Optimal operation of an integrated hybrid renewable energy system with demand-side management in a rural context

Ci a ion: Kuma , P.P.; Nu ula, R.S.S.; Hossain, M.A.; Shezan, S.A.; Su esh, V.; Jasinski, M.; Gono, R.; Leonowicz, Z. Op imal Ope a ion o an In eg a ed Hyb id Renewable Ene gy Sys em wi h Demand-Side Managemen in a Ru al Con ex . Ene gies 2022,15, 5176. h ps:// doi.o g/10.3390/en15145176 Academic Edi o : Albana Ilo Recei ed: 7 May 2022 Accep ed: 7 July 2022 Published: 17 July 2022 Publishe ’s No e: MDPI s ays neu al wi h ega d o ju isdic ional claims in published maps and ins i u ional a il- ia ions. Copy igh : © 2022 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps:// c ea i ecommons.o g/licenses/by/ 4.0/). ene gies A icle Op imal Ope a ion o an In eg a ed Hyb id Renewable Ene gy Sys em wi h Demand-Side Managemen in a Ru al Con ex Polama ase y P Kuma 1,*, Ramak ishna S. S. Nu ula 1, Md. Alamgi Hossain 2, SK. A. Shezan 3, Vishnu Su esh 4,* , Michal Jasinski 4,* , Radomi Gono 5and Zbigniew Leonowicz 4 1Depa men o Elec ical and Elec onics Enginee ing, GMR Ins i u e o Technology, Rajam 532127, India; [email p o ec ed] 2Queensland Mic o and Nano-Technology Cen e, G i i h Uni e si y, Na han, QLD 4113, Aus alia; mdalamgi .hossain@g i i h.edu.au 3Depa men o Elec ical Enginee ing, Enginee ing Ins i u e o Technology, Melbou ne, VIC 3001, Aus alia; [email p o ec ed] 4Facul y o Elec ical Enginee ing, W oclaw Uni e si y o Science and Technology, 50-370 W oclaw, Poland; zbigniew.leonowicz@pw .edu.pl 5Depa men o Elec ical Powe Enginee ing, Facul y o Elec ical Enginee ing and Compu e Science, VSB—Technical Uni e si y o Os a a, 708 00 Os a a, Czech Republic; adomi [email p o ec ed] *Co espondence: p a eenkuma [email p o ec ed] (P.P.K.); ishnu.su esh@pw .edu.pl (V.S.); michal.jasinski@pw .edu.pl (M.J.) Abs ac : A signi ican po ion o he Indian popula ion li es in illages, some o which a e loca ed in g id-disconnec ed emo e a eas. The supply o elec ici y o hese illages is no easible o cos - e ec i e, bu an au onomous in eg a ed hyb id enewable ene gy sys em (IHRES) could be a iable al e na i e. Hence, his s udy p oposed using a ailable enewable ene gy esou ces in he s udy a ea o p o ide elec ici y and eshwa e access o i e un-elec i ied g id-disconnec ed illages in he Odisha s a e o India. This s udy concen a ed on h ee di e en kinds o ba e y echnologies such as li hium-ion (Li-Ion), nickel-i on (Ni-Fe), and lead-acid (LA) along wi h a diesel gene a o o main ain an unin e up ed powe supply. Six di e en con igu a ions wi h wo dispa ch s a egies such as load ollowing (LF) and cycle cha ging (CC) we e modelled using nine me aheu is ic algo i hms o achie e an op imally con igu ed IHRES in he MATLAB © en i onmen . Ini ially, hese six con igu a ions wi h LF and CC s a egies we e e alua ed wi h he load demands o a low-e iciency appliance usage-based scena io, i.e., wi hou demand-side managemen (DSM). La e , he op imal con igu a ion ob ained om he low-e iciency appliance usage-based scena io was u he e alua ed wi h LF and CC s a egies using he load demands o medium and high-e iciency appliance usage-based scena ios, i.e., wi h DSM. The esul s showed ha he Ni-Fe ba e y-based IHRES wi h LF s a egy using he high-e iciency appliance usage-based scena io had a lowe li e cycle cos o USD 522,945 as compa ed o o he ba e y-based IHRESs wi h LF and CC s a egies, as well as o he e iciency- based scena ios. As compa ed o he o he algo i hms used in he s udy, he sugges ed Salp Swa m Algo i hm demons a ed i s as con e gence and obus ness e ec i eness in de e mining he global bes op imum alues. Finally, he sensi i i y analysis was pe o med o he p oposed con igu a ion using a iable inpu pa ame e s such as biomass collec ion a e, in e es a e, and diesel p ices. The in e es a e luc ua ions we e ound o ha e a subs an ial impac on he sys em’s pe o mance. Keywo ds: o -g id; in eg a ed enewable ene gy; demand-side managemen ; op imiza ion echniques; di e en ba e ies 1. In oduc ion 1.1. Need o Ene gy Managemen Sys ems Ene gy and eshwa e a e essen ial o humankind, bu he plane is su e ing g ea ly om u u e and cu en ene gy demands as well as eshwa e equi emen s due o he Ene gies 2022,15, 5176. h ps://doi.o g/10.3390/en15145176 h ps://www.mdpi.com/jou nal/ene gies Ene gies 2022,15, 5176 2 o 50 apid clima e change and popula ion g ow h [ 1 ]. To esol e his powe sho age, a backup powe sys em is needed. Diesel gene a o s (DGs) ha e been employed as a backup mecha- nism o a ange o o -g id applica ions, bu hey ace a numbe o key issues, including uel p ice ola ili y and high ope a ing and main enance cos s. Howe e , he op imum combina ion o RE esou ces and DGs esul s in a cos -e ec i e, e icien , and clean ene gy sys em ha educes he unce ain ies, ene gy p ices, and CO 2 emissions. A he same ime, an o -g id RE-based powe sys em comp ised o one o wo RE esou ces in conjunc ion wi h he ba e y s o age sys em and DG is an ideal combina ion o elec i ying he o -g id u al a eas. In he con ex o mic og id sizing, mic og ids a e ypically ei he unde sized o o e sized o mee he ene gy demands. An unde sized mic og id would esul in a loss o powe supply while an o e sized mic og id would esul in high sys em cos s and excess elec ici y p oduc ion. Hence, o esol e hese issues and eap he bene i s o he RE-based mic og id, a s ong ene gy managemen s a egy (EMS) is equi ed [2]. 1.2. The Impo ance o a Re e se Osmosis Desalina ion Plan o Remo e Village s In India, sa e d inking wa e is exceedingly limi ed, pa icula ly in emo e u al il- lages. Al hough some illages con inue o ecei e go e nmen wa e supply, almos 73% o Indian illages s ill ely on g oundwa e supply. Un o una ely, none o hese esou ces a e unsui able o p o iding a sa e d inking wa e supply. The usage o e ilize s in such a eas, as well as o he ac i i ies such as mining, has pollu ed he g oundwa e supply. S eams su ounding human a eas, such as illages, a e also hea ily pollu ed. Nume ous Indian soils ha e b ackish g oundwa e wi h o al dissol ed solid (TDS) concen a ions o mo e han 500 mg/L. I is g ea e han he Bu eau o Indian D inking Wa e S anda ds’ ecom- menda ion. Child en o all ages a e a ec ed by his con amina ed d inking wa e . Child en unde he age o i e a e especially ulne able since i equen ly kills hem and c ea es se- ious heal h- ela ed p oblems. As a esul , he usage o e e se osmosis Desalina ion (ROD) plan s is essen ial o he heal h and well-being o u al illage dwelle s. Memb anes and chemicals a e now widely a ailable in he ma ke as eplacemen componen s o he ROD uni s, and nowadays, ROD uni s can be powe ed by locally a ailable RE esou ces such as sola , wind, and biomass. As a esul , deploying ROD uni s in isola ed u al communi ies has become bo h simple and cos -e ec i e, as well as necessa y. 1.3. O e iew o he Op imiza ion Techniques Se e al s udies on mic og id size issues ha e been epo ed in he li e a u e. The p eceding app oaches can be di ided in o h ee ca ego ies: (i) so wa e ools such as RETSc een, HOMER, IHOGA, HOGA [ 3 ], e c., (ii) de e minis ic app oaches such as g aphi- cal cons uc ion, p obabilis ic, i e a i e, linea p og amming, and analy ical and nume ical me hods [ 4 ], and (iii) me aheu is ic algo i hms such as g asshoppe op imiza ion algo i hm (GOA) [ 5 ], g ey wol op imiza ion (GWO) [ 6 ], pa icle swa m op imiza ion (PSO) [ 7 ], ge- ne ic algo i hm (GA) [ 8 ], e c. Al hough he so wa e ools a e simple o use, use s canno selec he necessa y componen s in i and ha e no access o con ol o e he algo i hms and calcula ions con ained wi hin hem. Using so wa e ools, se e al assump ions and sequences can limi he mic og id size issues. Addi ionally, he de e minis ic app oaches ou pe o m he so wa e ools [ 9 ]. Howe e , because o he complexi ies o mic og id sizing, a he local op ima, he op imal solu ion is ex emely en apped. In hese ci cums ances, hey a e unable o con e ge o he global bes op imum solu ion. As a esul , he algo i hm mus be epea ed nume ous imes wi h he ini ial condi ion chosen a andom o a oid his local op ima en anglemen . Hence, he solu ion is unlikely o be he global bes op imal solu ion, and he algo i hm has o y se e al imes o disco e i . The e o e, me aheu is ic algo i hms ha e become one o he mos p omising and ex ensi ely used me hods [4]. Since he las decade, a numbe o me aheu is ic algo i hms ha e been de eloped and pa ed he way o conce ns such as mic og id sizing. In e es ingly, ew o hese me hods, such as pa icle swa m op imiza ion (PSO) and gene ic algo i hm (GA), a e well-known no only among he compu e scien is s bu also among a la ge numbe o scien is s om Ene gies 2022,15, 5176 3 o 50 o he ields. They a e adap i e app oaches ha ou pe o m de e minis ic me hods because hei solu ions a e no subs an ially en angled a local op ima. All o hese algo i hms ha e a ious bene i s, including he abili y o handle any ype o op imiza ion p oblem [ 9 ]. In con as , he no- ee-lunch heo em s a es ha a pa icula me aheu is ic algo i hm can achie e he global bes op imal solu ion o a speci ic objec i e unc ion bu i may p oduce ine ec i e ou comes o o he objec i e unc ions [ 10 ]. This has p omp ed mic og id size esea che s o look a he maiden me aheu is ic algo i hms [4]. 