Energetic and economic performance analyses of photovoltaic,parabolic trough collector and wind energy systemsfor Multan, Pakistan

S.M. Sajed Sadati, Fassahat Ullah Qureshi, Derek Baker n

Sustainable Environment and Energy Systems (SEES), Middle East Technical University, Northern Cyprus Campus (METU NCC), Ankara, Turkey

a r t i c l e i n f o

Article history:Received 16 November 2014Received in revised form10 February 2015Accepted 8 March 2015Available online 31 March 2015

Keywords:PhotovoltaicParabolic trough collectorsWind energyLevelized cost of energyPakistan

a b s t r a c t

Pakistan is going through a severe energy crisis due to an increasing gap between demand and supply. Itscurrent energy needs are heavily dependent upon conventional thermal power plants which mainly useoil and gas. In addition to the economic problems associated with importing oil for Pakistan, the burning offossil fuels for the production of electricity releases vast amounts of greenhouse gases. As an alternative tothe current scenario, in this paper the energetic and economic performance of green energy technologiessuch as photovoltaic (PV), parabolic trough collector (PTC) with and without storage, and wind energysystems are analyzed and compared with respect to their potential for electricity generation for the city ofMultan, Pakistan. Each system is designed taking a nominal 10 MWe capacity as a reference. Hourlymeteorological data are used to estimate hourly insolation on a fixed PV module and for PTC’s withEast–West and North–South tracking. Results show that PV and PTC systems without storage haveapproximately the same output with capacity factors of approximately 20%. The electrical energy output ofthe wind turbines was very low with a capacity factor of �2%. PTC’s with 7.5 h storage and a solar multipleof 3.5 showed the best result for electrical energy output with a capacity factor of 46%. A cost analysis isperformed assuming a 30 year lifetime for PV and a 35 year lifetime for PTC. The Levelized Cost ofElectricity (LCOE) is found to be 0.192 USD/kWh for PV systems, 0.273 USD/kWh for PTC systems withoutstorage, and 0.226 USD/kWh for PTC systems with 7.5 h of storage. The results of the economic study showthat based strictly on economic considerations green energy technologies can be utilized if thegovernment supports the investment by giving incentives and subsidies.

& 2015 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8452. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848

2.1. Meteorological model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8482.2. PV energetic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8482.3. PTC energetic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849

2.3.1. Thermal storage energetic model for PTC system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8502.4. Wind turbine energetic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8502.5. Capacity factor model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8512.6. Cost analysis for PTC and PV power plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 851

2.6.1. Initial costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8512.6.2. Annual costs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8512.6.3. Financial factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 851

3. Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8513.1. Energetic results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8513.2. Capacity factor results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/rser

Renewable and Sustainable Energy Reviews

http://dx.doi.org/10.1016/j.rser.2015.03.0841364-0321/& 2015 Elsevier Ltd. All rights reserved.

n Correspondence to: Mech. Engr. E-105, Middle East Technical University, 06800 Ankara, Turkey. Tel.: þ90 312 210 5217; fax: þ90 312 210 2536.E-mail addresses: sajed.sadati@metu.edu.tr (S.M.S. Sadati), fassahat.qureshi@metu.edu.tr (F.U. Qureshi), dbaker@metu.edu.tr (D. Baker).

Renewable and Sustainable Energy Reviews 47 (2015) 844–855

3.3. Cost analysis results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8534. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 854

1. Introduction

Renewable energy technologies are playing an increasinglyimportant role in the sustainable development and well-being ofstates as fossil fuel resources are being depleted throughout theworld. Wind and solar energy resources are considered to be twoof the most important sustainable energy resources in the world[1]. The worldwide growing demand for sustainable energy hasbeen investigated in a large number of studies [2–6]. The globalgrowth trend of renewable energy technologies in 2011 wasexplored in [7] by Awan et al., and according to it the largestgrowth rate is for PV which is 74% followed by Concentrated SolarPower (CSP) with 35%, solar water heating with 27%, wind powertechnology with 20% and then biodiesel with 16%. Other renew-able technologies have a growth rate which is less than 3%. Thepresent share of renewable energy sources in power generationwas only 5% in 2011 as shown in Fig. 1. According to this figure, thelargest share in global power generation scenario is from fossilfuels and nuclear which are contributing 77.9%, followed by

Nomenclature

A total area of photovoltaic solar plant (m2)A0 total area of CSP solar power plant (m2)Acoll parabolic trough collector area (m2)E electrical energy output (Wh)Emaxptc maximum electrical energy output of PTC system

Et annual energy output (Wh)Ft annual fuel cost (USD)Gstd standard photovoltaic reference irradiation (W m�2)I initial investment (USD)Ib,n beam insolation normal to the mirror (Wh m�2)IPV hourly insolation in the orientation of solar panel

(Wh m�2)LSCA length of a single solar collector assembly (m)Lspacing length of spacing between troughs (m)Ms solar multiple (dimensionless)Mt annual maintenance cost (USD)Pinstalled rated power capacity of the power plant (MW)Pmaxoutput;A ¼ 1 maximum output of the model with A¼1 m2 (W)

PR performance ratioQcoll thermal energy collected by the trough

collectors (Wh)QHE thermal energy available for heat engine (Wh)Qmax

HE maximum thermal energy capacity of heatengine (Wh)

Qmaxstor storage size in units of energy (Wh)

