output prediction of large-scale photovoltaics by wind-condition analysis using 3d topographic maps

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ScienceDirect

Solar Energy 105 (2014) 157–169

Output prediction of large-scale photovoltaics bywind-condition analysis using 3D topographic maps

Shin’ya Obara ⇑, Jorge Morel, Daisuke Konno, Yuta Utsugi

Kitami Institute of Technology, Power Engineering Lab., Dep. of Electrical and Electronic Engineering,

Koen-Cho 165, Kitami, Hokkaido 090-8507, Japan

Received 1 June 2013; received in revised form 28 December 2013; accepted 8 March 2014

Communicated by: Associate Editor Ursula Eicker

Abstract

The electrical conversion efficiency of large-scale photovoltaics is dependent on the operating temperature of the photovoltaic module.Therefore, if a tool for analyzing the annual electrical conversion efficiency could be developed on the basis of the climatic conditions atthe system installation site, it would be effective for assessing system economy. Accordingly, an analysis method was developed in thisstudy to determine the temperature distribution and electrical conversion efficiency of a photovoltaic module by considering the windconditions at the installation site. Modular temperature distribution and DC electrical conversion efficiency were obtained by introduc-ing the physical properties of the photovoltaic module, wind conditions, and climatic conditions (plane of array irradiance and ambienttemperature) by using a digital three-dimensional topographic map in a heat-transfer calculation. The case analysis results suggest a highvalue for the power-production efficiency for low ambient temperature. However, the difference in the temperature distribution of thephotovoltaic module in relation to the difference in the ambient temperature is strongly influenced by wind velocity and wind direction.Moreover, when a large-scale photovoltaic power plant is installed on a complicated mountain slope, the cooling effect is controlled sothat the indraft wind velocity on the photovoltaic module decreases. Therefore, in order to maintain high electrical conversion efficiencyin the photovoltaic module, the best location for installation is an airy and flat area, as much as possible. According to the case analysis,the electrical conversion efficiency of the photovoltaic module at the time of the analysis under wind condition increased 23% (maximum)compared with that without wind conditions.� 2014 Elsevier Ltd. All rights reserved.

Keywords: Large-scale photovoltaics; Wind-condition analysis; 3D topographic map; Heat-transfer analysis

1. Introduction

Currently, the introduction of large-scale photovoltaics(mega-solar power systems, hereafter MSPSs) in the thou-sands-of-kilowatts class is progressing, aiming to spreadgreen energy in Japan (e.g., Massi Pavana et al., 2013;Rakibuzzaman et al., 2012; Strzalka et al., 2012; Pearce,

http://dx.doi.org/10.1016/j.solener.2014.03.007

0038-092X/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel./fax: +81 157 26 9262.E-mail address: [email protected] (S. Obara).

2008; Wybo, 2013; Taha, 2013; Zhang et al., 2012).Furthermore, because the government of Japan hasimproved the system for acquiring surplus power from greenenergy, the introduction of photovoltaics is accelerating.The installation of a MSPS requires the measurement ofcommercial success by a highly accurate cost analysis. Thephotovoltaic module, the land, personnel expenses, mainte-nance costs, power transmission lines, and a transformer areincluded in the cost of large-scale photovoltaics. Further-more, because the electricity production of photovoltaics


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Nomenclature

Apm area of a photovoltaic module (m2)cp,a specific heat of air (J/(g K))cp,pm specific heat of the photovoltaic module

(J/(g K))Gpm mass of the photovoltaic module (kg)hpm heat-transfer rate on the surface of a photovol-

taic module (kW/(m2 h K))Ipm current of the solar cell (A)ka heat conductivity of the open air (kW/(m K))L representative length (m)Nu Nusselt numberPr Prandtl numberQca heat of air current (J)Qgh heat of the photovoltaic module (J)Qht heat by heat transfer (J)Qr reflection energy of solar radiation (J)Qtr amount of radiation by heat transfer (J)qca quantity rate of heat of air current (kW)qdc output rate of direct current (=load) (kW)qgh production rate of heat of the photovoltaic

module (kW)

qht amount of heat by heat transfer (kW)qpm rate of electricity production of the solar cell