1.4. Li e a u e Re iew on Op imiza ion Techniques and Di e en Ba e y Technologies Recha geable ba e ies, soil physics, and chemical enginee ing a e jus a ew o he many ields ha make use o elec oly e di usion in elec oly e solu ions [ 11 , 12 ]. Fo he s andalone un-elec i ied illages in he Chikmagalu dis ic o Ka na aka, Ramesh and Saini [ 13 ] used he HOMER P o o conduc a easibili y analysis o he PV/diesel gene a o (DG)/mic o hyd o powe (MHP)/WT/BAT con igu a ion wi h LA and Li-Ion ba e y echnologies and h ee dispa ch s a egies such as cycle cha ging (CC), combined dispa ch (CD), and load ollowing (LF) and i was e ealed ha he Li-Ion ba e y-based IHRES wi h CD s a egy had he lowes ne p esen cos (NPC) and cos o ene gies (COEs) when compa ed o CC and LF s a egies. Alpesh and Sunil [ 14 ] used a PV/biogas gene a o (BGG)/biomass gene a o (BMG)/WT/ LA ba e y con igu a ion o powe a simple o -g id illage o 123 hamle s nea he Guja a - Rajas han s a e bo de in India and conduc ed an assessmen using he echnique o op imum componen selec ion wi h widely a ailable ypes o equipmen using a mul i- a iable linea eg ession algo i hm (MVLRA) and PSO o ob ain he op imal esul s wi h he MVLRA. Rajanna and Saini [ 15 ] employed a gene ic algo i hm o elec i y i e independen un-elec i ied hamle s in India’s Chama ajanaga dis ic o Ka na aka s a e using a con- igu a ion o PV/BMG/BGG/WT/MHP/LA ba e y echnology. Anki e al. [ 16 ] used he HOMER P o ® so wa e ool o elec i y i e independen un-elec i ied hamle s in he Almo a dis ic o U a akhand s a e in India using a PV/BGG/DG/MHP/BMG/LA ba e y con igu a ion o minimize he sys em’s NPC. Upadhyay and Sha ma [ 17 ] used CC and LF s a egies wi h GA, biogeog aphy-based op imiza ion (BBO), and PSO algo i hms o powe se en s andalone illages in he Indian s a e o U a akhand wi h a con igu a ion o PV/BMG/DG/BGG/MHP/LA ba e y ech- nology. F om he esul s, i was obse ed ha he BBO algo i hm p oduced he op imal esul s. Chong Li e al. [ 18 ] used he HOMER p o ® so wa e ool o conduc a s udy o 280 single- amily homes in Gansu P o ince, China, employing a WT/DG/BAT con igu a- ion wi h Li-Ion, LA, and zinc-b omine (ZB) ba e y echnologies. Acco ding o he indings, he ZB ba e y echnology p oduced he op imal esul s. Ba e al. [ 19 ] used a Simap o so wa e ool o conduc a li e cycle assessmen o PV ligh ing p oduc s in a soli a y u al a ea in Sou h-Eas Asia and ound ha sola PV ligh ing has a lowe en i onmen al e ec han adi ional ligh ing op ions. Shezan e al. [ 20 ] used he HOMER P o ® so wa e ool o conduc a s udy in a soli a y u al egion o KLIA Sepang S a ion in he Malaysian s a e o Selango , employing a con igu a ion o PV/DG/WT/LA ba e y echnology o lowe he sys em’s NPC. Ca los e al. [ 21 ] used GA-based algo i hms o analyze how o powe an Indonesian island wi h PV/DG/Li-Ion ba e y echnologies. Chhunheng and Supacha [22] used he HOMER P o®so wa e ool o analyze how o elec i y a soli a y u al egion in Cambodia u ilizing PV/DG/LA ba e y echnology o lowe he sys em’s NPC. Sompol e al. [ 23 ] used he LABVIEW so wa e ool o conduc a s udy o o -g id applica ions in Thailand wi h a BMG/PV/Li-Ion ba e y con igu a ion. Haein and Tae [ 24 ] used he HOMER P o ® so wa e ool o conduc an analysis o powe a ees anding egion in Myanma u ilizing a PV/DG/BAT con igu a ion wi h LA and Li-Ion ba e y echnologies o lowe he sys em’s NPC, and om he esul s, i was iden i ied ha he LA ba e y echnology p o ided he op imal esul s. Lo a e e al. [ 25 ] used he HOMER P o ® so wa e ool o analyze how PV/LA ba e y echnology could be used o powe Sou h-Eas Asian islands: Philippines, Gilu ongan, Ene gies 2022,15, 5176 4 o 50 Cebu, and Co do a wi h minimum NPC. Sa ah e al. [ 26 ] conduc ed an analysis in Dodoma and Tanzania u ilizing he PV/DG/WT/BAT con igu a ion wi h LA and Li-Ion ba e y echnologies and ound ha he Li-Ion ba e y-based con igu a ion wi h GA had he lowes COE. Kaabeche and Bakelli [ 27 ] conduc ed an assessmen using a WT/PV/BAT con igu a- ion wi h Li-Ion, LA, and nickel-cadmium ba e y echnologies. The ALO, GWO, JAYA, and K ill He d algo i hms we e used o examine he sys em’s uni elec ici y cos and i was disco e ed ha he JAYA algo i hm p o ided a iable solu ion wi h an LA ba e y-based sys em, ollowed by Li-Ion and Ni-cd ba e ies. 1.5. Demand-Side Managemen In gene al, uncoo dina ed peak and alley load demands inc ease ene gy cos s by expanding he gene a ion and dis ibu ion ne wo ks, as well as o cing gene a o s o un ou o hei a ed capaci y du ing peak load pe iods [ 11 ]. Hence, i would be ad an ageous o lowe some o hese demands in o de o a oid he need o cos ly ex a ins alla ions [ 12 ]. Fu he mo e, he ene gy demand cu es mus be as smoo h as possible o se e al easons, including minimizing he s ain on powe gene a ion equipmen and o he p o ec i e componen s o he mic og id, as well as lowe ing ene gy cos s and de e ing o a oiding u u e equipmen in es men . In his con ex , se e al demand-side managemen (DSM) s a egies in he powe sys em indus y ha e been applied using a a ie y o me hods such as “peak clipping, alley illing, load shi ing, ene gy conse a ion, load building and lexible load shape” [13], which a e illus a ed in Figu e 1and desc ibed as ollows [28]: (a) Peak clipping: peak clipping is a echnique o educing load demand du ing peak hou s. I is equen ly accomplished by ei he limi ing he use o appliances du ing peak hou s o mo i a ing cus ome s o modi y hei demand beha io by o e ing a ac i e p ice signals. (b) Valley illing: he pu pose o alley illing is o s imula e ene gy use du ing o - peak hou s in o de o inc ease a e age ene gy u iliza ion. I can be done by encou aging cus ome s o do hings such as loading and cha ging du ing o -peak hou s when u ili ies p e e o use less ene gy o mee he load demand. (c) Load shi ing: his is in ended o shi he loads om on-peak o o -peak hou s wi hou al e ing he ene gy use pa e n. Fo example, du ing o -peak hou s, cus ome s can s o e he mal hea and use i o keep he oom wa m all day. Simila ly, o he household ac i i ies such as washing clo hes and washing dishes can be done a nigh o p e en peak loading. (d) Ene gy conse a ion: he goal o ene gy conse a ion is o educe he ene gy demand by using ene gy-e icien de ices. Changing o e icien de ices can educe he load demand as well as change he load shape. (e) Load building: load building and lexible loads a e connec ed o he ne wo k suppo ed unde he p inciple o sma g ids. Load building imp o es load sha ing as well as ene gy s o age sys ems o imp o e g id esponsi eness. ( ) Flexible load shape: lexible loads can be handled in e u n o he bene i s. This implies ha he load shape is esponsible o he eliabili y condi ions which means ha he loads can be modi ied acco ding o he eliabili y o he sys em. Ene gies 2022,15, 5176 5 o 50 Figu e 1. All ypes o demand-side managemen . 1.6. Li e a u e Re iew o Demand-Side Managemen The concep o DSM has inspi ed he a en ion o esea che s wo king on au onomous IHRESs. Rajanna and Saini [ 29 ] used GA and PSO algo i hms o analyze he pe o mance o Ene gies 2022,15, 5176 6 o 50 ou un-elec i ied illage zones in India wi h a DSM s a egy using h ee in es men -based scena ios, such as low in es men wi h high a ing appliances, medium in es men wi h mode a e a ing appliances, and high in es men wi h low a ing appliances and ound ha he sys ems wi h DSM s a egy had he lowes cos s using PSO. Upadhyay and Sha ma [ 17 ] p oposed h ee ene gy managemen schemes based on he HOMER p o ® so wa e ool and GA and PSO algo i hms, claiming ha peak sha ing wi h he CC s a egy u ilizing he PSO me hod would be mo e cos -e ec i e han o he me hods. Chauhan and Saini [ 30 ] in es iga ed he echno-economic aspec s o an IHRES using an ene gy managemen app oach by conside ing a load-shi ing s a egy based on DSM o mee he ene gy demands o he popula ion o U a akhand s a e illages in India, inding ha he DSM s a egy was a mo e cos -e ec i e solu ion han he NON-DSM s a egy. Zheng e al. [ 31 ] used linea economic p og amming o design a a i -based load- shi ing algo i hm o lowe he ope a ional cos s o a biomass-based mic og id wi h com- bined hea and powe . Wang e al. [ 32 ] combined he eceding ho izon op imiza ion echnique wi h DSM o lowe he maximum ope a ing and en i onmen al expenses o a s andalone PV/WT ne wo k-based single- amily dwelling. To ob ain he bes pe o mance in s andalone sys ems, Ma zband e al. [ 33 ] p esen ed a s ochas ic op imiza ion echnique ha akes in o accoun luc ua ions in he design o load u iliza ion. Ma allanas e al. [ 34 ] sugges ed a DSM con ol echnique o enhancing business planning in PV sys ems using neu al ne wo ks wi h he goal o inc easing ene gy e iciency. Gudi e al. [ 35 ] used a bina y pa icle swa m op imiza ion o apply he DSM s a egy in he home sec o o cos sa ings o he sugges ed sys em. Ky iaka akos e al. [ 36 ] p oposed a sma DSM solu ion based on he g ey p edic ion algo i hm o mee sys em a chi ec u al p inciples and ensu e he e ec i eness o a ees anding mul i-gene a ed mic og id ope a ing in emo e places. Randa Kallel e al. [ 37 ] in es iga ed he bene i s o he p oposed in eg a ed sys em s a egic plan unde a ious scena ios and conduc ed a compa ison be ween he DSM and NON-DSM ene gy managemen s a egies. 