The Impacts of Electric Charging Stations on Distribution Power Grids under Different Simulations using Jellyfish Swarm Algorithm

Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH Resea ch A icle THE IMPACTS OF ELECTRIC CHARGING STATIONS ON DISTRIBUTION POWER GRIDS UNDER DIFFERENT SIMULATIONS USING JELLYFISH SWARM ALGORITHM Dao T ong TRAN1,∗, Minh Phuc DUONG 2 1Di ision o MERLIN, Facul y o Elec ical and Elec onics Enginee ing, Ton Duc Thang Uni e si y, Ho Chi Minh ci y, Vie nam 2Powe Sys em Op imiza ion Resea ch G oup, Facul y o Elec ical and Elec onics Enginee ing, Ton Duc Thang Uni e si y, Ho Chi Minh Ci y, Vie nam an[email p o ec ed], duongph[email p o ec ed] ∗Co esponding au ho : Dao T ong T an; [email p o ec ed] DOI: 10.15598/aeee. 23i1.250101 A icle his o y: Recei ed Jan 1, 2025; Re ised Feb 14, 2025; Accep ed Ma 06, 2025; Published Ma 31, 2025. This is an open access a icle unde he BY-CC license. Abs ac . This esea ch p esen s di e en implemen- a ions o placing elec ic cha ging s a ions (ECSs) in dis ibu ion powe ne wo ks (DPNs) o achie e he bes o al ac i e powe loss (TAPL). Sola gene a o s (SGs) a e also used o alle ia e he ad e se e ec s esul ing om he p esence o ECSs in he ne wo ks in e ms o powe loss and ol age p o ile. A i icial hummingbi d algo i hm (AHA), Jelly ish swa m algo i hms (JS), and No he n goshawk op imiza ion (NGO) a e execu ed o de e mine he bes placemen o ECSs and SGs in an IEEE 33-node ne wo k o eaching a minimum loss and sa is ying all he ela ed cons ain s. The e a e ou cases conduc ed in he whole esea ch. In he i s case, JS ou pe o ms bo h AHA and NGO by p o iding he highes s abili y h oughou all he ial uns and as es con e gence speed o he op imal solu ion in he bes uns. Besides, he quan i a i e compa ison also consolida es he obus ness and eliabili y o JS com- pa ed o o he s. Based on he su p ising pe o mance, JS is con inuously eapplied o sol e ano he h ee cases o he conside ed p oblem. Th ough hose h ee cases wi h he applica ion o JS, he TAPL alues o ou sce- na ios wi h di e en numbe s o ECSs a e e alua ed. Speci ically, he esul s achie ed by JS indica e ha he highe numbe o ECSs leads o a highe alue o TAPL and a highe ol age d op. On he o he hand, he si- mul aneous placemen o SGs and ECSs can esul in smalle luc ua ions o he ol age p o ile and smalle TAPL. Thus, he op imiza ion placemen o ECSs and SGs is c ucial o DPNs o economic and echnical pu - poses. Keywo ds Elec ic cha ging s a ions, dis ibu ion powe ne wo ks, sola gene a o s, o al ac i e powe loss, ol age p o ile. 1. In oduc ion Nowadays, global wa ming is one o he mos conce n- ing p oblems due o i s nega i e e ec s, which can ap- pa en ly be expe ienced all a ound he globe [1]. The main con ibu ion o hese nega i e e ec s is he signi - ican inc ease in CO2 concen a ion p oduced by di e - en mode n ac i i ies, including diesel-based ehicles [2]. Mo eo e , due o he apid g ow h o he wo ld economy, he lee o gasoline and diesel ca s in la ge ci ies and coun ies has imp essi ely g own, damaging he en i onmen he e [3]. In his ci cums ance, he shi o elec ic ehicles is acknowledged o be he a - o dable solu ion o educing CO2 emissions and im- p o ing ai quali y in such hus ling ci ies and places [4, 5]. The g ow h o he use o elec ic ehicles leads o a demand o elec ic cha ging s a ions (ECSs) con- ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 72 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH nec ed o he dis ibu ion powe ne wo k (DPN) [6]. Howe e , he placemen o ECS in he DPNs also has some nega i e e ec s, such as powe loss enla gemen o ol age de egula ion. Renewable powe sou ces a e hen p oposed o educe ol age d op and powe loss [7]. By ho oughly unde s anding he ad an ages and disad an ages o placing ECS in he DPN, many e- sea che s ha e conduc ed hei esea ch o enhance he ad an ages o placing