Qstored thermal energy stored (Wh)Un friction velocity (m s�1)Uz wind speed at an elevation of z meters (m s�1)W collector aperture width (m)Weff effective width of mirror aperture (m)Z elevation (m)Z0 roughness length (m)

f focal length of collectors (m)k universal von Kármán constantn lifetime of power plant (years)r discount ratet number of the years in LCOE calculationtstor storage size in hours (h)

Indices

AEDB Alternative Energy Development BoardCF capacity factorCSP concentrating solar powerDNI direct normal insolation (kWh m�2 day�1)EW east westIEA International Energy AgencyIRENA International Renewable Energy AgencyLCOE Levelized Cost of Electricity (USD/kWh)NREL National Renewable Energy LaboratoryNS north southPTC parabolic trough collectorPV photovoltaicTES thermal energy storageTMY typical meteorological year

Greek letters

ηHE efficiency of heat engineηP solar cell efficiencyηtotal total efficiency of the PTC systemηst efficiency of thermal energy storage systemsθ angle of incidence (radians)θZ solar zenith angle (radians)

Fig. 1. Global share of resources in electrical power generation. Adapted from [8].

S.M.S. Sadati et al. / Renewable and Sustainable Energy Reviews 47 (2015) 844–855 845

hydropower (15.3%). The finite amount of conventional sources ofenergy and the environmental impacts of these sources are two ofthe main issues that make them unsustainable and undesirable interms of being friendly to the environment [6]. The problem ofglobal warming and climate change is associated with the emis-sion of greenhouse gases, especially CO2 emissions from thecombustion of fossil fuels [6].

On the other hand, Zeb et al. [9] identified that one of the maincauses for environmental degradation, food crisis and biodiversityloss is the distribution of wealth where the central emphasis isplaced on investment in non-renewable sources of energy thusboosting the “brown economy” instead of the “green economy”.The results of this study emphasis the positive social impacts ofinvestment on renewable energy technologies in order to achievesustainable development and discourage brown growth which“relies heavily on fossil fuels and does not consider the negativeside effects that economic production and consumption have onthe environment” [10].

In terms of green technologies, the main renewable sources ofelectricity are solar (concentrating solar power (CSP) plants andphotovoltaic (PV) plants), wind, geothermal, bio-fuels and hydro[3,6]. As a sign of the current efforts to increase the contribution ofrenewable energies to the energy mix, the road map for futuresolar energy production by the United States Department ofEnergy envisions the increase in solar energy usage and decreasein solar technology costs through “SunShot” targets [11,12].

As a developing country, Pakistan is an energy deficientcountry and although it has abundant renewable energy resources,the share of these resources is very low in the overall energy mixof the country [13]. The major sources of energy which contributeto the overall energy mix of the country are oil and gas [14].Recognizing the importance of alternative energy sources, thegovernment of Pakistan created an Alternative Energy Develop-ment Board (AEDB)1 in order to encourage the green economy byimplementing policies, projects and programs in alternativeenergy resources to support the sustainable social and economicgrowth of the country [15].

Khalil et al. [16] investigated the correlation between theenergy supply and demand in Pakistan and reviewed differentcities of Pakistan where renewable energy technologies can bedeployed. In Pakistan the total installed capacity is currently

21 GW. The actual electric power generation remains restrictedbetween 9 GW and 13 GW since the hydropower is seasonal andnot available in winter and also some independent power plants,which operate on oil, face fuel shortages throughout the year [17].Nevertheless, the actual demand varies between 16 GW to 19 GW[18]. As a result, Pakistan is facing electricity load shedding crisesthese days. Fig. 2 shows the share of all energy resources inelectricity production in Pakistan and indicates that where oil andgas are playing a dominant role and despite of having goodpotential of wind and solar their share is negligible.

Solar and wind energy resources in Pakistan have been broadlystudied by several researchers [19]. Asif [20] presented sustainableenergy options and discussed the significance of both solar andwind energy potential in Pakistan. Mirza et al. [21] discussed thestatus and outlook of solar energy use in Pakistan and concludedthat the initial high capital cost is the main barrier to exploitationof solar energy technologies. Sahir et al. [19] analyzed renewableenergy resource potentials and identified barriers to their signifi-cant utilization in Pakistan. Ullah et al. [22] reported that Jacoba-bad, Pakistan, has high solar resources and carried out a feasibilityanalysis for installing Stirling dish solar power plants in thisregion. It was concluded that it is feasible to install such systemsin Jacobabad, as the insolation is suitable for a solar power plant.Khalid and Junaidi [23] investigated the feasibility of a PV powerplant for the city Quetta, which is in one of the best regions inPakistan in terms of having available solar resources. The averagesolar radiation in Quetta is 2023 kWh m�2 year�1 and the analysisperformed by the software RETScreen shows the comparativeresults for fixed, one axis tracking and two axis tracking PVsystems. The results of the analysis indicate that the cheapestelectricity is achieved using one axis tracking systemwith an LCOEof 0.157 USD/kWh.

