Research ArticleFeasibility and Optimal Design of a Stand-Alone PhotovoltaicEnergy System for the Orphanage
Vincent Anayochukwu Ani
Department of Electronic Engineering University of Nigeria (UNN) Nsukka 410001 Nigeria
Correspondence should be addressed to Vincent Anayochukwu Ani vincent aniyahoocom
Received 10 February 2014 Revised 2 April 2014 Accepted 10 April 2014 Published 30 April 2014
Academic Editor Nuri Azbar
Copyright copy 2014 Vincent Anayochukwu Ani This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited
Access to electricity can have a positive psychological impact through a lessening of the sense of exclusion and vulnerability oftenfelt by the orphanagesThis paper presented the simulation and optimization study of a stand-alone photovoltaic power system thatproduced the desired power needs of an orphanage Solar resources for the design of the system were obtained from the NationalAeronautics and Space Administration (NASA) Surface Meteorology and Solar Energy website at a location of 6∘511015840N latitudeand 7∘351015840E longitude with annual average solar radiation of 492 kWhm2d This study is based on modeling simulation andoptimization of energy system in the orphanageThe patterns of load consumption within the orphanage were studied and suitablymodeled for optimization Hybrid OptimizationModel for Electric Renewables (HOMER) software was used to analyze and designthe proposed stand-alone photovoltaic power system model The model was designed to provide an optimal system configurationbased on an hour-by-hour data for energy availability and demands A detailed design description and expected performance ofthe system were presented in this paper
1 Introduction
Isolated (remote) sites are locations far from the places wheremost people live and often lack grid power supply Theprice of conventional energy sources in remote areas suchas candles paraffin gas coal and batteries is often moreexpensive than in places where most people live becauseof the remoteness of retailers Providing grid electricity inremote areas is often associated with higher costs to thegrid supplier Power may be supplied through stand-alonesystems (serving just one or two users) These systems canprovide power for domestic uses such as lighting coolingTV radio and communication The power may be generatedfrom various resources using diesel biomass wind PVor small hydrogenerators or hybrid combinations of theseresources Depending on the characteristics of a specific use(ie the load profile) and the local supply options the leastcost solution for an orphanagemay consist of any of the aboveoptionsThe attraction of these sources lies primarily in theirabundance and ready access Many of the isolated areas lying
remotely from the grid have a high potential of renewableenergy with solar energy being the most abundant
Solar home system (SHS) typically includes a photo-voltaic (PV) module a battery a charge controller wiringsetup and a DCAC inverter A standard small SHS can oper-ate several lights a television (black-and-white or coloured)a radio or cassette player and a small fan SHS can eliminateor reduce the need for candles kerosene liquid propane gasandor battery charging and provide increased convenienceand safety improved indoor air quality and a higher qualityof light than kerosene lamps for reading [1] The size of thesystem (typically 10 to 100 Watts peak (Wp)) determinesthe number of ldquolight hoursrdquo or ldquoTV-hoursrdquo available Forexample a 35Wp SHS provides enough power for four hoursof lighting from four 7W lamps each evening as well asseveral hours of televisionThere are more than 500000 SHSnow installed in rural areas of developing countries [2ndash7]
Orphanages are often located in an isolated area andaccess to electricity can bring tangible social and economicbenefits to themThe possible benefits can include household
Hindawi Publishing CorporationJournal of Renewable EnergyVolume 2014 Article ID 379729 8 pageshttpdxdoiorg1011552014379729
2 Journal of Renewable Energy
0 6 12 18 24
010
020
Load
(kW
)
Daily profile
Hour
000
030
Figure 1 Load daily profile of typical orphanage electricity con-sumption
(orphanage) lighting the ability to refrigerate food and makewashing clothes more convenient The presence of electricityin an orphanage also can result in better reading cultureFinally electricity can have a positive psychological impactthrough a lessening of the sense of exclusion and vulnerabilityoften felt by orphanages hence the need for the provisionof an alternative sustainable electric power supply systemIt is always convenient to perform a thorough simulationof the energy system to obtain an optimal output using thenatural resources around it before its constructionThereforethe purpose of this paper is to simulate and optimize arenewable (PV-battery) energy system that will produce thedesired power needs of the orphanage and the optimizationparameter proposed here as a base is the offered service
2 Methodology
In order to design a power system one has to providesome information from the remote location of the orphanagesuch as the load profile that should be met by the systemsolar radiation for PV generation the initial cost of eachcomponent (PV panels a charge controller battery andinverter) annual interest rate and project lifetime
21 The Reference Orphanage From the acquired data aprofile of the orphanage was created This profile consists ofthe orphanage load variations and electrical usage patternswithin the orphanage Figure 1 shows the daily profile elec-tricity consumption in an orphanage inNsukka (Enugu StateNigeria) The orphanage in Nsukka is simple and does notrequire large quantities of electrical energy used for lightingand electrical appliances Table 1 shows an estimation of eachappliancersquos rated power its quantity and the hours of use bythe orphanage in a single day
22 The Pattern of Using Electricity Power within the Orphan-age The lights in the orphanage will always be on as from6 am (0600 h) to 7 am (0700 h) By this time (6 am to7 am) the orphans start preparing for school They leave theorphanage to school by 8 am (0800 h) and come back to theorphanage by 2 pm (1400 h) By 7 am the light will go offsince the rays of light come in through the windows during
00
02
04
06
08
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
1
2
3
4
5
6
Dai
ly ra
diat
ion
(kW
hm
sup2d)
Global horizontal radiation
Clea
rnes
s ind
ex
Daily radiationClearness index
Figure 2 Graphics of monthly solar radiation profile for Nsukka
day time [7 amndash7 pm (0700 hndash1900 h)] The light comes onagain by 7 pm (1900 h) till 10 pm (2200 h) to enable themto read their books Once it is 10 pm there will be light outand they will go to bed The light out will be there till 6 ambefore the light comes in again Meanwhile between 1 pm(1300 h) and 2 pm (1400 h) when the radiation is at the apexthewashingmachinewill be used towash orphans clothes Asfrom 4 pm (1600 h) the orphans will be in the waiting roomwatching television (programmes from the satellite dish)while the television the satellite decoder and the fans will allbe ON till 7 pm Once it is 7 pm they will go and read theirbooks till 10 pm
23 Study Area This research focuses on the simulation ofphotovoltaic power generation system for an orphanage sitedin Nsukka located in a valley with poor wind but goodsolar energies It is geographically located at 6∘511015840N latitudeand 7∘351015840E longitude with annual average solar radiation of492 kWhm2d The data for solar resource were obtainedfrom the National Aeronautics and Space Administration(NASA) Surface Meteorology and Solar Energy website [8]For this study only solar PV technology was consideredFigure 2 shows the solar resource profile of this locationFebruary is the sunniest month of the year During thismonth the solar energy resource is 57 kWhm2d while inAugust it is only 39 kWhm2d In the months of SeptemberOctober November December January and February thesolar radiation increases with differences from month tomonth as (028) (038) (054) (035) (022) and (006)respectively whereas in the months of March April MayJune July and August the solar radiation decreases withdifferences from month to month as (017) (032) (031)(04) (04) and (023) respectively These differences will beconsidered during system sizing
3 Modeling of Energy System Components
Themathematicalmodel of the proposed energy system com-ponents contains photovoltaic system with battery storage
Journal of Renewable Energy 3
Table 1 The electrical load data
Description of item Qty Load (watts per unit) Load (watts)total
systemThe theoretical aspects are given below and based on[9ndash11]
Mathematical Model of Solar Photovoltaic Using the solarradiation available the hourly energy output of the PVgenerator (119864PV) can be calculated according to the followingequation [9 12 13]
119864PV = 119866 (119905) times 119860 times 119875 times 120578PV (1)
where 119866(119905) is the hourly irradiance in kWhm2 119860 is thesurface area in m2 119875 is the PV penetration level factor and120578PV is the efficiency of PV generator
Mathematical Model of Charge Controller To prevent over-charging of a battery a charge controller is used to sensewhen the batteries are fully charged and to stop or decreasethe amount of energy flowing from the energy source to thebatteries The model of the charge controller is presentedbelow [9]
119864CC-OUT (119905) = 119864CC-IN (119905) times 120578CC
119864CC-IN (119905) = 119864SUR-DC (119905) (2)
where 119864CC-OUT(119905) is the hourly energy output from chargecontroller kWh 119864CC-IN(119905) is the hourly energy input tocharge controller kWh 120578CC is the efficiency of a chargecontroller and 119864SUR-DC(119905) is the amount of surplus energyfrom DC sources kWh
Mathematical Model of Battery Bank The battery stateof charge (SOC) is the cumulative sum of the dailychargedischarge transfers The battery serves as an energysource entity when discharging and a load when chargingAt any hour t the state of the battery is related to theprevious state of charge and to the energy production andconsumption situation of the system during the time from119905 minus 1 to t
During the charging process when the total output fromrenewable sources exceeds the load demand the availablebattery bank capacity at hour t can be described by [9 12ndash14]
119864BAT (119905) = 119864BAT (119905 minus 1) minus 119864CC-OUT (119905) times 120578CHG (3)
where 119864BAT(119905) is the energy stored in the battery at hour tkWh 119864BAT(119905 minus 1) is the energy stored in the battery at hour119905 minus 1 kWh and 120578CHG is the battery charging efficiency
On the other hand when the load demand is greaterthan the available energy generated the battery bank isin discharging state Therefore the available battery bankcapacity at hour 119905 can be expressed as [9 12ndash14]
119864BAT (119905) = 119864BAT (119905 minus 1) minus 119864Needed (119905) (4)
where 119864Needed(119905) is the hourly load demand or energy neededat a particular period of time
Let 119889 be the difference between minimum allowableSOC voltage limit and the maximum SOC voltage across thebattery terminals when it is fully charged which is equal to1 minus DOD100