1.7. Mo i a ion o he A icle o Conside Ene gy Conse a ion-Based DSM An ene gy conse a ion-based elec i ica ion is highly ecommended in India. On 5 Janua y 2015, he Go e nmen o India launched Unna Jee an by A o dable LEDs and Appliances o All (UJALA) scheme, which will p o ide people wi h cos -e ec i e ene gy- e icien LED bulbs compa ed o ma ke p ices h ough Ene gy E iciency Se ices L d. (EESL) in a join en u e managed by he Indian Minis y o Powe p o iding widesp ead dis ibu ion o LED bulbs and ene gy-e icien elec ical appliances. I has been dis ibu ed mo e han 21.7 c o es o ene gy-e icien LED bulbs wi h i s ne wo k sp ead o e 24 s a es in India, esul ing in ene gy and elec ical bill sa ings as well as he educ ion in bo h he CO 2 emissions and peak load demands. The Na ional Ene gy E icien Fan P og am (NEEFP) was also in oduced by he EESL o p omo e ene gy conse a ion h ough in- c eased esiden ial use o ene gy-e icien ans and EESL also de eloped a se ice model such as he S ee Ligh ing Na ional P og am (SLNP) scheme ha allows municipali ies o eplace con en ional ligh s wi h LEDs wi hou any up on cos s, whe e he balance o cos s is eco e ed by mone izing he ene gy sa ings h ough local municipali ies [ 38 ]. These a e all schemes o he Go e nmen o India ha inspi ed us o w i e his a icle abou ene gy conse a ion-based DSM. 1.8. No el y and O e iew o he A icle I was iden i ied ha he e has been no esea ch on he supply o eshwa e o he isola ed egions in he Indian scena io using a con igu a ion o PV/BMG/DG/BAT wi h Ni-Fe, LA, and Li-Ion ba e y echnologies using LF and CC s a egies. Fu he mo e, se e al esea che s conduc ed a echno-economic easibili y s udy o p o ide an unin e up able powe supply using only one o wo ypes o ba e y echnologies, such as Li-Ion and LA. Mo eo e , a ious esea che s ha e conduc ed s udies on di e en ypes o DSM app oaches. Howe e , no s udies ha e been a emp ed o conside he e iciency-based Ene gies 2022,15, 5176 7 o 50 scena ios (ene gy conse a ion-based DSM) such as high powe a ed appliances o low cos (HPRALC), medium powe a ed appliances o mode a e cos (MPRAMC), and low powe a ed appliances o high cos (LPRAHC) wi h di e en dispa ch s a egies using di e en ba e y echnologies o o -g id u al a eas, which was iden i ied as a signi ican gap in he exis ing li e a u e. Finally, se e al esea che s ha e compa ed he p oposed algo i hm’s con e gence and obus ness e iciency o hose o only one o wo o he algo i hms. Fo a ealis ic analysis o an o -g id IHRES, he a o emen ioned gaps and limi a ions mus be esol ed. To keep in his iew, i e un-elec i ied o -g id illages in he Odisha s a e o Rayagada dis ic we e iden i ied as a s udy a ea in o de o p o ide powe and eshwa e a ailabili y using accessible RE esou ces in he s udy a ea, such as biomass and sola . Owing o he in e mi en na u e o hese RE esou ces, he powe supply is no con inuous. To ensu e a con inuous powe supply, he s udy conduc ed easibili y s udies wi h h ee di e en ypes o ba e y echnologies such as li hium-ion (Li-Ion), nickel-i on (Ni-Fe), and lead-acid (LA), as well as a diesel gene a o (DG), by aking in o accoun LF and CC s a egies. In gene al, he u al people’s load usage pa e n is almos he same h oughou he day. Regula ly, he peak loads occu in he e ening due o he p io i y loads such as lamps, ans, and TVs; hese loads canno be al e ed by hei habi ual pa e n o use. The e o e, peak clipping and load shi ing a e no possible o o -g id u al illage s, especially in he e ening ime. Howe e , wi h p ope ene gy conse a ion managemen , hese peak loads can be educed wi hou peak clipping and load shi ing. I is one o he mos success ul and a o ed demand esponse p og ams o o -g id u al illage s due o i s easy- o-adop bene i s, no main enance, and no need o shi ing p io i y loads. Hence, he s udy conside ed ene gy conse a ion-based DSM using consume s’ loads usage pa e ns such as high powe a ed appliances o low cos (HPRALC), medium powe a ed appliances o mode a e cos (MPRAMC), and low powe a ed appliances o high cos (LPRAHC) wi h di e en dispa ch s a egies using di e en ba e y echnologies. Six di e en con igu a ions we e modelled in o de o de e mine he op imum con igu- a ion o elec i ying he s udy a ea using a ailable RE esou ces and he p oposed ba e y echnologies wi h hei di e en dep h o discha ges (DODs) such as PV/BMG/DG/LA a 70% DOD, PV/BMG/DG/LA a 80% DOD, PV/BMG/DG/Li-Ion a 50% DOD, PV/BMG/ DG/Li-Ion a 70% DOD, PV/BMG/DG/Li-Ion a 80% DOD, and PV/BMG/DG/Ni-Fe a 80% DOD, which we e es ed wi h wo di e en dispa ch s a egies such as LF and CC using consume s load usage pa e ns such as HPRALC, MPRAMC, and LPRAHC-based scena ios. To ob ain an op imum con igu a ion om hese six con igu a ions, a maiden algo i hm called a Salp Swa m Algo i hm om he me aheu is ic amily was p oposed in he s udy [ 10 ]. To demons a e i s con e gence and obus ness e iciency in iden i ying he global bes op imal alues, i was compa ed wi h eigh o he p o en and well-known algo- i hms, namely: pa icle swa m op imiza ion (PSO) [ 7 ], di e en ial e olu iona y algo i hm (DE) [ 39 ], gene ic algo i hm (GA) [ 8 ], an lion op imiza ion (ALO) [ 40 ], g asshoppe op i- miza ion algo i hm (GOA) [ 5 ], g ey wol op imiza ion (GWO) [ 6 ], mo h lame op imiza ion (MFO) [ 41 ], and d agon ly algo i hm (DA) [ 42 ]. Finally, he op imal con igu a ion’s sensi- i i y analysis was in es iga ed using a ious inpu pa ame e s such as biomass oliage collec ion a e, in e es a e, and diesel p ices. 2. De elopmen o he IRES A sys ema ic p ocess is essen ial o he implemen a ion o an IHRES o isola ed u al communi ies, which is ou lined in he ollowing s eps: 2.1. S ep 1—S udy A ea Iden i ica ion In he Rayagada dis ic o Odisha s a e in India, a g oup o i e un-elec i ied illages o Muniguda block we e conside ed as a s udy a ea. Figu e 2shows i s geog aphic loca ion on he map. I is loca ed a 19 ◦ 37 0 16.6944 00 N la i ude and 83 ◦ 29 0 50.6688 00 E longi ude, a a heigh o 206 m om he abo e mean sea le el. In his a ea, a o al o 1213 people li e in 266 households, none o which ha e access o elec ici y o a sa e d inking wa e supply. Ene gies 2022,15, 5176 8 o 50 Because o i s emo e loca ion, i has ye o be elec i ied, so people s ill ely on sola lamps, ke osene lan e ns, and candles o ligh ing. Figu e 2. The loca ion o he s udy a ea on he map. 2.2. S ep 2—Es ima ion o Elec ical Ene gy Demand and Hou ly F eshwa e Requi emen s This s udy con ains h ee di e en ypes o ene gy-e iciency scena ios based on he household elec ical appliance usage pa e ns such as high powe a ed appliances o low cos (HPRALC), medium powe a ed appliances o mode a e cos (MPRAMC), and low powe a ed appliances o high cos (LPRAHC). Each scena io was classi ied in o communi y, domes ic, comme cial, ag icul u al, o small-scale indus ial sec o s. HPRALC appliances a e incandescen bulbs, LCD TVs, incandescen s ee ligh lamps, and low- e iciency ceiling ans. MPRAMC appliances a e CFL ligh s, LCD TVs, CFL s ee ligh s, and medium-e iciency ceiling ans. LPRAHC appliances a e LED TVs, LED ligh s, LED s ee ligh s, and high-e iciency ceiling ans. The co esponding hou ly load demands o bo h he summe and win e seasons and hei ela ed de ails a e gi en in Tables 1–3and he associa ed load cu es a e shown in Figu e 3. Ene gies 2022,15, 5176 9 o 50 Table 1. HPRALC-based scena io load demand o bo h win e and summe seasons. Load Sec o →Domes ic Load Communi y Load Ag icul u al Load Comme cial Load SIL Hou ly Ene gy Demand (kWh) School Hospi al Communi y Hall SL PW MCTM Shops MDP Flou Mill Saw Mills Appliance → LED Lamp Fan TV + Dish MC LED Lamp Fan Compu e LED Lamp Fan Re ige a o LED Lamp Fan LED Ligh s Mo o (2 hp) Mo o (5 hp) LED Lamp Fan Mo o (4 hp) Mo o (5 hp) Saw Machine Ra ed Powe → 20 W 75 W 150 W 5 W 20 W 75 W 250 W 20 W 75 W 200 W 20 W 75 W 40 W 1.5 KW 3.73 kW 20 W 75 W 2.983kW 3.73kW 1.8kW Quan i y →2 1 1 1 12 12 12 5 5 1 3 3 27 6 1 5 5 3 1 1 Time (h) ↓S/W S/W S/W S/W S/W S/W 0:00–1:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 1:00–2:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 2:00–3:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 3:00–4:00 19.95/0 0.3 0.38/0 0.2 1.08 2.98 24.89/4.56 4:00–5:00 31.92 19.95/0 0.3 0.38/0 0.2 1.08 2.98 56.81/36.48 5:00–6:00 31.92 19.95/0 9.98 0.3 0.38/0 0.2 1.08 2.98 66.79/46.46 6:00–7:00 19.95/0 9.98 1.33 0.38/0 0.2 9 2.98 43.82/23.49 7:00–8:00 19.95/0 9.98 1.33 0.38/0 0.2 9 2.98 43.82/23.49 8:00–9:00 19.95/0 9.98 0.67 0.38/0 0.2 9 2.98 43.16/22.83 9:00–10:00 9.98/0 9.98 0.72 0.9/0 30.38/0 0.2 3.73 0.3 0.38/0 8.95 1.8 40.32/28.68 10:00–11:00 9.98/0 9.98 0.72 0.9/0 30.38/0 0.2 3.73 0.3 0.38/0 8.95 1.8 40.32/28.68 11:00–12:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 3.73 0.3 0.38/0 8.95 1.8 70.65/58.78 12:00–13:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 3.73 0.3 0.38/0 8.95 1.8 70.65/58.78 13:00–14:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 3.73 0.3 0.38/0 2.98 3.73 66.61/54.74 14:00–15:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 62.88/51.01 15:00–16:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 62.88/51.01 16:00–17:00 9.98/0 39.9 0.72 0.9/0 30.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 62.88/51.01 17:00–18:00 9.98/0 39.9 0.38/0 0.2 0.18 0.23/0 0.3 0.38/0 2.98 3.73 58.26/47.29 18:00–19:00 31.92 19.95/0 19.95 1.33 0.3 0.38/0 0.2 0.18 0.23/0 1.08 0.3 0.38/0 2.98 79.18/58.24 Ene gies 2022,15, 5176 16 o 50 is 26 ◦ C. The s udy a ea is su ounded by a high dense o es co e ing abou 87 hec a es, wi h a collec ion a e o 60% o o es oliage such as lea es, pine needles, and i ewood. The biomass supply is p ojec ed o be 9 ons/yea . The s udy conside ed in he simula ion an a e age o 10 yea s (2005–2015) o hou ly sola adia ion and ambien empe a u es, which we e aken om he Na ional Renewable Ene gy Labo a o y (NREL) and a e shown in Figu es 5and 6. Figu e 5. Annual global sola adia ion o he s udy a ea. Figu e 6. The annual ambien empe a u e o he s udy a ea. 3. The IHRES Componen Ma hema ical Modelling Be o e op imal sizing o he IHRES, p ope ma hema ical modelling o he componen s is needed. The s udy p oposed an IHRES model ha inco po a es biomass and sola ene gy Ene gies 2022,15, 5176 17 o 50 esou ces as well as a ba e y bank and diesel gene a o as a backup powe supply. I s schema ic diag am is depic ed in Figu e 7and he co esponding ma hema ical models a e desc ibed as ollows. Figu e 7. The schema ic diag am o he IRES. 3.1. Sola Ene gy Sys em Va ious models o measu ing PV ou pu powe ha e been p oposed in he li e a u e. In his analysis, a simpli ied model was used o calcula e he ou pu powe o a PV panel Ene gies 2022,15, 5176 18 o 50 (PPV( )) by using Equa ion (1) based on he hou ly ambien empe a u e (Tamb( )) and hou ly sola i adia ion (G( ))in he s udy a ea [9]. PPV( ) = PV a ed ×(G( )/G e )×h1+KT×TC−T e i (1) whe e, G e is a e e ence condi ion sola adia ion, i s alue is 1000 W/m 2 , KT is he maximum powe empe a u e coe icien , i s alue is 3.7 × 10 − 3 (1/ ◦ C), T e is a he s anda d es condi ion PV cell empe a u e, i s alue is 25 ◦ C, and PV a ed is he PV panel a ed powe . The cell empe a u e (TC)is calcula ed as: TC=Tamb( ) + (0.0256 ×G( )). (2) whe e, Tamb( )is he hou ly ambien empe a u e (◦C). The PV panel ene gy gene a ion (EPV) is calcula ed as ollows: EPV ( ) = NPV ×PPV( )×∆ (3) whe e, ∆ is he ime span and is conside ed as one hou . 