The efficiency and performance of PV and PTC systems dependupon the solar resources at a certain location. Pakistan is a richcountry in terms of solar resources and the mean global insolationon a horizontal surface is between 1.9 and 2.3 MWhm�2 year�1

[24]. Fig. 3 shows the annual Direct Normal Insolation (DNI) mapof Pakistan which identifies that the Baluchistan province ofPakistan is especially rich in solar resources. Several programs likethe solar village electrification program have been initiated andabout 3000 solar home systems have been installed in about 49villages of Tharparkar in the Sind province [25]. Pakistan hasseveral institutions working to deploy solar energy projects. Theimport of solar panels and solar water heaters are being encour-aged by the Government of Pakistan to encourage AlternativeEnergy Resources usage [26,27]. To promote the usage of solar PVenergy systems in the country, 500 houses, mosques, and schoolsand 265-street/garden lights were successfully electrified by 300solar PV systems with a total capacity of 100 kW by the Pakistancouncil of Renewable and Electrical Energy [25]. Pakistan is alsoencouraging private sector industries such as Nizam Energy2, SolarSystems Pakistan3, etc. to sell solar panels for domestic, industrialand agricultural use in the country. In order to encourage thedeployment of solar power plants in Pakistan, the local govern-ment of Sindh, Pakistan, recently signed a Memorandum ofUnderstanding with the German company AzurSolar to build a50 MWe solar power plant at Dhabeji, Thatta District. As a firststep, the company has set up a 60 kW solar power station toprovide free electricity to the villages, schools and basic healthcenters of Badin [22]. Furthermore, as of 2013, 341 MWe of solarenergy projects were undergoing feasibility and assessment

Fig. 2. Share of resources in electrical power generation, Pakistan. Adapted from[16].

1 AEDB website: http://www.aedb.org/Main.htm.

2 http://www.nizamsolar.com/.3 http://www.solarsystemspk.com/.

S.M.S. Sadati et al. / Renewable and Sustainable Energy Reviews 47 (2015) 844–855846

Fig. 3. Annual direct normal solar radiation map, Pakistan [29].

Fig. 4. Annual average wind speed map at 50 m, Pakistan [29].

S.M.S. Sadati et al. / Renewable and Sustainable Energy Reviews 47 (2015) 844–855 847

studies, and these could be completed by 2015 if they are deemedfeasible [28].

The wind map of the country (Fig. 4) shows that coastal areas ofSind and some parts of Baluchistan are very rich in wind resources.Currently, one wind energy project which has a gross capacity of50 MWe has been completed by Fauji Fertilizer Company, and hasbeen providing electricity to the national grid since December2010. Another project has been completed by the Turkish companyZorlu Energy Ltd., which has 56.3 MWe capacity [27]. Farooqui [30]carried out a survey on renewable energy resources in Pakistanand predicted that Pakistan has the ability to generate 50 GWe

from wind by 2030. The largest feasible wind power potential inPakistan is in the Gharo–Keti Bandar wind corridor with 44 GWe ofgross resources and 11 GWe of available potential with 25%average capacity factor. The Alternative Energy and DevelopmentBoard (AEDB) of Pakistan in collaboration with the United Nationsdevelopment program installed wind masts in the Gharo–KetiBandar wind corridor to measure wind speeds [24].

In summary, several studies exist in the literature analyzing thepotential for solar and wind in Pakistan at the national level.However, no study could be found in the literature integrating solarand wind electrical energy output performance with cost analysesfor any specific region in Pakistan using hourly meteorological data.Therefore, the primary objective of this paper is to analyze theenergetic and economic performance of PV, PTC and wind technol-ogies for Multan in the Punjab province of Pakistan. Multan has 5 to5.5 kWh m�2 day�1 average direct normal insolation solarresources [29]. Additionally, only six stations in Pakistan measureglobal horizontal solar radiation [22], one of these stations is inMultan, and these solar radiation data are very important for thedesign of PV and PTC systems [31]. Multan’s wind resources arevery low compared to solar resources. However, for completeness inestablishing a comprehensive methodology that can be applied toother regions in Pakistan and other locations globally, the feasibilityof wind technology in Multan is also analyzed in the current study.

Models appropriate for initial feasibility studies and preliminarydesign purposes for PV, PTC, and wind power plants are used herein.The models are presented in sufficient detail that this methodologycan easily be applied to other locations. The results of these threemodels are compared based on the performance of the system andcost of the produced electricity, and conclusions are drawn.

2. Methodology

In this section the methodologies for modeling the energeticand economic performance of PV, PTC, and wind turbine systemsare presented. These methodologies are general and can easily beadopted or adapted for similar studies of other locations. Theresults from the application of these models to Multan, Pakistan,are presented in Section 3.

2.1. Meteorological model

Meteorological data in Typical Meteorological Year (TMY) formatare used for the Meteorological Model. TMY data sets provide hourlylevel meteorological data at a specific location over a typical year,including insolation, wind speed, and temperature. Two TMY datasets were considered. The first set is from the US National RenewableEnergy Laboratory (NREL) [29] and is in the first generation TMYformat, termed simply TMY. The second was generated by Meteo-norm software and is in the second generation TMY format, termedTMY2. The two data sets yield substantially different annual DNIvalues of 1828 kWhm�2 for the NREL data and 1363 kWhm�2 forthe Meteornorm data. To resolve this discrepancy, DNI maps fromboth NREL and Meteonormwere consulted and both indicate annual

DNI values of �1800 kWhm�2. Therefore the NREL data are used inthis analysis as a first and best case scenario. TMY data sets containboth DNI and diffuse horizontal insolation, which can be used toestimate insolation on a surface with any arbitrary fixed or trackingorientation [32]. Solar resources for four characteristic orientationsdefined as follows are investigated:

1. Fixed: Surface faces due south and with the tilt that maximizesthe annual solar resources;

2. NS tracking: Tracking in the North-South direction with rota-tion about a horizontal East–West axis;

3. EW tracking: Tracking in the East–West direction with rotationabout a horizontal North–South axis;

4. 2A: 2-axis tracking.

