ECONOMIC AND ENVIRONMENTAL ASSESSMENT OF
RENEWABLE ENERGY AND ENERGY STORAGE INTEGRATION IN
STANDALONE POLYGENERATION SYSTEMS FOR RESIDENTIAL
BUILDINGS
Edwin S. Pin o, Luis M. Se a, Ana Láza o
GITSE I3A, Depa men o mechanical enginee ing, Uni e si y o Za agoza, Spain
Abs ac
Ene gy consump ion and CO2 emissions in esiden ial sec o plays an impo an ole o ace he clima e change.
Technologies and s a egies which allow he educ ion o uel consump ion and CO2 emissions in ene gy supply
sys ems o esiden ial buildings a e equi ed. This wo k analyses he in eg a ion o di e en enewable ene gy
and ene gy s o age echnologies o a esiden ial building loca ed in Za agoza, Spain. I is selec ed a s andalone
ene gy sys em in o de o s udy in a sys ema ic way how di e en echnologies in e ac and a ec he op imal
design, om con en ional o polygene a ion sys ems. A Mixed In ege Linea P og amming (MILP) model was
de eloped o op imize he sys em om he economic poin o iew whe eas CO2 emissions a e calcula ed
simul aneously. Resul s show ha pho o ol aic echnology p o ides a ema kable educ ion o cos s and along
wi h cogene a ion allow a signi ican CO2 emissions educ ion as well. In addi ion, i was de e mined he syne gies
o di e en echnologies, e.g. ba e ies capaci y is educed when cogene a ion and he mal ene gy s o age a e
conside ed. Fu he mo e, a sensi i i y analysis o he numbe o cycles o he Ion Li hium ba e ies was ca ied
ou o show i s compe i i eness wi h espec o lead acid ba e ies.
Keywo ds: Polygene a ion, MILP, Ene gy s o age, Renewable Ene gy, Ene gy sys ems in eg a ion.
1. In oduc ion
Residen ial sec o plays an impo an ole in he policies o ace he clima e change since his sec o ep esen s
abou 27% o wo ld ene gy consump ion and abou 17% o wo ld CO2 emissions (Neja e al., 2015). Se e al
s udies ha e demons a ed he ad an ages o in eg a ing ene gy sys ems in o de o ob ain a mo e e icien use o
na u al esou ces as well as a signi ican educ ion o CO2 emissions in esiden ial buildings applica ions
(Manca ella, 2014; Se a e al., 2009). To achie e i , he design o ene gy sys ems mus be add essed aking in o
accoun he syn hesis o he sys em (ins alled echnologies and capaci ies, e c.) and he ope a ional planning
(s a egy conce ning he ope a ional s a e o he equipmen , ene gy low a es, e c.); howe e , inding he op imal
con igu a ion is a complex ask, gi en he wide a ie y o echnologies op ion a ailable and g ea diu nal and
annual luc ua ions in ene gy demands, among o he s (Tapia-Ahumada e al., 2013). O he ac o s ha inc ease
e en mo e he complexi y a e: i) he inco po a ion o enewable ene gy echnologies which a e cha ac e ized by
in e mi en beha iou and non-simul anei y be ween consump ion and p oduc ion, and ii) he in eg a ion o
ene gy s o age, ei he elec ical and/o he mal, which allow o decouple p oduc ion om consump ion.
The aim o his wo k is o ca y ou a sys ema ic economic and en i onmen al e alua ion o he impac o he
in eg a ion o di e en ene gy and ene gy s o age echnologies in he ene gy supply sys em o esiden ial buildings
loca ed in Za agoza, Spain. The app oach is based on s andalone ene gy sys ems in o de o iden i y clea e he
in e ac ions o di e en echnologies, e.g. ba e ies, allowing a deepe unde s anding o hei e ec on he op imal
design o he ene gy sys em, om con en ional o polygene a ion sys ems. To do his, a MILP (Mixed In ege
Linea P og amming) model has been de eloped o ob ain he op imal design o ene gy sys ems based on di e en
condi ions and es ic ions.
ISES Sola Wo ld Cong ess 2019 IEA SHC In e na ional Con e ence on
Sola Hea ing and Cooling o Buildings and Indus y 2019
© 2019. The Au ho s. Published by In e na ional Sola Ene gy Socie y
Selec ion and/o pee e iew unde esponsibili y o Scien iic Commi ee
doi:10.18086/swc.2019.08.06 A ailable a h p://p oceedings.ises.o g
2. Me hodology
MILP model is used o design he ene gy sys em o a esiden ial building loca ed in Za agoza, composed o 40
dwellings wi h 102.4 m2 o su ace a ea and an a e age occupancy o 3 people pe dwelling. To do his, ene gy
demands and na u al esou ces mus be de ined be o ehand. Ideally, whole yea da a should be used o e alua e
he ene gy sys ems; howe e , his can be in ac able compu a ionally, he e o e, ep esen a i e days a e used.
Supe s uc u e which conside s candida e echnologies is de ined and inally MILP model is de eloped.
