Energy Conversion and Management 127 (2016) 233–244

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Energy Conversion and Management

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Techno-economic feasibility of hybrid diesel/PV/wind/battery electricitygeneration systems for non-residential large electricity consumers undersouthern Iran climate conditions

http://dx.doi.org/10.1016/j.enconman.2016.09.0080196-8904/� 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail addresses: mbaneshi@shirazu.ac.ir, mehdi.baneshi@gmail.com

(M. Baneshi).

Mehdi Baneshi ⇑, Farhad HadianfardSchool of Mechanical Engineering, Shiraz University, Shiraz 71936-16548, Iran

a r t i c l e i n f o

Article history:Received 25 July 2016Received in revised form 26 August 2016Accepted 2 September 2016

Keywords:Hybrid energy systemPhotovoltaic panelsGrid-connected systemsHOMERCost of electricity

a b s t r a c t

This paper aims to study the techno-economical parameters of a hybrid diesel/PV/wind/battery powergeneration system for a non-residential large electricity consumer in the south of Iran. As a case study,the feasibility of running a hybrid system to meet a non-residential community’s load demand of9911 kWh daily average and 725 kW peak load demand. HOMER Pro software was used to model theoperation of the system and to identify the appropriate configuration of it based on comparative techni-cal, economical, and environmental analysis. Both stand alone and grid connected systems were modeled.The impacts of annual load growth and governmental energy policies such as providing low interest loanto renewable energy projects, carbon tax, and modifying the grid electricity price on viability of the sys-tem were discussed. Results show that for off-grid systems the cost of electricity (COE) and the renewablefraction of 9.3–12.6 ₵/kWh and 0–43.9%, respectively, are achieved with photovoltaic (PV) panel, windturbine, and battery sizes of 0–1000 kW, 0–600 kW, and 1300 kWh, respectively. For on grid systemswithout battery storage the range of COE and renewable fraction are 5.7–8.4 ₵/kWh and 0–53%, respec-tively, for the same sizes of PV panel and wind turbine.

� 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Electricity is the fastest-growing final form of energy. The worldelectricity generation has increased from 17573.3 TWh in 2004–23536.5 TWh in 2014 with an average annual growth of 3% [1].The share of fossil fuels, hydro, nuclear and non-hydro renewables(including solar PV, solar thermal, wind, geothermal and tide) inworld electricity generation is 68%, 16.5%, 11%, and 3%, respectively[2]. In 2014, almost 271.2 TWh of electricity was generated in Iran,of which 94% was from fossil-fuel sources. The share of hydro andnuclear was 5%, and 0.7%, respectively, and the non-hydro renew-ables make up the remaining fuel sources used to generate electric-ity in Iran which is not impressive.

The annual average growth rate of electricity generation in Iranhad been 5.7% which is twice the average growth rate of the world.This swiftly rising energy consumption in Iran results in reducingthe exports of crude oil and natural gas to meet the domesticdemand and would have drastic impacts on the developing

economy of the country. By the end of Iran’s 20-year (2005–2025)vision plan, 2000MW electricity must be generated from non-hydro renewable resources with an investment of 3 billion USD bythe government and private sector.

Besides enjoying extensive fossil fuel resources such as crude oiland natural gas, Iran is blessed with high potential of renewableresources such as wind and solar. The nominal capacity of windenergy in Iran is estimated to be 60,000 MW which potentiallycan produce 18,000 MW of electricity [1]. Located on the world’sSun Belt, Iran enjoys 300 sunny days during a year in two–thirdsof its land and its average solar gain is estimated to be5.4–5.5 kWh/m2 per day [3].

A hybrid energy system is defined as a system in which differ-ent energy resources (solar, wind, Hydro, diesel generator, etc.) areused to supply the energy load demand. The most importantadvantage of hybrid systems is, when the variety of energy produc-tions are used together, the reliability of the system improves.Moreover, the reduction in renewable energy technologies’ costsas a result of R&D and accelerated deployment makes them moreattractive to the investors. We can refer to availability of theseenergies as another privilege of them in remote and rural areawhere other energy resources such as electricity and natural gasgrid are not available.

Nomenclature

ACC annualized capital cost ($)AGC annual grid charge ($)AMC annual maintenance cost ($)ARC annualized replacement cost ($)ASE annual supplied electricity (kWh)C battery capacity ratioCOE cost of electricityCR replacement cost ($)Ct cost in year t ($)CTR carbon tax rate ($/ton CO2 emission)fPV derating factorGT solar irradiation (W/m2)GT,NOCT irradiation at nominal operation (W/m2)GT,STC standard irradiation (W/m2)i interest rateI0 initial capital cost ($)Imax maximum charge current (A)IRR internal rate of return (%)k rate constant (h�1)N life timeNbatt number of batteriesNOCT nominal operating cell temperatureNPC net present cost ($)NPV net present value ($)nR number of replacementPbatt,cmax battery maximum charge power (kW)Pbatt,dmax battery maximum discharge power (kW)Pinv,out power output of inverter (kW)PPV power output of PV array (kW)PV photovoltaicPWTG power output of wind turbine (kW)PWTG,STP power output of wind turbine under standard condi-

tions (kW)