ECS while simul aneously y- ing o mi iga e he nega i e impac s. Fi s ly, a gen- e al look a he impac s caused by ha ing he ECSs in DPN is gi en in [6]. Nex , he p esence o ECSs in he DPN is e alua ed ega ding powe demand, ha - monics, ol age sag, and ans o me powe loss [8]. Then, [9] ocuses on iden i ying and analysing he im- pac o ECSs on he eliabili y o he IEEE-33 bus es sys em. A e ha , he impac o ECSs on he DPN planning p oblems is assessed and e alua ed in [10]. A e ha , he e ec s o ha ing ECSs in a eal DPN in a La in Ame ican in e media e ci y a e in es iga ed [11]. Fu he mo e, he in luences o ECSs on a esiden- ial dis ibu ion ne wo k in Bangladesh a e also s ud- ied in [12]. Since all he p oblems and nega i e impac s caused by ECSs in DPN ha e been ully iden i ied, a lo o pape s ha e been p oposed o pa ly mi iga e all hese downsides o ECSs, conside ing di e en as- pec s and o ien a ions. Fo example, he au ho s in [13, 14] o e ed a me hod o sol e he p oblem o in- c easing peak hou s caused by he cha ging p ocess o elec ic ca s, which did no happen in he pas when gasoline ca s we e in high demand. Mo eo e , he au- ho s also a gue ha he inc ease in peak hou s leads o o e load s a us, which badly a ec s all elec ical de- ices in he ne wo k, such as ans o me s and dis ibu- ion lines. Besides he nega i e e ec s on he elec ical de ices, he luc ua ion o ol age and cu en a e o- cused on in [15, 16]. In [17], an op imiza ion model is sugges ed o minimize he ol age s abili y index and e alua e he cha ging demand and he economy aspec while building ECSs in he IEEE-33 node. In [18], a mul i-objec i e unc ion has been o mula ed o mini- mize ol age de ia ion and powe loss simul aneously. The au ho s in [19] add essed he inadequacy o build- ing an uncon olled numbe o ECSs on he g id, and hen a cos model o ECS ope a ion was buil o di - e en ci cums ances. A e ha , an op imiza ion ool is applied o de e mine he op imal loca ion o ECSs. In [20], he au ho s ocused on sho ening he capi al o ECS while conside ing he ol age bounda ies and eac i e powe loss o he whole sys em. In p ac ice, enewable ene gy gene a o s (REGs), mos ly sola gene a o s (SGs) and capaci o banks (CBs), ha e been deployed independen ly o com- pounded o alle ia e he nega i e impac s caused by ECS in he g id and also educe he p essu e on he ansmission ne wo k. Fo example, CBs a e combined wi h dis ibu ed gene a o s (DGs) in [21] o op imize he econ igu a ion o DPN based on he employmen o a mul i-objec i e unc ion o each di e en indices such as powe loss, in eg a ing le el, and ol age s a- bili y in di e en scena ios. Nex , he au ho s in [22] ocused on maximizing he powe supplied by sola gen- e a o s by combining bo h he placemen o ECSs and enewable ene gy gene a o s in DPN o educe he e- liance on ansmission powe ne wo ks. Besides, CBs a e also in eg a ed in o he IEEE-33 bus and 34-bus DPN oge he wi h ECS o lessen he powe loss and main ain he eliabili y o DPN [23]. On op o ha , he au ho s in [24] sugges ed ha using he CB is one o he mos e icien me hods o handle he nega i e e - ec s caused by he imp ope posi ion o placing ECSs in he DPN. The au ho also added ha geog aphi- cal con enience is he op p io i y while es ablishing an ECS, no powe low op imiza ion o DPN; he e- o e, using auxilia y enginee ing solu ions is highly ec- ommended o main ain he designed capabili y o he DPN. Howe e , he combina ion o CBs, SGs, and DGs a e simul aneously in eg a ed wi h he DPN along he ECSs o maximize he e iciency, as conduc ed in [25]. Besides, he ela ed modelling, simula ion, and con ol me hods in e ms o ope a ions and cha ging me h- ods o EVs unde di e en condi ions, bo h in heo y and p ac ice, a e highly essen ial o p o iding e e - ences and da a analysis in he de eloping and es - ing phases [26, 27]. Addi ionally, he au ho in [28] p esen s he app oach o