(kW)qr reflection energy rate of solar radiation (kW)qs rate of solar radiation (kW/m2)qtr heat-transfer rate of amount of radiation (kW)q0s effective rate of amount of insolation (kW)RLoad resistance of load (X)Rir internal resistance (X)Re Reynolds numberTa ambient temperature (�C)Tpm temperature on the module surface (�C)t sampling time (h)ua flow velocity of air (m/s)

Greek characters

cr reflectivity of solar radiation on the surface ofphotovoltaic module

ma kinematic viscosity of air (cm2/s)qa density of air (kg/cm3)r Stefan–Boltzmann constant (J/(s cm2 K4))

dc

Ta

Fig. 1. Heat input and output of photovoltaic module.

158 S. Obara et al. / Solar Energy 105 (2014) 157–169

strongly influences the installation plan of MSPSs, the anal-ysis tools that predict electricity production in Japan are ser-viced by national organizations. For example, theMONSOLA05 (801) of the Japan Weather Association(National amount-of-insolation normal data map, 2013),the handbook and review support tool for large-scale photo-voltaics introduction (New Energy and IndustrialTechnology Development Organization (NEDO), 2013a),and the annual amount-of-insolation data base of the MET-PV-11 (New Energy and Industrial TechnologyDevelopment Organization (NEDO), 2013b), etc., can beaccessed on the Internet. Because the slope amount of inso-lation in each area in Japan can be obtained from the toolsdescribed above, the electricity production of the MSPS canbe forecasted by giving the electrical conversion efficiency ofa photovoltaic module. The electrical conversion efficiencyof the photovoltaic module can be calculated from the oper-ating temperature of the module obtained in the heat-trans-fer analysis compared to the relationship between electricalconversion efficiency and temperature characteristics inves-tigated beforehand. Investigation of the past of photovol-taic and conversion efficiency is known (e.g., Haider, 2013;Skoplaki and Palyvos, 2009).

However, the temperature of an actual photovoltaicmodule changes with the heat-transfer rates between thesurrounding wind and the modular surface. A decrease inthe electrical conversion efficiency caused by a temperatureincrease in a photovoltaic module may significantlydecrease the annual electricity production in the MSPS.

Accordingly, the heat-transfer rate between the photovol-taic module and the open air is calculated on the basis ofthe wind conditions obtained from the geography of theactual site by introducing the wind-condition analysis usingthe applicable digital three-dimensional (3D) topographicmap (Ministry of Land, Infrastructure, Transport andTourism of Japan, 2013). By introducing the heat-transferrate described previously into the heat-transfer analysis ofthe photovoltaic module, the modular temperature distri-bution is determined; as a result, the average electrical con-version efficiency can be obtained. The effectiveness of theelectricity production prediction using the wind-conditionanalysis coupled with the 3D topographic map is clarifiedby determining the difference between the electricity pro-

S. Obara et al. / Solar Energy 105 (2014) 157–169 159

duction of the MSPS in the consideration of wind condi-tions (the proposed production method) and the electricityproduction calculated from the slope amount of insolation(the conventional method).

2. Analysis method

2.1. Heat-transfer model and electrical conversion efficiency

of photovoltaic module

Fig. 1 shows a model of the input/output of energy andfluid about a photovoltaic module. The quantity of heat qca

by the solar radiation qs and the flow of the open air areinputs to the photovoltaic module, and the direct currentpower qdc is the output. Moreover, the production of heatqgh occurs from electric resistance by the semiconductorinside the photovoltaic module. The heat emitted by heattransfer from the modular surface qht and the heat by radi-ation heat transfer qtr correspond to the difference betweentemperature on the module surface Tpm and ambient tem-perature Ta. Although the solar modular surface is coveredwith a special glass, generally, the reflection energy qr ofsolar radiation on the glass surface is less than 1%. Thetemperature of the photovoltaic module Tpm is determinedby the balance of energy described previously. Fig. 2 showsan example of the relation between the temperature of thephotovoltaic module and electrical conversion efficiency. Inthe example in Fig. 2, the electrical conversion efficiency is18% when the module temperature is 25 �C. The relationbetween module temperature and electrical conversion effi-ciency is denoted by a linear function.