So the depth of discharge (DOD) is as follows
DOD = (1 minus 119889) times 100 (5)
DOD is a measure of how much energy has been withdrawnfrom a storage device expressed as a percentage of fullcapacity The maximum value of SOC is 1 and the minimumSOC measured in percentage is determined by maximumdepth of discharge (DOD)
SOCMin = 1 minusDOD100 (6)
Mathematical Model of Inverter In the proposed scheme thePV panel and battery systems are connected with DC buswhile the electric loads are connected with AC bus as shownin Figure 3
The invertermodels for photovoltaic and battery bank aregiven below [15]
119864PV-INVBAT-INV (119905)
= (119864PV (119905) +119864BAT (119905 minus 1) minus 119864LOAD (119905)
120578INV times 120578DCHG) times 120578REC
(7)
where 119864PV-INVBAT-INV(119905) is the hourly energy output frominverter kWh 119864BAT(119905 minus 1) is the energy stored in the battery
4 Journal of Renewable Energy
Figure 3 Schematic diagram of photovoltaic energy system
at hour 119905 minus 1 kWh 119864Load(119905) is the hourly energy consumedby the load side kWh 120578INV is the efficiency of inverter and120578DCHG is the battery discharging efficiency
31 Power Generation Model Total power generated at anytime t is given by [9 12ndash14]
119875 (119905) =
119873119875
sumPV=1119875PV (8)
where119873119875are number of PV cells This generated power will
feed the loads and when this generated power exceeds theload demand then the surplus of energy will be stored inthe battery bank This energy (battery) will be used whena deficiency of power occurs to meet the load The chargedquantity of the battery bank has the constraint SOCmin leSOC(119905) le SOCmax The SOCmin is at 40 while that ofSOCmax is at 80 The approach involves the minimizationof a cost function subject to a set of equality and inequalityconstraints
32 Cost Model (Economic and Environmental Costs) ofEnergy Systems The equation for estimating the level of opti-mization of photovoltaic energy solution being consideredfor the orphanage and a location is derived as economic andenvironmental cost (carbon credit of CO
2) model of running
solar-photovoltaic + batteries and calculated as [15]
where 119862acap119904 is annualized capital cost of solar power 119862arep119904is annualized replacement cost of solar power 119862aop119904 isannualized operating cost of solar power 119862emissions is cost ofemissions119862acap119887 is annualized capital cost of batteries power119862arep119887 is annualized replacement cost of batteries power and119862aop119887 is annualized operating cost of batteries power
The mathematical model derived in (9) estimates thelife-cycle cost of the systems (solar-photovoltaic) which is
Table 2 Simulation results of electricity production consumptionlosses and excess (kWhyr)
ComponentQuantity
ofelectricity(kWhyr)
Production from PV array 1916Losses from the battery 122Losses from the inverter 234Other losses such as cables 52Consumption from AC load 1329Excess electricity 179
the total cost of installing and operating the system over itslifetime The output when run with HOMER softwaretoolwill give the optimal configuration of the energy system thattakes into account technical and economic performance ofsupply options
Net Present Cost (NPC) for Energy Systems The total netpresent cost (NPC) of a system is the present value of all thecosts that it incurs over its lifetimeminus the present value ofall the revenue that it earns over its lifetime Revenues includesalvage value and grid sales revenue The net present cost(NPC) for each component is derived using [9 12ndash14 16 17]
119862NPC =119862anntot
CRF (119894 119877proj) (10)
where the capital recovery factor is [9 12ndash14 16 17]
CRF = 119894 sdot (1 + 119894)119873
(1 + 119894)119873
minus 1 (11)
The economic optimization identifies the most financiallyattractive solution For this research paper HOMER version28 beta has been used as the sizing and optimization softwaretool It contains a number of energy component modelsand evaluates suitable technology options based on cost andavailability of resources [18]
33 Configuration and Optimization of Stand-Alone Pho-tovoltaic Energy System Stand-alone photovoltaic systemtypically has an electricity generation device equipped witha wiring setup and supporting structure as well as thenecessary BOS (balance of system) components (ie thebattery bank the charge controller and the DCAC inverter)The selection of components of energy system is doneusing Hybrid Optimization Model for Electric Renewables(HOMER)design software developed by theNational Renew-able Energy Laboratory accurate enough to reliably predictsystem performance HOMER is an optimization modelwhich performsmany hundreds or thousands of approximatesimulations in order to design the optimal system Thediagram of the completed stand-alone photovoltaic energysystem can be seen in Figure 3
Journal of Renewable Energy 5
Table 3 Simulation results of economic cost
Component Capital ($) Replacement ($) O and M ($) lowastSalvage ($) Total NPC ($)PV 2800 0 1606 0 4406Surrette 6CS25P 54960 23855 28 minus4989 73853Converter 200 0 0 0 200System 57960 23855 1633 minus4989 78459lowastSalvage value is the value remaining in a component of the power system at the end of the project lifetime that is the salvage value of a component is directlyproportional to its remaining life
Jan Feb Mar Apr May June JulyAug Sep Oct Nov Dec000005010015020025030035
Pow
er (k
W)
Monthly average electric production
PV
Figure 4 Electrical production of PV energy system
00
02
04
06
08
10
12
Jan Feb Mar AprMayJune July Aug Sep Oct NovDec40
50
60
70
80
90
100
Batte
ry st
ate o
f cha
rge (
)
Pow
er (k
W)
Excess electricityBattery state of charge
Figure 5 Battery state of charge versus excess electricity
4 Results and Discussion
41 Results The optimization result shows that sixteen solu-tions were simulated one was feasible which is PV-batteryoption with 14 kW PV 48 Surrette 6CS25P battery and 1 kWinverter fifteen were infeasible due to the capacity shortageconstraint Twenty-four were omitted (twenty-two due toinfeasibility one for lacking a converter and the remainingone for having an unnecessary converter) The obtainedresults provide information concerning the electricity pro-duction consumption losses excess and economic costs ofthe feasible system and are given in Tables 2 and 3 and shownin Figures 4 5 and 6
PV Surrette 6CS25P Converter0
20000
40000
60000
80000
Net
pre
sent
cost
($)
Cash flow summary
PVSurrette 6CS25P
Converter
Figure 6 Net present cost of component of PV energy system
42 Discussion
Electricity Production The PV array in this orphanage gen-erates 1916 kWh of electricity per year which effectivelypowers the load demand of 1329 kWh per year with littleexcess electricity of 179 kW per year as shown in Table 2 andthe electrical production of PV energy system is shown inFigure 4
Losses from the System A battery is used to store excess energyfor later use The conversion efficiency of batteries is notperfect and energy is usually lost as heat during chemicalreaction that is during charging or recharging Also theamount of energy that will be delivered from the battery ismanaged by the inverterThe inverter connects directly to thebattery bank and converts the direct current (DC) electricalenergy from the battery bank to alternative current (AC)electrical energy which is the energy that orphanages andmost residential homes use During the conversion energy isalso lost Other losses such as cables were calculated and theamount of energy that is lost from the system was tabulatedFrom Table 2 it was shown that losses from the battery havea total of 122 kWhyr losses from the inverter have a total of234 kWhyr and other losses have 52 kWhyrmaking a grandtotal of 408 kWhyr energy losses from the system as shownin Table 2Excess Electricity Excess electricity always occurs whenthe battery state of charge (SOC) is at 98 upwards andthis is between Januaries and Aprils As of May when thesolar radiation is low the battery is at 96 downward anddischarges much and there will be no excess electricity from
6 Journal of Renewable Energy
Figure 7 HOMER simulator diagram of photovoltaic energysystem and the optimization results
Figure 8 HOMER showing the simulation results of economic costof component of PV energy system
Figure 9 HOMER showing the electricity production of PV energysystem
Figure 10 HOMER showing the battery state of charge and losses
Figure 11 HOMER showing the inverter losses
Figure 12 HOMER showing the result of the emissions
Figure 13HOMER showing the battery state of charge versus excesselectricity
Figure 14 HOMER showing the optimization report
Journal of Renewable Energy 7
this point downwardThe battery state of charge versus excesselectricity is shown in Figure 5
Economic Costs Batteries are considered as a major costfactor in small-scale stand-alone power systems [15] Theoptimization of the system is carried out by modifyingthe size of the batteries until a configuration that ensuressufficient autonomy was achieved with the least net presentcost (NPC) The salvage value was used to calculate theannualized replacement cost Battery is the only componentthat has replacement cost (23855$) and therefore has salvagevalue (minus4989$) because it did not last till project lifetimeand the replacement extended the estimated project lifetimewhich was deducted from the system cost (73853$) as showninTable 3 and the net present cost of component of PV energysystem is shown in Figure 6
The software solutions showing the runningprogram with the results are shown inFigures 7 8 9 10 11 12 13 and 14
5 Conclusion
The optimal design of PVbattery energy system was carriedout minimizing the net present cost (NPC) by varying thesize of the batteries until a configuration that produces thedesired power needs of the orphanage is achieved This opti-mization study indicates that energy requirements to provideelectricity for an orphanage in Nigeria can be accomplishedby 14 kW PV 48 Surrette 6CS25P battery and 1 kW inverterThe PV system is in significant mode during the day timeparticularly in the dry season but at night and other cloudydays the battery compensates Due to the abundance of solarresource in Nigeria and having no environmental impact interms of CO
2 solar energy can be a choice for green power
solutions in powering the orphanages located in remote areas
Abbreviations
NASA National Aeronautics and SpaceAdministration
HOMER Hybrid Optimization Model for ElectricRenewables
SHS Solar home systemPV PhotovoltaicDC Direct currentAC Alternate currentSOC State of chargeDOD Depth of dischargeNPC Net present costBOS Balance of systemMin MinimumMax Maximum
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The author would like to thank Professor Chinedu Ositad-inma Nebo of Ministry of Power Nigeria for his usefuldiscussion on the subject
References
[1] H Von ldquoMini-grid system for rural electrification in the greatMekong sub-regional countriesrdquo in Renewable Energies andEnergy Efficiency vol 6 University of Kassel Kassel Germany2007
[2] F Gerald ldquoPhotovoltaic applications in rural areas of the devel-opingworldrdquo Tech Rep no 304World BankWashington DCUSA 1995