3.2. Biomass Gene a o (BMG) The biomass gene a o is made up o ou majo componen s such as “p oduce gas- based engine cum gene a o se , gas cleaning sys em, gas cooling sys em and biomass gasi ie . Fo he biomass gasi ie , he s udy used a downd a gasi ie design; in his gasi ie , mainly se en pa s a e he e such as d ying zone, hoppe lid, combus ion zone, educ ion zone, py olysis zone, ash emo al ank and smoke al e. The cleaning sys em consis s o pan il e , a cyclone, co on il e and sawdus il e and he cooling sys em consis s o a chille plan ” [ 43 , 44 ]. The on and ea iews o he biomass gene a o a e shown in Figu es 8and 9, espec i ely. Figu e 8. F on iew o he biomass gene a o [44]. Ene gies 2022,15, 5176 19 o 50 Figu e 9. Rea iew o he biomass gene a o [44]. The powe gene a ed by he biomass gene a o (PBMG) is calcula ed as [14]: PBMG( ) = QBM ×ηBMG ×CVBM ×1000 DOHBMG ×365 ×860 (4) whe e, ηBMG is he e iciency o he BMG, CVBM is he calo i ic alue o he biomass, i s alue is 4015 kcal/kg , DOHBMG is he daily ope a i e hou s o he BMG, he 860 alue used in he o mula is a con e ing ac o om kcal o kWh, and QBM is he a ailabili y o he quan i y o biomass ( ons/yea ). The ene gy gene a ed by he BMG (EBMG) is calcula ed as ollows: EBMG( ) = PBMG( )×∆ (5) whe e, ∆ is he ime pe iod and is conside ed o be one hou . 3.3. Ba e y Bank When RE esou ces a e una ailable o he sys em is expe iencing peak load demand, he ba e y bank usually supplies he powe . Whene e excess ene gy is gene a ed by he RE esou ces, i is s o ed in he ba e y bank. The ene gy s o ed in he ba e y bank a any hou ‘ ’ is exp essed as ollows [9]: EBa ( ) = (1−σ)×EBa ( −1)+(EG( )−EL( )/ηCon )×ηCC ×η ba (6) whe e, σ is he ba e y hou ly sel -discha ge a e, EG is he elec ical ene gy gene a ed, EL is he elec ical ene gy demand, ηCon is he bi-di ec ional con e e e iciency, ηCC is he cha ge con olle e iciency, η ba is he ba e y ound ip e iciency, and EBa ( ) and EBa ( −1)a e ba e y bank ene gy le els a ime ‘ 0and ‘ −10, espec i ely. The elec ical ene gy gene a ed (EG)by he RE esou ces a e calcula ed as ollows: EG( ) = [EDC( ) + EAC( )]×ηCon (7) whe e he DC ene gy gene a ed (EDC) by he RE esou ces a e calcula ed as ollows: EDC( ) = EPV( )(8) Ene gies 2022,15, 5176 20 o 50 The AC ene gy gene a ed (EAC) by he RE esou ces a e calcula ed as ollows: EAC( ) = EBMG( )(9) The elec ical powe gene a ed by he RE esou ces du ing he discha ge p ocess is less han he load demand. The e o e, he ba e y bank can p o ide he necessa y de ici load, which can be exp essed as ollows: EBa ( ) = (1−σ)×EBa ( −1)−(EL( )/ηCon −EG( ))/η ba (10) 3.4. Diesel Gene a o (DG) Diesel gene a o s a e bene icial in o -g id a eas because hey p o ide powe when ba e ies ail o ul ill he load demand o when enewable ene gy supplies a e dis up ed by p olonged cloudy wea he o ainy seasons. The main eason o including DG in he s udy is ha many households, p ima y heal h ca e cen e s, and businesses a e le in he da k du ing he blackou s caused by supe s o ms and o he unexpec ed occu ences. Fu he mo e, in he las en yea s, blackou cases ha e doubled. Thus, inco po a ing a DG se in o in eg a ed RE sys ems imp o es he e iciency o a mic og id by p o iding a eliable powe sou ce in eme gency si ua ions and sha ing peak load demands when ba e ies ail o mee he peak load demands [ 45 , 46 ]. The DG hou ly uel consump ion ( FDG ) can be calcula ed using a linea law based on he equi ed load demand as ollows [9]: FDG( ) = aDG ×PDG,gen( ) + bDG ×PDG, a l/h(11) whe e, aDG and bDG a e he DG uel consump ion cu e coe icien s and hei alues a e aDG = 0.246 (l/kWh) and bDG = 0.08145 (l/kWh). PDG,gen( ) and PDG, a a e he hou ly gene a ed powe and a ed powe o he DG, espec i ely. The DG annual uel consump ion (AFC) is calcula ed as ollows: AFC = 8760 ∑ =1 FDG( )(12) CO2Emissions The hou ly CO 2 emissions o DG es ima ed wi h espec o he hou ly uel consump- ion a e as ollows [47]: CO2( ) = SECO2(kg/l)×FDG( )(l/h)(13) whe e, SECO2is he speci ic CO2emissions pe L o diesel and i s alue is 2.7 kg/L. The DG annual CO2emissions a e es ima ed as ollows: ACO2emission = 8760 ∑ =1 CO2( )(14) 3.5. Bi-Di ec ional Con e e wi h a Cha ge Con olle (BDC-CC) In gene al, he BDC-CC con e s elec ical ene gy in o ec i ie and in e e modes o ope a ion. In he in e e mode, i con e s he di ec cu en (DC) in o an al e na e cu en (AC) and in he ec i ie mode, i con e s AC in o DC. The cha ge con olle is use ul o ensu ing ha he ba e y bank is no o e cha ged o o e -discha ged. The BDC-CC powe a ing (PBDC-CC) is calcula ed as ollows [14]: PBDC-CC =ET,max ×1.1 (15) whe e, he mul iplica ion ac o 1.1 ep esen s he con e e ’s 10% o e loading capabili y and ET,max is he maximum amoun o ene gy ans e ed h ough he con e e . Ene gies 2022,15, 5176 21 o 50 3.6. Re e se Osmosis Desalina ion (ROD) Plan In ela ion o he speci ic ene gy consump ion ( SEC ), he powe ( PDEM ) equi ed by he ROD uni o gene a e an hou ly eshwa e demand ( HVDW ) o he desalina ion p ocess is exp essed as ollows [1]: PDEM( ) = HVDM( )×SEC (16) In his s udy, he ROD uni was expec ed o consume 2 kWh/m 3 ( SEC ) o speci ic ene gy. The ROD uni consis s o pumps, a desalina ion uni , memb anes, and ene gy eco e y de ices. The ROD uni ’s daily olume ic demand o eshwa e ( DVDW ) is calcula ed as ollows: DVDW =24 ×PDEM SEC (17) To analyze he cha ac e is ic cu es o RO memb anes, he ROD sys em was designed o ope a e in be ween he ins alled powe ( PI ) and minimum load equi emen ( PMLD ), i.e., PMLD ≤PDEM ≤PI(18) whe e, he ROD uni ’s minimum load demand (PMLD) is used o esol e he osmo ic p essu e p oduced by he ROD uni , which was es ima ed o be 25% o he ins alled powe (PI). The ROD uni au onomy assumes a wo-day s o age pe iod o a eshwa e ank o calcula e he olume ic capaci y o a eshwa e ank (VCWT) , which is calcula ed as ollows: VCWT =2×DVDW (19) 4. Economic Analysis o he IRES Se e al app oaches ha e been used o in es iga e he economic easibili y o he IHRES such as ne p esen cos , annual le elized cos , li e cycle cos (LCC), and payback pe iod. In hese scena ios, he LCC me hodology o economic analysis is ex ensi ely employed since i p o ides an accu a e o e iew o p ojec expenses o e he p ojec ’s li espan. In his s udy, he LCC o he IHRES was calcula ed using Equa ion (20) [ 46 ] by summing he e ec ion cos s, ini ial capi al cos s, O&M cos s, uel cos s, and eplacemen cos s o all sys em componen s. The analysis comp ised he ollowing assump ions. The e ec ion cos s o he PV, BMG, DG, BAT, BDC-CC, and ROD uni we e aken as 20% [14], 5% [14], 5%, 3% [14], 3% [14], and 3% [46] o hei capi al cos s, espec i ely. The eplacemen cos o he BMG, DG, BAT, BDC-CC, MEM, and CHEM we e consid- e ed as 70% [ 14 ], 100% [ 46 ], 100% [ 14 ], 100% [ 14 ], 100% [ 14 ], and 100% [ 14 ] o hei capi al cos s, espec i ely. LCC =ICC +PV,O&M+PV,REP +PV,FUEL (20) The ini ial capi al cos (ICC) o he IRES componen s a e calcula ed as ollows [14]: ICC =CBMG,cap+NPV ×CPV,cap+NBAT ×CBAT,cap+ CROD,cap+CBDC-CC,cap+CMEM,cap+CWTA,cap+CCHE,cap(21) whe e, CBMG,cap , CPV,cap , CBAT,cap , CROD,cap , CBDC-CC,cap , CMEM,cap , CWTA,cap , and CCHE,cap a e he ini ial capi al cos s o he BMG, PV, BAT, ROD uni , BDC-CC, MEM, WTA, and CHE, espec i ely. The e ec ion cos s (EREC) o he IRES componen s a e calcula ed as ollows [14]: EREC =       (NPV ×CPV,e ec )+(CROD,e ec )+ (NBAT ×CBAT,e ec )×N ∑ b=1 (1+x)bNc−1 (1+y)bNc+ CBDC-CC,e ec ×N ∑ d=1 (1+x)dNc−1 (1+y)dNc+ CBMG,e ec ×N ∑ g=1 (1+x)gNc−1 (1+y)gNc!        (22) Ene gies 2022,15, 5176 22 o 50 whe e, CPV,e ec , CROD,e ec , CBAT,e ec , CBDC-CC,e ec , and CBMG,e ec a e e ec ion cos s o PV, ROD uni , BAT, BDC-CC, and BMG, espec i ely. The p esen alue o annual O&M ( PV,O&M ) cos s o he IRES componen s a e calcu- la ed as ollows [14]: PV,O&M=(NPV ×CPV,o&m)+(CBMG,o&m) (NBAT ×CBAT,o&m)+(CBDC-CC,o&m)+(CROD,o&m)× N ∑ i=1 (1+x)i−1 (1+y)i(23) whe e, CPV,o&m , CBMG,o&m , CBAT,o&m , CBDC-CC,o&m , and CROD,o&m a e O&M cos s o he PV, BMG, BAT, BDC-CC, and ROD uni , espec i ely, and y is de ined as ollows [14]: y=Inom −x 1+x(24) whe e, Inom ,y, N , and xa e he nominal in e es a e, discoun a e, li espan, and he in la ion a e o he p ojec , espec i ely. The componen s’ li espan such as ha o ba e ies, biomass gene a o , bi-di ec ional con e e wi h a cha ge con olle , memb anes, chemicals, and DG a e sho e han he p ojec li e ime. The e o e, hey need o be eplaced a some s age du ing he p ojec ’s li e ime. The p esen alue o annual eplacemen cos ( PV,REP ) o he IRES is calcula ed as ollows: PV,REP =          NBAT ×CBAT, ep ×N ∑ b=1 (1+x)bNc−1 (1+y)bNc+ CBMG, ep ×N ∑ g=1 (1+x)gNc−1 (1+y)gNc!+ CCHE, ep ×N ∑ c=1 (1+x)cNc−1 (1+y)cNc+CBDC-CC, ep ×N ∑ d=1 (1+x)dNc−1 (1+y)dNc+ NMEM ×CMEM, ep ×N ∑ i=1 (1+x)mNc−1 (1+y)mNc          (25) whe e, CBAT, ep , CBMG, ep , CCHE, ep , CBDC-CC, ep , and CMEM, ep a e he eplacemen cos s o he BAT, BMG, CHE, BDC-CC, and MEM, espec i ely, and he N is de ined as ollows [ 14 ]: N =in N−Nc Nc(26) whe e, N and Nc a e he numbe o eplacemen s needed o he sys em componen s and li espan o each sys em componen , espec i ely. The p esen alue o annual uel cos (PV,FUEL) o he IRES is calcula ed as [14]: PV,FUEL =[(CBM ×QBM)+(AFCDG)] × N ∑ i=1 (1+x)i−1 (1+y)i(27) whe e CBM and QBM a e he cos and quan i y o he biomass, espec i ely, and AFCDG is he annual uel consump ion o he DG. 5. The Objec i e Func ion and I s Cons ain s The sys em’s objec i e unc ion, i.e., li e cycle cos (LCC), and i s cons ain s a e discussed as ollows. Ene gies 2022,15, 5176 23 o 50 5.1. Li e Cycle Cos The objec i e unc ion as exp essed in Equa ion (28) was used o calcula e he sys em’s li e cycle cos . The objec i e unc ion is p ima ily dependen on wo in ege decision a iables such as he numbe o ba e ies (NBAT) and PV panels (NPV). min LCC(NPV,NBAT)= min ∑ C=PV,BMG,BAT,ROD,BDC−CC (LCC)C(28) 5.2. Uppe and Lowe Bounds In his s udy, i was p esumed ha he biomass gene a o ope a es as a ixed ene gy esou ce wi h a a ed powe o 5 kW and wo ks daily o i e hou s du ing he peak load demands, i.e., om 6 P.M. o 10 P.M., o gene a e 4 kWh o ene gy pe hou . Hence, i was no bound by any cons ain s. Fu he mo e, he emaining sola ene gy esou ce was subjec o he ollowing cons ain . 