2.2. Heat-transfer analysis

2.2.1. Heat-transfer rate between module surface and open

air

Because the solar modular surface and the heat transferbetween it and the open air are considered to be

14

15

16

17

18

19

20

0 20 40 60 80

Temperature of solar module [ºC]

Ele

ctri

cal c

onve

rsio

n ef

fici

ency

[%

]

Fig. 2. Relation between photovoltaic module temperature and electricalconversion efficiency (measured).

forced-convection heat transfer on a flat surface, qht, inFig. 1 is calculated by Eq. (1). In the case of forced-convec-tion heat transfer, the Nusselt number (Nu) defined by Eq.(2) is arranged with the Reynolds number (Re) and the Pra-ndtl number (Pr), and the Nusselt number is calculated byEqs. (3) and (4).

qht ¼ Apm � hpm � ðT pm � T aÞ ð1ÞNu ¼ hpm � L=ka ð2Þ

Nu ¼ 0:064 � Re1=2 � Pr1=3 Here; Re < 105 ð3Þ

Nu ¼ 0:037 � Re4=5 � Pr1=3 ð4Þ

Eq. (5) is the definitional equation of the Reynolds num-ber, and the flow velocity ua in the equation is the flowvelocity of the open air.

Re ¼ ðua � LÞ=ma ð5Þ

The heat-transfer rate between the open air and the pho-tovoltaic module hpm is obtained by calculating Nu fromEq. (3) or (4) and substituting the obtained Nu value inEq. (2). Here Pr is obtained by introducing the physicalproperties of the open air, and the Re number is obtainedby substituting the flow velocity of the open air ua, thecharacteristic length L of the photovoltaic module, andthe coefficient of kinematic viscosity of open air ma to Eq.(5). When the hpm value is known, the quantity-of-heatqth can be obtained by substituting the area of thephotovoltaic module Apm and the temperature gradient(Tpm � Ta) of the open air and the module into Eq. (1).

2.2.2. Heating value of module

The electricity production of a solar cell qpm consists ofthe heating value qgh, the load qdc, and the modular internalresistance Rir, as shown in Eq. (6). The heating value ofmodular qgh is dependent on the electricity production ofthe solar cell qpm and the magnitude of the load qdc. TheMSPS power production in this paper is interconnectedwith a commercial power system and is controlled to oper-ate at maximum output in each sampling time. Ipm is thecurrent of the solar cell.

qpm ¼ qdc þ qgh ¼ RLoad � I2pm þ Rir � I2

pm ð6Þ

2.2.3. Reflection of solar radiation and radiation heat

transfer

The reflectivity of solar radiation of the glass coating ofthe photovoltaic module is set at cr, and Eq. (7) defines theeffective amount of insolation q0s that contributes to powerproduction. Moreover, the energy reflected from the pho-tovoltaic module qr can be calculated by Eq. (8). Eq. (9)is the amount of heat radiation by radiation heat transferqtr corresponding to the temperature gradient of the photo-voltaic module and the open air.

q0s ¼ ð1� crÞ � qs ð7Þqr ¼ cr � qs ð8Þ


qtr ¼ r � Apm � ðT 4pm � T 4

aÞ ð9Þ

2.2.4. Power balance and heat balance

Eqs. (10) and (11) are balance formulas of the electricityand the heat of the photovoltaic module shown in Fig. 1,respectively. The left side of each equation contains theterms of input and production, and the right-hand sidecontains the terms of output. qgh by modular internal resis-tance is obtained from Eq. (6), and the thermal power byreflection and radiation of solar radiation, qr and qtr, areobtained from Eqs. (7)–(9). Therefore, qgh in Eq. (10),and qgh, qr, and qtr in Eq. (11) can be calculated by Eqs.(6)–(9). Qdc in Eq. (10) is the power output of the photovol-taic module. Moreover, qca on the left-hand side of Eq. (11)is the quantity of heat in the open air, and qht is the quan-tity of heat emitted to the open air from the module.