A theoretical limit to solar resources is the extraterrestrial solarresources that would exist if there was no night for a surfacenormal to the sun–earth line. Baker et al. [33] define severalquantities that facilitate interpreting why actual solar resourcesare less than this theoretical limit.

Night losses: The decrease in the normal extraterrestrialresources due to nighttime.Atmospheric losses: The difference between the extraterrestrialand total terrestrial (beamþdiffuse) resources on a normalsurface.Orientation losses: The difference between the total terrestrialresources on a normal and surface with arbitrary orientation.Diffuse resources: The diffuse resources striking a terrestrialsurface with arbitrary orientation.Beam resources: The beam resources striking a terrestrial sur-face with arbitrary orientation.

Details for the mathematical model to quantify these losses andresources are in [33]. Significantly PV systems can convert bothdiffuse and beam solar radiation into electricity. However, sinceonly beam (but not diffuse) solar radiation can be concentrated,PTC systems can only convert beam solar radiation into electricity.Therefore diffuse solar radiation is a resource for PV systems but aloss for PTC systems, while beam solar radiation is a resource forboth PV and PTC systems.

TMY formatted data also contain hourly wind speed measuredat 10 m above the ground level [34]. These data are used toestimate the energy output of wind technology assuming thatVestas V-90 wind turbines will be installed.

A PTC power plant typically has a capacity in the range of 10 to300 MWe as reported by the International Renewable EnergyAgency (IRENA) [35]. Accordingly, in order to have a comparisonbetween the performance of PV, PTC and wind systems, in thecurrent study the capacity for preliminary design of each powerplant is considered to be 10 MWe.

2.2. PV energetic model

The objective of the PV Energetic Model is to introduce amathematical model to estimate the electricity output of the PVpower plant having a specific installed capacity. The introducedmodel is used for the insolation based on the hourly meteorolo-gical data of a specific location for three characteristic orienta-tions: (1) fixed; (2) EW tracking; and (3) NS tracking. Since PVpanels convert both beam and diffuse insolation to electricity, bothof these resources are considered as the energy input to the PVpanel. In order to simplify the model the shading by terrestrialobjects on the panels during the daylight is assumed to be smalland therefore is neglected.

S.M.S. Sadati et al. / Renewable and Sustainable Energy Reviews 47 (2015) 844–855848

Given TMY formatted data, the PV model calculates the hourlyinsolation on these three orientations. The remaining parametersrequired to calculate the energy output of the PV power systemsare the area of the PV panels, solar cell efficiency ηP

� �, and the

performance ratio PRð Þ. The performance ratio accounts for lossessuch as inverter losses, temperature losses, etc. and is an impor-tant factor to have a realistic estimation of electricity production.In Table 1 the list given for PR components covers the mostimportant losses in a PV power system [36]. In the current study,PR is taken as 75% based on the losses stated in Table 1 [37]. Thetotal area of the PV power plant is found using Eq. (1).

A¼ Pinstalled

ηPGstdð1Þ

HerePinstalled is the installed capacity of the power plant to bebuilt. Gstdis the standard PV reference irradiation which is1000 Wm�2 [38,39]. Hence, by using Eq. (1) the panel areaneeded for a 10 MWe power plant can be calculated. AssumingPV panels produced by Canadian Solar Company (CS6X-P) withpanel efficiency ηP

� �of 15.9%, the required area is 62,900 m2 for a

10 MWe power plant. Similarly, Khalid et al. estimated a panel areaof 62,112 m2 for a 10 MWe PV plant with a panel efficiency of 16.1%for Quetta, Pakistan [40]. This area is universal (i.e. not locationspecific) and any power plant with the same panel efficiency andperformance ratio will need the same area to have a 10 MWe

capacity. Eq. (2) is used to calculate the electricity output (E) of thePV system [36]. The detailed list of input parameters and thenominal values can be seen in Table 1.

E¼ AIPVηpPR ð2Þ

Here IPV is the hourly insolation in the orientation of thePV panel.

2.3. PTC energetic model

The objective of this section is to introduce the mathematicalmodel of the PTC systemwhich yields the electrical energy output.The PTC model used in the current study to estimate the outputelectricity is adapted from [33]. The electrical energy produced perhour by the PTC system (E) is modeled using Eq. (3) [33]. The factthat PTC system only uses DNI for the generation of electricity, andnot diffuse, limits PTC to sunny locations where beam insolation ishigh, hence it is necessary to study the performance of PTC

systems for each specific region.

E¼ ηtotalAcollIb;n ð3ÞHere ηtotal is the total solar to electrical energy efficiency, Acoll is

the collector area and Ib;n is the beam insolation normal to themirror. More detailed PTC system models can be found inreferences [33,41]. The overall efficiency ηtotal

� �is the collector

efficiency, calculated in the model, multiplied by the heat enginethermal efficiency. The heat engine efficiency is assumed as 34%[42]. In order to estimate the mirror area needed, the maximumpower output over the course of a year for one unit area ofcollector is calculated using the PTC model shown in Eq. (3) [33].The calculated maximum power output is used to find therequired area to have a 10 MWe power plant by Eq. (4).