[3] A Cabraal M Cosgrove Davies and L Schaeffer ldquoBestpractices for photovoltaic household electrification programslessons from experiences in selected countriesrdquo Tech Rep no324 World Bank Washington DC USA 1996
[4] A Cabraal M Cosgrove Davies and L Schaeffer ldquoAcceleratingsustainable photovoltaic market developmentrdquo Progress in Pho-tovoltaics Research and Applications vol 6 no 5 pp 297ndash3061998
[5] D Kammen ldquoPromoting appropriate energy technologies inthe developing worldrdquo Environment vol 41 no 5 pp 11ndash15 34ndash41 1999
[6] K Kapadia ldquoOff-grid in Asia the solar electricity businessrdquoRenewable Energy World vol 2 no 6 pp 22ndash33 1999
[7] G Loois and B van Hemert Stand-Alone Photovoltaic Applica-tions Lessons Learned James amp James London UK 1999
[8] NASA 2013 httpseosweblarcnasagov[9] V A Ani ldquoOptimal energy system for single household in
Nigeriardquo International Journal of Energy Optimization andEngineering vol 2 no 3 26 pages 2013
[10] S Ashok ldquoOptimised model for community-based hybridenergy systemrdquo Renewable Energy vol 32 no 7 pp 1155ndash11642007
[11] A Gupta R P Saini andM P Sharma ldquoSteady-state modellingof hybrid energy system for off grid electrification of cluster ofvillagesrdquo Renewable Energy vol 35 no 2 pp 520ndash535 2010
[12] D K Lal B B Dash and A K Akella ldquoOptimization ofPVWindMicro-Hydrodiesel hybrid power system in homerfor the study areardquo International Journal on Electrical Engineer-ing and Informatics vol 3 no 3 pp 307ndash325 2011
[13] K Sopian A Zaharim Y Ali Z M Nopiah J A Razak andN S Muhammad ldquoOptimal operational strategy for hybrid
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
2 Journal of Renewable Energy
0 6 12 18 24
010
020
Load
(kW
)
Daily profile
Hour
000
030
Figure 1 Load daily profile of typical orphanage electricity con-sumption
(orphanage) lighting the ability to refrigerate food and makewashing clothes more convenient The presence of electricityin an orphanage also can result in better reading cultureFinally electricity can have a positive psychological impactthrough a lessening of the sense of exclusion and vulnerabilityoften felt by orphanages hence the need for the provisionof an alternative sustainable electric power supply systemIt is always convenient to perform a thorough simulationof the energy system to obtain an optimal output using thenatural resources around it before its constructionThereforethe purpose of this paper is to simulate and optimize arenewable (PV-battery) energy system that will produce thedesired power needs of the orphanage and the optimizationparameter proposed here as a base is the offered service
2 Methodology
In order to design a power system one has to providesome information from the remote location of the orphanagesuch as the load profile that should be met by the systemsolar radiation for PV generation the initial cost of eachcomponent (PV panels a charge controller battery andinverter) annual interest rate and project lifetime
21 The Reference Orphanage From the acquired data aprofile of the orphanage was created This profile consists ofthe orphanage load variations and electrical usage patternswithin the orphanage Figure 1 shows the daily profile elec-tricity consumption in an orphanage inNsukka (Enugu StateNigeria) The orphanage in Nsukka is simple and does notrequire large quantities of electrical energy used for lightingand electrical appliances Table 1 shows an estimation of eachappliancersquos rated power its quantity and the hours of use bythe orphanage in a single day
22 The Pattern of Using Electricity Power within the Orphan-age The lights in the orphanage will always be on as from6 am (0600 h) to 7 am (0700 h) By this time (6 am to7 am) the orphans start preparing for school They leave theorphanage to school by 8 am (0800 h) and come back to theorphanage by 2 pm (1400 h) By 7 am the light will go offsince the rays of light come in through the windows during
00
02
04
06
08
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
1
2
3
4
5
6
Dai
ly ra
diat
ion
(kW
hm
sup2d)
Global horizontal radiation
Clea
rnes
s ind
ex
Daily radiationClearness index
Figure 2 Graphics of monthly solar radiation profile for Nsukka
day time [7 amndash7 pm (0700 hndash1900 h)] The light comes onagain by 7 pm (1900 h) till 10 pm (2200 h) to enable themto read their books Once it is 10 pm there will be light outand they will go to bed The light out will be there till 6 ambefore the light comes in again Meanwhile between 1 pm(1300 h) and 2 pm (1400 h) when the radiation is at the apexthewashingmachinewill be used towash orphans clothes Asfrom 4 pm (1600 h) the orphans will be in the waiting roomwatching television (programmes from the satellite dish)while the television the satellite decoder and the fans will allbe ON till 7 pm Once it is 7 pm they will go and read theirbooks till 10 pm
23 Study Area This research focuses on the simulation ofphotovoltaic power generation system for an orphanage sitedin Nsukka located in a valley with poor wind but goodsolar energies It is geographically located at 6∘511015840N latitudeand 7∘351015840E longitude with annual average solar radiation of492 kWhm2d The data for solar resource were obtainedfrom the National Aeronautics and Space Administration(NASA) Surface Meteorology and Solar Energy website [8]For this study only solar PV technology was consideredFigure 2 shows the solar resource profile of this locationFebruary is the sunniest month of the year During thismonth the solar energy resource is 57 kWhm2d while inAugust it is only 39 kWhm2d In the months of SeptemberOctober November December January and February thesolar radiation increases with differences from month tomonth as (028) (038) (054) (035) (022) and (006)respectively whereas in the months of March April MayJune July and August the solar radiation decreases withdifferences from month to month as (017) (032) (031)(04) (04) and (023) respectively These differences will beconsidered during system sizing
3 Modeling of Energy System Components
Themathematicalmodel of the proposed energy system com-ponents contains photovoltaic system with battery storage
Journal of Renewable Energy 3
Table 1 The electrical load data
Description of item Qty Load (watts per unit) Load (watts)total
systemThe theoretical aspects are given below and based on[9ndash11]
Mathematical Model of Solar Photovoltaic Using the solarradiation available the hourly energy output of the PVgenerator (119864PV) can be calculated according to the followingequation [9 12 13]
119864PV = 119866 (119905) times 119860 times 119875 times 120578PV (1)
where 119866(119905) is the hourly irradiance in kWhm2 119860 is thesurface area in m2 119875 is the PV penetration level factor and120578PV is the efficiency of PV generator
Mathematical Model of Charge Controller To prevent over-charging of a battery a charge controller is used to sensewhen the batteries are fully charged and to stop or decreasethe amount of energy flowing from the energy source to thebatteries The model of the charge controller is presentedbelow [9]
119864CC-OUT (119905) = 119864CC-IN (119905) times 120578CC
119864CC-IN (119905) = 119864SUR-DC (119905) (2)
where 119864CC-OUT(119905) is the hourly energy output from chargecontroller kWh 119864CC-IN(119905) is the hourly energy input tocharge controller kWh 120578CC is the efficiency of a chargecontroller and 119864SUR-DC(119905) is the amount of surplus energyfrom DC sources kWh
Mathematical Model of Battery Bank The battery stateof charge (SOC) is the cumulative sum of the dailychargedischarge transfers The battery serves as an energysource entity when discharging and a load when chargingAt any hour t the state of the battery is related to theprevious state of charge and to the energy production andconsumption situation of the system during the time from119905 minus 1 to t
During the charging process when the total output fromrenewable sources exceeds the load demand the availablebattery bank capacity at hour t can be described by [9 12ndash14]
119864BAT (119905) = 119864BAT (119905 minus 1) minus 119864CC-OUT (119905) times 120578CHG (3)
where 119864BAT(119905) is the energy stored in the battery at hour tkWh 119864BAT(119905 minus 1) is the energy stored in the battery at hour119905 minus 1 kWh and 120578CHG is the battery charging efficiency
On the other hand when the load demand is greaterthan the available energy generated the battery bank isin discharging state Therefore the available battery bankcapacity at hour 119905 can be expressed as [9 12ndash14]
119864BAT (119905) = 119864BAT (119905 minus 1) minus 119864Needed (119905) (4)
where 119864Needed(119905) is the hourly load demand or energy neededat a particular period of time
Let 119889 be the difference between minimum allowableSOC voltage limit and the maximum SOC voltage across thebattery terminals when it is fully charged which is equal to1 minus DOD100
So the depth of discharge (DOD) is as follows
DOD = (1 minus 119889) times 100 (5)
DOD is a measure of how much energy has been withdrawnfrom a storage device expressed as a percentage of fullcapacity The maximum value of SOC is 1 and the minimumSOC measured in percentage is determined by maximumdepth of discharge (DOD)
SOCMin = 1 minusDOD100 (6)
Mathematical Model of Inverter In the proposed scheme thePV panel and battery systems are connected with DC buswhile the electric loads are connected with AC bus as shownin Figure 3
The invertermodels for photovoltaic and battery bank aregiven below [15]
119864PV-INVBAT-INV (119905)
= (119864PV (119905) +119864BAT (119905 minus 1) minus 119864LOAD (119905)
120578INV times 120578DCHG) times 120578REC
(7)
where 119864PV-INVBAT-INV(119905) is the hourly energy output frominverter kWh 119864BAT(119905 minus 1) is the energy stored in the battery
4 Journal of Renewable Energy
Figure 3 Schematic diagram of photovoltaic energy system
at hour 119905 minus 1 kWh 119864Load(119905) is the hourly energy consumedby the load side kWh 120578INV is the efficiency of inverter and120578DCHG is the battery discharging efficiency
31 Power Generation Model Total power generated at anytime t is given by [9 12ndash14]
119875 (119905) =
119873119875
sumPV=1119875PV (8)
where119873119875are number of PV cells This generated power will
feed the loads and when this generated power exceeds theload demand then the surplus of energy will be stored inthe battery bank This energy (battery) will be used whena deficiency of power occurs to meet the load The chargedquantity of the battery bank has the constraint SOCmin leSOC(119905) le SOCmax The SOCmin is at 40 while that ofSOCmax is at 80 The approach involves the minimizationof a cost function subject to a set of equality and inequalityconstraints
32 Cost Model (Economic and Environmental Costs) ofEnergy Systems The equation for estimating the level of opti-mization of photovoltaic energy solution being consideredfor the orphanage and a location is derived as economic andenvironmental cost (carbon credit of CO
2) model of running
solar-photovoltaic + batteries and calculated as [15]