sol ing he o ans e ing powe he wi eless cha ging echnology and by enhanc- ing he e iciency o he ba e y managemen sys em (BMS) on EVs [29]. On op o ha , he ba e y is a c ucial elemen in an EV, among o he s. The e o e, manu ac u e s and esea che s also need an o e iew o he ma ke o elec ic ehicle ba e ies [30]. By deeply unde s anding he posi i e e ec s b ough by placing ECS combined wi h REGs o he dis ibu- ion gi ds, his esea ch applied h ee me a-heu is ic algo i hms including A i icial hummingbi d algo i hm (AHA) [31], jelly ish algo i hm (JS) [32], and No h- e n goshawk op imiza ion (NGO) [33] o op imize he placemen o ECS in he gi en DPN o achie e he minimum alue o he main objec i e unc ion which is minimizing he o al ac i e powe loss (TAPL). The h ee algo i hms a e p oposed based on he simula- ion o li ing beha iou s o di e en species in na- u e such as, hummingbi d, jelly ish, and no he n goshawk. Besides, hose algo i hms a e also e alu- a ed wi h a ious es s bo h in heo ical and eal- wo ld p oblems and hey ha e p o en hei capabili- ies while compa ed o o he published p e iously. Fo AHA, speci ically, when es ed on he Mul iple Disc Clu ch B ake design p oblem, he algo i hm demon- s a ed i s supe io i y o e se e al o he s, including A i icial Bee Colony (ABC), Teaching-Lea ning-Based ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 73 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH Op imiza ion (TLBO), and Passing Vehicle Sea ch (PVS). JS, on he o he hand, exhibi ed high pe o - mance compa ed o a ious algo i hms, such as Pa i- cle Swa m Op imiza ion (PSO), i s imp o ed e sion (IPSO), and Enhanced Colliding Bodies Op imiza ion (ECBO), when add essing he 582-ba owe op imal design p oblem. Las ly, NGO was also es ed on di - e en p ac ical op imiza ion p oblems, such as P es- su e Vessel Design, Welded Beam Design, and Speed Reduce Design, du ing i s de elopmen phase. The esul s ob ained ac oss hese p oblems indica ed ha NGO ou pe o ms he Whale Op imiza ion Algo i hm (WOA), Ma ine P eda o s Algo i hm (MPA), Tunica e Swa m Algo i hm (TSA), and o he s. Compa ed o p e ious s udies, he s udy has main no el ies as ollows: •Apply h ee me a-heu is ic algo i hms, including AHA, JS, and NGO, o sol e he gi en p oblem o op imizing he placemen o ECSs and REGs in he gi en DPN. •P opose di e en cases o op imizing REGs and ECSs: 1) The placemen o ECSs is op imized in he i s s ep, and hen he placemen o REGs is op imized in he second s ep, and 2) The place- men o EGSs is op imized in he i s s ep and hen he placemen o ECSs is op imized in he second s ep. •Di e en pene a ion le els o ECSs a e es ed, and hen he co esponding capaci y o REGs is op imized. A e unning he h ee algo i hms o simula ion cases in an IEEE 33-node sys em, he con ibu ions o he s udy can be summa ized as ollows: •JS is he mos sui able algo i hm among he h ee applied algo i hms o he p oblem o op imally ins alling ECSs and REGs in dis ibu ion powe g ids. •The use o high pene a ion le els o ECSs in dis- ibu ion powe g ids leads o a high ol age d op and a high powe loss. Howe e , he use o REGs can imp o e he ol age and o al powe loss in he dis ibu ion sys em. •The op imal placemen o REGs in he i s s age and he op imal placemen o ECS in he nex s age ha e a be e ol age p o ile and a smalle powe loss. In addi ion o he In oduc ion, o he sec ions o he esea ch a e s uc u ed as ollows: Sec ion 2 p esen s he ma hema ical model o he gi en p oblem in e ms o he main objec i e unc ion and he ela ed con- s ain s; Sec ion 3 b ie ly in oduces he applied algo- i hms; Sec ion 4 p o ides he discussion on he esul s achie ed by he applied algo i hms on di e en cases; inally, Sec ion 5 e eals he impo an conclusions o he whole esea ch. 2. P oblem o mula 2.1. The main objec i e unc ion This s udy minimizes he alue o ac i e powe loss in he dis ibu ed powe ne wo