A0 ¼Pinstalled

Pmaxoutput;A ¼ 1

ð4Þ

Here Pinstalled is the desired installed capacity of the PTC powerplant and Pmax

output;A ¼ 1 is the maximum output of the model withA¼1 m2. Another important parameter in calculating the actualaperture area is the solar multiple Msð Þ, which is the ratio of actualaperture area to the area whose maximum output is the designinput to the power block. For Ms ¼ 1, the maximum output of thecollectors is equal to the design input of the power block, and formost hours throughout the year the collectors are undersized withrespect to the power block, which leads to inefficiencies in thepower block due to running at part load conditions. For Ms41, forat least some hours during the year the collector field producesmore thermal output than the nominal input to the power block,and at these times some of the PTCs may need to be defocused.The annual energetic and economic performance of a PTC systemcan be improved by having Ms41. Therefore the actual collectorarea Acollð Þ is calculated by multiplying the solar multiple Msð Þ andthe calculated area A0ð Þ which is shown by Eq. (5).

Acoll ¼MsA0 ð5ÞTypical values for the solar multiple for a PTC system without

and with storage are 1.25 and 3.0, respectively [43]. The calculatedarea for a 10 MWe PTC system at Multan is shown in Table 2.

The solar energy collected by PTC are affected by two geometricloss parameters termed “shadow loss” and “endloss” [44]. Shadowloss typically occurs in the early morning and late evening whenone PTC row shades another, which decreases the PTC energeticperformance. Shadow loss is a function of zenith angle and ismodeled by Eq. (6) [44].

Shadow Loss¼Weff

W¼ Lspacing

W� cos θz

� �cos θ

� � ð6Þ

Here Weff is the effective width of mirror aperture, θz is thesolar zenith angle, Lspacing is the length of spacing between thetroughs (taken as 15 m), W is the collector aperture width(assumed as 6 m [42]), and θ is the angle of incidence [44]. Onthe other hand, endloss is the result of high angle of incidences,which cause the end of the receiver closest to the sun not to beirradiated with concentrated solar radiation. The endloss of PTCs ismodeled by Eq. (7) [44].

Endloss¼ 1� f tan θ� �

LSCAð7Þ

Table 2Solar multiple and Area calculated for 10 MWe PTC system for Multan.

Parameter Ms Acoll [m2]

No-storage 1.25 45,1007.5 h Storage 3.50 126,400

Table 1Nominal constants and inputs for PV model [36,37].

Variable Value Units

Power plant installed capacityPinstalled 10 MWe

Total solar panel areaA 62,900 m2

InsolationIPV From meteorological

dataWh m�2

PV reference irradianceGstd 1000 W m�2

Solar panel efficiencyηP 15.9% –

Performance ratio coefficient for losses(PR)

PR 75% –

PRincludes:4 Inverter losses 4–15% –

4 Temperature losses 5–18% –

4 DC cables losses 1–3% –

4 Losses weak radiation 3–7% –

4 Losses due to dust, snow 2% –

S.M.S. Sadati et al. / Renewable and Sustainable Energy Reviews 47 (2015) 844–855 849

In Eq. (7) f is focal length of collector and LSCA is the length of asingle solar collector assembly. In the current study f is assumed tobe 1.71 m and LSCA is assumed to be 115 m based on [42].

2.3.1. Thermal storage energetic model for PTC systemOne of the main advantages of CSP plants is the ability to store

thermal energy cheaply, and therefore it is important to model astorage system to estimate the overall performance of a PTCsystem with storage. As per one of the US National RenewableEnergy Laboratory reports a typical capacity factor for a PTCsystem with �7 h storage is about 45% [43]. In order to achievethis value it is necessary to consider a solar multiple equal to3.5 for a PTC system having 7.5 h storage. The efficiency for atypical thermal storage system, ηst, is reported to be 50% [45]. Thethermal energy storage (TES) model is generated based on theflowchart shown in Fig. 5. This model is based on the storage sizein units of energy Qmax

stor

� �and the hourly available energy, and is

run for the whole typical year. The storage size is calculated usingEq. (8) where tstor is the storage size in hours and ηHE is theefficiency of the heat engine.

Qmaxstor ¼

tstorPinstalled

ηHEηstð8Þ

Installing a storage system will increase installation costs,however the resulting higher capacity factor can compensate forthe higher installed cost which can make installing the storagesystem economically feasible in case of having sufficient solarresources. In the flowchart shown in Fig. 5, Q coll is the thermalenergy collected by the collector, Q stor is the energy stored in thestorage system, Qmax

stor is the maximum capacity of the storagesystem for thermal energy storage, QHE is the thermal energyavailable for the heat engine, Qmax

HE is the maximum thermalenergy capacity of the heat engine, E is the electrical energyoutput and Emax

PTC is the maximum capacity of the electrical energyoutput of the system.

2.4. Wind turbine energetic model

The wind turbine energetic model is developed assuming aVestas V-90 wind turbine. Since the wind speed data in the TMYformatted meteorological model are measured at 10 m aboveground level rather than at hub height, a correction to these wind

speed data is made to estimate the wind speed at wind turbinehub height. Eq. (9) is a model for the wind velocity profile in termsof elevation Zð Þ [46].