2.1 Ene gy demands and enewable ene gy p oduc ion
Space hea ing and Cooling demands a e es ima ed om annual da a (IDAE, 2009). Daily da a a e ob ained by
using deg ee days me hod and hou ly da a by applying hou ly p o iles (Ramos, 2012). To apply he deg ee days
me hod, base empe a u e o space hea ing and cooling is se in 15ºC and 21ºC espec i ely. Domes ic Ho Wa e
(DHW) is calcula ed conside ing he e e ence empe a u e 60ºC and he mean mon hly empe a u e o he ne
wa e (AENOR, 2005). Mon hly dis ibu ion is ca ied ou by applying a mon hly consump ion ac o (Vi i, 1996).
I is assumed ha e e y day o each mon h ha e he same consump ion. An hou ly p o ile (Ramos, 2012) is applied
o ob ain he hou ly demand. In he case o elec ici y demand, annual elec ici y demand o appliances acco ding
o IDAE (2011a) is mon hly dis ibu ed by applying a dis ibu ion ac o , which is di ided by he days o he
mon h and dis ibu ed by an hou ly dis ibu ion unc ion (Ma ín-Giménez, 2004), ha conside s di e en hou ly
consump ion o each season. P ocedu es b ie ly desc ibed abo e p o ide he hou ly demand da a se ies o hea ing
𝑄𝑄𝑑𝑑, cooling 𝑅𝑅𝑑𝑑 and elec ici y 𝐸𝐸𝑑𝑑, whe e hea ing demand consis s o space hea ing and DHW.
Hou ly pho o ol aic ene gy p oduc ion pe squa e me e , EPV, is calcula ed ollowing he p ocedu e desc ibed by
(Du ie and Beckman, 2013) as a unc ion o he sola adia ion o e a il ed su ace 36º and azimu h angle 0º
(Me eo es , 2017).
Hou ly sola he mal ene gy p oduc ion pe squa e me e , EST, is calcula ed as a unc ion o he sola adia ion
o e a il ed su ace 36º and azimu h angle 0º as well, he mean di e ence empe a u e be ween he collec o
empe a u e 60ºC and ambien empe a u e, and he collec o pa ame e s (Sal ado Escoda S.A, 2017).
The elec ical p oduc ion o a wind u bine, is calcula ed based on he p oduc ion cu e o he u bine wi h nominal
capaci y o 30 kW (Aeolos, 2006) and he wind speed (Me eo es , 2017), ollowing he p ocedu e desc ibed by
(Manwell e al., 2009).
Tab. 1. Annual and peak alues o ene gy demands and enewable ene gy p oduc ion
A ibu e
Annual Value
Peak Value
Hea ing demand (Q
d
)
69985
kWh
65.6
kW
Cooling demand (R
d
)
14008
kWh
70.3
kW
Elec ici y demand (E
d
)
35268
kWh
7.2
kW
Pho o ol aic p oduc ion (E
PV
)
285
kWh/m2
0.16
kW/m2
Wind ene gy (E
W
)
6397
kWh/ud
3.42
kW/ud
Sola The mal P oduc ion (E
ST
)
995
kWh /m2
0.79
kW /m2
2.2. Rep esen a i e days
The op imiza ion o polygene a ion sys ems should be ca ied ou by using whole yea da a bu his can become
in ac able compu a ionally, mainly when in ege a iables a e in ol ed. The e o e, ep esen a i e days a e
selec ed o ackle his issue. In his case, i was applied a me hod based on he combina ion o k-medoids
(Dominguez-Muñoz e al., 2011) and OPT (Poncele e al., 2017) me hods o ob ain he se o 12 ep esen a i e
days wi h hei espec i e weigh s ω p esen ed in Tab. 2. Two addi ional days co esponding o peak ene gy
demands a e conside ed in he op imiza ion p ocess wi h ω=0. The e o e, he numbe o days N ep used in he
op imiza ion p ocess is 14.
Tab. 2. Se o ep esen a i e days used o he op imiza ion o he ene gy sys em
Mon h
d
ω
Mon h
d
ω
Mon h
d
ω
Janua y
21
6
May
147
51
Augus
240
15
Feb ua y
37
47
June
158
62
Oc obe
300
41
Ap il
116
21
June
166
15
No embe
339
48
May
136
16
June
175
34
Decembe
352
9
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
2.3 Supe s uc u e, echnical, economic and en i onmen al da a
The supe s uc u e, depic ed in he Fig. 1, conside s he candida e echnologies and he easible connec ions
be ween hem in he ene gy sys em. I is made up o an elec ical and he mal pa . The elec ical pa conside s
a gene a o GE o p oduce elec ici y om gasoil; pho o ol aic modules PV; wind u bines WT; in e e In ,
which con e s he di ec cu en o al e na ing cu en ; Lead acid LA o Ion Li hium Ion Ba e ies Ba , which can
s o e elec ic ene gy; and in e e -cha ge In -Ch, which con e s al e na ing cu en o di ec cu en and
con e sely. The he mal pa conside s con en ional boile GB ha consumes gasoil o p oduce hea ; sola he mal
collec o s ST; a single-e ec abso p ion chille ACH ha uses hea and a small quan i y o elec ici y o p oduce
cooling; and inally he mal ene gy s o age o hea ing TSQ and cooling TSR, which can cha ge/discha ge he mal
ene gy. O he componen s such as cogene a ion module CM, con e ing gasoil in o elec ici y and hea , and
e e sible hea pumps HP, con e ing he elec ical ene gy in o he mal ene gy ei he hea ing o cooling, allow
he in eg a ion o elec ic and he mal pa s. When HP only p oduces cooling, i is conside ed as a mechanical
chille .