k (DPN). The ma he- ma ical exp ession o he objec ion unc ion is gi en as ollows [14]: Minimize T AP L = NDL X n=1 Rn×I2 n(1) whe e T AP L is he o al ac i e powe loss in he con- side ed DPN; nis he dis ibu ion line n;NDL is he numbe o dis ibu ion lines o he conside ed DPN; Rnand Ina e espec i ely he esis ance and cu en alues o he dis ibu ion line n. 2.2. The ela ed cons ain s 1) The powe balance cons ain s These cons ain s mean ha he o al ac i e and e- ac i e powe supplied by all he gene a ing sou ces in he g id mus equal he ac i e and eac i e powe de- manded by he end use and powe loss. The ma h- ema ical exp ession o he cons ain s is gi en below [34]: PSL + NSGs X m=1 PSG,m =PLD + NCS1 X i=1 P L1,i + NCS2 X j=1 P L2,j + NCS3 X k=1 P L3,k +Ploss (2) and QSL + NCBs X c=1 QCB,c =QLD +Qloss (3) In Equa ions (2) and (3), PSL and QSL a e ac i e and eac i e powe ecei ed om he ansmission ne - wo k a slack node; P L1,i is he powe supplied by he i h ECS le el 1 wi h i= 1. . . NCS1and NCS1is he numbe o ECS le el 1 in g id; P L2,j is he powe supplied by he j h ECS le el 2 wi h j= 1. . . NCS2 and NCS2is he numbe o ECS le el 2 in g id; P L3,k is he powe supplied by he k h ECS le el 3 wi h ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 74 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH k= 1. . . NCS3and NCS3is he numbe o ECS le el 3 in g id; PSG,m is he powe supplied by he SGs m, wi h m= 1. . . NSGs and NSGs is he numbe o he SGs in g id; QCB,c is he eac i e powe supplied by c h CB wi h c= 1. . . NCBs and NCBs is he numbe o capaci o banks; PLD and QLD a e ac i e and eac i e powe demanded by load; inally, PLoss and QLoss a e espec i ely ac i e and eac i e powe loss caused by he ansmission p ocess. 2) The ope a ing cons ain s o capaci o banks (CBs) and sola gene a o s (SGs) Simila o o he elec ical de ices, bo h CBs and SGs will wo k sa ely and e ec i ely i hei ou pu s a e a - ied in he allowed anges as desc ibed in [35]: Plow SG,m ≤PSG,m ≤Phigh SG,m (4) Qlow CB,c ≤QCB,c ≤Qhigh CB,c (5) whe e Plow SG,m and Phigh SG,m a e he lowes and highes alue o ac i e powe gene a ed by he m h SG; Qlow CB,c and Qhigh CB,c a e he lowes and highes alues o eac i e powe supplied by he c h CB. 3) The cons ain s o ol age and cu en ampli ude The p esence o CBs and SGs in he conside ed DPN leads o a a ia ion o bo h ol age and cu en ampli- ude in he whole ne wo k. Howe e , hese alues can only change wi hin pa icula bounda ies o ensu e he s abili y and eliabili y o he ne wo k [36]. Ulow nd ≤Und ≤Uhigh nd (6) In≤Ihigh n(7) whe e Ulow nd and Uhigh nd a e he lowes and highes alue o ol age a he nd h node; Ihigh nd is he highes alue o cu en allowed o sen h ough he dis ibu ion line n; Und is he ol age alue a he nd h node wi h nd h = 1. . . Nnd and Nnd is he numbe o node in g id. 4) The cons ain s o elec ical cha ging s a ions (ECSs) This cons ain means ha only all he ECSs can be placed om node wo onwa d on he sys em. Mo e- o e , each node is allowed o place only one ECS. The o mula ion o he cons ain is gi en as ollows [35]: 2≤P oECS−L1, P oECS−L2, P oECS−L3≤Nnd (8) P oECS−L1=P oECS−L2=P oECS−L2(9) whe e P oECS−L1,P oECS−L2,P oECS−L3a e he posi- ion o he ECS le el 1, 2, and 3 in he g id. 5) Cons ain o posi ion o placing CBs and SGs Simila o EVSs, bo h CBs and SGs can be placed om node wo onwa ds in he ne wo k, as desc ibed below [37]: 2≤P oSG,m, P oCB,c ≤Nnd (10) whe e P oSG,m and P oCB,c a e he posi ion o he SGs and CBs in he g id. 6) The cons ain s o SGs’ powe ac o SGs a e supposed o ha e powe ac o s in he ange o 0.85 o 1.0. so, he op imal powe ac o is cons ained wi hin he ange below: P F low SG,m ≤P FSG,m ≤P F high SG,m (11) whe e Plow SG,m and P F high SG,m a e he lowe and uppe lim- i s o he m h SG’s powe ac o . 