Q total energy at the beginning of the time step (kWh)Q1 available energy at the beginning of the time step (kWh)Qmax total capacity of the battery bank (kWh)Rt revenue in year tt year numberTa ambient temperature (�C)Ta,NOCT ambient temperature at nominal operation (�C)TC PV cell temperature (�C)Tc,NOCT PV cell temperature at nominal operation (�C)Tc,STC standard PV cell temperature (�C)tR replacement timeUanem anemometer height wind speed (m/s)Uhub hub height wind speed (m/s)UL heat transfer coefficient (W/m2 K)Vmax nominal voltage (V)WT wind turbineYPV rated capacity of PV array (kW)Z0 surface roughness length (m)Zanem anemometer height (m)Zhub hub height (m)a solar absorptance of PV arrayac maximum change rate (A/Ah)aP temperature coefficient (%/�C)Dt time step (h)gbatt,c battery charge efficiencygbatt,d battery discharge efficiencygbatt,rt battery roundtrip efficiencygc conversion efficiency of PV arrayginv inverter efficiencyq air density (kg/m3)q0 air density under standard conditions (kg/m3)r variances transmittance of PV cover

234 M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244

Many researches have been developed in hybrid energy systemsand their results indicate that the application of hybrid energy sys-tems can significantly improve system reliability to overcomeimportant deficiencies of stand-alone systems. Türkay and Telli[4] examined various grid connected PV/wind/fuel cell hybrid sys-tems. They show that grid connected hybrid systems includinggrid, PV and hydrogen system have been the most feasible solutionin view of the monthly average solar irradiation, wind energycapacity, and equipment costs. Zoubeidi et al. [5] defined severaloptions to provide electricity for remote area of a safari camp inAl-Ain city’s suburb in UAE with renewable energy sources. Theyconsidered two different load profiles and found the optimum sys-tem configuration for each. Asrari et al. [6] used HOMER to design ahybrid energy system for a rural village in north-west of Iran. Theyevaluated the feasibility of various hybrid diesel-RES and grid-RESenergy systems.

Prasetyaningsari et al. [7] modeled a hybrid power system usingPV panel, battery and converter for an off-grid fish pond in SlemanRegency, Indonesia. Güler et al. [8] analyzed four different scenar-ios by HOMER in order to fulfill a hotel electricity demand usingrenewable energy resources. In first scenario, optimal solutionwas achieved only for wind turbine and grid with a renewable frac-tion of 74%. The three other scenarios fulfill 100% of demand asrenewable and grid is used only for selling excess electricity togrid. These three systems become suitable for investment if thesale price of the electricity is increased. Li et al. [9] studied the fea-sibility of a hybrid power system for households in Urumqi, China

using the HOMER simulation software. Fazelpour et al. [10] studiedthe feasibility of satisfying electrical energy needs of a medium-sized hotel on Kish Island, south of Iran by employing renewableenergy resources of wind and solar. They used HOMER for simula-tion and showed that the most economic system is the diesel-battery system but they suggested that the wind-diesel-batteryhybrid system appears to be superior to the diesel-battery system,as it achieves a 14% reduction in emissions of carbon dioxide andonly a 0.3% rise in net present cost (NPC).

Kumar and Manoharan [11] analyzed the installation of PV/die-sel hybrid energy systems under different climatic zones in thestate of Tamil Nadu in India. The hybrid system economy was ana-lyzed for each climatic zone on the basis of NPC, consumption ofdiesel and renewable fraction. Finally, they found the optimum cli-matic zone to install PV/diesel system. They also mentioned thatdue to the high initial costs of implementing hybrid systems, gov-ernment subsidies and tariff concession need to be established.Adaramola et al. [12] studied the economic analysis of the feasibil-ity of utilizing a hybrid energy system consisting of solar, wind anddiesel generators for application in remote areas of southern Ghanabased on cost of electricity (COE) and net present cost of the sys-tem using HOMER. Kolhe et al. [13] studied techno-economic siz-ing of an off-grid hybrid renewable energy system for ruralelectrification in Sri Lanka and offered an optimal hybrid systemusing wind, solar, diesel generator and a battery bank.

Olatomiwa et al. [14] chose six different geo-political zones ofoff-grid locations in Nigeria. Their simulations concentrated on

M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244 235

the net present costs, cost of energy and renewable fraction ofthe given hybrid configurations for all the climatic zones. Theiranalysis indicates that the PV/diesel/battery hybrid renewablesystem configuration is found as optimum architecture, it alsodisplayed better performance in fuel consumption and CO2

reduction. Bhattacharjee and Acharya [15] used HOMER softwareto conduct a techno-economic analysis of a photovoltaic/windhybrid energy system for small scale application in an educa-tional building. Baghdadi [16] studied feasibility of stand-alonehybrid renewable energy system and optimized it by means ofHOMER software. Sinha and Chandel [17] utilized HOMER soft-ware to study photovoltaic–micro wind based hybrid systemsfor 12 locations of the western Himalayan state in India. Theyalso analyzed a fixed tilt and sun tracking PV-micro wind hybridpower systems in a low windy Indian hilly terrain with goodsolar resource [18].

Bahramara et al. [19] had a review on optimal planning ofhybrid renewable energy systems using HOMER software in south-ern Algeria. Amutha and Rajini [20] estimated the domestic, indus-trial, and agricultural load in a remote village in South India andfind the optimal option for a hybrid system using HOMER software.They show that a hybrid combination of solar/wind/hydro/batteryis a cost effective, sustainable, techno-economically and environ-mentally viable alternative to grid extension. Kumar et al. [21]used HOMER to study the feasibility of a stand-alone hybrid sys-tem for typical ATM machines in remote locations concerning themaximum use of non-conventional generation systems at mini-mum system cost.