Technical da a
Hea pump ope a es in hea ing mode assuming a cons an coe icien o pe o mance COP, o in cooling mode
assuming a cons an Ene gy E iciency Ra io EER wi h a cons an cooling/hea ing capaci y a io β. Bo h COP and
EER ha e been es ima ed conside ing he ope a ional empe a u e o he ese oi s expec ed o Za agoza (Spain).
In he case o engines GE and CM, hey can modula e up o pa ial load o 15%. Fo CM, he elec ical and he mal
p oduc ions a e p opo ional o αw and αq ac o s espec i ely. Single e ec abso p ion chille ope a es wi h a
cons an COPACH. The pe o mance ηGB o con en ional boile is assumed cons an . Rega ding he mal ene gy
s o age anks, he s o ed ene gy 𝑆𝑆𝑞𝑞and 𝑆𝑆𝑟𝑟 o hea ing and cooling espec i ely, a e calcula ed in each ime s ep
aking in o accoun he ene gy loss by applying a 𝜆𝜆 ac o . In he case o ba e ies, he ound ip e iciency 𝜂𝜂𝑟𝑟𝑟𝑟,
de e mines he ene gy loss du ing he cha ging and discha ging p ocess in each ime s ep. Fu he , maximum deep
o discha ge 𝐷𝐷𝐷𝐷𝐷𝐷 is de ined o ba e ies o a oid p ema u e ailu es. Du ing he ba e ies li e ime ope a ion, he
numbe o cha ge-discha ge cycles has o be lowe han he maximum numbe o cycles ha p o oke he ailu e
𝑁𝑁𝑐𝑐,𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑟𝑟𝑓𝑓, indica ed by he manu ac u e . This is e i ied by applying he equi alen ull cycle o ailu e ageing
me hod desc ibed by Du o-López e al. (2014). The e a e wo echnologies o ba e ies p oposed o his s udy,
ion li hium and lead acid, bu in he op imal con igu a ion, only one o hem is selec ed. Models o capaci y 𝑞𝑞, a e
applied o calcula e hei dynamic beha iou in he equipmen . Ion li hium ba e ies capaci y 𝑞𝑞𝑓𝑓𝑖𝑖𝑖𝑖, a e modelled
acco ding o DiO io e al.(2015), aking in o accoun bo h, he maximum cha ge cu en Imax,c s ablished by
manu ac u e and he cha ge a io 𝛼𝛼𝑐𝑐 in A/Ah desc ibed by Home Ene gy (2016). Fo lead acid ba e ies, he
echnology used o his s udy is he OPz ba e ies applying he KiBaM model (Manwell and McGowan, 1993),
which equi es h ee pa ame e s, calcula ed on he basis o manu ac u e s’ da a ca alogues: 𝑘𝑘, he a e cons an ; 𝑐𝑐,
he ac ion o he capaci y ha may hold a ailable cha ge; and he maximum capaci y o he ba e y 𝑞𝑞𝑚𝑚𝑓𝑓𝑚𝑚, as a
unc ion o 𝑘𝑘 and 𝑐𝑐. Taking in o accoun ha his s udy is based on ep esen a i e days, o bo h, he mal and
elec ical s o age, he ene gy s o ed a he beginning o each ep esen a i e day mus be equal o he ene gy s o ed
a he end o each ep esen a i e day. Technical da a a e shown in Tab. 3.
Economic da a
The in es men cos o e e y componen is calcula ed om he uni cos 𝐶𝐶𝐶𝐶 and he ins alled capaci y Cap.
Ins alla ion and main enance cos s a e conside ed by applying he ac o 𝐹𝐹𝑚𝑚. In o de o calcula e he ixed annual
cos , a Capi al Reco e y Fac o 𝐶𝐶𝑅𝑅𝐹𝐹=0.082 y -1 is applied based on a li e ime o he ins alla ion o 20 yea s and
Hea ing
Cooling
Elec ici y
CM GB
Q
HP
[OR]
R
HP
ACH
Ba
PV
PW
ST
TSQ
TSR
In e e -Cha ge HP
Gasoil
E
Q
R
GE
In e e
Fig. 1. Supe s uc u e. Nodes a e ep esen ed by ci cles
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
an in e es a e =5% . Howe e , some componen s ha e di e en li e ime 𝑛𝑛𝑟𝑟, hence, a ne p esen alue ac o
𝐹𝐹𝑁𝑁𝐹𝐹𝐹𝐹 is calcula ed o e e y componen o conside he o al eposi ions ca ied ou du ing he li e ime o he
ins alla ion. The indi ec cos s a e conside ed by applying a ac o 𝐹𝐹𝑓𝑓𝑖𝑖𝑑𝑑 o 0.2. Fo all in es men s, he Value-
Added Tax 𝐹𝐹𝑉𝑉𝑉𝑉, is applied, whose alue o Spain case is 0.21. All economic da a a e shown in Tab. 3.
Rega ding uel F, wo ypes o gasoil ha e been conside ed in he ene gy sys em: Gasoil A which is used in he
GE and CM and gasoil o hea ing used in he GB. The p ice o gasoil A and gasoil o hea ing is 0.1174 €/kWh
and 0.0678 €/kWh espec i ely (IDAE, 2018).