0≤NPV ≤NPV−max (29) whe e, NPV is he numbe o PV panels. Fu he mo e, he ba e y bank was subjec ed o he ollowing cons ain . 0≤NBAT ≤NBAT−max (30) whe e, NBAT is he numbe o ba e ies. 5.3. Ba e y Bank Ene gy S o age Limi s The amoun o ene gy s o ed in he ba e y bank a any hou ‘ ’ is de e mined by he ollowing cons ain [45]: EBa _min ≤EBa ( )≤EBa _max (31) The maximum and minimum ene gy s o age le els o he ba e y bank is calcula ed as ollows: EBa _max =NBAT ×VBAT ×SBAT 1000 ×SOCmax−ba (32) EBa _min =NBAT ×VBAT ×SBAT 1000 ×SOCmin−ba (33) whe e, VBAT and SBAT a e he ol age and a ed capaci y (Ah) o he ba e y, espec i ely. The minimum and maximum s a e o cha ges o he ba e y is calcula ed as ollows: SOCmin−ba =1−DOD SOCmax−ba =SOCmin−ba +DOD whe e, DOD is he dep h o discha ge o he ba e y. 5.4. Diesel Gene a o Ope a ing Limi s A highe loads, he diesel gene a o is much mo e e icien . As a esul , he minimum load equi ed o he DG ope a ion is se a 40% o i s a ed capaci y. Acco dingly, he DG uns in he ope a ing mode a e adhe ing o he limi a ions men ioned below [48]: EL( ) ηcon ≥40% o P dg ×∆ (34) whe e EL( ) is he hou ly ene gy demand, ηcon is he e iciency o he con e e , P dg is he a ed powe o he diesel gene a o , and ∆ is he ime pe iod. Ene gies 2022,15, 5176 24 o 50 5.5. Powe Reliabili y Index The powe sys em’s eliabili y is desc ibed as i s abili y o supply powe o a speci ied pe iod o ime unde speci ic condi ions. In his s udy, The IHRES powe eliabili y was assessed using he loss o powe supply p obabili y (LPSP), which is calcula ed by summing he hou s o a powe ou age o he sum o hou ly ene gy demands. The loss o powe supply (LPS) a any hou ‘ ’ is calcula ed as ollows [1]: LPS( ) = EL( ) ηCon −EG( )−[(1−σ)×EBa ( −1)−EBa _min]×η ba (35) The LPSP is calcula ed as ollows [1]: LPSP =∑T =1LPS( ) ∑T =1EL( )(36) Du ing he op imiza ion p ocess, he ollowing cons ain is use ul o analyzing he maximum pe missible loss o powe supply p obabili y (LPSP*). LPSP∗≥LPSP (37) 6. Me hodology 6.1. Load ollowing S a egy The main ea u e o he LF s a egy is ha he DG can sa is y he de iciency load demand when he ba e ies and RE esou ces a e unable o supply he elec ici y demand. The key conce n is ha i jus p o ides he de ici load demand only and does no cha ge he ba e ies. The o e all ope a ion o he LF s a egy is ou lined in he ollowing modes [24]. 6.2. Cycle Cha ging S a egy The CC s a egy is dis inguished by he ac ha he DG u ns on o sa is y he de ici load demand while also s o ing ene gy in he ba e y bank h ough he cha ging p ocess. The o e all ope a ion o he CC s a egy is ou lined in he ollowing modes [49]: The comple e EMS ope a ion was conduc ed in he MATLAB © en i onmen by sim- ula ing he inpu pa ame e s such as echno-economic alues o he componen s, load demand, ambien empe a u e, and sola i adia ion o 8760 h, i.e., o 1 yea . The sys em’s elec ical ene gy demand a any hou ‘ ’ is de e mined as ollows: EL( ) = (ELoad( ) + EROD( ))/ηCon (38) The elec ici y p o ided by he RE esou ces (EG)is compu ed a any hou ‘ ’ as ollows: EG( ) = [EDC( ) + EAC( )]×ηCon (39) whe e he gene a ed AC ene gy (EAC)and DC ene gy (EDC) a e calcula ed as ollows: EDC( ) = EPV( )(40) EAC( ) = EBMG( )(41) Du ing he peak load ime om 6 P.M. o 10 P.M., he biomass gene a o wo ks daily. The minimum and maximum ba e y bank ene gy s o age limi s a e calcula ed as ollows: EBa _min =NBAT ×VBAT ×SBAT 1000 ×SOCmin−ba (42) EBa _max =NBAT ×VBAT ×SBAT 1000 ×SOCmax−ba (43) Ene gies 2022,15, 5176 25 o 50 whe e, SBAT and VBAT a e he a ed capaci y (Ah) and ol age o he ba e y, espec i ely. The minimum and maximum s a e o cha ge (SOC) o he ba e y bank a e es ima ed as ollows: SOCmax−ba =SOCmin−ba +DOD (44) SOCmin−ba =1−DOD (45) whe e, DOD is he dep h o discha ge o he ba e ies. A any hou ‘ ’, he ne ene gy o he sys em is es ima ed as he di e ence be ween he hou ly ene gy gene a ed by he RE esou ces and he p ojec ed load demand: Ene ( ) = EG( )−EL( )(46) Now, he ‘ o ’ loop begins o 8760 h o simula ion. Fo = 1:8760 i Ene ( ) = 0 (47) Mode 1: in his ope a ing mode, he o al ne ene gy p o ided by he sys em is equal o 0, and he ene gy le el o he ba e y bank a ha ime ‘ ’ is equal o he ene gy le el o he p e ious hou . This mode o ope a ion is desc ibed pic o ially in Figu e 10a, which explains ha he swi ches S 1 and S 3 a e in he closed posi ion and he swi ches S 2 , S 4 , and S 5 a e in he open posi ion. The expec ed load demand is me and he e is no powe ou age, which is ma hema ically exp essed as ollows: EBa ( ) = EBa ( −1)(48) LPS( ) = 0 (49) ELoad_supplied( ) = EL( )(50) elsei Ene ( )>0 (51) Ech( ) = EG( )−EL( )(52) i Ech( )≤EBa _max −EBa ( −1)(53) Mode 2: in his ope a ing mode, he RE esou ces i s mee he load demand and hen s o e he p oduced su plus ene gy in he ba e y bank i he ene gy le els in he ba e y bank a e be ween he minimum and maximum ange, i.e., i (EBa _min ≤EBa ( )≤EBa _max) . This mode o ope a ion is desc ibed pic o ially in Figu e 10b, which explains ha he swi ches S 1 , S 2 , and S 3 a e in he closed posi ion and he swi ches S 4 and S 5 a e in he open posi ion. The expec ed load demand is me and he e is no powe ou age, which is ma hema ically exp essed as ollows: EBa ( ) = (1−σ)∗EBa ( −1)+Ech( )∗ηCC ∗η ba (54) LPS( ) = 0 (55) ELoad_supplied( ) = EL( )(56) else Mode 3: in his ope a ing mode, ene gy om he RE esou ces ini ially sa is ies he load demand, and i he ene gy le el o he ba e y bank is a i s maximum limi , i.e., i (EBa ( ) = EBa _max) , hen he su plus ene gy is used o ope a e he dump load. In his mode o ope a ion, as shown in Figu e 10c, S 1 , S 3 , and S 5 swi ches a e in a closed posi ion and S 2 and S 4 swi ches a e in an open posi ion. The expec ed load demand is me and he e is no powe ou age, which is ma hema ically exp essed as ollows: EBa ( ) = EBa _max (57) Ene gies 2022,15, 5176 32 o 50 Figu e 15. Flowcha illus a ing he p ocedu e o e alua ing he op imal IRES sizing wi h SSA. 8. Resul s and Discussion In his s udy, an op imal IHRES con igu a ion was used o supply eshwa e and elec ici y demands o i e u al un-elec i ied illages in he Indian s a e o Odisha. These illages a e en iched wi h RE esou ces such as biomass and sola , which can be used o elec i y hem. Because o he unce ain ies associa ed wi h hese RE sou ces, a eliable ba e y s o age sys em in conjunc ion wi h a diesel gene a o is equi ed o p o ide a con inuous powe supply. The e o e, he s udy ocused on h ee di e en ypes o ba - e y echnologies, namely li hium-ion (Li-Ion), lead-acid (LA), and nickel-i on (Ni-Fe), o p o ide a con inuous powe supply. The pu pose o examining a ious ba e y echnologies is he LA ba e y echnology since i is less expensi e in all egions o he wo ld han all kinds o ba e y echnologies. As a esul , de eloping coun ies such as Pakis an, India, S i Lanka, Bangladesh, e c., a e employing his ba e y echnology o elec i y he s andalone emo e egions wi hou aking in o accoun signi ican d awbacks such as hei du abili y and li espan. This ba e y echnology has a sho e li espan in compa ison o o he ba e y echnologies and i s li espan is dependen on he ambien empe a u e a which he ba e ies a e ins alled. The e o e, e e y h ee o i e yea s, hey mus be eplaced. F equen ly, eplacing ba e ies in emo e egions ia di icul oads causes plen y o echnical, physical, and economic issues. Hence, be o e beginning a p ojec , i is indeed essen ial o unde s and he echnological and economic ea u es o a ba e y echnology such as, echnically: du abili y, high ope a ing Ene gies 2022,15, 5176 33 o 50 empe a u e capabili y, longe i y, and ound ip e iciency, and economically: ope a ion and main enance cos s, eplacemen equency, and capi al cos s [43]. 8.1. Robus ness and His o y o he Ni-Fe Ba e y Technology This s udy p oposed a Ni-Fe ba e y echnology o add ess he a o emen ioned issues. Al hough he Ni-Fe ba e ies a e s ill in he ea ly s ages o de elopmen , hey a e he mos powe ul and eliable ba e y echnology a ailable oday and a e an excellen op ion o o -g id RE and sola applica ions. Ni-Fe ba e ies ha e a ack eco d o mo e han 100 yea s. Thomas Edison in en ed and manu ac u ed Ni-Fe ba e ies in he ea ly 1900s o make hem “much s onge han ba e ies using lead pla es and acid”. In he ea ly 1910s, he i s elec ic ca was ou i ed wi h Ni-Fe ba e ies. While hey we e ne e used o he s a ing ba e ies o in e nal combus ion engines a he pe iod o he au omobile in en ion, hei oo hold was ound in he wen ie h cen u y in many ail oads, o kli s, and s andby powe applica ions. Because o hei long li e, obus ness, and du abili y, Ni-Fe ba e ies ha e been ebo n in he wen y- i s cen u y o use in RE applica ions. In compa ison o many o he ypes o ba e ies, he dep h o discha ge (DOD) o he Ni-Fe ba e ies has no impac on hei li e cycle. As a esul , consume s can discha ge hem up o 80% o hei a ed capaci y and ha e a ba e y li e o 30+ yea s. I is a well-known ac ha i an LA ba e y is o e -discha ged e en once, i s li e ime is signi ican ly educed. This is ue o he majo i y o ba e y echnologies bu no o he Ni-Fe ba e ies because discha ging hem up o 80% o mo e does no sho en hei li e ime. Fu he mo e, Ni-Fe ba e ies can be o e cha ged wi hou losing hei li e expec ancy [50]. 