3. Applied me hods This sec ion will b ie ly in oduce he upda e mecha- nisms o he h ee applied algo i hms o new solu ions. No e ha he upda e mechanism is c i ical in di e en i- a ing a pa icula me a-heu is ic algo i hm om many o he s. 3.1. The A i icial hummingbi d algo i hm (AHA) The upda e me hod o new solu ions o AHA is in- spi ed by he a ia ion on posi ion o he hummingbi d in i s o aging p ocess in na u e. The upda e p ocess is subsequen ly execu ed using h ee phases and hei speci ic exp essions will be gi en as ollows [31]: Xnew,P 1 n=Xsl +ε1×NV ×(Xn−Xsl)(12) Xnew,P 2 n=Xn+ε2×NV ×Xn(13) Xnew,P 3 n=HBn+Rnd ×(HBn−LBn)(14) In he h ee equa ions abo e, Xnew,P 1 n,Xnew,P 2 n, and Xnew,P 3 na e espec i ely he new posi ion o he hummingbi d n a each phase, espec i ely wi h n= 1,...NP s and NP s is ini ial popula ion size; Xsl is he andom selec ed posi ion in he sea ch space; ε1and ε2 a e he amplying ac o s; NV is he na iga ing ac o ; HBnand LBn he highes and lowes bounda ies o he sea ch space; Rnd is he andom numbe be ween 0 and 1. ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 75 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH 3.2. The Jelly ish algo i hm (JS) As men ioned ea lie , he de elopmen o JS is based on he li ing p ac ices o jelly ish in na u e, pa icula ly hei mo emen p ac ices in he ocean. These mo e- men s a e also he main idea o he upda e me hod o p oducing new solu ions in he sea ch p ocess o he op imal solu ion. The pa icula exp ession o he upda e me hod o JS is gi en below [32]: Xnew n=(Xn+m ×Rnd ×(HBn−LBn) Xn+ST wi h n= 1 . . . NP S (15) Wi h ST =Rnd ×DT (16) DT =(XR−Xni FR> FXn Xn−XRi FXn< FR (17) In Equa ions (15) – (17), Xnew nand Xna e he new upda ed posi ion and he conside ed posi ion belonged o he jelly ish no he popula ion; m is he mo ing ansi ion ac o and acco ding o he au ho s m is se by 0.1 o op imize he sea ching abili y o he al- go i hm; ST is he leng h o he jump s ep; DT is he di ec ion e m; XRand FRa e espec i ely he an- dom jelly ish selec ed om he ini ial popula ion and i s i ness alue. 3.3. The No he n goshawk op imiza ion (NGO) Simila o AHA and JS, he upda e me hod o new NGO solu ions is also de eloped by simula ing he li - ing p ac ices o he no he n goshawk, pa icula ly he hun ing beha io , which is sepa a ed in o wo phases. The ma hema ical exp essions o each phase will be gi en as ollows [33]: Xnew,P 1 n =(Xn+AF1(XR−AF2×Xn), FXR< Xn Xn+AF1(Xn−XR), FXR≥Xn (18) whe e Xnew,P 1 nis he new posi ion o he he no he n no he popula ion wi h n= 1,2, . . . NP s and NP s is he ini ial popula ion size; Xn he cu en posi ion o he no he n n; AF1and AF2a e, espec i ely, he ampli ying ac o s a e hei alues is andom gene a ed be ween 0 and 1; XRis he andom selec ed posi ion in he sea ch space. A e he upda e p ocess o new solu ions in he i s phase is comple ed, he upda e o new solu ion in he second phase is execu ed using he exp ession below [33] Xnew,P 1 n=Xn+SP HA ×(2 ×AF1−1) ×Xn(19) Wi h SP HA = 0.02 1−IT IT max (20) In Equa ions (19) and (20), Xnew,P 1 nis he new posi- ion o he no he n nin phase 2; SP HA is he ac eage o he posible hun ing a ea; IT and IT max a e, espec- i ely, he cu en index o i e a ion and he maximum index o i e a ion. 4. Resul s AHA, JS and NGO a e implemen ed o he simula- ion and e alua ion. Each algo i hm is un i y ials o collec he op imal solu ions, he bes and wo s solu ions wi h he smalles and highes powe losses, he bes un con e gence cha ac e is ic and he mean con e gence cha ac e is ic o all uns. The p og am o sol ing s udy case is coded in MATLAB on a compu e wi h 2.6 GHz o CPU and 8GB o RAM. Fo each case, he popula ion and i e a ion numbe a e se o 30 and 100. The selec ion o he popula ion and he maxi- mum i e a ion numbe mus be selec ed sui ably o ge ing he mos op imal solu ion and he simula ion ime is no long [38]. Besides, he h ee applied algo- i hms a e execu ed o 50 ial uns o he bes solu- ion be o e all he compa isons ake place. This sec ion