HOMER software used in this research has previously been val-idated experimentally in many types of distributed generation sys-tems [22]. For example, AbouJaoudeh [23] created a HOMER modelfor optimizing a PV/Battery micro power plant in Lebanon and thenvalidated this setup with data obtained from an actual referencerenewable energy installation. HOMER is also validated with othernumerical models. Al-Sharafi et al. [24] used a MATLAB computercode to simulate and optimize a hybrid power generation systemcontaining PV modules, wind turbines, and a battery bank. Theoptimum numbers of PV modules, wind turbines as well as storagebatteries were determined and the results were validated withHOMER software.

The main drawback of renewable energy systems is the overdependency of these systems on environmental conditions, whichvaries place to place and time to time. One approach to overcomethis problem is to combine these energy resources with traditionalenergy systems by means of utilizing the strengths of one energysource to balance the weaknesses of others. A hybrid solar-windelectrical generation system with a diesel generator and electricalstorage and/or grid connection can provide such a reliable powersupply. In such systems the main challenge is to compute the opti-mal size and configuration of components to meet the electricityload demand at minimum cost.

This paper studies the feasibility of utilizing a hybrid electric-ity generation system for a large electricity consumer in Shiraz,located in southern Iran. The HOMER Pro software is used to sim-ulate and to find the most optimized configuration of compo-nents for our case study. Both off grid and grid connectedsystems with and without battery storage were investigated.Then, the financial and environmental matters of different casesincluding the cost of electricity (COE), net present value (NPV),internal rate of return (IRR), renewable fraction, and CO2 emis-sion are discussed. The impacts of uncertainty in load demandand annual change in electricity consumption are discussed.Finally, the effects of governmental energy policies such as pro-viding low interest loan to renewable energy projects, carbontax, and modifying the grid electricity price on viability of renew-able systems were examined.

2. Methodology

2.1. The power output of a PV array

The following equation is used to evaluate the power output ofa PV array in HOMER [25]:

PPV ¼ YPV f PVGT

GT;STC

� �1þ apðTc � Tc;STCÞ� � ð1Þ

where YPV (kW) is the rated capacity of the PV array, fPV is thederating factor which accounts for losses from the DC nameplatepower rating and is the mathematical product of the derate factorsfor the components of the PV system, GT (W/m2) is the solarirradiation, GT,STC (W/m2) is irradiation at standard test conditions(i.e. 1000 W/m2), ap (%/�C) is the temperature coefficient, Tc (�C) isthe PV cell temperature, and Tc;STC (�C) is the PV cell temperatureunder standard conditions. The cell temperature can be calculatedusing the energy balance for the PV array as follows:

TC ¼ Ta þ GTsaUL

� �1� gC

sa

� �ð2Þ

where Ta (�C) is the ambient temperature, s is the transmittance ofthe cover over PV array, a is the solar absorptance of the PV array, UL

(W/m2 K) is the coefficient of heat transfer to the surrounding, andgc is the electrical conversion efficiency of the PV array. The celltemperature under the condition of GT = 800 W/m2, Ta = 20 �C, andgc = 0 (no load operation) is called nominal operating cell tempera-ture (NOCT) and is reported by manufacturers. Thus,

saUL

¼ Tc;NOCT � Ta;NOCT

GT;NOCTð3Þ

and

Tc ¼ Ta þ GTTc;NOCT � Ta;NOCT

GT;NOCT

� �1� gC

sa

� �ð4Þ

The inverter converts DC electricity of PV panels to AC electric-ity with an efficiency of ginv as follows

Pinv ;Out ¼ ginvPPV ð5Þ

2.2. The power output of a wind turbine

The hub height wind speed was evaluated by HOMER using thelogarithmic law as follows [25]:

Uhub ¼ UanemlnðZhub=Z0ÞlnðZanem=Z0Þ ð6Þ

where Uanem (m/s) is the wind speed at anemometer height, Zhub (m)and Zanem (m) are the hub height of the wind turbine and theanemometer height, and z0 (m) is the surface roughness length.The wind turbine power output then is calculated as follows:

PWTG ¼ qq0

� �PWTG;STP ð7Þ

Here, q (kg/m3) is actual air density, q0 (kg/m3) is the air density atstandard pressure and temperature, and PWTG,STP (kW) is the outputof wind turbine under standard temperature and pressure condi-tions calculated using the turbine’s power curve. When the windspeed at the turbine height is below the cutoff or above the cutoutwind speeds, no power is produced.

0.4

0.5

0.6

0.7

0.8

0.9

1

ntal

Irra

dia�

on (k

Wh/

m2 )

(a)

236 M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244

2.3. Battery charge and discharge power

The maximum battery charge power considered by HOMER isthe minimum of three separate limitations on the battery bank’smaximum charge power, namely [25]:

Pbatt;cmax ¼ MINðPbatt;cmax;kbm; Pbatt;cmax;mcr; Pbatt;cmax;mccÞgbatt;c

ð8Þ

where

Pbatt;cmax;kbm ¼ kQ1e�kDt þ Qkcð1� e�kDtÞ1� e�kDt þ cðkDt � 1þ e�kDtÞ ð9Þ

Pbatt;cmax;mcr ¼ ð1� e�acDtÞðQmax � QÞDt

ð10Þ

Pbatt;cmax;mcc ¼ NbattImaxVmax

1000ð11Þ

and

gbatt;c ¼ffiffiffiffiffiffiffiffiffiffiffiffiffigbatt;rt

p ð12ÞHere, Q1 (kWh) is the available energy in the battery at the begin-ning of the time step, Q (kWh) is the total amount of energy inthe battery at the beginning of the time step, Qmax (kWh) is the totalcapacity of the battery bank, c, k (h�1), and ac (A/A h) are the batterycapacity ratio, rate constant, and maximum charge rate. Also, Dt (h)is the length of time step, Nbatt is the number of batteries, Imax (A)and Vnom (V) are the battery’s maximum charge current and nomi-nal voltage, respectively, and gbatt,c and gbatt,rt are the battery chargeefficiency and round trip efficiency.