8.2. Technical Compa ison o he Ba e y Technologies Used in he S udy The ollowing a e he echnical cha ac e is ics o he h ee di e en ypes o ba e y echnologies: Li-Ion, Ni-Fe, and LA. 8.2.1. The Li e ime o he Ba e ies The ba e y’s li e ime mainly depends on i s dep h o discha ge (DOD); he DOD simply desc ibes he deg ee o which he ba e y has been discha ged in ela ion o i s o e all capaci y. I he ba e y is ully discha ged, hen i s DOD is 100%. Acco ding o he manu ac u e s, he h ee ba e ies, Ni-Fe, Li-Ion, and LA used in he s udy, ha e di e en li espans depending on hei use o he allowable DODs. • The LA ba e y used in he s udy can be usable in wo allowable DODs, such as 70% and 80%; i i is used a 70% DOD, i s li espan is 3 yea s; i i is used a 80% DOD, hen i s li espan is 2.5 yea s. • The Li-Ion ba e y used in he s udy can be usable in h ee allowable DODs, such as 50%, 70%, and 80%; i i is used a 50% DOD, i s li espan is 15 yea s; i i is used a 70% DOD, i s li espan is 9 yea s; i i is used a 80% DOD, i s li espan is 7.5 yea s. • The Ni-Fe ba e y used in he s udy can be usable in wo allowable DODs, such as 50% and 80%; i i is used a 50% DOD, i s li espan is 30+ yea s; i i is used a 80% DOD, i s li espan is also 30+ yea s. This is why i is he mos obus ba e y echnology because he DOD does no a ec i s li espan and i is mo e sui able o o -g id u al elec i ica ion o people li ing in emo e a eas since i does no need o be eplaced du ing he li espan o he p ojec . 8.2.2. Round T ip E iciency o he Ba e ies In iew o he ound ip e iciency o he ba e ies, he Li-Ion ba e y has he highes e iciency wi h 92%, he second bes is he LA ba e y which has an e iciency o 85%, and he hi d bes is he Ni-Fe ba e y which has an e iciency o 80%. 8.2.3. The Sel -Discha ge Ra e o he Ba e ies The Li-Ion ba e y has a sel -discha ge a e o 0.3%/day, he LA ba e y echnology has a a e o 0.2%/day, and he Ni-Fe ba e y has a a e o 1%/day. Howe e , in e ms o Ene gies 2022,15, 5176 34 o 50 sel -discha ge ene gy losses, he Ni-Fe ba e y may ha e negligible losses. Fo example, i he Ni-Fe ba e y consumes 25 kWh o ene gy pe day, he sel -discha ge ene gy loss is only 0.25 kWh, allowing he emaining 24.75 kWh o be used wi hou any issue. Hence, he sel -discha ge losses wi h his ba e y echnology a e no ha much highe . 8.2.4. Ope a ing Tempe a u e Capabili ies o he Ba e ies Wi h he excep ion o he Ni-Fe ba e y echnology, mos ba e ies do no ha e high- empe a u e capabili ies. The Ni-Fe ba e y o e s wo y- ee se ice in ex eme cold and ho condi ions wi h wo king empe a u es anging om − 30 ◦ C o +60 ◦ C. The second bes is he Li-Ion ba e y echnology wi h ope a ing empe a u es anging om − 20 ◦ C o +50 ◦ C. Finally, he hi d bes is he LA ba e y echnology wi h ope a ing empe a u es anging om −20 ◦C o +45 ◦C. 8.2.5. Replacemen F equency o he Ba e ies du ing he Li espan o he P ojec The cu en s udy assumed a p ojec li e o wen y- i e yea s, and i he LA ba e y was used in he s udy, i mus be eplaced 9 o 10 imes a 70% and a 80% usage o DODs, espec i ely. I he s udy conside ed Li-Ion ba e ies, hey would need o be eplaced 2, 3, and 4 imes, espec i ely, a 50%, a 70%, and a 80% usage o DODs. I he s udy conside ed he Ni-Fe ba e y, no eplacemen would be equi ed du ing he li espan o he p ojec , ei he a 50% usage o DOD o a 80% usage o DOD, and i would ope a e o ano he i e mo e yea s ou side o he li espan o he p ojec . 8.2.6. Cycle Li e o he Ba e ies The numbe o cha ging and discha ging cycles a ba e y can comple e be o e losing i s capaci y is e e ed o as i s cycle li e. In his s udy, he LA ba e y echnology has wo-cycle li es, such as 800 and 750 cycles a 70% and a 80% DODs, espec i ely. The Li-Ion ba e y has h ee cycle li es, such as 5000, 3000, and 2500 cycles a 50%, a 70%, and a 80% DODs, espec i ely. The Ni-Fe ba e y has wo cycle li es, such as 11,000+ and 11,000+ cycles a 50% and a 80% DODs, espec i ely. Compa ed o o he ba e y echnologies, he cycle li e o he Ni-Fe ba e y echnology is much highe . 8.3. Modelling o Di e en Con igu a ions Using Ba e y Technologies and RE Resou ces The Li-Ion ba e y can wo k a h ee di e en DODs, i.e., a 50%, a 70%, and a 80%; he e- o e, h ee con igu a ions we e modelled using he Li-Ion ba e y echnology: PV/BMG/DG/Li- Ion a 50% DOD, PV/BMG/DG/Li-Ion a 70% DOD, and PV/BMG/DG/Li-Ion a 80% DOD. Simila ly, he LA ba e y can wo k a wo DODs such as a 70% and a 80%; he e o e, wo con igu a ions we e modelled using he LA ba e y echnology: PV/BMG/DG/LA a 70% DOD and PV/BMG/DG/LA a 80% DOD. Simila ly, he Ni-Fe ba e y can wo k a wo di - e en DODs, such as a 50% and a 80%; howe e , i s p ima y s eng h is ha i has a li espan o mo e han 30 yea s a bo h he DODs. Hence, o he cu en s udy, he echno-economic analysis wi h Ni-Fe ba e y echnology was accomplished wi h a 80% DOD only. As a esul , a PV/BMG/DG/Ni-Fe a 80% DOD con igu a ion was modelled. In o de o elec i y he s udy a ea wi h an op imum con igu a ion, he six con igu a- ions men ioned abo e we e e alua ed a an LPSP alue o 0% wi h LF and CC s a egies using he HPRALC-based scena io, i.e., wi hou DSM. To simpli y he analysis, a e de- e mining he op imal con igu a ion om he HPRALC-based scena io, i was u he e alua ed wi h LF and CC s a egies using MPRAMC and LPRAHC-based scena ios, i.e., wi h DSM a an LPSP alue o 0%. 8.4. Op imiza ion Algo i hms and Componen s Technical and Cos Values The p oposed SSA algo i hm’s con e gence e iciency and obus ness we e compa ed o hose o eigh o he well-known and p o en algo i hms, namely: PSO, GA, GWO, DE, ALO, MFO, DA, and GOA in he MATLAB © en i onmen wi h a popula ion o 100 and 100 i e a ions. The nine algo i hms’ con ol pa ame e alues a e gi en in Table 4. All Ene gies 2022,15, 5176 35 o 50 componen s’ cos and echnical alues used in he s udy a e gi en in Tables 5–7. The peak load demand in he HPRALC-based scena io was 79.18 kW, hence he con e e - a ed powe was 87 kW ( o sa e y easons, he con e e ’s powe a ing should be 10% g ea e han he peak load demand). The peak load demand in he MPRAMC-based scena io was 54.44 kW. As a esul , o he MPRAMC-based scena io, he con e e - a ed powe was conside ed o be 60 kW. The peak load demand o he LPRAHC-based scena io was 52.02 kW. Hence, o MPRAMC-based scena ios, he con e e - a ed powe was aken as 57 kW. Simila ly, in o de o mee he peak load demand o he HPRALC-based scena io, he DG- a ed powe was conside ed o be 80 kW, whe eas in o de o mee he peak load demand o he MPRAMC and LPRAHC-based scena ios, he DG- a ed powe was conside ed o be 60 kW because he comme cially a ailable a ed powe o he DG o hese wo scena ios is only 60 kW. Tables 8and 9gi e he op imal esul s o he six con igu a ions desc ibed abo e wi h LF and CC s a egies, espec i ely, using he nine me aheu is ic algo i hms a an LPSP alue o 0% using he HPRALC-based scena io. The op imal con igu a ion om he HPRALC-based scena io was u he e alua ed wi h LF and CC s a egies a an LPSP alue o 0% wi h MPRAMC and LPRAHC-based scena ios and hei op imal esul s p o ided in Tables 10–13, espec i ely. Table 4. Con ol pa ame e s o he algo i hms. Algo i hm Pa ame e s GA Pop I e max µCR 100 100 0.1 0.9 PSO Pop I e max wmax wmin c1 c2 100 100 0.9 0.2 2 2 DE Pop I e max F CR 100 100 0.5 0.9 GWO Pop I e max a C1 C2 C3 100 100 0 o 2 2 × and(0,1) 2 × and(0,1) 2 × and(0,1) ALO Pop I e max I weigh s 100 100 1 (1,5,3,15,8,1) DA Pop I e max w s a c e 100 100 0.9 o 0.2 0.1 0.1 0.7 1 1 MFO Pop I e max a b 100 100 −1 o −2 1 GOA Pop I e max cmax cmin 100 100 1 0.00004 SSA Pop I e max c1 c2 c3 100 100 and(0,1) and(0,1) and(0,1) Ene gies 2022,15, 5176 36 o 50 Table 5. Ba e ies’ echno-economic pa ame e s and hei alues. Ba e y Type Lead-Acid (PbSO4) Li hium I on Phospha e (LiFePO4) Nickel-I on (Ni-Fe) Manu ac u e T ojan [51] Vic on [52] I on Edison [50] Model SSIG 06 490 LFP-12.8/200-a TN 1000 Nominal capaci y (SBAT) 490 Ah 300 Ah 1000 Ah Nominal ol age (VBAT) 6 V 12.8 V 1.2 V Round ip e iciency (η ba )85% 92% 80% Li espan in yea s 3 yea s a 70% DOD 15 yea s a 50% DOD 30 yea s+ a 50% DOD 2.5 yea s a 80% DOD 9 yea s a 70% DOD 30 yea s+ a 80% DOD 7.5 yea s a 80% DOD Sel -discha ge a e (%/day) (σ)0.3% 0.2% 1% Capi al cos (CC) in USD USD 410 USD 3317 USD 1057 Annual O&M cos in USD 2.5% o CC No main enance 2% o CC Ope a ing empe a u e −20◦C o +45 ◦C−20◦C o +50 ◦C−30 ◦C o +60 ◦C Cycle li e o he ba e ies 800 cycles a 70% DOD 5000 cycles a 50% DOD 11,000+ cycles a 50% DOD 750 cycles a 80% DOD 3000 cycles a 70% DOD 11,000+ Cycles a 80% DOD 2500 cycles a 80% DOD Table 6. Technical and cos alues o he biomass gene a o . Manu ac u e [44] Ene sol Bio Powe Wa e Tank Capaci y [44] 300 L Ra ed Powe o BMG [44] 5 kW F equency [44] 50 Hz Fuel Mode [44] 100% P oduce Gas Based Li e ime o BMG [14] 15,000 h Plan Size (L * W * H) [44] 10 * 9 * 6 Fee BMG Capi al cos [44] USD 4505 Numbe o Phases [44] Single Phase AO&M cos o BMG [14] USD 27 Ra ed Cu en [44] 26 Ampe es Quan i y o biomass 9 /yea Al e na o Make [44] Ki loska Manu ac u e s Cos o biomass [14] 15 USD/ Vol age [44] 230 V, AC E iciency o BMG [14] 20% Table 7. Technical and cos alues o he IHRES. Pa ame e s Value Pa ame e s Value P ojec li e ime 25 yea s No. o MEM Repl./yea [1] 2 Nominal in e es a e [14] 13% MEM Replacemen cos [1]0.06 USD/m3 In la ion a e [53] 5% Repl. cos o chemicals [1]0.06 USD/m3 Manu ac u e o PV Panel [54] Vik am sola Ra ed powe o con e e o HPRALC scena io 87 kW Model No. o PV Panel [54] Some a 385 Ra ed powe o con e e o MPRAMC scena io 60 kW Ra ed powe o PV Panel [54] 385 Wp Ra ed powe o con e e o LPRAHC scena io 57 kW Li e ime o PV Panel [54] 25 yea s Li e ime o con e e [14] 10 yea s Capi al cos o PV Panel [54] USD 128 C&R o con e e pe kW USD 108 AO&M cos o PV Panel [14] USD 3.2 AO&M cos o con e e [14] USD 15 Mechanical s uc u e cos o PV Panel [55]USD 41 E iciency o con e e [14] 95% Ene gies 2022,15, 5176 37 o 50 Table 7. Con . Pa ame e s Value Pa ame e s Value Li e ime o mechanical s uc u e o PV panel [55]25 yea s DG (Company: Cummins, Model No: C100D5) o HPRALC scena io [56] 100 KVA 80 kW ROD capi al cos (1 m3/day) [1]USD 532 C&R o DG o HPRALC scena io [56] USD 9144 Capi al cos o Wa e ank [1]256 USD/m3DG (Company: Ki loska , Model No: KEC-T75-II) o MPRAMC