consderng DR effects. Te model ncluded four dfferent obectves: 1) mnmzaton of te total power purcasng (from te upstream grd or DG unts), 2) mnmzaton of te total cost of power losses payment, 3) fndng optmal ESS capactes and power rates wt respect to ter nvestment cost, and 4) fndng an optmal scedulng of ESSs as well as DR. A modfed 33 bus IEEE test system and dfferent renewable and non-renewable DGs were consdered. Obtaned results for tree dfferent cases were compared wt eac oter. Accordng to te obtaned results, te total cost of te network for fve years n case 1, was $ 110,182,550 and ts value n case 2, consderng ESS nvestment, fell to $ 105,134,450. Moreover, for case 3 wt DR consderaton, te total cost was equal to $ 89,151,100. It can be concluded tat te optmal scedulng of ESS and DR togeter n case 3, can reduce te network costs by 15.2 % n comparson wt case 2, and by 19 % n comparson wt case 1. Wle n case 2, wt only ESS allocaton te save was about 4.6% n comparson wt case 1. Te ger save n case 3 s because of DR consderaton. DR ad sfted some percentage of te consumpton from peak tmes wt g energy prces to off-peak tmes wt lower energy prces. As future works, te autors are nterested n te nvestgaton of uncertanty modelng of renewable-based generaton outputs and load forecasts n te ESS plannng problem. Te optmal allocaton and scedulng of ESS unts for partcpaton n te power market envronments are anoter remarkable felds wc wll be nvestgated n next works. References [1] Tuballa, M. L., M. L. Abundo. A revew of te development of Smart Grd tecnologes, Renewable and Sustanable Energy Revews, (2016). [2] Marzband, M., M. Javad, S. A. ourmousav, and G. Lgtbody. An advanced retal electrcty market for actve dstrbuton systems and ome mcrogrd nteroperablty based on game teory, Electr. ower Syst. Res., (2018). [3] Tavakol, M., F. Sokrdeak, M. Marzband, R. Godna, and E. ouresmael, A two stage erarccal control approac for te optmal energy management n commercal buldng mcrogrds based on local wnd power and EVs, Sustan. Ctes Soc., (2018).

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