employs he o iginal IEEE 33-node dis ibu ion powe g id o in es iga e he ins alla ion o SGs and ECSs. The single-line diag am o he g id is plo ed in Figu e 1 [25]. Inpu da a o he g id consis ing o load demand a each node, esis ance, and eac ance o each line a e aken om [25]. The o al load demand is 213.41 kW [25]. In he s udy, we simula e h ee scena ios and ou s udy cases o each scena io, including: •Case 1: Op imize he placemen o h ee SGs in he o iginal g id. •Case 2: Use esul s om Case 1 and con inue op- imize he placemen o ECSs. •Case 3: Op imize he placemen o ESCs in he o iginal g id. •Case 4: Use esul s om Case 3 and con inue o op imize hee SGs. In Scena io 1, 1 Le el-1 EVS, 1 Le el-2 EVS and 1 Le el-3 EVS a e conside ed. In he scena ios 2 and 3, each EVS ype has wo and h ee s a ions, espec i ely. I is assumed ha he Le el-1 EVS can cha ge 1000 ca s simula aneously and he s a ion needs he supply ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 76 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH Fig. 1: The con igu a ion o he IEEE 33-node sys em. o 206.kW. Simila ly, he ca numbe and he capaci y o Le el-2 and Le el-3 s a ions a e 1000 ca s and 435 kW, and 10 ca s and 1,087 kW, espec i ely. The a ed powe o each cha ge is 1.9 kW in he Le el-1 s a ion, 4.0 kW in he Le el-2 s a ion and 100 kW in he Le el- 3 s a ion [16]. The e iciency o each cha ge is selec ed o be 0.92. 4.1. Op imal placemen o h ee SGs The h ee cu es in Figu e 2a p esen he powe loss o he ial uns and he summa y o all ial uns, in- cluding he bes , mean and wo se loss, and s anda d de ia ion a e gi en in Figu e 2b. JS’s powe losses in ed cu e a e less han hose o NGO in black cu e and AHA in g een cu e. The sho es ba s o JS, NGO and AHA ha e he same loss o 14.52; howe e , JS eaches sho e maximum and mean loss ba s han o he s. Fu he mo e, JS ge s he smalles s anda d de- ia ion (STD). In addi ion, he bes un and mean un o i y ials a e gi en in Figu e 3a and 3b. In Figu e 3a, JS is as e han AHA and NGO om he ou y h i e a ion o he eigh y h i e a ion, hen he h ee al- go i hms each he same loss a he inal i e a ion. In Figu e 3b, he mean loss o NGO is always g ea e han ha o AHA and JS. AHA can each be e mean loss alues han JS o he i s ou y i e a ion bu hen JS eaches be e mean alues han AHA o o he i e a- ions. Clea ly, JS is as e and mo e s able han AHA and NGO. In summa y, he h ee algo i hms could each he same bes solu ion bu he mean solu ion and wo s solu ions om JS a e less. JS each as e and mo e s able con e gence. Thus, JS is he mos sui able algo- i hm o Case 1, and JS is selec ed o unning o he emaining cases. Table 1 p esen s he op imal solu ions achie ed by JS in Case 1. (a) Fi y op imal solu ions (b) Summa y o esul s om i y op imal solu ions Fig. 2: Resul s ob ained by execu ed algo i hms o Case 1. 4.2. Resul s ob ained o all s udy cases The esul s ob ained o ou cases o h ee scena ios by unning JS a e shown in Figu e 4. Case 1 is he same o all scena ios, bu Case 2, Case 3 and Case 4 a e di e en in all scena ios. In gene al, he powe ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 77 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH (a) The bes un (b) The mean o all uns Fig. 3: Compa ison o con e gence cha ac e is ics ob ained by algo i hms o Case 1. loss is inc eased om Scena io 1 o Scena io 3. The losses a e 17.858, 35.040, and 74.701 kW o Case 2 in Scena ios 1, 2 and 3, espec i ely. Simila ly, he losses a e 224.496, 270.355, and 357.126 kW o Case 3, and 20.832, 42.609, and 74.295 kW o Case 4, espec i ely. The esul s a e ob ious because he numbe o ECSs is inc eased om 3 o 6 and 9 in Scena ios 1, 2 and 3. Among ou s udy cases, Case 1 each he smalles powe loss because no ECSs bu h ee SGs a e placed in he g id. Case 3 su e s om he highes loss because load demand is g ea e due o he adding mo e ECSs and no SGs a e