where 119862acap119904 is annualized capital cost of solar power 119862arep119904is annualized replacement cost of solar power 119862aop119904 isannualized operating cost of solar power 119862emissions is cost ofemissions119862acap119887 is annualized capital cost of batteries power119862arep119887 is annualized replacement cost of batteries power and119862aop119887 is annualized operating cost of batteries power
The mathematical model derived in (9) estimates thelife-cycle cost of the systems (solar-photovoltaic) which is
Table 2 Simulation results of electricity production consumptionlosses and excess (kWhyr)
ComponentQuantity
ofelectricity(kWhyr)
Production from PV array 1916Losses from the battery 122Losses from the inverter 234Other losses such as cables 52Consumption from AC load 1329Excess electricity 179
the total cost of installing and operating the system over itslifetime The output when run with HOMER softwaretoolwill give the optimal configuration of the energy system thattakes into account technical and economic performance ofsupply options
Net Present Cost (NPC) for Energy Systems The total netpresent cost (NPC) of a system is the present value of all thecosts that it incurs over its lifetimeminus the present value ofall the revenue that it earns over its lifetime Revenues includesalvage value and grid sales revenue The net present cost(NPC) for each component is derived using [9 12ndash14 16 17]
119862NPC =119862anntot
CRF (119894 119877proj) (10)
where the capital recovery factor is [9 12ndash14 16 17]
CRF = 119894 sdot (1 + 119894)119873
(1 + 119894)119873
minus 1 (11)
The economic optimization identifies the most financiallyattractive solution For this research paper HOMER version28 beta has been used as the sizing and optimization softwaretool It contains a number of energy component modelsand evaluates suitable technology options based on cost andavailability of resources [18]
33 Configuration and Optimization of Stand-Alone Pho-tovoltaic Energy System Stand-alone photovoltaic systemtypically has an electricity generation device equipped witha wiring setup and supporting structure as well as thenecessary BOS (balance of system) components (ie thebattery bank the charge controller and the DCAC inverter)The selection of components of energy system is doneusing Hybrid Optimization Model for Electric Renewables(HOMER)design software developed by theNational Renew-able Energy Laboratory accurate enough to reliably predictsystem performance HOMER is an optimization modelwhich performsmany hundreds or thousands of approximatesimulations in order to design the optimal system Thediagram of the completed stand-alone photovoltaic energysystem can be seen in Figure 3
Journal of Renewable Energy 5
Table 3 Simulation results of economic cost
Component Capital ($) Replacement ($) O and M ($) lowastSalvage ($) Total NPC ($)PV 2800 0 1606 0 4406Surrette 6CS25P 54960 23855 28 minus4989 73853Converter 200 0 0 0 200System 57960 23855 1633 minus4989 78459lowastSalvage value is the value remaining in a component of the power system at the end of the project lifetime that is the salvage value of a component is directlyproportional to its remaining life
Jan Feb Mar Apr May June JulyAug Sep Oct Nov Dec000005010015020025030035
Pow
er (k
W)
Monthly average electric production
PV
Figure 4 Electrical production of PV energy system
00
02
04
06
08
10
12
Jan Feb Mar AprMayJune July Aug Sep Oct NovDec40
50
60
70
80
90
100
Batte
ry st
ate o
f cha
rge (
)
Pow
er (k
W)
Excess electricityBattery state of charge
Figure 5 Battery state of charge versus excess electricity
4 Results and Discussion
41 Results The optimization result shows that sixteen solu-tions were simulated one was feasible which is PV-batteryoption with 14 kW PV 48 Surrette 6CS25P battery and 1 kWinverter fifteen were infeasible due to the capacity shortageconstraint Twenty-four were omitted (twenty-two due toinfeasibility one for lacking a converter and the remainingone for having an unnecessary converter) The obtainedresults provide information concerning the electricity pro-duction consumption losses excess and economic costs ofthe feasible system and are given in Tables 2 and 3 and shownin Figures 4 5 and 6
PV Surrette 6CS25P Converter0
20000
40000
60000
80000
Net
pre
sent
cost
($)
Cash flow summary
PVSurrette 6CS25P
Converter
Figure 6 Net present cost of component of PV energy system
42 Discussion
Electricity Production The PV array in this orphanage gen-erates 1916 kWh of electricity per year which effectivelypowers the load demand of 1329 kWh per year with littleexcess electricity of 179 kW per year as shown in Table 2 andthe electrical production of PV energy system is shown inFigure 4
Losses from the System A battery is used to store excess energyfor later use The conversion efficiency of batteries is notperfect and energy is usually lost as heat during chemicalreaction that is during charging or recharging Also theamount of energy that will be delivered from the battery ismanaged by the inverterThe inverter connects directly to thebattery bank and converts the direct current (DC) electricalenergy from the battery bank to alternative current (AC)electrical energy which is the energy that orphanages andmost residential homes use During the conversion energy isalso lost Other losses such as cables were calculated and theamount of energy that is lost from the system was tabulatedFrom Table 2 it was shown that losses from the battery havea total of 122 kWhyr losses from the inverter have a total of234 kWhyr and other losses have 52 kWhyrmaking a grandtotal of 408 kWhyr energy losses from the system as shownin Table 2Excess Electricity Excess electricity always occurs whenthe battery state of charge (SOC) is at 98 upwards andthis is between Januaries and Aprils As of May when thesolar radiation is low the battery is at 96 downward anddischarges much and there will be no excess electricity from
6 Journal of Renewable Energy
Figure 7 HOMER simulator diagram of photovoltaic energysystem and the optimization results
Figure 8 HOMER showing the simulation results of economic costof component of PV energy system
Figure 9 HOMER showing the electricity production of PV energysystem
Figure 10 HOMER showing the battery state of charge and losses
Figure 11 HOMER showing the inverter losses
Figure 12 HOMER showing the result of the emissions
Figure 13HOMER showing the battery state of charge versus excesselectricity
Figure 14 HOMER showing the optimization report
Journal of Renewable Energy 7
this point downwardThe battery state of charge versus excesselectricity is shown in Figure 5
Economic Costs Batteries are considered as a major costfactor in small-scale stand-alone power systems [15] Theoptimization of the system is carried out by modifyingthe size of the batteries until a configuration that ensuressufficient autonomy was achieved with the least net presentcost (NPC) The salvage value was used to calculate theannualized replacement cost Battery is the only componentthat has replacement cost (23855$) and therefore has salvagevalue (minus4989$) because it did not last till project lifetimeand the replacement extended the estimated project lifetimewhich was deducted from the system cost (73853$) as showninTable 3 and the net present cost of component of PV energysystem is shown in Figure 6
The software solutions showing the runningprogram with the results are shown inFigures 7 8 9 10 11 12 13 and 14
5 Conclusion
The optimal design of PVbattery energy system was carriedout minimizing the net present cost (NPC) by varying thesize of the batteries until a configuration that produces thedesired power needs of the orphanage is achieved This opti-mization study indicates that energy requirements to provideelectricity for an orphanage in Nigeria can be accomplishedby 14 kW PV 48 Surrette 6CS25P battery and 1 kW inverterThe PV system is in significant mode during the day timeparticularly in the dry season but at night and other cloudydays the battery compensates Due to the abundance of solarresource in Nigeria and having no environmental impact interms of CO
2 solar energy can be a choice for green power
solutions in powering the orphanages located in remote areas
Abbreviations
NASA National Aeronautics and SpaceAdministration
HOMER Hybrid Optimization Model for ElectricRenewables
SHS Solar home systemPV PhotovoltaicDC Direct currentAC Alternate currentSOC State of chargeDOD Depth of dischargeNPC Net present costBOS Balance of systemMin MinimumMax Maximum
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The author would like to thank Professor Chinedu Ositad-inma Nebo of Ministry of Power Nigeria for his usefuldiscussion on the subject
References
[1] H Von ldquoMini-grid system for rural electrification in the greatMekong sub-regional countriesrdquo in Renewable Energies andEnergy Efficiency vol 6 University of Kassel Kassel Germany2007
[2] F Gerald ldquoPhotovoltaic applications in rural areas of the devel-opingworldrdquo Tech Rep no 304World BankWashington DCUSA 1995
[3] A Cabraal M Cosgrove Davies and L Schaeffer ldquoBestpractices for photovoltaic household electrification programslessons from experiences in selected countriesrdquo Tech Rep no324 World Bank Washington DC USA 1996
[4] A Cabraal M Cosgrove Davies and L Schaeffer ldquoAcceleratingsustainable photovoltaic market developmentrdquo Progress in Pho-tovoltaics Research and Applications vol 6 no 5 pp 297ndash3061998
[5] D Kammen ldquoPromoting appropriate energy technologies inthe developing worldrdquo Environment vol 41 no 5 pp 11ndash15 34ndash41 1999
[6] K Kapadia ldquoOff-grid in Asia the solar electricity businessrdquoRenewable Energy World vol 2 no 6 pp 22ndash33 1999
[7] G Loois and B van Hemert Stand-Alone Photovoltaic Applica-tions Lessons Learned James amp James London UK 1999
[8] NASA 2013 httpseosweblarcnasagov[9] V A Ani ldquoOptimal energy system for single household in
Nigeriardquo International Journal of Energy Optimization andEngineering vol 2 no 3 26 pages 2013
[10] S Ashok ldquoOptimised model for community-based hybridenergy systemrdquo Renewable Energy vol 32 no 7 pp 1155ndash11642007
[11] A Gupta R P Saini andM P Sharma ldquoSteady-state modellingof hybrid energy system for off grid electrification of cluster ofvillagesrdquo Renewable Energy vol 35 no 2 pp 520ndash535 2010
[12] D K Lal B B Dash and A K Akella ldquoOptimization ofPVWindMicro-Hydrodiesel hybrid power system in homerfor the study areardquo International Journal on Electrical Engineer-ing and Informatics vol 3 no 3 pp 307ndash325 2011
[13] K Sopian A Zaharim Y Ali Z M Nopiah J A Razak andN S Muhammad ldquoOptimal operational strategy for hybrid
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
systemThe theoretical aspects are given below and based on[9ndash11]
Mathematical Model of Solar Photovoltaic Using the solarradiation available the hourly energy output of the PVgenerator (119864PV) can be calculated according to the followingequation [9 12 13]
119864PV = 119866 (119905) times 119860 times 119875 times 120578PV (1)
where 119866(119905) is the hourly irradiance in kWhm2 119860 is thesurface area in m2 119875 is the PV penetration level factor and120578PV is the efficiency of PV generator