The battery bank’s maximum discharge power is

Pbatt;dmax ¼ gbatt;dPbatt;dmax;kbm ð13Þwhere

Pbatt;dmax;kbm ¼ �kQmax þ kQ1e�kDt þ Qkcð1� e�kDtÞ1� e�kDt þ cðkDt � 1þ e�kDtÞ ð14Þ

and

gbatt:d ¼ gbatt:c ð15Þ

Fig. 1. General configuration of a hybrid electricity generation system.

2.4. Financial evaluation of the projects

Net present value (NPV) involves conversion of all annual ben-efits and costs stream occurring at different points in the life timeof the project to their present value equivalents and adding upthem to get the overall worth of all benefits and costs of the projectwhich is mathematically written as [26]

NPV ¼XNt¼1

Rt � Ct

ð1þ iÞt � I0 ð16Þ

where Rt is the revenue in year t, Ct is the cost in year t, i is the inter-est rate, I0 is the initial capital cost, and N is the life time of theproject.

The cost of electricity (COE) per kWh is obtained by adding upthe net costs on an annual basis and divides it by the annual sup-plied electricity (ASE) as follows

COE ¼ ACC þ ARC þ AMC þ AGCASE

ð17Þ

where

ACC ¼ ið1þ iÞNð1þ iÞN � 1

I0 ð18Þ

is the annualized capital cost, and

ARC ¼ ið1þ iÞNð1þ iÞN � 1

XnR

CR

ð1þ iÞtR ð19Þ

0

0.1

0.2

0.3

Glo

bal H

oriz

o

Jan Feb March April May June July Aug Sep Oct Nov Dec

0

2

4

6

8

10

12

14

16

18

20

Win

d sp

eed

(m/s

)

Jan Feb March May June July Aug Sep Oct Nov DecApril

(b)

Fig. 2. Hourly (a) solar irradiation and (b) wind speed in Shiraz.

0

100

200

300

400

500

600

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mon

thly

Loa

d De

man

d (M

Wh)

Peak

Middle

Low

Summer Winter

19:00-23:00 17:00-21:00

7:00-19:00 5:00-17:00

23:00-7:00 21:00-5:00

Fig. 3. Monthly profile of electricity load demand at low, middle, and peak loadhours.

M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244 237

is the annualized replacement cost. Here, CR is the replacement cost,tR is the replacement time, and nR is the number of replacementduring the life time of the project. Also, AMC and AGC are annualmaintenance cost and net grid charge, separately.

Fig. 4. Hourly lo

Internal rate of return (IRR) is very widely used in project eval-uation and it is the interest rate for which NPV is zero (i.e. costsequal benefits). It is written as [26]

XNt¼1

Rt � Ct

ð1þ iÞt ¼ I0 ð20Þ

A project with IRR greater than a predetermined acceptable level ofreturn, which is usually the market interest rate for private inves-tors, is economically attractive.

2.5. CO2 emission and carbon tax

The CO2 emission in hybrid electric systems results from theproduction of electricity by the generator and the consumptionof grid electricity. For the generator the annual emission is calcu-lated by multiplying the emissions factor by the total annual fuelconsumption. In a grid-connected system, the CO2 emission is eval-uated by multiplying the net grid purchases (in kWh) by the emis-sion factor (in g/kWh) [25].

The carbon tax is a fee that the users of fossil fuels pay for cli-mate damage by releasing carbon dioxide into the atmosphere.No tax on CO2 production has been established in Iran but it canbe an incentive to make the renewable projects more attractive.

ad profile.

Table 1Technical characteristics of system components.

Component

Diesel generator Capacity 800 kWMin load ratio 30%Slope of fuel curve 0.2372 L/h/

kW

Flat plate photovoltaicpanel

Nominal operation celltemperature

47 �C

Temperature coefficient �0.5%/�CEfficiency at standard testcondition

13%

Derating factor 80%Open circuit voltage 21 VShort circuit current 6.4 AVoltage at maximum power 17 VCurrent at maximum power 5.9 A

System converter Efficiency 90%

CELLCUBE� FB 20-130battery

Nominal capacity 130 kWhNominal voltage 48 VRound trip efficiency 64%Max charge current 383 AMax discharge current 599 A

NPS60-24 wind turbine Rated capacity 60 kWRotor diameter (m) 24.4 mNumber of blades 3Hub height 30 mRated wind speed 11 m/sCut-in wind speed 3 m/sCut-out wind speed 25 m/sExtreme wind speed 52.5 m/s

238 M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244

The most common approach to set a carbon tax rate (CTR) is to usethe social cost of carbon. However, the CTR varies in a wide rangein countries where carbon is taxed.

3. System simulation and required data

Every hybrid energy system mainly consists of: 1. Renewableenergy generators such as wind turbines and photovoltaic panels,2. Non-renewable energy generators like as diesel generator, 3.Power storage units, 4.AC/DC power convertor, 5. Load, and 6. Grid(if available). Fig. 1 sketches a general configuration for a hybridenergy system.