and LPRAHC scena ios [57] 75 KVA 60 kW Capi al cos o memb ane [1]0.06 USD/m3 C&R o DG o MPRAMC and LPRAHC scena ios [57]USD 6858 Capi al cos o chemicals [1]0.06 USD/m3Diesel P ice USD 1.08 AO&M cos o ROD [1]0.2 USD/m3AO&M cos o DG [46] 3% o TAOHDG” Table 8. Op imiza ion esul s o he HPRALC-based IHRESs using LF s a egy a LPSP alue o 0%. Con igu a ion Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 1282 1280 1281 1275 1280 1280 1280 1280 1275 BMG/ NBAT 917 917 879 893 917 917 917 917 893 DG/ AFC 1574 887 2192 1600 887 887 887 887 1600 Ni-Fe ACO24251 2395 5917 4320 2395 2395 2395 2395 4320 a DOD = 80% LCC (USD) 918,176 918,040 921,542 916,728 918,040 918,040 918,040 918,040 916,728 PV/ NPV 1243 1237 1237 1238 1237 1237 1237 1237 1237 BMG/ NBAT 342 340 340 340 340 340 340 340 340 DG/ AFC 7338 7499 7499 7498 7499 7499 7499 7499 7499 LA ACO219,813 20,247 20,247 20,246 20,247 20,247 20,247 20,247 20,247 a DOD = 70% LCC (USD) 1,516,213 1,511,891 1,511,891 1,512,129 1,511,891 1,511,891 1,511,891 1,511,891 1,511,891 PV/ NPV 1237 1237 1301 1237 1237 1237 1237 1237 1237 BMG/ NBAT 297 297 299 297 297 297 297 297 297 DG/ AFC 7590 7590 7123 7590 7590 7590 7590 7590 7590 LA ACO220,494 20,494 19,232 20,494 20,494 20,494 20,494 20,494 20,494 a DOD = 80% LCC (USD) 1,492,491 1,492,491 1,506,249 1,492,491 1,492,491 1,492,491 1,492,491 1,492,491 1,492,491 PV/ NPV 1136 1136 1183 1138 1136 1136 1136 1136 1136 BMG/ NBAT 339 339 338 339 339 339 339 339 339 DG/ AFC 7859 7859 7895 7858 7859 7859 7859 7859 7859 Li-Ion ACO221,220 21,220 21,315 21,216 21,220 21,220 21,220 21,220 21,220 a DOD = 50% LCC (USD) 2,397,086 2,397,086 2,403,931 2,397,563 2,397,086 2,397,086 2,397,086 2,397,086 2,397,086 PV/ NPV 1135 1135 1135 1135 1135 1150 1211 1135 1135 BMG/ NBAT 242 242 242 242 242 242 244 242 242 DG/ AFC 7891 7891 7891 7891 7891 7781 7172 7891 7891 Li-Ion ACO221,307 21,307 21,307 21,307 21,307 21,009 19,364 21,307 21,307 a DOD = 70% LCC (USD) 2,446,102 2,446,102 2,446,102 2,446,102 2,446,102 2,447,773 2,467,615 2,446,102 2,446,102 PV/ NPV 1135 1135 1135 1135 1203 1326 1135 1199 1135 BMG/ NBAT 212 212 212 212 211 210 212 213 212 DG/ AFC 7771 7771 7771 7771 7837 7723 7771 7337 7771 Li-Ion ACO220,981 20,981 20,981 20,981 21,161 20,853 20,981 19,810 20,981 a DOD = 80% LCC (USD) 2,605,751 2,605,751 2,605,751 2,605,751 2,611,090 2,624,535 2,605,751 2,621,122 2,605,751 Ene gies 2022,15, 5176 38 o 50 Table 9. Op imiza ion esul s o he HPRALC-based IHRES using CC s a egy a LPSP alue o 0%. Con igu a ion Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 1268 1265 1332 1268 1265 1265 1265 1265 1265 BMG/ NBAT 933 928 925 933 928 928 928 928 928 DG/ AFC 1045 871 821 1045 871 871 871 871 871 Ni-Fe ACO22822 2352 2217 2822 2352 2352 2352 2352 2352 a DOD = 80% LCC (USD) 927,077 926,800 934,345 927,077 926,800 926,800 926,800 926,800 926,800 PV/ NPV 1178 1202 1178 1178 1178 1178 1219 1178 1178 BMG/ NBAT 347 394 347 347 347 347 346 347 347 DG/ AFC 7839 5823 7839 7839 7839 7839 7864 7839 7839 LA ACO221,166 15,723 21,166 21,166 21,166 21,166 21,233 21,166 21,166 a DOD = 70% LCC (USD) 1,525,952 1,629,822 1,525,952 1,525,952 1,525,952 1,525,952 1,531,881 1,525,952 1,525,952 PV/ NPV 1174 1174 1226 1174 1174 1213 1174 1174 1174 BMG/ NBAT 305 305 306 305 305 304 305 305 305 DG/ AFC 7640 7640 7192 7640 7640 7615 7640 7640 7640 LA ACO220,628 20,628 19,419 20,628 20,628 20,561 20,628 20,628 20,628 a DOD = 80% LCC (USD) 1,503,917 1,503,917 1,511,744 1,503,917 1,503,917 1,509,935 1,503,917 1,503,917 1,503,917 PV/ NPV 1098 1098 1133 1098 1098 1082 1098 1098 1098 BMG/ NBAT 340 340 340 340 340 341 340 340 340 DG/ AFC 9083 9083 9009 9083 9083 9059 9083 9083 9083 Li-Ion ACO224,525 24,525 24,324 24,525 24,525 24,458 24,525 24,525 24,525 a DOD = 50% LCC (USD) 2,416,367 2,416,367 2,423,727 2,416,367 2,416,367 2,417,493 2,416,367 2,416,367 2,416,367 PV/ NPV 1084 1084 1084 1084 1195 1930 1195 1225 1084 BMG/ NBAT 243 243 243 243 241 235 241 241 243 DG/ AFC 9083 9083 9083 9083 9009 7366 9009 8735 9083 Li-Ion ACO224,525 24,525 24,525 24,525 24,324 19,889 24,324 23,585 24,525 a DOD = 70% LCC (USD) 2,463,960 2,463,960 2,463,960 2,463,960 2,474,258 2,579,041 2,474,258 2,476,585 2,463,960 PV/ NPV 1177 1116 1116 1176 1486 1516 1116 1176 1116 BMG/ NBAT 211 212 212 211 208 208 212 211 212 DG/ AFC 9083 9108 9108 9083 8138 7964 9108 9083 9108 Li-Ion ACO224,525 24,593 24,593 24,525 21,972 21,502 24,593 24,525 24,593 a DOD = 80% LCC (USD) 2,629,251 2,627,111 2,627,111 2,629,041 2,645,568 2,648,554 2,627,111 2,629,041 2,627,111 Table 10. Op imiza ion esul s o he MPRAMC-based IHRES using LF s a egy a LPSP alue o 0%. Con igu a ion Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 904 904 921 904 904 904 904 904 904 BMG/ NBAT 569 569 563 569 569 569 569 569 569 DG/ AFC 854 854 1010 854 854 854 854 854 854 Ni-Fe ACO22306 2306 2726 2306 2306 2306 2306 2306 2306 a DOD = 80% LCC (USD) 613,841 613,841 617,660 613,841 613,841 613,841 613,841 613,841 613,841 Table 11. Op imiza ion esul s o he MPRAMC-based IHRES using CC s a egy a LPSP alue o 0%. Con igu a ion Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 877 890 903 891 890 877 890 877 890 BMG/ NBAT 594 599 588 596 599 594 599 594 599 DG/ AFC 989 653 915 747 653 989 653 989 653 Ni-Fe ACO22671 1764 2469 2016 1764 2671 1764 2671 1764 a DOD = 80% LCC (USD) 623,772 623,484 625,477 623,808 623,484 623,772 623,484 623,772 623,484 Ene gies 2022,15, 5176 39 o 50 Table 12. Op imiza ion esul s o he LPRAHC-based IHRES using LF s a egy a LPSP alue o 0%. Con igu a ion Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 814 814 834 814 814 814 814 814 814 BMG/ NBAT 450 450 458 450 450 450 450 450 450 DG/ AFC 837 837 532 837 837 837 837 837 837 Ni-Fe ACO22259 2259 1438 2259 2259 2259 2259 2259 2259 a DOD = 80% LCC (USD) 522,945 522,945 526,708 522,945 522,945 522,945 522,945 522,945 522,945 Table 13. Op imiza ion esul s o he LPRAHC-based IHRES using CC s a egy a LPSP alue o 0%. Con igu a ion Q&C GA PSO DE GWO ALO DA MFO GOA SSA PV/ NPV 791 791 794 791 791 791 801 791 791 BMG/ NBAT 496 496 495 496 496 496 487 496 496 DG/ AFC 131 131 149 131 131 131 280 131 131 Ni-Fe ACO2353 353 403 353 353 353 756 353 353 a DOD = 80% LCC (USD) 529,795 529,795 530,336 529,795 529,795 529,795 530,042 529,795 529,795 8.5. Op imal Con igu a ion om he LA Ba e y-Based IHRESs The LA ba e y can ope a e a wo di e en DODs, i.e., a 70% and a 80%. In such a way ha a o al o wo con igu a ions we e modelled using LA ba e y echnology and which we e es ed wi h wo di e en dispa ch s a egies such as LF and CC, hei co - esponding esul s using he HPRALC-based scena io a e p o ided in Tables 8and 9, espec i ely. F om he esul s, i was obse ed ha he LA ba e y-based IHRES a 80% DOD wi h LF s a egy is economically easible as compa ed o i s CC s a egy, as well as LA ba e y-based IHRES a 70% DOD using LF and CC s a egies, wi h an LCC o USD 1,492,491. I is abou 1% lowe han i s CC s a egy’s LCC, and i is abou 1% and 2% lowe han he LCCs o LA ba e y-based IHRES a 70% DOD using LF and CC s a e- gies, espec i ely. The co esponding op imum componen alues we e N PV = 1237 and N BAT (LA) = 297. The e o e, o u he compa isons wi h o he ba e y-based IHRESs, he LA ba e y-based IHRES a 80% DOD wi h LF s a egy was aken in o accoun . The E ec o Dispa ch S a egies on LA Ba e y-Based IHRESs The abo e discussion e eals ha he LA ba e y-based IHRES a 80% DOD wi h LF s a egy was iden i ied as an op imal con igu a ion; i s annual uel consump ion (AFC) and annual ca bon dioxide (ACO 2 ) emissions compa ed o o he dispa ch s a egies (LF and CC) o he emaining LA ba e y-based IHRESs a e discussed as ollows: F om he esul s gi en in Tables 8and 9, i is obse ed ha he AFC o he LA ba e y- based IHRES a 80% DOD wi h LF s a egy was 7590 L, which is 50 L less han i s CC s a egy’s AFC, as well as 91 L mo e and 249 L less han he LA ba e y-based IHRES a 70% DOD’s LF and CC s a egies’ AFCs, espec i ely. F om he esul s gi en in Tables 8and 9, i is obse ed ha he ACO 2 emissions o he LA ba e y-based IHRES a 80% DOD wi h LF s a egy was 20,494 kg, which is 134 kg less han he ACO 2 emissions o i s CC s a egy, as well as 316 and 247 kg mo e and 672 kg less han he ACO 2 emissions o he LA ba e y-based IHRES a 70% DOD’s LF and CC s a egies, espec i ely. 8.6. Op imal Con igu a ion om he Li-Ion Ba e y-Based IHRESs The Li-Ion ba e y can ope a e a h ee di e en DODs, i.e., a 50%, a 70%, and a 80%. In such a way ha a o al o h ee con igu a ions we e modelled using Li-Ion ba e y echnology and which we e es ed wi h wo dispa ch s a egies such as LF and CC, hei co esponding Ene gies 2022,15, 5176 40 o 50 op imal esul s using he HPRALC-based scena io a e p o ided in Tables 8and 9, espec i ely. F om he esul s, i was obse ed ha he Li-Ion ba e y-based IHRES a 50% DOD wi h LF s a egy is economically easible as compa ed o i s CC s a egy, as well as Li-Ion ba e y-based IHRESs a 70% and a 80% DOD’s LF and CC s a egies, wi h an LCC o USD 2,397,086. I is abou 1% lowe han i s CC s a egy’s LCC, and i is abou 2%, 3%, 8%, and 9% lowe han he LCCs o Li-Ion ba e y-based IHRESs a 70% and a 80% DOD’s LF and CC s a egies. The co esponding op imum componen alues o N PV and N BAT (Li-Ion) we e 1136 and 339, espec i ely. The e o e, o u he compa isons wi h o he ba e y-based IHRESs, he Li-Ion ba e y-based IHRES a 50% DOD wi h LF s a egy was aken in o accoun . The E ec o Dispa ch S a egies on Li-Ion Ba e y-Based IHRESs The abo e discussion e eals ha he Li-Ion ba e y-based IHRES a 50% DOD wi h LF s a egy was iden i ied as an op imal con igu a ion; i s AFC and ACO 2 emissions compa ed o o he dispa ch s a egies (LF and CC) o he emaining Li-Ion ba e y-based IHRESs a e discussed as ollows: F om he esul s gi en in Tables 8and 9, i is obse ed ha he AFC o he Li-Ion ba e y-based IHRES a 50% DOD wi h LF s a egy was 7859 L, which is 1224 L less han i s CC s a egy’s AFC and 32 and 1224 L less han he Li-Ion