Uz ¼Un

kln

ZZ0

� �ð9Þ

Here Un is the friction velocity, k is a universal constant and Z0

is the roughness length. Since Unand k are constants for allelevation values in a specific region, and considering that TMYwind speed data are measured at 10 m above the ground level[34], by using Eq. (10) the wind speed at 105 m is estimated.

U105 ¼U10 lnð105=Z0Þ

lnð10=Z0Þð10Þ

In Eq. (10), U10 is the wind speed at Z¼10 m which is obtainedfrom TMY formatted data and U105 is the wind speed at Z¼105 m.Due to the lack of the data on the local roughness length value,different typical values of roughness [46] are considered toestimate the wind speed at hub height. In this study in order tojustify the comparability of the wind farm with PTC and PV powerplants, six V-90 wind turbines are considered in the farm whichresults in a farm with rated capacity of 10.8 MWe. The operationalcharacteristics of the V-90 wind turbine are shown in Table 3 andthe power output versus wind speed curve for this wind turbine isshown in Fig. 6. The hub height wind speed data are correlatedwith the power versus wind speed characteristic curve of the windturbine and the corresponding electrical energy output wasestimated.

Fig. 5. The flowchart of the thermal energy storage system (TES) model for PTC.

Table 3Operational data for V-90 wind turbine.

Parameter Value

Rated power 1.8 MWe

Cut in speed 3 m s�1

Cut out speed 25 m s�1

Nominal wind speed 12 m s�1

Hub height 105 mRotor diameter 90 m

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2.5. Capacity factor model

The capacity factor (CF) of a power plant is the ratio of actualelectricity produced divided by the maximum electricity producedassuming the plant operates continuously at its installed capacityover some time period [47] which is shown in Eq. (11). CF isusually defined on an annual basis.

CF¼ Actual Energy OutputInstalled Energy Capacity

ð11Þ

In this study, in order to calculate CF, the estimated annualproduction which is found from the energetic models in Sections2.2–2.4, is divided by the production assuming the plants operateat their nominal output for the entire year (24 h day�1

�365 days year�1¼8760 h year�1).

2.6. Cost analysis for PTC and PV power plants

In order to analyze the cost of the produced electrical energythe levelized cost of energy (LCOE) is calculated by using theanalytical model for PTC and PV systems proposed by Hernandez-Moro and Martinez-Duart [48]. Because of the low CF found for thewind farm for Multan, a cost analysis was not performed for awind power plant. LCOE is defined as the cost of energy per unitoutput energy of the system during its lifetime. The unit for LCOEin this study is considered as USD/kWh which can be calculatedusing Eq. (12).

LCOE¼Iþ Pn

t ¼ 1ðMtþFtÞ= 1þrð Þt

Pnt ¼ 1

Et= 1þrð Þtð12Þ

Here I is the initial investment, Mt is the annual maintenancecost, Ft is the annual fuel cost, Et is the annual energy produced inkWh, r is the annual discount rate and n is the lifetime of thepower plant in years. Since this is an initial feasibility study with aprimary goal being to define directions for more detailed studies,several simplifying assumptions are made for calculating LCOE andthese are shown in Table 4. It should be noted that all the costsgiven in the following sections are based on the 2010 USD.

2.6.1. Initial costs2.6.1.1. Installed cost. The initial investment (or total capital cost)includes system installed cost and land cost. There are severalproposed values available in different reported studies to estimatesystem installed costs and land costs [48]. The values vary fordifferent system designs. Based on the data for PTC power plantsgiven in [48] installed cost are assumed to be approximately4.6 USD/W for a power plant which does not have storage. Wheninstalling a storage system and for the same power plant capacity,more collectors will be needed and consequently the installed costwill increase. If a storage system of 7.5 h is installed the costincreases to approximately 8.7 USD/W [43,48]. The installed costof a PV power plant is around 3.09 USD/W for an installed capacityof 10 MWe power plant with one axis tracking [49].

2.6.1.2. Land use cost. A wide range of land use costs from 12 to 60USD/kW has been reported for PV [48]. In this work both the lowerlimit (12 USD/kW) and upper limit (60 USD/kW) of this range havebeen analyzed and the change in LCOE due to this difference is lessthan 1.5% which means LCOE is not very sensitive to land cost. Thereason is that the installed cost is much more than land cost forboth PTC and PV systems. Therefore as a first approximation a landcost of 30 USD/kW is considered in this study for PV. PTC powerplants also have a land use cost of approximately 24 USD/kW [48].

2.6.2. Annual costs2.6.2.1. Maintenance cost. Maintenance cost is assumed to be 1.5%of total initial cost for PV and 2% of total initial cost for CSP [48].These values are also close to the reported values by NREL for PTCand PV costs [49].

2.6.2.2. Insurance cost. Due to the risks of the investment,insurance cost is considered to be 0.25% of total capital cost forPV and 0.5% of total capital cost for PTC [49].

2.6.3. Financial factors2.6.3.1. Discount rate. Discount rate is one of the importantparameters in financial studies since it takes the time value ofmoney and also the investment risks into account. Conservativeassumptions for discount rates given by the International EnergyAgency (IEA) are 10% to 12% for PV and 10% to 15% for CSP systems(including PTC) [48,50]. In this study the discount rate isconsidered as 10%.