En i onmen al da a
In o de o e alua e he en i onmen al impac o he polygene a ion sys em, i has been conside ed he uni CO2eq
emissions embodied CO2U in e e y componen o he supe s uc u e based on he li e cycle assessmen LCA o
e e y componen (Tab. 3). The CO2 emissions eleased due o he uel combus ion (gasoil) a e calcula ed
conside ing a cons an alue o CO2 emissions associa ed o gasoil CO2 uel o abou 0.294 kgCO2eq/kWh (Ca bon
oo p in , 2016).
Tab. 3. Technical, economic and en i onmen al da a o componen s
Componen
j Technical da a
Economic da a
En i onmen al
da a
Re e ences
Cu [€/*] Fm
n
[Yea s]
CO
2
U
[kgCO2eq/*]
PV ηPV= 15.66% 113.4 €/m2 0.9 20 161 kgCO2eq/m2
(Fu e al., 2017)(A e sa,
2017)(F ischknech e al., 2015)
WT Manu ac u e
cu e 51230 €/ud 0.7 20 21600 kgCO2eq/ud
(Aeolos, 2006)(O ell and
Poehlman, 2017)(T emeac and
Meunie , 2009)
ST
𝜂𝜂𝑖𝑖= 0.801
𝑎𝑎
1
=
3.188 W/m2K
𝑎𝑎2=
0.011
W/m
2
K
2
254 €/ m2 1.5 20 95 kgCO2eq/m2 (Guadal aja a, 2016; IDAE, 2011b;
Sal ado Escoda S.A, 2017)
GB
η
b
: 0.96
80 €/kW
0.5
15
10 kgCO
2
eq/kW
(BAXI, 2017; Pina e al., 2017)
HP
COP=3.0, EER=
4.0, β=0.9 500 €/kW 0.5 20 160 kgCO2eq/kW
(Beccali e al., 2016; ENERTRES,
2017; Pina e al., 2017)
ACH COPACH= 0.7 485 €/kW 1.5 20 165 kgCO2eq/kW
(Beccali e al., 2016; Pina e al.,
2017; U.S. Depa men o Ene gy,
2017)
GE αw= 0.28 600 €/kW 0.2 10
65 kgCO2eq/kWe
(Aye be, 2018)
CM αw= 0.28,αq= 0.56 1150 €/kWe 0.7 10
(Da ow e al., 2017; Pina e al.,
2017; Yanma , 2017)
TSQ
λ= 1%
212 €/kWh
0.1 15
31 kgCO
2
eq/kWh
(ENERTRES, 2017)(Beccali e al.,
2016)
TSR
λ= 3%
257 €/kWh
62 kgCO
2
eq/kWh
Ba LA
k=0.11, c=0.53
η =82%;
DOD=50%;
Nc, ailu e =1500
129 €/kWh 0.25 9 60 kgCO2eq/kWh (IRENA, 2017)(Hi ema h e al.,
2015; McManus, 2012)
Ba Ion
η
=90%; α
c
=0.4
DOD=90%;
Nc, ailu e =2000
370 €/kWh 0.25 12 160 kgCO2eq/kWh (IRENA, 2017)(Pe e s e al., 2017)
2.4 Op imiza ion Model
MILP model is de eloped by using he so wa e LINGO (LINDO Sys ems Inc, 2013). The objec i e unc ion is
o minimize he o al annual cos . A he same ime, en i onmen al cos which encompasses CO2 emissions
embodied in he equipmen 𝐶𝐶𝐷𝐷2𝑓𝑓𝑓𝑓𝑚𝑚 and elease due o he uel combus ion du ing he ope a ion 𝐶𝐶𝐷𝐷2𝑖𝑖𝑜𝑜𝑓𝑓 is also
calcula ed.