Electricity : q0s ¼ qdc þ qgh ð10ÞHeat : qca þ qgh ¼ qht þ qr þ qtr ð11Þ

The temperature change (Tpm,t � Tpm,t�1) in the photo-voltaic module from sampling time t � 1 to t is obtainedfrom Eq. (12). Qca,t, Qgh,t, Qht,t, Qr,t, and Qtr,t in Eq. (12)can be calculated by Eqs. (13)–(17), respectively, and thetemperature Tpm,t of the photovoltaic module can beobtained by employing these results to Eq. (12).

Qca;t þ Qgh;t ¼ Gpm � cp;pm � ðT pm;t � T pm;t�1Þ þ Qht;t

þ Qr;t þ Qtr;t ð12ÞQca;t ¼ qa;t � cp;a;t � ðT a;t � T pm;tÞ ð13Þ

Qgh;t ¼ Rir � I2pm;t ð14Þ

Qht;t ¼ Apm � hpm;t � ðT 4pm;t � T 4

a;tÞ ð15ÞQr;t ¼ cr � qs;t ð16Þ

Qtr;t ¼ r � Apm � ðT 4pm;t � T 4

a;tÞ ð17Þ

2.3. Wind conditions in the neighborhood of the mega-solar

power system

The flow velocity ua of inflowing air to the photovoltaicmodule is included in the Reynolds number described inEq. (5). However, the rate vector of air is always changing,and it cannot be set up uniformly. Accordingly, in thisstudy, the average wind velocity and wind direction at eachtime are introduced as characteristics of inflowing air to thephotovoltaic module used for heat-transfer calculations.The average wind velocity and wind direction at each timecan be known from past meteorological data (JapanMeteorological Agency, 2013a). However, the characteris-tics of the open air that flows into the MSPS are greatlyinfluenced by surrounding geographical features. There-fore, a 3D digital topographic map has been prepared onthe basis of base map information from the Geospatial

Information Authority of Japan (Ministry of Land,Infrastructure, Transport and Tourism of Japan, 2013),and the MSPS is arranged on this topographic map. Veloc-ity, direction, heat-transfer rate, etc., of air in the neighbor-hood of the photovoltaic module are calculated byintroducing the wind-condition analysis, described in thefollowing section, into the 3D digital topographic map.

2.4. Analysis flow

A flow chart of the wind-condition analysis is shown inFig. 3; (a)–(d) in the figure are blocks containing calcula-tions. The flow chart consists of obtaining topographicalmap information and altitude data [block (a)], preparingthe 3D topographic map and the model of the mega-solarpower generator [block (b)], and modeling and input of anal-ysis conditions [block (c)], and thermal fluid analysis andheat-transfer calculation [block (d)]. The topographic mapof the MSPS site and map information are prepared in block(a). The topographic map and map information use basemap information (Ministry of Land, Infrastructure,Transport and Tourism of Japan, 2013) from the GeospatialInformation Authority of Japan and the digital-national-information download service (Japan MeteorologicalAgency, 2013b). Next, based on the topographic map infor-mation and the map information, a 3D map is preparedusing SolidWorks 2012R of the MathWorks, Inc. [block(b)]. The model of a photovoltaic module with a previouslyfixed installation angle, angle of direction, and installationmodes (distribution, etc.) is installed on the 3D plan.

Meteorological data (ambient temperature, slope solarradiation, wind direction, wind velocity) and values forthe solid and fluid properties used in the Flow Simulation2012 analysis are given in block (c). Past meteorologicaldata (Japan Meteorological Agency, 2013c; JapanWeather Association, 2013; New Energy and IndustrialTechnology Development Organization (NEDO), 2013b)is used as the meteorological data.