2.6.3.2. Lifetime of the system. The reported lifetime for PV systemsis 25 to 30 years and for PTC systems is 30 to 40 years [48]. Hence,in this study lifetimes of 30 years for PV and 35 years for PTC areassumed.

3. Results and discussion

3.1. Energetic results

The average daily insolation achieved from 2 axis (2A), EW andNS tracking are shown in Fig. 7 for non-concentrating systems and

Fig. 6. Power versus speed curve of V-90 wind turbine.

Table 4Different components in calculating LCOE for 10 MWe PV and PTC power plants.

Parameter PV PTC

No storage 7.5 h Storage

Installed [USD/W] 3.09 4.6 8.7Land [USD/W] 0.030 0.024 0.035Maintenance in USD 1.5% Of total cost 2% 2%Discount rate 10% 10% 10%Insurance of total cost in USD 0.25% Of total cost 0.5% 0.5%

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Fig. 8 for concentrating systems. 2A tracking is not applicable forPTCs and also it is costly so in this study the focus is on EW and NStracking.

As it is shown in Figs. 7 and 8 for both concentrating and non-concentrating solar resources, EW tracking has larger summer andannual solar resources as compared to NS tracking. This is due tothe fact that overall orientation losses are more in NS tracking as isshown in Fig. 9. Recall orientation loss is the difference ininsolation received by a surface with 2A tracking (oriented normal

to the sun–earth line) and a surface with arbitrary orientation, andis sometimes referred to as cosine losses. In two axis tracking casethere are no orientation losses since the surface is always normalto the sun–earth line. As presented in Fig. 9, orientation losses arevery low during late April to early August for EW tracking. This isdue to the fact that EW tracking’s angle of incidence is very smallat this time of the year.

Nonzero incidence angle also causes end losses which areshown in Fig. 10 for both EW and NS tracking. As presented inFig. 10, end losses are low during May to early August for EWtracking. This is again the result of small angle of incidences forthis time of the year.

In Fig. 11, the solar losses, including night, atmospheric,orientation, others (containing shadow and end), and resourcesincluding diffuse and beam (concentrating), are presented for EWtracking. The total height of the bar represents the average dailyextraterrestrial resources assuming no night (i.e. the sun shines24 h day�1). The decrease in this total height in the summer is dueto the earth being farther from the sun. Orientation loss andendloss are much less for EW tracking from May to Septemberbecause the incidence angle is very small due to the position ofsun with respect to the Earth. In general endloss is very smallcompared to other losses. The average daily orientation loss in EWtracking is approximately 17 times larger than the endloss for thecollector dimensions given in the methodology section. Sinceincluding endloss is only affecting the output electrical energyby �0.5% and it is subject to change with different collector

Fig. 7. Trends in average daily insolation of non-concentrating solar resourcesbased on TMY formatted data for Multan, Pakistan.

Fig. 8. Trends in average daily insolation for concentrating solar resources based onTMY formatted data for Multan, Pakistan.

Fig. 9. Orientation loss for both EW and NS tracking based on TMY formatted datafor Multan, Pakistan.

Fig. 10. Endloss for both EW and NS tracking based on TMY formatted data forMultan, Pakistan.

Fig. 11. Insolation contribution for EW tracking based on TMY formatted data forMultan, Pakistan.

S.M.S. Sadati et al. / Renewable and Sustainable Energy Reviews 47 (2015) 844–855852

dimensions, in subsequent parts of this study endloss has beenneglected. According to these results EW tracking system is morepreferable for Multan, hence EW tracking should be used.

In Fig. 12 the average daily electricity output for PTC, PV andwind power plants for Multan, Pakistan is presented. It can be seenthat electrical energy output of EW Tracking systems for both PTC(no-storage) and PV systems are approximately equal but the windenergy output is very small as compared to both solar energysystems. Also correcting the wind speed from 10 m above groundto hub height using Eq. (10) increases daily average energy outputfrom 2 MW MWh to 5.4 MWh. This increase in energy output isdue to the fact that wind speed typically increases with altitudeand the correction estimates the wind speed at the center of windturbine rotor 105 m above ground level.

The average daily electricity produced by PTC with 7.5 h ofstorage is the highest among all. As it is shown in Fig. 12, PV andPTC systems can be considered feasible for Multan with respect toelectrical energy output. Conversely wind turbines deployment isnot feasible in this region.

Fig. 13 presents the average daily electrical energy output for PVfor each month for the 3-characteristices surfaces for Multan,Pakistan. The optimum fixed tilt angle is calculated to be 211 forthis specific region, which is consistent with an experimental studymade for southern parts of Sindh, Pakistan which reported anoptimum yearly tilt angle of 231 [51]. Between February andOctober, the large differences between EW and the other twocharacteristic surfaces can be clearly seen in Fig. 13. Especially fromJune to August the EW tracking PV system output is significantlyhigher than NS and fixed tilted surface. Considering the annual daily

average for tilted surface, it is lower than the tracking surface asexpected. The annual average daily energy production by NStracking is 4% more than the energy produced by the tilted system.EW tracking surface generates 10% more electricity than the tiltedsystem and 6% more than NS tracking system. However, the energyneeded for tracking and maintenance costs are disadvantages of EWand NS tracking relative to the fixed tilted system.