HOMER performs three principal tasks: simulation, optimiza-tion, and sensitivity analysis. It can model the performance of aparticular system configuration each hour of the year to deter-mine its technical feasibility and life-cycle cost [25]. HOMERneeds four types of input data including climate data, load data,system components data, and financial data as will be describedbelow.

3.1. Climate data

Shiraz is located at 29�370N and 52�320E with a relatively rainymild winters, and hot dry summers. The average temperature inShiraz is about 17.9 �C, ranging from �4.9 �C to 39.8 �C and its

Table 2Type and costs of system components.

Component Lifetime Purchase cost (

Diesel generator 15,000 h 160Flat plate photovoltaic panel 25 yrs 1400System converter 15 yrs 600CELLCUBE� FB 20-130 battery 20 2300Wind turbine 20 yrs 2000

elevation is 1481 m from the sea level [27]. Hourly meteorologicaldata including dry bulb temperature, relative humidity, atmo-spheric pressure, direct and diffuse solar irradiations, wind speedand direction, and cloud factor are extracted from Ref. [27].

Solar and wind pattern data for Shiraz are presented in Fig. 2(a)and (b), respectively. According to the meteorological data, thenumber of clear and sunny days in a year is more than 300 dayswith an average global irradiation of 5.4 kWh/m2 per day andr2 = 27.18. The maximum hourly global irradiation is 900 W/m2

at noon of September 1st. The wind speed ranges from 0 to17.3 m/s with overall mean of 2.2 m/s and r2 = 5.1. The numberof hours with wind speed less than mean value is 5010 while thenumber of hours with wind speed greater than 3 m/s (i.e. the typ-ical cut in speed of wind turbines) is 2452. It seems an acceptableidea to run a hybrid dual source system - with wind and solarresources - to meet the electricity load demand.

3.2. Load data

According to the electricity bills of the case study the total loaddemand is 3618 MWh with daily average of 9911 kWh, peak loaddemand of 725 kW, and base load demand of 186 kW. Fig. 3 showsthe monthly electricity load demand during low, middle, and peakload hours. The low, middle, and peak load hours are 23:00–7:00,7:00–19:00, and 19:00–23:00 in summer and 21:00–5:00, 5:00–17:00, and 17:00–21:00 in winter. The monthly average hourlyload profile was determined using electricity bills. Then, the dailyand time step perturbation, which are <1% for this case study, wereapplied to these profiles to find the whole year hourly load profile.More details can be found in [25]. Fig. 4 shows the sample hourlyload profiles for the first three days of each month.

3.3. Components data

The use of diesel generators especially in off grid systems isessential to ensure the supply continuity. Also, the storage unit isused for balancing electricity generation and electricity usage.However, in area with grid access the diesel generator and batterystorage can be omitted. Considering about 10% uncertainty in peakload demand (i.e. 725 kW), a 800 kW diesel generator with mini-mum load ratio of 30% is utilized. The technical properties of sys-tem components are given in Table 1.

The total nameplate capacity of PV panels and converters is lim-ited to 1000 and 800 kW, respectively. Also, the upper limit for thenumber of wind turbines and batteries is considered to be 10 and20, respectively, in calculations. It is worth to mention that practi-cally the actual limits are decided by investors with regard torequired investment and cost of electricity production, availabilityof equipment, required land, governmental incentives, and techni-cal issues.

3.4. Financial data

Table 2 shows the costs of system components. According toIran’s energy prices liberalization program, at the end of this

$/kW) Replacement cost ($/kW) Maintenance cost

145 0.01 $/h1400 10 $/kW/yr600 0 $/kW/yr2250 0 $/h1800 500 $/turbine/yr

Fig. 5. Hourly electricity production of (a) 1 kW rated capacity photovoltaic systemand (b) NPS60-24 wind turbine.

M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244 239

program the price of energy carriers must reach to 75% of theirexport prices which means that the electricity and gas oil pricebecome 7.5 ₵/kWh and 24 ₵/l, respectively. Moreover, accordingto government’s incentive policies the sellback price of electricityto the grid is about 0.2 $/kWh for wind and 0.3 $/kWh for solarpower generation [3]. The project life time and real interest rateare 20 years and 5%, respectively.

4. Results and discussion

4.1. Electricity production of diesel generator, PV panels, and windturbines

To meet the load demand, the diesel generator produces3,648,400 kWh of electricity at the cost of 9.4 ₵/kWh from which30,888 kWh is the excess electricity.

Fig. 5(a) represents the hourly output of a 1 kW rated capacityphotovoltaic system. The maximum output power reaches to0.8 kW and thus a converter capacity of 0.8 kW is a suitable choice.The annual electricity production of a 1 kW rated capacity photo-voltaic system is 1564.3 kWh with the COE of 10.3 ₵/kWh.

The COE of a wind turbine strongly depends on its rated capac-ity and power curve. Here, several wind turbines with differentrated capacities and power curves were examined. The results

Table 3The COE of different wind turbines.