Furthermore, block (d) contains the thermal fluid calcu-lation and the heat-transfer calculation; the temperaturedistribution of the solar modular surface and the stream-line in the space surrounding the mega-solar power gener-ators are obtained by Flow Simulation 2012. The methodfor calculating the temperature distribution of the solarmodular surface follows the heat-transfer analysisdescribed in Section 2.2. On the other hand, the Navier–Stokes equation for laminar flow and turbulence of fluidis used, and a transport equation (k–e model) is appliedto turbulence kinetic energy (SolidWorks Corporation,2012) for the analysis of the streamline surrounding themega-solar power generators. The mean temperature ofthe photovoltaic module is calculated from the resultsobtained in the heat-transfer analysis by Flow Simulation2012, and the average efficiency of the photovoltaic moduleis obtained from the characteristics shown in Fig. 2.

Fig. 3. Analysis flow.


3. Analysis example

3.1. Outline of Kitami

The MSPS is situated at a racetrack site in Kitami inHokkaido, Japan. Fig. 4 shows the position in Kitami,which is the largest city in the Sea of Okhotsk block ofHokkaido (population of 125,000 with 61,214 residences).The installation of wind power production in the coastalarea and an inland mega-solar power production systemis actually planned. The inland temperature is intense, withthe maximum air temperature in summer being about 35 �C

Fig. 4. Kita

and the lowest winter temperature being about �20 �C.Moreover, the annual mean air temperature is about6 �C, although such low temperatures are mostly recordedin winter. As in most urban areas of Japan, this areareceives marginal rainfall and snowfall.

3.2. Analysis conditions

3.2.1. Mega-solar power system

The MSPS, with a maximum output of 1.7 MW, isinstalled in a suburb of Kitami. An electricity of 1.7 MWcorresponds to 28% of the power consumption of individ-

mi city.


ual houses in the city. Fig. 5 shows the layout of the pho-tovoltaic modules. Considering snow cover, the angle ofinclination of the photovoltaic modules is 40 degrees, theinstalled height from the ground is 1 m, and a pair of iden-tical south-facing photovoltaic modules are installed, asshown in Fig. 5(c). In order to investigate on the basis ofDC output, it does not take into consideration about aninverter. The maximum electrical conversion efficiency ofthe modular output terminal is 18% assuming single-crystalsilicon. Generally, the internal resistance of a photovoltaicmodule is about 0.5–1.0 X=m2. The internal resistance inthis study is set to 1.0 X=m2. Fig. 6 shows the 3D topo-graphic map and model of the mega-solar power generatorprepared on the basis of blocks (a) and (b) in Fig. 3. TheMSPS is installed on flat ground at an altitude of about160 m. There are mountains at an altitude of 280 m sur-rounding the solar cell for about 3.5 km; hence, the settingposition is complicated by a geographical feature, as shownin Fig. 6.

3.2.2. Climatic conditions

The climatic conditions of the area surrounding theMSPS, shown in Fig. 6, have been determined from pastmeteorological data. Fig. 7 shows the ambient tempera-ture, mean wind, and wind direction at 9:00, 12:00, and15:00 on a representative day of every month in 2012(Japan Weather Association, 2013). Fig. 7(a) correspondsto Ta in Eqs. (1), (9), (13), and (17). Furthermore,Fig. 7(b) corresponds to ua in Eq. (5). Although the ambi-ent temperature in June to September is high, as shown inFig. 7(a), Fig. 7(b) shows that the mean wind during thesame periods is low; thus, a decrease in the electrical con-version efficiency caused by a temperature rise in the pho-tovoltaic module is forecasted from June to September.Because the direction of the wind shown in Fig. 7(c) signif-icantly influences the heat-transfer rate between the solarmodular surface and the open air, predicting the

300 m

Solar module

(a) Top plan view

(b) Front view

Fig. 5. Schematic layout of the m

temperature distribution of the photovoltaic module isextremely difficult. Furthermore, Fig. 8 shows the amountof insolation inputted into the plane of array irradiance ofthe same arrangement as the photovoltaic module shown inFig. 5 (New Energy and Industrial TechnologyDevelopment Organization (NEDO), 2013b). The amountof insolation of the representation day in every monthexcept August is changed sharply. The values in Fig. 8 cor-respond to qs in Eq. (7). Because the amounts of insolationin this analysis example are low in March, May, August,and November, it is expected that temperature increase inthe photovoltaic module in the summer season (August)is controlled.