Mathematical Model of Charge Controller To prevent over-charging of a battery a charge controller is used to sensewhen the batteries are fully charged and to stop or decreasethe amount of energy flowing from the energy source to thebatteries The model of the charge controller is presentedbelow [9]
119864CC-OUT (119905) = 119864CC-IN (119905) times 120578CC
119864CC-IN (119905) = 119864SUR-DC (119905) (2)
where 119864CC-OUT(119905) is the hourly energy output from chargecontroller kWh 119864CC-IN(119905) is the hourly energy input tocharge controller kWh 120578CC is the efficiency of a chargecontroller and 119864SUR-DC(119905) is the amount of surplus energyfrom DC sources kWh
Mathematical Model of Battery Bank The battery stateof charge (SOC) is the cumulative sum of the dailychargedischarge transfers The battery serves as an energysource entity when discharging and a load when chargingAt any hour t the state of the battery is related to theprevious state of charge and to the energy production andconsumption situation of the system during the time from119905 minus 1 to t
During the charging process when the total output fromrenewable sources exceeds the load demand the availablebattery bank capacity at hour t can be described by [9 12ndash14]
119864BAT (119905) = 119864BAT (119905 minus 1) minus 119864CC-OUT (119905) times 120578CHG (3)
where 119864BAT(119905) is the energy stored in the battery at hour tkWh 119864BAT(119905 minus 1) is the energy stored in the battery at hour119905 minus 1 kWh and 120578CHG is the battery charging efficiency
On the other hand when the load demand is greaterthan the available energy generated the battery bank isin discharging state Therefore the available battery bankcapacity at hour 119905 can be expressed as [9 12ndash14]
119864BAT (119905) = 119864BAT (119905 minus 1) minus 119864Needed (119905) (4)
where 119864Needed(119905) is the hourly load demand or energy neededat a particular period of time
Let 119889 be the difference between minimum allowableSOC voltage limit and the maximum SOC voltage across thebattery terminals when it is fully charged which is equal to1 minus DOD100
So the depth of discharge (DOD) is as follows
DOD = (1 minus 119889) times 100 (5)
DOD is a measure of how much energy has been withdrawnfrom a storage device expressed as a percentage of fullcapacity The maximum value of SOC is 1 and the minimumSOC measured in percentage is determined by maximumdepth of discharge (DOD)
SOCMin = 1 minusDOD100 (6)
Mathematical Model of Inverter In the proposed scheme thePV panel and battery systems are connected with DC buswhile the electric loads are connected with AC bus as shownin Figure 3
The invertermodels for photovoltaic and battery bank aregiven below [15]
119864PV-INVBAT-INV (119905)
= (119864PV (119905) +119864BAT (119905 minus 1) minus 119864LOAD (119905)
120578INV times 120578DCHG) times 120578REC
(7)
where 119864PV-INVBAT-INV(119905) is the hourly energy output frominverter kWh 119864BAT(119905 minus 1) is the energy stored in the battery
4 Journal of Renewable Energy
Figure 3 Schematic diagram of photovoltaic energy system
at hour 119905 minus 1 kWh 119864Load(119905) is the hourly energy consumedby the load side kWh 120578INV is the efficiency of inverter and120578DCHG is the battery discharging efficiency
31 Power Generation Model Total power generated at anytime t is given by [9 12ndash14]
119875 (119905) =
119873119875
sumPV=1119875PV (8)
where119873119875are number of PV cells This generated power will
feed the loads and when this generated power exceeds theload demand then the surplus of energy will be stored inthe battery bank This energy (battery) will be used whena deficiency of power occurs to meet the load The chargedquantity of the battery bank has the constraint SOCmin leSOC(119905) le SOCmax The SOCmin is at 40 while that ofSOCmax is at 80 The approach involves the minimizationof a cost function subject to a set of equality and inequalityconstraints
32 Cost Model (Economic and Environmental Costs) ofEnergy Systems The equation for estimating the level of opti-mization of photovoltaic energy solution being consideredfor the orphanage and a location is derived as economic andenvironmental cost (carbon credit of CO
2) model of running
solar-photovoltaic + batteries and calculated as [15]
where 119862acap119904 is annualized capital cost of solar power 119862arep119904is annualized replacement cost of solar power 119862aop119904 isannualized operating cost of solar power 119862emissions is cost ofemissions119862acap119887 is annualized capital cost of batteries power119862arep119887 is annualized replacement cost of batteries power and119862aop119887 is annualized operating cost of batteries power
The mathematical model derived in (9) estimates thelife-cycle cost of the systems (solar-photovoltaic) which is
Table 2 Simulation results of electricity production consumptionlosses and excess (kWhyr)
ComponentQuantity
ofelectricity(kWhyr)
Production from PV array 1916Losses from the battery 122Losses from the inverter 234Other losses such as cables 52Consumption from AC load 1329Excess electricity 179
the total cost of installing and operating the system over itslifetime The output when run with HOMER softwaretoolwill give the optimal configuration of the energy system thattakes into account technical and economic performance ofsupply options
Net Present Cost (NPC) for Energy Systems The total netpresent cost (NPC) of a system is the present value of all thecosts that it incurs over its lifetimeminus the present value ofall the revenue that it earns over its lifetime Revenues includesalvage value and grid sales revenue The net present cost(NPC) for each component is derived using [9 12ndash14 16 17]
119862NPC =119862anntot
CRF (119894 119877proj) (10)
where the capital recovery factor is [9 12ndash14 16 17]
CRF = 119894 sdot (1 + 119894)119873
(1 + 119894)119873
minus 1 (11)
The economic optimization identifies the most financiallyattractive solution For this research paper HOMER version28 beta has been used as the sizing and optimization softwaretool It contains a number of energy component modelsand evaluates suitable technology options based on cost andavailability of resources [18]
33 Configuration and Optimization of Stand-Alone Pho-tovoltaic Energy System Stand-alone photovoltaic systemtypically has an electricity generation device equipped witha wiring setup and supporting structure as well as thenecessary BOS (balance of system) components (ie thebattery bank the charge controller and the DCAC inverter)The selection of components of energy system is doneusing Hybrid Optimization Model for Electric Renewables(HOMER)design software developed by theNational Renew-able Energy Laboratory accurate enough to reliably predictsystem performance HOMER is an optimization modelwhich performsmany hundreds or thousands of approximatesimulations in order to design the optimal system Thediagram of the completed stand-alone photovoltaic energysystem can be seen in Figure 3
Journal of Renewable Energy 5
Table 3 Simulation results of economic cost
Component Capital ($) Replacement ($) O and M ($) lowastSalvage ($) Total NPC ($)PV 2800 0 1606 0 4406Surrette 6CS25P 54960 23855 28 minus4989 73853Converter 200 0 0 0 200System 57960 23855 1633 minus4989 78459lowastSalvage value is the value remaining in a component of the power system at the end of the project lifetime that is the salvage value of a component is directlyproportional to its remaining life
Jan Feb Mar Apr May June JulyAug Sep Oct Nov Dec000005010015020025030035
Pow
er (k
W)
Monthly average electric production
PV
Figure 4 Electrical production of PV energy system
00
02
04
06
08
10
12
Jan Feb Mar AprMayJune July Aug Sep Oct NovDec40
50
60
70
80
90
100
Batte
ry st
ate o
f cha
rge (
)
Pow
er (k
W)
Excess electricityBattery state of charge
Figure 5 Battery state of charge versus excess electricity
4 Results and Discussion
41 Results The optimization result shows that sixteen solu-tions were simulated one was feasible which is PV-batteryoption with 14 kW PV 48 Surrette 6CS25P battery and 1 kWinverter fifteen were infeasible due to the capacity shortageconstraint Twenty-four were omitted (twenty-two due toinfeasibility one for lacking a converter and the remainingone for having an unnecessary converter) The obtainedresults provide information concerning the electricity pro-duction consumption losses excess and economic costs ofthe feasible system and are given in Tables 2 and 3 and shownin Figures 4 5 and 6
PV Surrette 6CS25P Converter0
20000
40000
60000
80000
Net
pre
sent
cost
($)
Cash flow summary
PVSurrette 6CS25P
Converter
Figure 6 Net present cost of component of PV energy system
42 Discussion
Electricity Production The PV array in this orphanage gen-erates 1916 kWh of electricity per year which effectivelypowers the load demand of 1329 kWh per year with littleexcess electricity of 179 kW per year as shown in Table 2 andthe electrical production of PV energy system is shown inFigure 4
Losses from the System A battery is used to store excess energyfor later use The conversion efficiency of batteries is notperfect and energy is usually lost as heat during chemicalreaction that is during charging or recharging Also theamount of energy that will be delivered from the battery ismanaged by the inverterThe inverter connects directly to thebattery bank and converts the direct current (DC) electricalenergy from the battery bank to alternative current (AC)electrical energy which is the energy that orphanages andmost residential homes use During the conversion energy isalso lost Other losses such as cables were calculated and theamount of energy that is lost from the system was tabulatedFrom Table 2 it was shown that losses from the battery havea total of 122 kWhyr losses from the inverter have a total of234 kWhyr and other losses have 52 kWhyrmaking a grandtotal of 408 kWhyr energy losses from the system as shownin Table 2Excess Electricity Excess electricity always occurs whenthe battery state of charge (SOC) is at 98 upwards andthis is between Januaries and Aprils As of May when thesolar radiation is low the battery is at 96 downward anddischarges much and there will be no excess electricity from
6 Journal of Renewable Energy
Figure 7 HOMER simulator diagram of photovoltaic energysystem and the optimization results
Figure 8 HOMER showing the simulation results of economic costof component of PV energy system
Figure 9 HOMER showing the electricity production of PV energysystem
Figure 10 HOMER showing the battery state of charge and losses
Figure 11 HOMER showing the inverter losses
Figure 12 HOMER showing the result of the emissions
Figure 13HOMER showing the battery state of charge versus excesselectricity
Figure 14 HOMER showing the optimization report
Journal of Renewable Energy 7
this point downwardThe battery state of charge versus excesselectricity is shown in Figure 5
Economic Costs Batteries are considered as a major costfactor in small-scale stand-alone power systems [15] Theoptimization of the system is carried out by modifyingthe size of the batteries until a configuration that ensuressufficient autonomy was achieved with the least net presentcost (NPC) The salvage value was used to calculate theannualized replacement cost Battery is the only componentthat has replacement cost (23855$) and therefore has salvagevalue (minus4989$) because it did not last till project lifetimeand the replacement extended the estimated project lifetimewhich was deducted from the system cost (73853$) as showninTable 3 and the net present cost of component of PV energysystem is shown in Figure 6