Name Rated capacity (kW) Prod

Endurance S-250 5 2046Generic 10 kW 10 3587Corp Jacobs 31-20 20 0Endurance 3120 50 0Northern Power NPS60-24 60 64,8Northern Power NPS100c-24 95 73,9Northern Power NPS100c-21 100 54,6Vergent MP-c 275 148,Windflow 45-500 500 239,Windflow 33-500 500 109,Generic 1.5 MW 1500 1,06

are shown in Table 3. Among all turbines, the NPS60-24 with ratedcapacity of 60 kW, annual electricity production of 64,840 kWh,capacity factor of 12.3% and the COE of 16.7 ₵/kWh has the mosteconomical performance. Fig. 5(b) shows the hourly electricity pro-duction of this wind turbine. The Jacobs 31-20 and E-3120 withrated capacities of 20 and 50 kW, respectively, almost have nopower generation because of their high cutoff speeds as theirpower curve indicate while the annual average wind speedreported in Shiraz is only 2.2 m/s.

4.2. Off-grid hybrid systems

In this layout the system is assumed to be off grid and thedemand is met with the aid of a diesel generator, wind turbines,PV panels, converters and batteries as shown in Fig. 6. Fig. 7 pre-sents both COE and renewable fraction of different combinationsof components. The COE and the renewable fraction are 9.3–14.2 ₵/kWh and 0–30.6%, respectively, when PV panel and windturbine sizes are 0–1000 kW and 0–600 kW, respectively. Themaximum reduction in CO2 emission is 791,560 kg per year.

The drawback of the off-grid system is that the excess electric-ity cannot be sold to the grid. The excess electricity for differentcombinations of components is shown in Fig. 8. For diesel genera-tor system the annual excess electricity is 27.2 MWh which is dueto the minimum load ratio of the diesel generator that is 30% forour diesel generator. Although the reduction in minimum loadratio reduces the excess electricity but it makes some operationalproblems and decreases the lifetime or at least increases the main-tenance cost. Adding the renewable components increases theexcess electricity. For example adding a 100 kW photovoltaic and300 kW wind systems increases the excess electricity to152.2 MWh.

Using battery can reduce the excess electricity and increase therenewable fraction in expense of increasing the capital cost. Fig. 9(a)–(c) shows the impact of adding batteries to systems on excesselectricity, renewable fraction, and COE, respectively. Results showthat the number of batteries greater than 10 does not have signif-icant impact on renewable fraction. Moreover, using batteries ismore efficient in systems with higher PV capacity. For exampleadding 10 batteries to the system with 100 kW PV capacitydecreases the excess electricity by 30.6% and increases the renew-able fraction by 8.5% in expense of 9% increase in COE whilst thesevalues are 83%, 58.6% and �4% for the system with 1000 kW PVcapacity. The results also show that for all systems with PV capac-ity greater than 600 kW the optimum number of batteries is 10based on minimum COE criteria.

Adding batteries to the system also prevents stability problemsdue to the high renewable penetration. Hours of autonomy is thenumber of hours a battery bank can supply load demand withoutrecharging. For a battery bank of 10 batteries, the hours of auton-omy ranges from 1.8 h in September to 7 h in March with an aver-age value of 3.1 h.

uction (kWh/yr) COE (₵/kWh) Capacity factor (%)

44.1 4.6750.3 4.09# 0.00# 0.00

40 16.7 12.3480 23.2 8.8964 33.0 6.24614 33.4 6.17342 37.7 5.46123 82.7 2.496,822 25.4 8.12

240 M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244

4.3. On-grid hybrid systems

For an on-grid system, as shown in Fig. 10, it is assumed that thesystem is connected to the electricity grid, and as a result the diesel

Fig. 6. General configuration of an off-grid hybrid electricity generation system.

0

10

20

30

40

50

0

0.03

0.06

0.09

0.12

0.15

0 100 200 300 400 500 600 700 800 900 1000

Rene

wab

le fr

ac�o

n (%

)

COE

($/k

Wh)

PV capacity (kW)

COERen Frac�onNo. of wind turbines

No. of wind turbines

Fig. 7. The COE and renewable fraction of different configurations of off-gridsystems.

0

200

400

600

800

1000

1200

1400

0 100 200 300 400 500 600 700 800 900 1000

Exce

ss e

lect

ricity

(MW

h/yr

)

PV capacity (kW)

No. of wind turbines

Fig. 8. Excess electricity of different configurations of off-grid systems.

generator can be omitted while all other components remainunchanged. Fig. 11(a) and (b) shows the COE and the renewablefraction for different configurations of components. As shown inthis figure, up to 400 kW PV capacity, both COE and renewablefraction increase. The maximum COE is 8.4 ₵/kWh for the systemwith 400 kW PV capacity and 5 number of wind turbines. Furtherincrease in the PV capacity decreases the COE significantly. The

0

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10 12 14 16 18 20

Exce

ss e

lect

ricity

(MW

h)

Number of ba�eries

100

400

700

1000

PV (kW) Converter (kW)

80

160560800

WT (kW)

300300300300

0.1

0.11

0.12

0.13

0.14

0.15

0 2 4 6 8 10 12 14 16 18 20

COE

($/k

Wh)

Number of ba�eries

100

400

700

1000

PV (kW) Converter (kW)

80

160560800

WT (kW)

300300300300

0

10

20

30

40

50

60

0 5 10 15 20

Rene

wab

le fr

ac�o

n (%

0)

Number of ba�eries

100

400

700

1000

PV (kW) Converter (kW)

80

160560800

WT (kW)

300300300300

(a)

(b)

(c)

Fig. 9. Impact of adding batteries to off-grid systems on (a) excess electricity, (b)renewable fraction, and (c) COE.