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
𝑀𝑀𝑀𝑀𝑁𝑁 =𝑉𝑉𝑇𝑇𝑇𝑇𝑎𝑎𝑇𝑇 𝑎𝑎𝑛𝑛𝐶𝐶𝑎𝑎𝑇𝑇 𝑐𝑐𝑇𝑇𝑐𝑐𝑇𝑇 (eq. 1)
𝑉𝑉𝑇𝑇𝑇𝑇𝑎𝑎𝑇𝑇 𝑎𝑎𝑛𝑛𝑛𝑛𝐶𝐶𝑎𝑎𝑇𝑇 𝑐𝑐𝑇𝑇𝑐𝑐𝑇𝑇= 𝐶𝐶𝑀𝑀𝑉𝑉+𝐶𝐶𝑔𝑔 (eq. 2)
𝐶𝐶𝑀𝑀𝑉𝑉=(1 + 𝐹𝐹𝑉𝑉𝑉𝑉)∙(1 + 𝐹𝐹𝑓𝑓𝑖𝑖𝑑𝑑)∙𝐶𝐶𝑅𝑅𝐹𝐹∙∑𝐶𝐶𝐶𝐶𝑗𝑗∙𝐶𝐶𝑎𝑎𝐶𝐶𝑗𝑗∙�1 + 𝐹𝐹𝑁𝑁𝐹𝐹𝐹𝐹𝑗𝑗��1 + 𝐹𝐹𝑚𝑚𝑗𝑗�
𝑗𝑗=𝑐𝑐𝑖𝑖𝑚𝑚𝑜𝑜𝑖𝑖𝑖𝑖𝑓𝑓𝑖𝑖𝑟𝑟 (eq. 3)
𝐶𝐶𝑔𝑔=∑𝜔𝜔𝑑𝑑∙�∑𝑐𝑐𝐶𝐶𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 ∙𝐹𝐹(ℎ)
24
ℎ=1 �𝑑𝑑∙(1 + 𝐹𝐹𝑉𝑉𝑉𝑉)
𝑁𝑁𝑟𝑟𝑟𝑟𝑟𝑟
𝑑𝑑=1 (eq. 4)
The objec i e unc ion is subjec o he nex cons ain s:
Balance equa ions:
An ene gy balance is ca ied ou in each node 𝑚𝑚 (In e sec ion poin s o ene gy luxes) o he supe s uc u e:
∑(𝐸𝐸𝑓𝑓𝑖𝑖
𝑚𝑚−𝐸𝐸𝑖𝑖𝑓𝑓𝑟𝑟
𝑚𝑚)
𝑚𝑚= 0 (eq. 5)
Equipmen e iciency:
GB: 𝜂𝜂𝐺𝐺𝐺𝐺 ∙𝐹𝐹𝐺𝐺𝐺𝐺 −𝑄𝑄𝐺𝐺𝐺𝐺 = 0 (eq. 6)
HP: 𝑄𝑄𝐻𝐻𝐻𝐻 −𝐸𝐸𝐻𝐻𝐻𝐻 ∙𝐶𝐶𝐷𝐷𝐹𝐹 = 0 (eq. 7)
HP: 𝑅𝑅𝐻𝐻𝐻𝐻 −𝐸𝐸𝐻𝐻𝐻𝐻 ∙𝐸𝐸𝐸𝐸𝑅𝑅 = 0 (eq. 8)
GE: 𝛼𝛼𝑤𝑤∙𝐹𝐹𝐺𝐺𝐺𝐺 −𝑊𝑊𝐺𝐺𝐺𝐺 = 0 (eq. 9)
CM: 𝛼𝛼𝑤𝑤∙𝐹𝐹𝐶𝐶𝐶𝐶 −𝑊𝑊𝐶𝐶𝐶𝐶 = 0 (eq. 10)
CM: 𝛼𝛼𝑞𝑞∙𝐹𝐹𝐶𝐶𝐶𝐶 −𝑄𝑄𝑐𝑐= 0 (eq. 11)
ACH: 𝑅𝑅𝑓𝑓𝑐𝑐ℎ=𝐶𝐶𝐷𝐷𝐹𝐹𝑓𝑓𝑐𝑐ℎ∙𝑄𝑄𝑓𝑓𝑐𝑐ℎ (eq. 12)
Fo he mal ene gy s o ages o hea ing q and cooling :
𝑆𝑆𝑞𝑞,𝑟𝑟(𝑇𝑇)=𝑆𝑆𝑞𝑞,𝑟𝑟(𝑇𝑇−1)∙𝜆𝜆𝑞𝑞,𝑟𝑟+𝐸𝐸𝑓𝑓𝑖𝑖𝑞𝑞,𝑟𝑟−𝐸𝐸𝑖𝑖𝑓𝑓𝑟𝑟𝑞𝑞,𝑟𝑟 (eq. 13)
Equipmen ’s capaci ies:
Fo enewable ene gy p oduc ion componen s:
PV: 𝑊𝑊𝐻𝐻𝑃𝑃 =𝐸𝐸𝐻𝐻𝑃𝑃 ∙𝑉𝑉𝐻𝐻𝑃𝑃 (eq. 14)
ST: 𝑄𝑄𝑆𝑆𝑆𝑆 =𝐸𝐸𝑆𝑆𝑆𝑆 ∙𝑉𝑉𝑆𝑆𝑆𝑆 (eq. 15)
WT: 𝑊𝑊𝑊𝑊=𝐸𝐸𝐻𝐻𝑊𝑊 ∙𝑁𝑁𝑊𝑊𝑆𝑆 (eq. 16)
Fo each componen j, he ene gy p oduc ion is equal o lowe han i s nominal capaci y. Thus, o hea ing 𝑄𝑄,
cooling 𝑅𝑅 o elec ici y 𝑊𝑊 p oduc ion:
𝑄𝑄𝑗𝑗≤𝐶𝐶𝑎𝑎𝐶𝐶𝑗𝑗 (eq. 17)
𝑅𝑅𝑗𝑗≤𝐶𝐶𝑎𝑎𝐶𝐶𝑗𝑗 (eq. 18)
𝑊𝑊𝑗𝑗≤𝐶𝐶𝑎𝑎𝐶𝐶𝑗𝑗 (eq. 19)
S o ed ene gy S is equal o lowe o nominal capaci y o he ene gy s o age.