The software solutions showing the runningprogram with the results are shown inFigures 7 8 9 10 11 12 13 and 14
5 Conclusion
The optimal design of PVbattery energy system was carriedout minimizing the net present cost (NPC) by varying thesize of the batteries until a configuration that produces thedesired power needs of the orphanage is achieved This opti-mization study indicates that energy requirements to provideelectricity for an orphanage in Nigeria can be accomplishedby 14 kW PV 48 Surrette 6CS25P battery and 1 kW inverterThe PV system is in significant mode during the day timeparticularly in the dry season but at night and other cloudydays the battery compensates Due to the abundance of solarresource in Nigeria and having no environmental impact interms of CO
2 solar energy can be a choice for green power
solutions in powering the orphanages located in remote areas
Abbreviations
NASA National Aeronautics and SpaceAdministration
HOMER Hybrid Optimization Model for ElectricRenewables
SHS Solar home systemPV PhotovoltaicDC Direct currentAC Alternate currentSOC State of chargeDOD Depth of dischargeNPC Net present costBOS Balance of systemMin MinimumMax Maximum
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The author would like to thank Professor Chinedu Ositad-inma Nebo of Ministry of Power Nigeria for his usefuldiscussion on the subject
References
[1] H Von ldquoMini-grid system for rural electrification in the greatMekong sub-regional countriesrdquo in Renewable Energies andEnergy Efficiency vol 6 University of Kassel Kassel Germany2007
[2] F Gerald ldquoPhotovoltaic applications in rural areas of the devel-opingworldrdquo Tech Rep no 304World BankWashington DCUSA 1995
[3] A Cabraal M Cosgrove Davies and L Schaeffer ldquoBestpractices for photovoltaic household electrification programslessons from experiences in selected countriesrdquo Tech Rep no324 World Bank Washington DC USA 1996
[4] A Cabraal M Cosgrove Davies and L Schaeffer ldquoAcceleratingsustainable photovoltaic market developmentrdquo Progress in Pho-tovoltaics Research and Applications vol 6 no 5 pp 297ndash3061998
[5] D Kammen ldquoPromoting appropriate energy technologies inthe developing worldrdquo Environment vol 41 no 5 pp 11ndash15 34ndash41 1999
[6] K Kapadia ldquoOff-grid in Asia the solar electricity businessrdquoRenewable Energy World vol 2 no 6 pp 22ndash33 1999
[7] G Loois and B van Hemert Stand-Alone Photovoltaic Applica-tions Lessons Learned James amp James London UK 1999
[8] NASA 2013 httpseosweblarcnasagov[9] V A Ani ldquoOptimal energy system for single household in
Nigeriardquo International Journal of Energy Optimization andEngineering vol 2 no 3 26 pages 2013
[10] S Ashok ldquoOptimised model for community-based hybridenergy systemrdquo Renewable Energy vol 32 no 7 pp 1155ndash11642007
[11] A Gupta R P Saini andM P Sharma ldquoSteady-state modellingof hybrid energy system for off grid electrification of cluster ofvillagesrdquo Renewable Energy vol 35 no 2 pp 520ndash535 2010
[12] D K Lal B B Dash and A K Akella ldquoOptimization ofPVWindMicro-Hydrodiesel hybrid power system in homerfor the study areardquo International Journal on Electrical Engineer-ing and Informatics vol 3 no 3 pp 307ndash325 2011
[13] K Sopian A Zaharim Y Ali Z M Nopiah J A Razak andN S Muhammad ldquoOptimal operational strategy for hybrid
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
4 Journal of Renewable Energy
Figure 3 Schematic diagram of photovoltaic energy system
at hour 119905 minus 1 kWh 119864Load(119905) is the hourly energy consumedby the load side kWh 120578INV is the efficiency of inverter and120578DCHG is the battery discharging efficiency
31 Power Generation Model Total power generated at anytime t is given by [9 12ndash14]
119875 (119905) =
119873119875
sumPV=1119875PV (8)
where119873119875are number of PV cells This generated power will
feed the loads and when this generated power exceeds theload demand then the surplus of energy will be stored inthe battery bank This energy (battery) will be used whena deficiency of power occurs to meet the load The chargedquantity of the battery bank has the constraint SOCmin leSOC(119905) le SOCmax The SOCmin is at 40 while that ofSOCmax is at 80 The approach involves the minimizationof a cost function subject to a set of equality and inequalityconstraints
32 Cost Model (Economic and Environmental Costs) ofEnergy Systems The equation for estimating the level of opti-mization of photovoltaic energy solution being consideredfor the orphanage and a location is derived as economic andenvironmental cost (carbon credit of CO
2) model of running
solar-photovoltaic + batteries and calculated as [15]
where 119862acap119904 is annualized capital cost of solar power 119862arep119904is annualized replacement cost of solar power 119862aop119904 isannualized operating cost of solar power 119862emissions is cost ofemissions119862acap119887 is annualized capital cost of batteries power119862arep119887 is annualized replacement cost of batteries power and119862aop119887 is annualized operating cost of batteries power
The mathematical model derived in (9) estimates thelife-cycle cost of the systems (solar-photovoltaic) which is
Table 2 Simulation results of electricity production consumptionlosses and excess (kWhyr)
ComponentQuantity
ofelectricity(kWhyr)
Production from PV array 1916Losses from the battery 122Losses from the inverter 234Other losses such as cables 52Consumption from AC load 1329Excess electricity 179
the total cost of installing and operating the system over itslifetime The output when run with HOMER softwaretoolwill give the optimal configuration of the energy system thattakes into account technical and economic performance ofsupply options
Net Present Cost (NPC) for Energy Systems The total netpresent cost (NPC) of a system is the present value of all thecosts that it incurs over its lifetimeminus the present value ofall the revenue that it earns over its lifetime Revenues includesalvage value and grid sales revenue The net present cost(NPC) for each component is derived using [9 12ndash14 16 17]
119862NPC =119862anntot
CRF (119894 119877proj) (10)
where the capital recovery factor is [9 12ndash14 16 17]
CRF = 119894 sdot (1 + 119894)119873
(1 + 119894)119873
minus 1 (11)
The economic optimization identifies the most financiallyattractive solution For this research paper HOMER version28 beta has been used as the sizing and optimization softwaretool It contains a number of energy component modelsand evaluates suitable technology options based on cost andavailability of resources [18]
33 Configuration and Optimization of Stand-Alone Pho-tovoltaic Energy System Stand-alone photovoltaic systemtypically has an electricity generation device equipped witha wiring setup and supporting structure as well as thenecessary BOS (balance of system) components (ie thebattery bank the charge controller and the DCAC inverter)The selection of components of energy system is doneusing Hybrid Optimization Model for Electric Renewables(HOMER)design software developed by theNational Renew-able Energy Laboratory accurate enough to reliably predictsystem performance HOMER is an optimization modelwhich performsmany hundreds or thousands of approximatesimulations in order to design the optimal system Thediagram of the completed stand-alone photovoltaic energysystem can be seen in Figure 3
Journal of Renewable Energy 5
Table 3 Simulation results of economic cost
Component Capital ($) Replacement ($) O and M ($) lowastSalvage ($) Total NPC ($)PV 2800 0 1606 0 4406Surrette 6CS25P 54960 23855 28 minus4989 73853Converter 200 0 0 0 200System 57960 23855 1633 minus4989 78459lowastSalvage value is the value remaining in a component of the power system at the end of the project lifetime that is the salvage value of a component is directlyproportional to its remaining life
Jan Feb Mar Apr May June JulyAug Sep Oct Nov Dec000005010015020025030035
Pow
er (k
W)
Monthly average electric production
PV
Figure 4 Electrical production of PV energy system
00
02
04
06
08
10
12
Jan Feb Mar AprMayJune July Aug Sep Oct NovDec40
50
60
70
80
90
100
Batte
ry st
ate o
f cha
rge (
)
Pow
er (k
W)
Excess electricityBattery state of charge
Figure 5 Battery state of charge versus excess electricity
4 Results and Discussion
41 Results The optimization result shows that sixteen solu-tions were simulated one was feasible which is PV-batteryoption with 14 kW PV 48 Surrette 6CS25P battery and 1 kWinverter fifteen were infeasible due to the capacity shortageconstraint Twenty-four were omitted (twenty-two due toinfeasibility one for lacking a converter and the remainingone for having an unnecessary converter) The obtainedresults provide information concerning the electricity pro-duction consumption losses excess and economic costs ofthe feasible system and are given in Tables 2 and 3 and shownin Figures 4 5 and 6
PV Surrette 6CS25P Converter0
20000
40000
60000
80000
Net
pre
sent
cost
($)
Cash flow summary
PVSurrette 6CS25P
Converter
Figure 6 Net present cost of component of PV energy system
42 Discussion
Electricity Production The PV array in this orphanage gen-erates 1916 kWh of electricity per year which effectivelypowers the load demand of 1329 kWh per year with littleexcess electricity of 179 kW per year as shown in Table 2 andthe electrical production of PV energy system is shown inFigure 4
Losses from the System A battery is used to store excess energyfor later use The conversion efficiency of batteries is notperfect and energy is usually lost as heat during chemicalreaction that is during charging or recharging Also theamount of energy that will be delivered from the battery ismanaged by the inverterThe inverter connects directly to thebattery bank and converts the direct current (DC) electricalenergy from the battery bank to alternative current (AC)electrical energy which is the energy that orphanages andmost residential homes use During the conversion energy isalso lost Other losses such as cables were calculated and theamount of energy that is lost from the system was tabulatedFrom Table 2 it was shown that losses from the battery havea total of 122 kWhyr losses from the inverter have a total of234 kWhyr and other losses have 52 kWhyrmaking a grandtotal of 408 kWhyr energy losses from the system as shownin Table 2Excess Electricity Excess electricity always occurs whenthe battery state of charge (SOC) is at 98 upwards andthis is between Januaries and Aprils As of May when thesolar radiation is low the battery is at 96 downward anddischarges much and there will be no excess electricity from
6 Journal of Renewable Energy
Figure 7 HOMER simulator diagram of photovoltaic energysystem and the optimization results
Figure 8 HOMER showing the simulation results of economic costof component of PV energy system
Figure 9 HOMER showing the electricity production of PV energysystem
Figure 10 HOMER showing the battery state of charge and losses