3.2. Capacity factor results

The capacity factor for PV is approximately 19.85% and for PTC isapproximately 20.08% considering shadow losses. If shadow lossesare not considered PTC’s capacity factor increases to 20.82%. More-over, by considering 7.5 h storage the capacity factor for PTCincreases to approximately 45.96%. It is shown that even in thebest case for which the roughness value returns the highest hubheight speed, the wind resources are much less than the solarresources in Multan. Considering different typical values for groundroughness [52], the wind energy capacity factor ranges from 1.6% to2.1% for Multan which is low compared to PV and PTC systems.

3.3. Cost analysis results

The results of cost analysis for PV, PTC with no storage and PTCwith 7.5 h of storage systems are presented in Fig. 14. In Table 5 thecalculated LCOE of the analyzed systems is shown for their typicallifetime reported in [48]. Although the installed cost is higher forinstalling a storage system it does not have a significant effect onthe LCOE. The reason is the higher capacity factor in case ofinstalling storage system compensates for the higher cost.

The cost of electricity for a 10 MW PV power plant with oneaxis tracking from a study for Quetta [40], which has the highestsolar resources in Pakistan, assuming a discount rate of 9% and aninflation rate of 8% is found to be 0.157 USD/kWh. The mainreasons for this lower cost are the higher availability of solarresources in Quetta and the difference between the inputs of theeconomic analysis model which is mainly discount rate. Using thesame discount rate, which is 9%, the model developed for this

Fig. 12. Trends in average daily electrical energy output for PTC, PV and windturbine for Multan, Pakistan.

Fig. 13. Trends in average daily electrical energy output for a PV system in Multan,Pakistan. For the tilted surface optimum tilt angle is 211.

Fig. 14. Cost of energy in USD/kWh versus the lifetime of the PV, PTC (with andwithout storage) systems.

Table 5LCOE of PV, and PTC with and without storage calculated for their typical lifetime.

System Lifetime (yrs) LCOE (2010 USD/kWh)

PV 30 0.192PTC 35 0.273PTCþ7.5 h storage 35 0.226

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study would yield 0.17 USD/kWh for a PV system for Multan.Nevertheless, the results of the current study support that the PVinstallation in Multan, which is not as rich as Quetta in solarresources, is feasible considering the electricity prices in Pakistan.The tariff of electricity price in Pakistan has had an increasingtrend since 2005 which is shown in Fig. 15 [40,53]. According toNational Power Policy published by the Government of Pakistan,the Ministry of Water and Power has estimated the real cost ofdelivering a unit of electricity to the end consumer at greater than0.156 USD/kWh [54]. One of the major reasons for this increasewas the increasing price of oil and fossil fuels, however, due to thelocal grid and network problems in Pakistan the government is notlikely to decrease the electricity price as a response to the decreasein oil prices since late 2014 [54]. On the other hand, since Pakistanis dependent on the imported oil [17], deploying green energytechnologies will help for having more autonomous energyproduction grid as well as preserving the environment of thecountry. Accordingly estimating a 0.16 USD/kWh electricity pricefor 2015, installing PV, PTC and PTC with storage systems wouldneed a subsidy of 0.032 USD/kWh, 0.119 USD/kWh and 0.066 USD/kWh, respectively.

4. Conclusions

In this paper performance analysis for PV, PTC and wind powerplants with 10 MWe capacities is performed with respect toelectrical energy production based on hourly meteorological datain TMY format for Multan, Pakistan. Due to the low available windresources, the electrical energy output of a typical wind systemwas found to be very low compared to both PV and PTC systems,hence wind technology with �2% capacity factor is not recom-mended for this region. Furthermore, EW tracking shows betterperformance than NS tracking for solar resources. On the otherhand, PTC and PV systems are found to be feasible based on thecalculated LCOE. In terms of electrical energy output, PTC with7.5 h storage shows the best performance with 45.96% capacityfactor. Also PV and PTC with no storage exhibit approximately thesame performance with 19.85% and 20.08% capacity factors,respectively. With respect to cost analysis, PV shows the bestperformance having the LCOE of 0.192 USD/kWh for a 30 yearlifetime. Both PTC with storage and without storage are moreexpensive than PV with LCOE’s of 0.273 and 0.226 USD/kWh,respectively. Additionally the land use of PTC systems is more thanPV system for the same installed capacity, but the analysis showthat the sensitivity of LCOE to land cost is very low. More

specifically a 5 fold increase in land cost will only increase theLCOE by �1.5%.

If electrical energy output during non-sunny times isdemanded the PTC with storage seems feasible at the expense ofhigher capital cost. However, it is more economical to install PVsystems having lower capacity factor than PTC with storage sincethere is a tradeoff between cost and lower electrical energyoutput. Although it is necessary to have incentives and subsidiesfor deploying green technologies, the higher cost of electricity maybe compensated by improving environmental standards and hav-ing an autonomous energy production grid. Recall for the Meteor-ological Model TMY data from NREL and TMY2 data fromMeteonorm were considered, significant differences found, theNREL TMY data were used since they are consistent with DNI mapsfrom both NREL and Meteonorm. The possible reasons for thisdifference in NREL and Meteonorm data should be studied infuture works.

Acknowledgment

We would like to thank Mr. Mustafa Karadeniz for his colla-borations on the start of this study at Middle East TechnicalUniversity, Northern Cyprus Campus.

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