placed. Case 2 and Case 4 had bo h Tab. 1: The op imal solu ion achie ed by he JS in Case 1. Va iables Case 1 P oSG,1;PSG,1(kW) 30; 1199.92 P oSG,2;PSG,2(kW) 24; 1032.44 P oSG,3;PSG,3(kW) 13; 753.55 P FSG,10.85 P FSG,20.87 P FSG,30.86 Powe loss (kW) 14.51576 Fig. 4: Compa ison o powe loss o scena ios o Cases. ECSs and SGs in he g id. Howe e , Case 2 placed SGs i s and ECS hen bu Case 4 placed ECSs i s and SGs hen. In Scena io 1, he loss is 17.858 kW o Case 2, and 20.832 kW o Case 4. In Scena io 2, he loss is 35.040 kW o Case 2, and 42.609 kW o Case 4. In Scena io 3, he loss is 74.701 kW o Case 2, and 74.295 kW o Case 4. Case 4 su e s a highe loss han Case 2 by abou 3 kW in Scena io 1, 5.6 kW in Scena io 2. Bu Case 2 su e s a li le highe loss han Case 4 by abou 0.5 kW in Scena io 3. The ol age p o ile o h ee scena ios o placing ECSs o he g id in Case 2 is p esen ed in Figu e 5. The igu e clea ly shows ha he inc ease in he numbe o ECSs placed on he g id will lead o a highe ol age d op. Mainly, Scena io 3 esul ed in he highes ol age d op, while Scena io 1 showed he smalles one among he h ee conside ed scena ios. No e ha he ol age d op, in his case, is de e mined by op imizing he placemen o SGs i s a Case 1, and hen ECSs a e subsequen ly op imized. Figu e 6 shows he ol age p o ile in Case 3 wi h di e en scena ios o placing ECSs in he conside ed DPN. The deg ee o ol age d op a buses a e com- ple ely huge compa ed o Case 2. Addi ionally, he placemen o ECSs in Scena io 3 has iola ed he ol - age limi desc ibed by he wo ed lines in he igu e. No e ha he placemen o ECSs in h ee scena ios in his case is no suppo ed by SG as seen in Case 2. The e o e, he p esence o mo e ECSs will inc ease he load demand compa ed o he o iginal con igu a ion o he gi d and also lead o ano he ex ensi ely ol age d op. Case 4 is conduc ed wi h he addi ional placemen o SGs o imp o e he ol age p o ile a all buses in Case ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 78 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH Fig. 5: Vol age p o ile o sys ems o di e en scena ios o Case 2 ob ained by JS. Fig. 6: Vol age p o ile o sys ems o di e en scena ios o Case 3 ob ained by JS. 3, and he esul s a e displayed in Figu e 7. Mo eo e , o cou se, he placemen o SGs in he g id is also op i- mized, besides he op imal posi ion o ECSs execu ed in Case 3. The p esence o SGs, in his case, has sub- s an ially imp o ed ol age d op a all buses compa ed o Case 3. Howe e , compa ed o Case 2, whe e he placemen o SGs is i s op imized, he luc ua ion o ol age alue in Case 4 is s ill mo e signi ican , espe- cially a buses 19 o 25. Tables 2, 3, and 4 p esen he op imal solu ions achie ed by JS in he h ee scena ios o he las h ee cases. 5. Conclusions This pape applied h ee me a-heu is ic algo i hms o op imize he placemen o SGs and ECSs in di e en cases o TAPL e alua ion. The h ee algo i hms a e Tab. 2: The op imal esul s ob ained by JS in Scena io 1 o he las h ee cases. Va iable Case 2 Case 3 Case 4 P oECS−L130 20 20 P oECS−L219 19 19 P oECS−L32 2 2 P oSG,1;30; 1199.92 - 24; 1110.379 PSG,1(kW) P oSG,2;24; 1032.44 - 30; 1214.597 PSG,2(kW) P oSG,3;13; 753.55 - 13; 764.416 PSG,3(kW) P FSG,10.85 - 0.887 P FSG,20.87 - 0.85 P FSG,30.86 - 0.868 Powe 17.858 224.496 20.832 loss (kW) Tab. 3: The op imal esul s ob ained by JS in Scena io 2 o he las h ee cases. Va iable Case 2 Case 3 Case 4 P oECS−L120; 30 22; 3 22; 3 P oECS−L223; 3 20; 21 20; 21 P oECS−L319; 2 19; 2 19; 2 P oSG,1;30; 1199.92 - 12; 933.958 PSG,1(kW) P oSG,2;24; 1032.44 - 30; 1295.216 PSG,2(kW) P oSG,3;13; 753.55 - 21; 1450.431 PSG,3(kW) P FSG,10.85 - 0.873 P FSG,20.8722 - 0.85 P FSG,30.8635 - 0.95 Powe 35.04 270.355 42.609 loss (kW) Tab. 4: The op imal esul s ob ained by JS in Scena io 3 o he las h ee cases. Va iable Case 2 Case 3 Case 4 P oECS−L121; 24; 30 4; 23; 24 4; 23; 24 P oECS−L24; 20; 23 20; 