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
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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
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