𝑆𝑆 ≤𝐶𝐶𝑎𝑎𝐶𝐶𝑖𝑖𝑖𝑖𝑚𝑚𝑓𝑓𝑖𝑖𝑓𝑓𝑓𝑓 (eq. 20)
𝐸𝐸𝑛𝑛𝐸𝐸𝐸𝐸𝐸𝐸𝑇𝑇𝑛𝑛𝑚𝑚𝐸𝐸𝑛𝑛𝑇𝑇𝑎𝑎𝑇𝑇 𝑐𝑐𝑇𝑇𝑐𝑐𝑇𝑇 =𝐶𝐶𝐷𝐷2𝑓𝑓𝑓𝑓𝑚𝑚 +𝐶𝐶𝐷𝐷2𝑖𝑖𝑜𝑜𝑓𝑓 (eq. 21)
𝐶𝐶𝐷𝐷2𝑓𝑓𝑓𝑓𝑚𝑚 =∑𝐶𝐶𝐷𝐷2𝑈𝑈(𝑗𝑗)∙𝐶𝐶𝑉𝑉𝐹𝐹(𝑗𝑗)∙�1 + 𝑅𝑅𝐸𝐸𝐶𝐶𝑇𝑇𝑗𝑗�/𝑛𝑛𝑛𝑛𝐸𝐸
𝑗𝑗 (eq. 22)
𝐶𝐶𝐷𝐷2𝑖𝑖𝑜𝑜𝑓𝑓 =∑𝜔𝜔𝑑𝑑∙�∑�𝐶𝐶𝐷𝐷2𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 ∙𝐹𝐹(ℎ)�
24
ℎ=1 �𝑑𝑑
𝑁𝑁𝑟𝑟𝑟𝑟𝑟𝑟
𝑑𝑑=1 (eq. 23)
3. Resul s
In o de o e alua e he economic and en i onmen al impac o di e en echnologies, se en sys ems we e de ined,
om con en ional o polygene a ion sys ems. Tab. 5 shows he 6 di e en ene gy sys ems s udied, in which he
di e en echnologies a e p og essi ely inco po a ed as candida es. Sys em 0 ep esen s he e e ence sys em in
which elec ici y is p oduced in an elec ic gene a o GE suppo ed wi h a ba e y Ba ei he lead acid (LA) o ion-
li hium (Ion), hea is p oduced in a con en ional boile GB, and cooling is p oduced in a mechanical chille .
Sys em 1 u he includes he op ion o p oducing hea in a e e sible hea pump HP. Sys em 2 inco po a es he
possibili y o including a cogene a ion module CM and a single e ec abso p ion chille ACH, which combina ion
is well known as combined cooling, hea ing & powe CCHP. Sys em 3 conside s all p e ious candida e
echnologies as well as he mal ene gy s o age, bo h o hea ing and cooling. Sys ems 4, 5 and 6 inco po a e
p og essi ely he possibili y o ins alling enewable ene gies as ollows: sola he mal ST, in sys em 4; ST and
wind u bine WT, in sys em 5; and all candida e echnologies (Fig. 1), including pho o ol aic modules PV in
sys em 6.
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
Tab. 4 Di e en ene gy sys em om con en ional o polygene a ion sys ems wi h hei espec i e candida e echnologies.
: Candida e echnology. : Technology no conside ed as a candida e in ene gy sys em.
Sys em
Di e en echnologies
Con en ional sys em
CCHP
The mal
Ene gy s o age
Renewable
ene gy
GE
Ba
GB
Hea Pump
LA
Ion
Cooling
Hea ing
CM
ACH
TS
ST
WT
PV
Sys em 0
Sys em 1
Sys em 2
Sys em 3
Sys em 4
Sys em 5
Sys em 6
3.1 Op imiza ion o ene gy sys ems: Impac on sys em design
Tab. 5 p esen s he capaci y o e e y componen conside ed in he op imal con igu a ion o ene gy sys ems. I can
be obse ed he in luence o each echnology in he sizing o he ene gy sys em. F om e e ence sys em o sys em
1, GB capaci y is educed o he hal when e e sible HP is aken in o accoun . Sys em 2 conside s CCHP
echnology as a candida e; howe e , i is no selec ed in he op imal con igu a ion which is equal o he p e ious
one. Sys em 3 conside s TS, in his case he op imal con igu a ion changes by selec ing CCHP and TSR whe eas
Ba is no selec ed. Re e sible HP and GB capaci ies educe in abou 70% and 17% espec i ely. The op imal
con igu a ion does no change when ST is conside ed in he sys em 4, so i is equal o he p e ious one. In sys em
5, he op imal con igu a ion selec s CCHP, HP, GB, TSR, WT and LA Ba . CM capaci y educes in abou 6%,
HP capaci y inc eases abou 56% whe eas ACH and TSR capaci y educe abou 24%. In sys em 6, op imal
con igu a ion is composed o CM, HP, GB, PV, WT, TSR and LA Ba . CM capaci y educes in abou 47%, HP
and GB capaci ies inc ease in abou 43% and 66% espec i ely. TSR and LA Ba capaci ies inc ease in abou 40%
and 403% espec i ely. WT capaci y educes in abou 81% due o selec ion o PV echnology.
Acco ding o he esul s, LA Ba echnology is equi ed in he op imal con igu a ion om sys em 0 o 2 as auxilia y
componen due o he pa ial load o he p ime mo e and i is a oided when CCHP and TSR a e selec ed in he
op imal con igu a ion. On he o he hand, ba e ies s a o play an impo an ole, beyond he auxilia y componen
when enewable ene gy echnologies such as PV and WT a e conside ed in he op imal con igu a ion. I pe o ms
as s o age managemen o ake ad an age he enewable ene gy p oduc ion.