21; 22 20; 21; 22 P oECS−L32; 3; 19 2; 3; 19 2; 3; 19 P oSG,1;30; 1199.92 - 30; 1279.705 PSG,1(kW) P oSG,2;24; 1032.44 - 24; 1914.165 PSG,2(kW) P oSG,3;13; 753.55 - 12; 948.250 PSG,3(kW) P FSG,10.85 - 0.85 P FSG,20.8722 - 0.95 P FSG,30.8635 - 0.893 Powe 74.701 357.126 74.295 loss (kW) ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 79 Dao T.T e al. VOLUME: 23 |NUMBER: 1 |2025 |MARCH Fig. 7: Vol age p o ile o sys ems o di e en scena ios o Case 4 ob ained by JS. including he a i icial hummingbi d algo i hm (AHA), he jelly ish algo i hm (JS), and No he n goshawk op- imiza ion (NGO). These algo i hms a e execu ed on he IEEE 33-node o he i s case o ind ou he bes one. In he i s case, JS ou pe o med wo o he s in inding he op imal placemen o SGs and eaching he bes powe loss alue. Then, JS is used o in es iga e he TAPL alue o he whole g id in he h ee emain- ing cases wi h he numbe o ECSs inc easing om one o h ee o h ee scena io and he ixed numbe o SGs. The esul s om hese scena ios indica e ha he op imal placemen o SGs o he g id be o e ECSs esul s in a be e powe loss alue, excep o Sce- na io 3, whe e he numbe o ECSs a all le els is h ee o each. Besides, he mo e ECSs in eg a ed in o he g id, he g ea e he powe loss and he ol age d op. Addi ionally, se e al limi a ions emain ha should be add essed o enhance he p ac icali y and con ibu ion o his wo k: •The analysis is limi ed o he s anda d IEEE -node DPN con igu a ion; a p ac ical DPN should also be conside ed. •O he objec i e unc ions, such as minimizing he o al ol age de ia ion index, minimizing he powe sou ce, minimizing he ene gy cos , e c., should be e alua ed. •The s udy p ima ily ocuses on sol ing he gi en p oblem om a planning pe spec i e. The ope a- ional pe spec i e should be explo ed. •Gi en ha sola gene a o s (SGs) p o ide powe only du ing dayligh hou s, ene gy s o age sys- ems (ESSs) should be in eg a ed o compensa e o nigh ime sho ages when SGs a e inac i e. •The easibili y o SG and ESS placemen si es should be e alua ed, conside ing he geog aphic cons ain s o p ac ical nodes. Au ho Con ibu ions D. T. T. de eloped he applied me hods, pe o med he simula ion and esul s, and edi ed he inal e - sion o he manusc ip . Bo h D. T. T. and M. P. D. con ibu ed o he i s d a o he manusc ip and supe ised he p ojec . Re e ences [1] ABRAHMS, B., e al. Clima e change as a global ampli ie o human–wildli e con lic . Na u e Cli- ma e Change. 2023, ol. 13, iss. 3, pp. 224–234. DOI: 10.1038/s41558-023-01608-5. [2] HUANG, Y., N. UNGER, K. HARPER, C. HEYES. Global Clima e and Human Heal h E ec s o he Gasoline and Diesel Vehi- cle Flee s. GeoHeal h. 2020, ol. 4, iss. 3. DOI: 10.1029/2019GH000240. [3] CAMES, M., E. HELMERS. C i ical e alua ion o he Eu opean diesel ca boom - global compa - ison, en i onmen al e ec s and a ious na ional s a egies. En i onmen al Sciences Eu ope. 2013, ol. 25, iss. 1, p. 15. DOI: 10.1186/2190-4715-25- 15. [4] SKERLOS, S. J., J. J. WINEBRAKE. Ta ge ing plug-in hyb id elec ic ehicle policies o inc ease social bene i s. Ene gy Policy. 2010, ol. 38, iss. 2, pp. 705–708. DOI: 10.1016/j.enpol.2009.11.014. [5] DAI, Z., H. LIU, M.O. RODGERS, R. GUENSLER. Elec ic ehicle ma ke po en ial and associa ed ene gy and emissions educ- ion bene i s. Applied Ene gy. 2022, ol. 322. DOI: 10.1016/j.apene gy.2022.119295. [6] AHMAD, F., e al. Op imal loca ion o elec ic ehicle cha ging s a ion and i s im- pac on dis ibu ion ne wo k: A e iew. En- e gy Repo s. 2022, ol. 8, pp. 2314–2333. DOI: 10.1016/j.egy .2022.01.180. [7] TRAN, H. V., A. V. TRUONG, T. M. PHAN, T. T. NGUYEN. Op imal placemen and ope a ion o so open poin s, capaci o s, and enewable dis- ibu ed gene a o s in dis ibu ion powe ne wo ks o educe o al one-yea ene gy loss. Heliyon. 2024, ol. 10, iss. 5. DOI: 10.1016/j.heliyon.2024.e26845. ©2025 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 80