ba e y-based IHRES a 70% DOD’s LF and CC s a egies’ AFCs, espec i ely, as well as 88 L mo e and 1249 L less han he Li-Ion ba e y-based IHRES a 80% DOD’s LF and CC s a egies’ AFCs, espec i ely. F om he esul s gi en in Tables 8and 9, i is obse ed ha he ACO 2 emissions o he Li-Ion ba e y-based IHRES a 50% DOD wi h LF s a egy was 21,220 kg, which is 3305 kg less han i s CC s a egy’s ACO 2 emissions and 87 kg and 3305 kg less han he Li-Ion ba e y-based IHRES a 70% DOD’s LF and CC s a egies’ ACO 2 emissions, espec i ely, as well as 239 kg mo e and 3373 kg less han he Li-Ion ba e y-based IHRES a 80% DOD’s LF and CC s a egies’ ACO2emissions, espec i ely. 8.7. Op imal Con igu a ion om he Ni-Fe Ba e y-Based IHRESs The Ni-Fe ba e y can ope a e a wo di e en DODs, such as a 50% and a 80%, and i has a li espan o mo e han 30+ yea s a bo h he DODs. The e o e, he analysis was conduc ed a 80% DOD only wi h wo di e en dispa ch s a egies such as LF and CC, and i s co esponding op imal esul s using he HPRALC-based scena io a e p o ided in Tables 8and 9, espec i ely. F om he esul s, i is obse ed ha he Ni-Fe ba e y- based IHRES a 80% DOD wi h LF s a egy was iden i ied as an op imal con igu a ion as compa ed o i s CC s a egy, wi h an LCC o USD 916,728. I is abou 1% lowe han i s CC s a egy’s LCC. The co esponding op imum componen alues we e N PV = 1275 and N BAT (Ni-Fe) = 893. The e o e, o u he compa isons wi h o he ba e y-based IHRESs, he Ni-Fe ba e y-based IHRES a 80% DOD wi h LF s a egy was conside ed. The E ec o Dispa ch S a egies on Ni-Fe Ba e y-Based IHRESs The abo e discussion e eals ha he Ni-Fe ba e y-based IHRES a 80% DOD wi h CC s a egy was iden i ied as an op imal con igu a ion; i s AFC and ACO 2 emissions compa ed o i s LF s a egy a e discussed as ollows: F om he esul s gi en in Tables 8and 9, i is obse ed ha he AFC o he Ni-Fe ba e y-based IHRES a 80% DOD wi h LF s a egy was 1600 L, which is 729 L mo e han i s CC s a egy’s AFC. F om he esul s gi en in Tables 8and 9, i is obse ed ha he ACO 2 emission o he Ni-Fe ba e y-based IHRES a 80% DOD wi h LF s a egy was 4320 kg, which is 1968 kg mo e han i s CC s a egy’s ACO2emissions. Ene gies 2022,15, 5176 41 o 50 8.8. The Sys em Pe o mance wi h Di e en Ba e y Technologies Using HPRALC, MPRAMC, and LPRAHC-Based Scena ios The ollowing desc ibes he impac o di e en ba e y echnologies and dispa ch s a egies on e alua ing an op imal con igu a ion using h ee e iciency-based scena ios: HPRALC, MPRAMC, and LPRAHC. 8.8.1. Low-E iciency Appliance Usage-Based Scena io (HPRALC) (wi hou DSM) A low-e iciency appliance usage-based scena io e e s o he usage o high powe a ed appliances o low cos (HPRALC) by he consume s, which alls unde he concep wi hou DSM. Acco ding o he esul s gi en in Tables 8and 9 o he HPRALC-based scena io, i is obse ed ha he Ni-Fe ba e y-based IHRES a 80% DOD wi h LF s a egy (base case) p o ided an LCC o USD 916,728. I is an op imal alue when compa ed o o he ba e y-based IHRES LCCs wi h di e en dispa ch s a egies. The LA ba e y-based IHRES a 80% DOD wi h LF s a egy p o ided an op imal LCC o USD 1,492,491, which is abou 63% highe han he base case LCC. The Li-Ion ba e y-based IHRES a 50% DOD wi h LF s a egy p o ided an op imal LCC o USD 2,397,086, which is abou 162% highe han he base case LCC. The E ec o Dispa ch S a egies wi h Di e en Ba e y Technologies Using Low-E iciency Appliance Usage-Based Scena io (HPRALC) (wi hou DSM) F om he esul s gi en in Tables 8and 9, i is obse ed ha he AFC o he Ni-Fe ba e y-based IHRES a 80% DOD wi h LF s a egy was 1600 L, which is 5990 L less han he AFC o LA ba e y-based IHRES a 80% DOD’s LF s a egy, and i is 6259 L less han he AFC o Li-Ion ba e y-based IHRES a 50% DOD’s LF s a egy. F om he esul s gi en in Tables 8and 9, i is obse ed ha he ACO 2 emission o he Ni-Fe ba e y-based IHRES a 80% DOD wi h LF s a egy was 4320 kg, which is 16,174 kg less han he ACO 2 emissions o LA ba e y-based IHRES a 80% DOD’s LF s a egy, and i is 16,900 kg less han he ACO 2 emission o he Li-Ion ba e y-based IHRES a 50% DOD’s LF s a egy. Finally, i was ound ha he Ni-Fe ba e y-based IHRES wi h LF s a egy is mo e sui able o elec i ying he s udy a ea. I is clea om he p eceding Sec ion 8.8.1 ha Ni-Fe ba e y-based IHRES wi h LF s a egy is mo e economically easible as compa ed o i s CC s a egy and o he ba e y-based IHRESs wi h di e en dispa ch s a egies, and i is also mo e eco- iendly in e ms o annual uel consump ion and ca bon emissions due o i s lowe uel consump ion and ca bon emissions as compa ed o o he ba e y-based IHRESs wi h di e en dispa ch s a egies. The e o e, IHRESs based on LA and Li-Ion ba e ies a e no conside ed o u he analysis in Sec ion 8.8.2 (MPRAMC-based scena io) and Sec ion 8.8.3 (LPRAHC-based scena io), since i is clea om he abo e discussion ha hese wo ba e y echnologies a e no economically and en i onmen ally easible when compa ed o he Ni-Fe ba e y echnology. 8.8.2. The E ec o Ni-Fe Ba e y-Based IHRES Using Medium-E iciency Appliance Usage-Based Scena io (MPRAMC) (wi h DSM) F om he esul s o he low-e iciency appliance usage-based scena io (HPRALC), i was iden i ied ha he Ni-Fe ba e y-based IHRES wi h LF s a egy (base case) p o ided a minimum LCC when compa ed o i s CC s a egy, as well as o he ba e y-based IHRESs wi h di e en dispa ch s a egies. The e o e, his con igu a ion was u he analyzed wi h he medium-e iciency appliance usage-based scena io, i.e., wi h DSM. The medium- e iciency appliance usage-based scena io e e s o he usage o medium powe a ed appliances o mode a e cos (MPRAMC) by he consume s and i is a pa o he concep o ene gy conse a ion-based DSM. F om he esul s gi en in Tables 10 and 11, i is obse ed ha he base case LCC wi h he MPRAMC-based scena io wi h LF s a egy was USD 613,841, which is abou 2% lowe han i s CC s a egy’s LCC, as well as abou 33% lowe han he LCC using he HPRALC-based scena io wi h LF s a egy. The cu en scena io op imum componen alues a e N PV = 904 and N BAT (Ni-Fe) = 569. I hese alues we e Ene gies 2022,15, 5176 48 o 50 di e en ypes o ba e y echnologies such as li hium-ion (Li-Ion), nickel-i on (Ni-Fe), and lead-acid (LA) was conside ed in o de o p o ide a con inuous powe supply in conjunc ion wi h a diesel gene a o (DG). In o de o ind ou an op imal con igu a ion o elec i y he s udy a ea, six di e en con igu a ions we e modelled using a ailable RE esou ces and ba e y echnologies. Ini- ially, hese six con igu a ions we e e alua ed wi h load ollowing (LF) and cycle cha ging (CC) s a egies using he low-e iciency appliance usage-based scena io in he MATLAB © en i onmen a an LPSP alue o 0% wi h nine me aheu is ic algo i hms such as pa icle swa m op imiza ion, g ey wol op imiza ion, gene ic algo i hm, an lion op imiza ion, di - e en ial e olu iona y algo i hm, mo h lame op imiza ion, d agon ly algo i hm, g asshop- pe op imiza ion algo i hm, and Salp Swa m Algo i hm. Acco ding o he esul s, he Salp Swa m Algo i hm showed i s con e gence and obus ness e iciencies in compa ison o o he algo i hms in o de o ind he global bes op imal alues. F om he esul s o he low- e iciency appliance usage-based scena io, he Ni-Fe ba e y-based IHRES wi h LF s a egy was ound o be an op imal con igu a ion. This was u he e alua ed wi h medium and high-e iciency appliance usage-based scena ios. The summa y o hese esul s a e lis ed as ollows: The Ni-Fe ba e y-based IHRES wi h LF s a egy using he low-e iciency appliance usage-based scena io, i.e., wi hou DSM, ob ained an LCC o USD 916,728, which is abou 39% and 62% lowe han he LCCs o LA (a 80% DOD) and Li-Ion (a 50% DOD) ba e y- based IHRES’s LF s a egies, espec i ely. The Ni-Fe ba e y-based IHRES wi h LF s a egy using he medium-e iciency appli- ance usage-based scena io, i.e., wi h DSM ob ained an LCC o USD 613,841, which is abou 33% lowe han i s LCC using low-e iciency appliance usage-based scena io. The Ni-Fe ba e y-based IHRES wi h LF s a egy using he high-e iciency appliance usage- based scena io, i.e., wi h DSM, ob ained an LCC o USD 522,945, which is abou 43% and 15% lowe han i s low and medium-e iciency appliance usage-based scena ios, espec i ely. Finally, he sensi i i y analysis was pe o med by a ying he in e es a e, biomass oliage collec ion a e, and diesel p ices as compa ed o he o he pa ame e s, and he e ec o he in e es a e was ound o ha e a majo impac on he sys em pe o mance. The cu en s udy ocused on demand-side managemen based on ene gy conse a ion. Using his s udy, powe consump ion in u al households as well as powe p oduc ion componen s in he s udy a ea can be educed signi ican ly. As a esul , ene gy bills and o al in es men cos s in he s udy a ea can be educed. In u u e s udies, wi h p ope planning, peak load demands can be educed by shi ing non-peak load demand pe iods using ene gy conse a ion-based managemen . This me hodology can signi ican ly educe he in es men cos o he speci ic s udy a ea. Au ho Con ibu ions: Concep ualiza ion, P.P.K. and R.S.S.N.; me hodology, P.P.K. and V.S.; so wa e, P.P.K. and S.A.S.; alida ion, M.A.H. and M.J.; o mal analysis, P.P.K.; in es iga ion, P.P.K. and M.J.; esou ces, P.P.K. and R.S.S.N.; da a cu a ion, V.S. and M.J.; w i ing—o iginal d a p epa a ion, P.P.K.; w i ing— e iew and edi ing, M.A.H. and S.A.S.; isualiza ion, P.P.K. and R.S.S.N.; supe ision, R.G. and Z.L.; p ojec adminis a ion, M.J. and R.G.; unding acquisi ion, R.G. and Z.L. All au ho s ha e ead and ag eed o he published e sion o he manusc ip . Funding: This esea ch was unded by SGS G an om VSB—Technical Uni e si y o Os a a unde g an numbe SP2022/21. Ins i u ional Re iew Boa d S a emen : No applicable. In o med Consen S a emen : No applicable. Da a A ailabili y S a emen : Da a a ailable on eques on co espondence o i s au ho . Con lic s o In e es : The au ho s decla e no con lic o in e es . Ene gies 2022,15, 5176 49 o 50 Re e ences 1. Maleki, A. 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