The cos o he CM is app oxima ely he double o GE cos , he e o e, CM s a s o be easible as a p ime mo e
in he op imal con igu a ion when i s ins alled capaci y is app oxima ely he hal o he GE in p e ious sys em, as
can be obse ed in sys em 3. Mo eo e , he p esence o PV and/o WT educes he p ime mo e capaci y e en
mo e.
Due o he p esence o PV and/o WT, HP capaci y inc eases whe eas ACH capaci y dec eases up o be no
conside ed in he op imal con igu a ion o sys em 6. On he o he hand, TSR allows o educe he capaci y o
cooling p oduc ion componen s as well as ba e y capaci y, besides, i inc eases he lexibili y o he sys em o
manage he elec ici y om PV and/o WT. No e ha in sys ems 3 and 4 he a ailabili y o ela i ely cheap he mal
ene gy combined wi h TS allows o emo e he ba e ies, e.g. elec ic ene gy s o age. This ac shows a close and
deep in eg a ion be ween he mal and elec ical ene gy, showing i s s ong in e ac ion when ene gy con e sion
sys ems, such as e e sible HP, con e ing elec ical ene gy in o he mal ene gy a e a ailable.
Tab. 5. Resul s o ins alled capaci y in he design o ene gy sys ems
Technologies
Sys em 0
Sys em 1-2
Sys em 3-4
Sys em 5
Sys em 6
GE [kWe]
67
67
0
0
0
CM [kWe] 0 0 33 31 16
Mechanical Chille [kW ]
260
0
0
0
0
Re HP [kW ]
0
260
78
122
174
GB [kW ] 219 102 85 85 141
ACH [kW ]
0
0
91
69
0
TSQ [kWh ]
0
0
0
0
3
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
TSR [kWh ]
0
0
152
116
162
Ba LA [kWh]
19
18
0
13
66
Ba Ion [kWh]
0
0
0
0
0
ST [m2] 0 0 0 0 0
WT [kWe]
0
0
0
21
4
PV [kWe] 0 0 0 0 52
The in es men and ope a ional cos o each sys em a e p esen ed in Tab. 6. F om sys em 0 o sys em 2, he o al
annual cos educ ion is due o he educ ion in GB in es men cos , on he o he hand, om sys em 2 o sys em
6, he o al annual cos is educed because o he signi ican ope a ional cos sa ings, despi e he o al in es men
cos inc eases, o ins ance, compa ing sys em 6 wi h espec e e ence sys em, i is possible o educe he o al
annual cos up o 22% wi h an inc easing in he in es men cos o abou 41%.
Tab. 6. In es men and ope a ional annual cos [€/y ]
In es men cos [€/y ]
Technology
Sys em 0
Sys em 1-2
Sys em 3-4
Sys em 5
Sys em 6
GE
9038
9061
0
0
0
CM
0
0
11955
11276
5983
Mechanical
Chille
18197 0 0 0 0
HP
0
18197
5448
8515
12181
GB
4531
2122
1768
1760
2918
ACH
0
0
12899
9797
0
TSQ
0
0
0
0
131
TSR
0
0
7439
5648
7912
Ba LA
744
713
0
507
2552
In -Ch
1118
1081
0
592
1626
ST
0
0
0
0
0
WT
0
0
0
7044
1305
PV
0
0
0
0
8341
In
0
0
0
1725
4613
To al cos
33628
31175
39509
46865
47560
Ope a ional annual cos [€/y ]
71143
71380
59284
48527
34044
To al annual cos [€/y ]
104771
102555
98793
95392
81604
The in es men cos b eakdown o each sys em is shown in he Fig. 2. The weigh o he mechanical chille and
e e sible hea pump on he o al in es men cos is abo e 50% in he e e ence sys em and sys em 1-2. The weigh
o HP dec ease d as ically in subsequen sys ems. In sys ems 3-4 he CCHP echnology in es men cos is abo e
60% on he o al in es men cos . Howe e , in he sys ems 5 and 6, none o he echnologies exceed he 50% o
he o al in es men cos , in ac , in sys em 6 he highes in es men cos is 26% co esponding o HP echnology.
F om he poin o iew o eliabili y, his shows an ad an age o he use o polygene a ion sys ems, which a oid
a high dependency on a speci ic de ice o p oduce one p oduc , and educe he ope a ional and economic impac
on he sys em when a eplacemen o a componen mus be ca ied ou .
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
3.2 Economic and en i onmen al impac o ene gy sys em in eg a ion
The in eg a ion o ene gy sys ems allows o ob ain economic sa ings o CO2 emissions educ ions, o bo h o
hem. Fig. 3 shows he economic and en i onmen al impac o ene gy sys ems in eg a ion espec i ely. F om
sys em 0 o 2, he e is a educ ion in o al annual cos o abou 2% only when conside ing a e e sible hea pump
o cooling and hea ing ins ead o a mechanical chille , whe eas any change in CO2 emissions can be neglec ed.