3.2.3. Amount of heat generated in a photovoltaic module

With the internal resistance of a photovoltaic module setat 1.0 X=m2, Fig. 9 shows the calculation results for theamount of heat generated by the photovoltaic modulewhile generating electricity at 18% efficiency under theslope of solar radiation shown in Fig. 8. Because theamount of insolation influences strongly, Figs. 8 and 9show the well similar change. The calculation results shownin Fig. 9 correspond to qgh in Eqs. (6), (10), and (11).

4. Analysis results

4.1. Example of analysis results

The MSPS shown in Fig. 5 is installed on the Kitamiracetrack site, and the analysis results for 12:00 on August15 with pleasant weather and a wind speed of 10 m/s blow-ing from East to Northeast is shown in Fig. 10. To investi-gate the influence of temperature, the ambient temperaturewas 20 �C in this analysis. Fig. 10(a) shows the analysisresults for the wind streamline; the wind conditions in thevicinity of the pair of photovoltaic modules installed onthe mountain slope becomes complicated by rapidly

30 m

16 m

40

1 m

40

1 m

Solar module facing north.Area 4800 m2, 2 sets.Monocrystal solar module. Maximum power generation efficiency 18%.Internal resistance 1 .

(c) Side view

2m

ega-solar power generator.

Fig. 6. Former site of a Kitami racetrack.


changing elevations. Because the relation between thesurface temperature of the photovoltaic modules and thesurrounding wind conditions is strong, it is necessary toinvestigate in detail beforehand the wind conditions atthe MSPS site. On the other hand, Fig. 10(b) shows anexample of the analysis results for the surface temperatureof photovoltaic modules PV-A and PV-B shown in Fig. 5.

Since the flow of the indraft wind from East to Northeastbecomes complicated around the photovoltaic modules,the surface temperature results show a characteristic distri-bution. The temperature distribution of photovoltaic mod-ules are changing from about 35–65 �C. Moreover, sincethe distribution characteristics of surface temperature differbetween PV-A and PV-B, the difference occurs in the mean

JanuaryFebruary

MarchApril June

JulyAugust October

NovemberDecember

May September

Win

d di

rect

ion

N

NNE

NE

ENE

E

ESE

SE

SSE

S

SSW

SW

WSW

W

WNW

NW

NNWN: north, E: east, S: south, W: west

0

1

2

3

4

5

6

7

8

9

Mea

n w

ind

spee

d [m

/s]

(b) Mean wind speedJanuary

FebruaryMarch

April JuneJuly

August OctoberNovember

DecemberMay September

-20

-10

0

10

20

30

Out

side

air

tem

pera

ture

[ºC

]

January

(a)(b)

(c)

FebruaryMarch

April JuneJuly



(a) 9:00(b) 12:00(c) 15:00

(a) Ambient temperature

(c) Wind direction

Fig. 7. Meteorological data of Kitami city.

0

Sola

r ra

diat

ion

of s

lope

sur

face

[M

J/m

2 ]

0

1000

2000

3000

4000

January

(a)(b)

(c)

FebruaryMarch

April JuneJuly



(a) 9:00(b) 12:00(c) 15:00

Fig. 8. Solar radiation of slope surface.

0

Hea

ting

valu

e of

pho

tovo

ltaic

mod

ule

[W/m

2 ]

0

40

80

120

140

20

60

100

January

(a)(b)

(c)

FebruaryMarch

April JuneJuly



(a) 9:00(b) 12:00(c) 15:00

Fig. 9. Heating value of a solar cell.


temperature of each module. Therefore, the electrical con-version efficiency between PV-A and PV-B differs. Fromthe same examination method as in the example of surfacetemperature and electrical conversion efficiency describedpreviously, we obtained the results described below.