M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244 241

COE of 5.7 ₵/kWh can be achieved for optimal configuration whichincludes 1000 kW PV and 600 kW wind turbine capacity. This COEvalue is 24% cheaper than the grid electricity price. For the optimalsystem the renewable fraction is 53% and the reduction in CO2

emission is 61%. Thus, if facilities permit, investment on larger sys-tems is more economically viable.

Fig. 10. General configuration of an on-grid hybrid electricity generation system.

0.055

0.06

0.065

0.07

0.075

0.08

0.085

0 1 2 3 4 5 6 7 8 9 10

COE

($/k

Wh)

Number of wind turbines

0 200 400 600 800 1000PV capacity:

0

10

20

30

40

50

60

0 1 2 3 4 5 6 7 8 9 10

COE

($/k

Wh)

Number of wind turbines

0 200 400 600 800 1000PV (kW):

(a)

(b)

Fig. 11. (a) COE and (b) renewable fraction of different configurations of on-gridsystems.

Also, our results show that adding batteries to the grid con-nected systems increases the COE by 12–25% with no significanteffect on renewable fraction because the system prefers to sellbackthe excess electricity to the grid instead of storing it in batteries forlater uses. Thus, none of the grid connected systems are economi-cally attractive when battery is added.

Fig. 12 shows the average monthly distribution of inverter out-put, wind turbine output, purchased and sold electricity for a gridconnected system with PV, inverter, and wind turbine capacities of1000 kW, 800 kW, and 300 kW, respectively. The monthly maxi-mum and minimum inverter output are 144,061 kWh in Septem-ber and 109,522 kWh in February. These values for wind turbinesoutput are 50,716 kWh in May and 7837 kWh in November,respectively. The maximum purchased electricity is 332,635 kWhin September when there is no excess electricity for sale. Maxi-mum sold electricity of 77,484 kWh is achieved in March whenthe load demand is the least.

A sensitivity analyses on hourly electric load demand was con-ducted. This sensitivity analysis helps assess the effects of uncer-tainty or changes in the load demand over which the designerhas no control. The results showed that the optimum combination

0

100

200

300

400

500

600El

ectr

icity

(kW

h)AC load

Grid sold

Grid purchased

Wind turbine output

Inverter output

Jan Feb March April May June July Aug Sep Oct Nov Dec

Fig. 12. Average monthly distribution of inverter output, wind turbine output, gridpurchased and sold electricity for an on grid hybrid system with 1000 kW PV and300 kW wind turbine capacities.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

-20 -15 -10 -5 0 5 10 15 20

Valu

e re

la�v

e to

op�

mum

des

ign

Uncertainty in load demand (%)

COE

Ren frac�on

CO2 emission

Fig. 13. Impact of load uncertainty on COE, renewable fraction, and CO2 emissionfor the optimal grid connected system.

800

1000

1200

1400

$)

A

B

C

D

E

(a)

242 M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244

of components does not change with ±20% uncertainty in loaddemand. Fig. 13 shows the impact of load uncertainty on COE,renewable fraction, and CO2 emission for the optimal grid con-nected configuration. The relative steepness of the three curvesshows that CO2 emission is more sensitive to the load uncertaintywith up to ±51% change. Also, the uncertainties in COE and renew-able fraction are �36.6–22.6% and �11.7–13.5%, respectively.

-600

-400

-200

0

200

400

600

-5 -4 -3 -2 -1 0 1 2 3 4 5

NPV

(Tho

usan

ds

Annual change in load deman (%)

F

G

-15

-10

-5

0

5

10

15

-5 -4 -3 -2 -1 0 1 2 3 4 5

IRR

(%)

Annual change in load deman (%)

A B C D E F G

(b)

Fig. 14. Impact of annual growth/decline in load demand on (a) NPV and (b) IRR.

4.4. Financial affairs

4.4.1. Impact of interest rate and bank loanIn this section the financial affairs of the project are discussed.

The financial details of selected on grid systems are shown inTable 4. The results indicate that the impact of real interest rateon COE of systems with higher renewable fraction is more signifi-cant. For example, the increase of interest rate from 0 to 10%increases the COE of system A by only 4.3% while this value is94.2% for system G. Both NPV and IRR for selected systems aregiven in Table 4. Systems B and C have the lowest values of IRRwhich are �0.6% and 0.36%, respectively. The IRR is 1–2% for sys-tems A, D, and E and it reaches to 3.9% and 6.1%, for systems Fand G, respectively. Thus with interest rate of 5% only project Gis economically viable from both NPV and IRR criteria.

Although according to persuasive policies of Iran, governmentwill pay half of initial cost of PV panels for residential consumersbut for such large projects the incentive is limited to low-interestloans to fund some part of initial investment. The foresaid systemswere financially reconsidered under different loan shares (LS) insupplying the initial investment (i.e. 25%, 50%, 75%, and 100%)and different loan rates (LR) (i.e. 0–5%).

The impacts of different LSs and LRs on economic viability ofabove mentioned systems are shown in Table 5. Project G is alwayseconomically viable and it is obvious that increasing the loan shareand decreasing the loan rate make the project more attractive. Pro-ject F becomes economically attractive when LS = 25% withLR < 1%, LS = 50% with LR < 2%, and LS = 75%, 100% with LR < 4%.Systems A and E are viable only when LS = 100% and LR < 2%.

Table 5Economically viable systems under different loan shares and loan rates.

Loan rate Loan share

0% 25% 50% 75% 100%

0% G F, G F, G D, F, G A, C, D, E, F, G1% G G F, G F, G A, D, E, F, G2% G G F, G F, G F, G3% G G G F, G F, G4% G G G G G5% G G G G G

Table 4The financial details of selected on grid systems when i = 5%.