F om sys em 2 o 4, he e is a o al annual cos educ ion o abou 4% as well as a ema kable CO2 emissions
educ ion o abou 22% due o he selec ion o CCHP and TSR echnologies in he op imal con igu a ion. F om
sys em 4 o 5, he e is a o al annual cos educ ion o abou 3% and CO2 emissions educ ion o abou 14% because
WT echnology is selec ed. Finally, om sys em 5 o 6, bo h o al annual cos and CO2 emissions ha e a
ema kable educ ion o abou 14% and 22% espec i ely, when PV echnology is selec ed in he op imal
con igu a ion.
Fig. 2. In es men cos b eakdown o each ene gy sys em e alua ed. TICi: To al annual In es men Cos o he sys em i.
Fig. 3. Economic (le ) and en i onmen al ( igh ) impac o ene gy sys em in eg a ion
-25%
-20%
-15%
-10%
-5%
0%
0
20000
40000
60000
80000
100000
120000
Sys em 0 Sys em 1-2 Sys em 3-4 Sys em 5 Sys em 6
€/y
To al annual cos Reduc ion cos [%]
-50%
-45%
-40%
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
0
50000
100000
150000
200000
250000
Sys em 0 Sys em 1-2 Sys em 3-4 Sys em 5 Sys em 6
kgCO2eq/y
CO2 emissions Reduc ion CO2 emissions [%]
GE
27%
Mechanical Chille
54%
GB
14%
Ba LA
2%
In -Ch
3%
Re e ence Scena io
TIC
0
=33628 €
GE
29%
HP
58%
GB
7%
Ba LA
2%
In -Ch
4%
Sys em 1-2
TIC
1-2
=31175 €
CM
30%
HP
14%
GB
4%
ACH
33%
TSR
19%
Sys em 3-4
TIC
3-4
=39509 €
CM
24%
WT
15%
HP
18%
GB
4%
ACH
21%
TSR
12%
Ba LA
1%
In
4%
In -Ch
1%
Sys em 5
TIC
5
=46865 €
CM
13%
PV
17%
WT
3%
HP
26%
GB
6%
TSR
17%
Ba LA
5% In
10%
In -Ch
3%
Sys em 6
TIC
6
=47560 €
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
3.3 Lead Acid Vs Ion Li hium ba e ies echnology
Acco ding o he esul , when ba e ies appea in he op imal con igu a ion, Lead acid echnology is he mos
sui able om he economic poin o iew. Howe e , his is a ma u e echnology which is ha dly o imp o e i s
pe o mance o educe i s cos . On he con a y, Ion-Li hium echnology has a high po en ial o pe o mance
imp o emen and educ ion cos . Ac ually, he pe o mance and uni cos used in his wo k we e based only on
he NMC (Nickel Manganese Cobal ) echnology; howe e , he e is a wide ange o Ion-Li hium echnologies
which imp o e i s pe o mance. In addi ion, al hough hei cos is highe han Lead acid ba e ies nowadays, hey
ha e a pe spec i e o educ ion in a nea u u e (IRENA, 2017). Based on hese ac s, a sensi i i y analysis has
been ca ied ou a ying he numbe o cycles o ailu e Nc, ailu e o ion li hium ba e ies om 2000 o 10000,
which is he cu en a ailable ange in he ma ke , in o de o ob ain he op imal con igu a ion o he sys em 6.
The conside ed li e ime o he ba e ies is 12 yea s. Fig. 4 shows he ba e y capaci y, he o al numbe o execu ed
cycles du ing he ope a ion and o al annual cos as a unc ion o he numbe o cycles o ailu e. I is obse ed
ha abo e 4000 cycles he e is no change in he o al annual cos , which means ha he op imal con igu a ion
emains beyond his alue. The maximum numbe o ope a ion cycles is abou 3500, he e o e, when Nc, ailu e
inc eases beyond his alue, li e ime should be inc ease as well, and he o al annual cos mus dec ease.
This is an i e a i e p ocess which is depic ed in he Fig. 5. By applying his p ocedu e, i was ound ha , a cu en
cos , Ion-Li hium echnology could be easible when Nc, ailu e is abou 6000 cycles which allows o inc ease he
li e ime ba e y up o 20 yea s app oxima ely in his case o s udy. I is wo hy o say ha he o al numbe o
cycles execu ed is below 6000, which means ha inc ease he Nc, ailu e does no implies imp o e he objec i e
unc ion. The e o e, his could be conside ed he op imal design o he Ion-Li hium ba e y o his applica ion.
0
500
1000
1500
2000
2500
3000
3500
4000
0
5
10
15
20
25
30
2000 3000 4000 6000 8000 10000
[Cycles]
[kWh]
Nc, ailu e [cycles]
Ba Capaci y [kWh] To al ope a ion cycles
81700
81800
81900
82000
82100
82200
82300
82400
0
5
10
15
20
25
30
2000 3000 4000 6000 8000 10000
[€/y ]
[kWh]
Nc, ailu e [cycles]
y
Ba Capaci y [kWh] To al cos [€/y ]
Fig. 4. Ion- Li hium ba e y capaci y as a unc ion o N
c, ailu e
. To al numbe o execu ed cycles du ing he ope a ion as a
unc ion o Nc, ailu e (le ) and o al annual cos o he sys em as a unc ion o as a unc ion o Nc, ailu e ( igh ).
Fig. 5. I e a i e p ocess o calcula e he li e ime ba e y as unc ion o Nc, ailu e
A. Laza o e . al. ISES SWC2019 / SHC2019 Con e ence P oceedings (2019)