4.2. Influence of geographical features

The wind at the Kitami racetrack site shows a compli-cated flow corresponding to the surrounding geographicalfeatures. Because there is no interruption for the windwhen the MSPS is installed on the flat ground, the wind

will flow at a stable speed and direction into the photovol-taic modules. The characteristics (laminar flow or turbu-lence) of the wind flowing into the photovoltaic modulesare governed by the flat characteristics of the ground, theflow velocity, among others. Accordingly, this sectiondescribes the differences in wind-condition characteristicsat the Kitami racetrack site and the flat ground, along withthe electrical conversion efficiency obtained from the aver-age surface temperature of the photovoltaic modulesinstalled at each place.

Fig. 11 shows each case number and the conditions ofwind direction and wind velocity corresponding to the casenumbers used for analysis. The range of wind velocity actu-ally observed in Kitami is given (Fig. 11(a)). On the otherhand, only the eastern half of the omnidirection from theNorth to South is given for the wind direction(Fig. 11(b)). Figs. 12 and 13 show the analysis results formodular surface temperature and DC power-productionefficiency at the time of MSPS installation on flat landand at the Kitami racetrack site. Wind direction, windspeed, and geographical feature influence in the operatingtemperature of photovoltaic strongly. As for the tempera-tures of the solar modular surfaces when the ambient tem-perature is 30 �C (Figs. 12(b) and 13(b)), there are several

Fig. 10. Analysis results of the line of flow and temperature distribution on the surface of photovoltaic modules. (12:00, August 15, direction of windENE, wind speed 10 m/s, outside temperature 20 �C).

N

NE

E

SE

S

SW

Case number

Dir

ectio

n

NW

W

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Case number

Win

d sp

eed

[m

/s]

Fig. 11. Analysis conditions of the wind.


high values compared with the results at 20 �C (Figs. 12(a)and 13(a)). When the MSPS on the flat ground (Fig. 12(a)

and (b)) is compared with that on the Kitami racetrack site(Fig. 13(a) and (b)), the temperature of the solar modularsurface installed on the flat ground shows a low trend.Because the heat-transfer rate (Section 2.2.1) at the solarmodular surface on the flat ground is large, the decreasein surface temperature is large. The reason is because thereis marginal reduction in wind speed. Therefore, when theMSPS is installed on the complicated mountain slope, thewind speed may decrease, but the cooling effect of the pho-tovoltaic module is suppressed. Fig. 12(a) and (b) show theanalysis results of electrical conversion efficiency in the casein which the MSPS is installed on the flat ground, andFig. 13(a) and (b) shows the analysis results of electricalconversion efficiency in the case in which the MSPS isinstalled on the Kitami racetrack site. Because the modularsurface temperature is cooler, the electrical conversion effi-ciency is high when the solar photovoltaic module isinstalled on the flat ground.

The average values of temperature distribution and elec-trical conversion efficiency for PV-A and PV-B displayed inFigs. 12 and 13 are shown in Fig. 14. The differences shownin Fig. 14(a) and (b) are due to differences in the mean tem-perature and electrical conversion efficiency of the photo-voltaic module.

8

10

12

14

16

18

20

Case number

Eff

icie

ncy

[%

]

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 201 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Case numberTemperature Efficiency of power generation

PV-A

PV-B

PV-A

PV-B

(a) PV-A and PB-B, outside temperature 20 ºC

8

10

12

14

16

18

20

Case numberE

ffic

ienc

y [

%]

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 201 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Case number

Tem

pera

ture

[

]ºC

Tem

pera

ture

[

]ºC

Temperature Efficiency of power generation

(b) PV-A and PB-B, outside temperature 30 ºC

Fig. 12. Analysis results for the flat ground.

8

10

12

14

16

18

20

Case number

Eff

icie

ncy

[%

]

ºC(a) PV-A and PB-B, outside temperature 20 Temperature Efficiency of power generation

8

10

12

14

16

18

20

Case number

Eff

icie

ncy

[%

]

0

20

40

60

80

100

120

Case number

Tem

pera

ture

[

]ºC


ºC(b) PV-A and PB-B, outside temperature 30

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 201 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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Fig. 13. Analysis results for the former Kitami racetrack site.