Figure 11 HOMER showing the inverter losses
Figure 12 HOMER showing the result of the emissions
Figure 13HOMER showing the battery state of charge versus excesselectricity
Figure 14 HOMER showing the optimization report
Journal of Renewable Energy 7
this point downwardThe battery state of charge versus excesselectricity is shown in Figure 5
Economic Costs Batteries are considered as a major costfactor in small-scale stand-alone power systems [15] Theoptimization of the system is carried out by modifyingthe size of the batteries until a configuration that ensuressufficient autonomy was achieved with the least net presentcost (NPC) The salvage value was used to calculate theannualized replacement cost Battery is the only componentthat has replacement cost (23855$) and therefore has salvagevalue (minus4989$) because it did not last till project lifetimeand the replacement extended the estimated project lifetimewhich was deducted from the system cost (73853$) as showninTable 3 and the net present cost of component of PV energysystem is shown in Figure 6
The software solutions showing the runningprogram with the results are shown inFigures 7 8 9 10 11 12 13 and 14
5 Conclusion
The optimal design of PVbattery energy system was carriedout minimizing the net present cost (NPC) by varying thesize of the batteries until a configuration that produces thedesired power needs of the orphanage is achieved This opti-mization study indicates that energy requirements to provideelectricity for an orphanage in Nigeria can be accomplishedby 14 kW PV 48 Surrette 6CS25P battery and 1 kW inverterThe PV system is in significant mode during the day timeparticularly in the dry season but at night and other cloudydays the battery compensates Due to the abundance of solarresource in Nigeria and having no environmental impact interms of CO
2 solar energy can be a choice for green power
solutions in powering the orphanages located in remote areas
Abbreviations
NASA National Aeronautics and SpaceAdministration
HOMER Hybrid Optimization Model for ElectricRenewables
SHS Solar home systemPV PhotovoltaicDC Direct currentAC Alternate currentSOC State of chargeDOD Depth of dischargeNPC Net present costBOS Balance of systemMin MinimumMax Maximum
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The author would like to thank Professor Chinedu Ositad-inma Nebo of Ministry of Power Nigeria for his usefuldiscussion on the subject
References
[1] H Von ldquoMini-grid system for rural electrification in the greatMekong sub-regional countriesrdquo in Renewable Energies andEnergy Efficiency vol 6 University of Kassel Kassel Germany2007
[2] F Gerald ldquoPhotovoltaic applications in rural areas of the devel-opingworldrdquo Tech Rep no 304World BankWashington DCUSA 1995
[3] A Cabraal M Cosgrove Davies and L Schaeffer ldquoBestpractices for photovoltaic household electrification programslessons from experiences in selected countriesrdquo Tech Rep no324 World Bank Washington DC USA 1996
[4] A Cabraal M Cosgrove Davies and L Schaeffer ldquoAcceleratingsustainable photovoltaic market developmentrdquo Progress in Pho-tovoltaics Research and Applications vol 6 no 5 pp 297ndash3061998
[5] D Kammen ldquoPromoting appropriate energy technologies inthe developing worldrdquo Environment vol 41 no 5 pp 11ndash15 34ndash41 1999
[6] K Kapadia ldquoOff-grid in Asia the solar electricity businessrdquoRenewable Energy World vol 2 no 6 pp 22ndash33 1999
[7] G Loois and B van Hemert Stand-Alone Photovoltaic Applica-tions Lessons Learned James amp James London UK 1999
[8] NASA 2013 httpseosweblarcnasagov[9] V A Ani ldquoOptimal energy system for single household in
Nigeriardquo International Journal of Energy Optimization andEngineering vol 2 no 3 26 pages 2013
[10] S Ashok ldquoOptimised model for community-based hybridenergy systemrdquo Renewable Energy vol 32 no 7 pp 1155ndash11642007
[11] A Gupta R P Saini andM P Sharma ldquoSteady-state modellingof hybrid energy system for off grid electrification of cluster ofvillagesrdquo Renewable Energy vol 35 no 2 pp 520ndash535 2010
[12] D K Lal B B Dash and A K Akella ldquoOptimization ofPVWindMicro-Hydrodiesel hybrid power system in homerfor the study areardquo International Journal on Electrical Engineer-ing and Informatics vol 3 no 3 pp 307ndash325 2011
[13] K Sopian A Zaharim Y Ali Z M Nopiah J A Razak andN S Muhammad ldquoOptimal operational strategy for hybrid
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Renewable Energy 5
Table 3 Simulation results of economic cost
Component Capital ($) Replacement ($) O and M ($) lowastSalvage ($) Total NPC ($)PV 2800 0 1606 0 4406Surrette 6CS25P 54960 23855 28 minus4989 73853Converter 200 0 0 0 200System 57960 23855 1633 minus4989 78459lowastSalvage value is the value remaining in a component of the power system at the end of the project lifetime that is the salvage value of a component is directlyproportional to its remaining life
Jan Feb Mar Apr May June JulyAug Sep Oct Nov Dec000005010015020025030035
Pow
er (k
W)
Monthly average electric production
PV
Figure 4 Electrical production of PV energy system
00
02
04
06
08
10
12
Jan Feb Mar AprMayJune July Aug Sep Oct NovDec40
50
60
70
80
90
100
Batte
ry st
ate o
f cha
rge (
)
Pow
er (k
W)
Excess electricityBattery state of charge
Figure 5 Battery state of charge versus excess electricity
4 Results and Discussion
41 Results The optimization result shows that sixteen solu-tions were simulated one was feasible which is PV-batteryoption with 14 kW PV 48 Surrette 6CS25P battery and 1 kWinverter fifteen were infeasible due to the capacity shortageconstraint Twenty-four were omitted (twenty-two due toinfeasibility one for lacking a converter and the remainingone for having an unnecessary converter) The obtainedresults provide information concerning the electricity pro-duction consumption losses excess and economic costs ofthe feasible system and are given in Tables 2 and 3 and shownin Figures 4 5 and 6
PV Surrette 6CS25P Converter0
20000
40000
60000
80000
Net
pre
sent
cost
($)
Cash flow summary
PVSurrette 6CS25P
Converter
Figure 6 Net present cost of component of PV energy system
42 Discussion
Electricity Production The PV array in this orphanage gen-erates 1916 kWh of electricity per year which effectivelypowers the load demand of 1329 kWh per year with littleexcess electricity of 179 kW per year as shown in Table 2 andthe electrical production of PV energy system is shown inFigure 4
Losses from the System A battery is used to store excess energyfor later use The conversion efficiency of batteries is notperfect and energy is usually lost as heat during chemicalreaction that is during charging or recharging Also theamount of energy that will be delivered from the battery ismanaged by the inverterThe inverter connects directly to thebattery bank and converts the direct current (DC) electricalenergy from the battery bank to alternative current (AC)electrical energy which is the energy that orphanages andmost residential homes use During the conversion energy isalso lost Other losses such as cables were calculated and theamount of energy that is lost from the system was tabulatedFrom Table 2 it was shown that losses from the battery havea total of 122 kWhyr losses from the inverter have a total of234 kWhyr and other losses have 52 kWhyrmaking a grandtotal of 408 kWhyr energy losses from the system as shownin Table 2Excess Electricity Excess electricity always occurs whenthe battery state of charge (SOC) is at 98 upwards andthis is between Januaries and Aprils As of May when thesolar radiation is low the battery is at 96 downward anddischarges much and there will be no excess electricity from
6 Journal of Renewable Energy
Figure 7 HOMER simulator diagram of photovoltaic energysystem and the optimization results
Figure 8 HOMER showing the simulation results of economic costof component of PV energy system
Figure 9 HOMER showing the electricity production of PV energysystem
Figure 10 HOMER showing the battery state of charge and losses
Figure 11 HOMER showing the inverter losses
Figure 12 HOMER showing the result of the emissions
Figure 13HOMER showing the battery state of charge versus excesselectricity
Figure 14 HOMER showing the optimization report
Journal of Renewable Energy 7
this point downwardThe battery state of charge versus excesselectricity is shown in Figure 5
Economic Costs Batteries are considered as a major costfactor in small-scale stand-alone power systems [15] Theoptimization of the system is carried out by modifyingthe size of the batteries until a configuration that ensuressufficient autonomy was achieved with the least net presentcost (NPC) The salvage value was used to calculate theannualized replacement cost Battery is the only componentthat has replacement cost (23855$) and therefore has salvagevalue (minus4989$) because it did not last till project lifetimeand the replacement extended the estimated project lifetimewhich was deducted from the system cost (73853$) as showninTable 3 and the net present cost of component of PV energysystem is shown in Figure 6
The software solutions showing the runningprogram with the results are shown inFigures 7 8 9 10 11 12 13 and 14
5 Conclusion
The optimal design of PVbattery energy system was carriedout minimizing the net present cost (NPC) by varying thesize of the batteries until a configuration that produces thedesired power needs of the orphanage is achieved This opti-mization study indicates that energy requirements to provideelectricity for an orphanage in Nigeria can be accomplishedby 14 kW PV 48 Surrette 6CS25P battery and 1 kW inverterThe PV system is in significant mode during the day timeparticularly in the dry season but at night and other cloudydays the battery compensates Due to the abundance of solarresource in Nigeria and having no environmental impact interms of CO
2 solar energy can be a choice for green power
solutions in powering the orphanages located in remote areas
Abbreviations
NASA National Aeronautics and SpaceAdministration
HOMER Hybrid Optimization Model for ElectricRenewables
SHS Solar home systemPV PhotovoltaicDC Direct currentAC Alternate currentSOC State of chargeDOD Depth of dischargeNPC Net present costBOS Balance of systemMin MinimumMax Maximum
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The author would like to thank Professor Chinedu Ositad-inma Nebo of Ministry of Power Nigeria for his usefuldiscussion on the subject
References
[1] H Von ldquoMini-grid system for rural electrification in the greatMekong sub-regional countriesrdquo in Renewable Energies andEnergy Efficiency vol 6 University of Kassel Kassel Germany2007
[2] F Gerald ldquoPhotovoltaic applications in rural areas of the devel-opingworldrdquo Tech Rep no 304World BankWashington DCUSA 1995
[3] A Cabraal M Cosgrove Davies and L Schaeffer ldquoBestpractices for photovoltaic household electrification programslessons from experiences in selected countriesrdquo Tech Rep no324 World Bank Washington DC USA 1996
[4] A Cabraal M Cosgrove Davies and L Schaeffer ldquoAcceleratingsustainable photovoltaic market developmentrdquo Progress in Pho-tovoltaics Research and Applications vol 6 no 5 pp 297ndash3061998