A B C D E F G

PV panels capacity (kW) 100 100 300 500 500 800 1000Converter capacity (kW) 80 80 240 400 400 640 800Wind turbines capacity (kW) 0 300 300 0 300 300 300Capital cost (thousands $) 188 788 1164 940 1540 2104 2480O & M cost ($) 1000 1000 3000 5000 5000 8000 10,000Grid purchased (MWh) 3461.1 3143.8 2841.4 2845.6 2557.5 2234.9 2111.8

(thousands $) 259.6 235.8 213.1 213.4 191.8 167.6 158.4Grid sold (MWh) 0 6.9 17.4 10.3 46.4 193.1 382.9

(thousands $) 0 2.1 5.2 3.1 13.9 57.9 114.9COE (₵/kWh) 7.62 8.22 8.37 8.01 8.37 7.52 6.31Ren fraction (%) 4.3 13 22 22 30 41 47NPV (thousands $) �53.9 �331.4 �410.7 �242.0 �437.8 �189.3 234.5IRR (%) 1.32 �0.68 0.36 1.73 1.36 3.91 6.09

M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244 243

Project D becomes attractive when LS = 75% with LR < 1%, andLS = 100% with LR < 2%. Project C with LS = 100% and LR < 1% mightbe attractive for investors. System B is never interesting even withLS = 100% and LR = 0%.

0

500

1000

1500

2000

2500

3000

Grid A B C D E F G

(ton

/yr)

Reduc�on in CO2 emission CO2 emission

Fig. 15. CO2 emission reduction of systems A–G.

-300

-200

-100

0

100

200

300

400

500

600

700

0 5 10 15 20

NPV

(tho

usan

ds $

)

Rate of carbon tax ($/ton)

F (LS=0%)

G (LS=0%)

F (LS=50%, LR=4%)

G (LS=50%, LR=4%)

Fig. 16. Impact of carbon tax rate on NPV of systems F and G with and without loan.

0

0.03

0.06

0.09

0.12

0.15

A B C D E F G

Min

imum

grid

ele

ctric

ty p

rice

($/k

Wh)

Without incen�ve

With incen�ve (LS=50%, LR=4%, CTR=8 $/ton

Fig. 17. Threshold of grid electricity price which make systems A–G economicallyviable.

4.4.2. Impact of annual growth/decline in electric load demandA MATLAB code was used to investigate the impact of annual

rate of growth/decline in load demand during the life time of theproject. The annual rates in the range of �5% to +5% were exam-ined. Fig. 14(a) and (b) shows the impact of annual change in loaddemand on NPV and IRR of systems A–G when LS = 50% andLR = 4%. As indicated in these figures, the NPV of systems F and Gare more sensitive to the load annual change. Project F can be eco-nomically viable with less than 1% annual decline in electricityconsumption and project G becomes economically unviable withmore than 2.5% annual growth in load demand. It is obvious thatsaving in electricity consumption improves the IRR.

4.4.3. Impact of carbon taxFig. 15 shows the amount of CO2 emission reduction of systems

A–G which ranges from 98.9 to 1193.6 ton/yr. Our results showthat including carbon tax with typical rate of less than 20 $/tonin financial calculations has significant impact on economic viabil-ity of systems F and G while no interesting improvement isobserved in other systems. Fig. 16 indicates the impact of carbontax on NPV of systems F and G with and without loan. Withoutany loan, the carbon tax of greater than 15 $/ton makes project Feconomically viable while this value reduces to 8 $/ton withLS = 50% and LR = 4%.

4.4.4. Impact of grid electricity priceFor each project there is a threshold of grid electricity price

above which that project becomes economically viable. Fig. 17shows this threshold for projects A–G. Without any incentive, allprescribed projects become economically attractive when the gridelectricity price is higher than 0.13 $/kWh. With a loan of LS = 50%and LR = 4% and CTR = 8 $/ton, the grid price higher than0.12 $/kWh makes above systems economically viable.

5. Conclusions

In this article, it was tried to design a hybrid energy system tomeet the demand of a large electricity consumer in Shiraz, Iran.Several off-grid and on-grid configurations of diesel generator,wind turbine, PV panel, and battery storage were examined. Theresults can be concluded as follows:

– For off-grid systems the cost of electricity (COE) and the renew-able fraction of 9.3–12.6 ₵/kWh and 0–43.9%, respectively, areachieved with PV, wind turbine, and battery sizes of0–1000 kW, 0–600 kW, and 1300 kWh, respectively.

– Adding batteries to the off-grid system reduces the excess elec-tricity and increases the renewable fraction in expense ofincreasing capital cost.

– Among the grid connected systems the one with PV, inverter,and wind turbine sizes of 400, 320, and 300 kW, respectively,shows the highest COE of about 0.084 $/kWh.

– The optimal grid connected configuration based on minimumCOE has 1000 kW PV, 800 kW inverter, and 300 kW wind tur-bine capacities.

– Low interest loan increases the number of economically viablesystems. Some of projects are not economically attractive evenwith interest free loans.

– Taxing carbon emission can be a suitable policy to encouragethe investment in large renewable energy projects.

– Increasing the grid electricity price from 0.075 to 0.13 $/kWhmakes a considerable number of systems studied in this articleeconomically attractive.

244 M. Baneshi, F. Hadianfard / Energy Conversion and Management 127 (2016) 233–244

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