[5] D Kammen ldquoPromoting appropriate energy technologies inthe developing worldrdquo Environment vol 41 no 5 pp 11ndash15 34ndash41 1999
[6] K Kapadia ldquoOff-grid in Asia the solar electricity businessrdquoRenewable Energy World vol 2 no 6 pp 22ndash33 1999
[7] G Loois and B van Hemert Stand-Alone Photovoltaic Applica-tions Lessons Learned James amp James London UK 1999
[8] NASA 2013 httpseosweblarcnasagov[9] V A Ani ldquoOptimal energy system for single household in
Nigeriardquo International Journal of Energy Optimization andEngineering vol 2 no 3 26 pages 2013
[10] S Ashok ldquoOptimised model for community-based hybridenergy systemrdquo Renewable Energy vol 32 no 7 pp 1155ndash11642007
[11] A Gupta R P Saini andM P Sharma ldquoSteady-state modellingof hybrid energy system for off grid electrification of cluster ofvillagesrdquo Renewable Energy vol 35 no 2 pp 520ndash535 2010
[12] D K Lal B B Dash and A K Akella ldquoOptimization ofPVWindMicro-Hydrodiesel hybrid power system in homerfor the study areardquo International Journal on Electrical Engineer-ing and Informatics vol 3 no 3 pp 307ndash325 2011
[13] K Sopian A Zaharim Y Ali Z M Nopiah J A Razak andN S Muhammad ldquoOptimal operational strategy for hybrid
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
6 Journal of Renewable Energy
Figure 7 HOMER simulator diagram of photovoltaic energysystem and the optimization results
Figure 8 HOMER showing the simulation results of economic costof component of PV energy system
Figure 9 HOMER showing the electricity production of PV energysystem
Figure 10 HOMER showing the battery state of charge and losses
Figure 11 HOMER showing the inverter losses
Figure 12 HOMER showing the result of the emissions
Figure 13HOMER showing the battery state of charge versus excesselectricity
Figure 14 HOMER showing the optimization report
Journal of Renewable Energy 7
this point downwardThe battery state of charge versus excesselectricity is shown in Figure 5
Economic Costs Batteries are considered as a major costfactor in small-scale stand-alone power systems [15] Theoptimization of the system is carried out by modifyingthe size of the batteries until a configuration that ensuressufficient autonomy was achieved with the least net presentcost (NPC) The salvage value was used to calculate theannualized replacement cost Battery is the only componentthat has replacement cost (23855$) and therefore has salvagevalue (minus4989$) because it did not last till project lifetimeand the replacement extended the estimated project lifetimewhich was deducted from the system cost (73853$) as showninTable 3 and the net present cost of component of PV energysystem is shown in Figure 6
The software solutions showing the runningprogram with the results are shown inFigures 7 8 9 10 11 12 13 and 14
5 Conclusion
The optimal design of PVbattery energy system was carriedout minimizing the net present cost (NPC) by varying thesize of the batteries until a configuration that produces thedesired power needs of the orphanage is achieved This opti-mization study indicates that energy requirements to provideelectricity for an orphanage in Nigeria can be accomplishedby 14 kW PV 48 Surrette 6CS25P battery and 1 kW inverterThe PV system is in significant mode during the day timeparticularly in the dry season but at night and other cloudydays the battery compensates Due to the abundance of solarresource in Nigeria and having no environmental impact interms of CO
2 solar energy can be a choice for green power
solutions in powering the orphanages located in remote areas
Abbreviations
NASA National Aeronautics and SpaceAdministration
HOMER Hybrid Optimization Model for ElectricRenewables
SHS Solar home systemPV PhotovoltaicDC Direct currentAC Alternate currentSOC State of chargeDOD Depth of dischargeNPC Net present costBOS Balance of systemMin MinimumMax Maximum
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The author would like to thank Professor Chinedu Ositad-inma Nebo of Ministry of Power Nigeria for his usefuldiscussion on the subject
References
[1] H Von ldquoMini-grid system for rural electrification in the greatMekong sub-regional countriesrdquo in Renewable Energies andEnergy Efficiency vol 6 University of Kassel Kassel Germany2007
[2] F Gerald ldquoPhotovoltaic applications in rural areas of the devel-opingworldrdquo Tech Rep no 304World BankWashington DCUSA 1995
[3] A Cabraal M Cosgrove Davies and L Schaeffer ldquoBestpractices for photovoltaic household electrification programslessons from experiences in selected countriesrdquo Tech Rep no324 World Bank Washington DC USA 1996
[4] A Cabraal M Cosgrove Davies and L Schaeffer ldquoAcceleratingsustainable photovoltaic market developmentrdquo Progress in Pho-tovoltaics Research and Applications vol 6 no 5 pp 297ndash3061998
[5] D Kammen ldquoPromoting appropriate energy technologies inthe developing worldrdquo Environment vol 41 no 5 pp 11ndash15 34ndash41 1999
[6] K Kapadia ldquoOff-grid in Asia the solar electricity businessrdquoRenewable Energy World vol 2 no 6 pp 22ndash33 1999
[7] G Loois and B van Hemert Stand-Alone Photovoltaic Applica-tions Lessons Learned James amp James London UK 1999
[8] NASA 2013 httpseosweblarcnasagov[9] V A Ani ldquoOptimal energy system for single household in
Nigeriardquo International Journal of Energy Optimization andEngineering vol 2 no 3 26 pages 2013
[10] S Ashok ldquoOptimised model for community-based hybridenergy systemrdquo Renewable Energy vol 32 no 7 pp 1155ndash11642007
[11] A Gupta R P Saini andM P Sharma ldquoSteady-state modellingof hybrid energy system for off grid electrification of cluster ofvillagesrdquo Renewable Energy vol 35 no 2 pp 520ndash535 2010
[12] D K Lal B B Dash and A K Akella ldquoOptimization ofPVWindMicro-Hydrodiesel hybrid power system in homerfor the study areardquo International Journal on Electrical Engineer-ing and Informatics vol 3 no 3 pp 307ndash325 2011
[13] K Sopian A Zaharim Y Ali Z M Nopiah J A Razak andN S Muhammad ldquoOptimal operational strategy for hybrid
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Renewable Energy 7
this point downwardThe battery state of charge versus excesselectricity is shown in Figure 5
Economic Costs Batteries are considered as a major costfactor in small-scale stand-alone power systems [15] Theoptimization of the system is carried out by modifyingthe size of the batteries until a configuration that ensuressufficient autonomy was achieved with the least net presentcost (NPC) The salvage value was used to calculate theannualized replacement cost Battery is the only componentthat has replacement cost (23855$) and therefore has salvagevalue (minus4989$) because it did not last till project lifetimeand the replacement extended the estimated project lifetimewhich was deducted from the system cost (73853$) as showninTable 3 and the net present cost of component of PV energysystem is shown in Figure 6
The software solutions showing the runningprogram with the results are shown inFigures 7 8 9 10 11 12 13 and 14
5 Conclusion
The optimal design of PVbattery energy system was carriedout minimizing the net present cost (NPC) by varying thesize of the batteries until a configuration that produces thedesired power needs of the orphanage is achieved This opti-mization study indicates that energy requirements to provideelectricity for an orphanage in Nigeria can be accomplishedby 14 kW PV 48 Surrette 6CS25P battery and 1 kW inverterThe PV system is in significant mode during the day timeparticularly in the dry season but at night and other cloudydays the battery compensates Due to the abundance of solarresource in Nigeria and having no environmental impact interms of CO
2 solar energy can be a choice for green power
solutions in powering the orphanages located in remote areas
Abbreviations
NASA National Aeronautics and SpaceAdministration
HOMER Hybrid Optimization Model for ElectricRenewables
SHS Solar home systemPV PhotovoltaicDC Direct currentAC Alternate currentSOC State of chargeDOD Depth of dischargeNPC Net present costBOS Balance of systemMin MinimumMax Maximum
The author declares that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
The author would like to thank Professor Chinedu Ositad-inma Nebo of Ministry of Power Nigeria for his usefuldiscussion on the subject
References
[1] H Von ldquoMini-grid system for rural electrification in the greatMekong sub-regional countriesrdquo in Renewable Energies andEnergy Efficiency vol 6 University of Kassel Kassel Germany2007
[2] F Gerald ldquoPhotovoltaic applications in rural areas of the devel-opingworldrdquo Tech Rep no 304World BankWashington DCUSA 1995
[3] A Cabraal M Cosgrove Davies and L Schaeffer ldquoBestpractices for photovoltaic household electrification programslessons from experiences in selected countriesrdquo Tech Rep no324 World Bank Washington DC USA 1996
[4] A Cabraal M Cosgrove Davies and L Schaeffer ldquoAcceleratingsustainable photovoltaic market developmentrdquo Progress in Pho-tovoltaics Research and Applications vol 6 no 5 pp 297ndash3061998
[5] D Kammen ldquoPromoting appropriate energy technologies inthe developing worldrdquo Environment vol 41 no 5 pp 11ndash15 34ndash41 1999
[6] K Kapadia ldquoOff-grid in Asia the solar electricity businessrdquoRenewable Energy World vol 2 no 6 pp 22ndash33 1999
[7] G Loois and B van Hemert Stand-Alone Photovoltaic Applica-tions Lessons Learned James amp James London UK 1999
[8] NASA 2013 httpseosweblarcnasagov[9] V A Ani ldquoOptimal energy system for single household in
Nigeriardquo International Journal of Energy Optimization andEngineering vol 2 no 3 26 pages 2013
[10] S Ashok ldquoOptimised model for community-based hybridenergy systemrdquo Renewable Energy vol 32 no 7 pp 1155ndash11642007
[11] A Gupta R P Saini andM P Sharma ldquoSteady-state modellingof hybrid energy system for off grid electrification of cluster ofvillagesrdquo Renewable Energy vol 35 no 2 pp 520ndash535 2010
[12] D K Lal B B Dash and A K Akella ldquoOptimization ofPVWindMicro-Hydrodiesel hybrid power system in homerfor the study areardquo International Journal on Electrical Engineer-ing and Informatics vol 3 no 3 pp 307ndash325 2011
[13] K Sopian A Zaharim Y Ali Z M Nopiah J A Razak andN S Muhammad ldquoOptimal operational strategy for hybrid
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
8 Journal of Renewable Energy
renewable energy system using genetic algorithmsrdquo WSEASTransactions on Mathematics vol 4 no 7 pp 130ndash140 2008
[14] H Abdolrahimi and H K Karegar ldquoOptimization and sensi-tivity analysis of a hybrid system for a reliable load supply inKish Iranrdquo International Journal of Advanced Renewable EnergyResearch vol 1 no 4 pp 33ndash41 2012
[15] V A AniEnergy optimization at telecommunication base stationsites [PhD dissertation] University ofNigeria NsukkaNigeria2013
[16] V A Ani and A N Nzeako ldquoEnergy optimization at GSMbase station sites located in rural areasrdquo International Journalof Energy Optimization and Engineering vol 1 no 3 31 pages2012
[17] T Lambert ldquoHOMER The HybridOptimization Modelfor Electrical Renewablesrdquo 2009 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
[18] HOMER 2013 httpwwwnrelgovinternationaltoolsHOMERhomerhtml