4.3. Annual electricity yield

Fig. 15 shows the results of investigations on the surfacetemperature distribution and electrical conversion effi-ciency of the photovoltaic modules when the MSPS isinstalled at the Kitami racetrack site. The climatic condi-tions of Kitami shown in Fig. 7 and the conditions of slopesolar radiation shown in Fig. 8 were used in the analysis of

Fig. 15. Fig. 15(a) shows the analysis results using histori-cal wind characteristics (Fig. 7(b) and (c)) and geographicalfeatures of the Kitami racetrack site, whereas Fig. 15(b)and (c) show the analysis results at the same time for a flatground without changes in elevations around the photovol-taic module. Moreover, Fig. 15(b) shows results for wind-less conditions; the conditions with Kitami wind shownin Fig. 7(b) and (c) are introduced in Fig. 15(c). Because

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Flat groundThe former site of a racetrack

ºC20ºC20

ºC30ºC30

Fig. 14. Analysis results on the relation among installation location, wind velocity, direction of the wind, and modular temperature and efficiency.

(a) The former site of a racetrack in Kitami

PV-APV-B

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(a)(b)

(c)(a) 9:00(b) 12:00(c) 15:00

Flat ground with windFormer site of a racetrack with wind

Flat ground without windFormer site of a racetrack with wind

Flat ground without wind

Flat ground with wind

Fig. 15. Analysis results with some conditions for the former Kitami racetrack site.


Fig. 16. Analysis results of rate of the electrical conversion efficiency toresults of the flat ground without wind.


of the combination of ambient temperature, wind direc-tion, wind velocity, and amount of insolation change overtime, the surface temperature and electrical conversion effi-ciency of the photovoltaic module forecast significantlychange with each condition at the introductory area. Thesurface temperature of the modules is high in June, andthe results in Fig. 15(a) (simulated geographical feature,with the wind being historical values for Kitami) show thatduring this season, the electrical conversion efficiencydecreases the maximum at the Kitami racetrack site. Incontrast, the electrical conversion efficiency is low through-out the year at the conditions of Fig. 15(b) (flat ground,windless), and this condition is disadvantageous for theinstallation of the MSPS in other cases. Moreover, the caseof Fig. 15(c) (flat ground, with wind being historical valuesfor Kitami) has the smoothest indraft of wind to the pho-tovoltaic modules compared with Fig. 15(a) and (b)described above, because the wind is not obstructed aroundthe photovoltaic modules. Since the temperature of thephotovoltaic modules is less controlled compared withthe other cases (as shown in Fig. 15(c)), the electrical con-version efficiency is high throughout the year, as shown inFig. 15(c). Fig. 15(d) shows the average values of tempera-ture distribution and electrical conversion efficiency forPV-A and PV-B. The indraft of the wind to the photovol-taic modules significantly influences their annual electricityproduction according to the results of Fig. 15 (d). There-fore, the wind conditions at a proposed installation siteshould serve as a strong factor in the evaluation of MSPSapplicability.

Fig. 16 shows the results for the rate of electrical conver-sion efficiency under conditions based on the geographicalfeatures of the Kitami racetrack site (Fig. 15(a)) and on theflat ground with wind from Fig. 15(c) when the analysisresults (flat ground, windless) of Fig. 15(b) are assumedat 100%. Electricity production based on the geographicalfeatures of the Kitami racetrack site increases about 16%each month as the result of the windless state(Fig. 15(b)). On the other hand, electricity production(Fig. 15(c)) increases by about 23% when the vicinity ofthe photovoltaic module is the flat ground. Therefore, itis economically more advantageous to install the MSPSon the flat ground without a mountain etc. rather thaninstall it at the Kitami racetrack site. A wind condition

investigation needs to be initiated for the study of MSPSinstallation.

5. Conclusions

To predict the electrical conversion efficiency of large-scale photovoltaics, the surface temperature and electricalconversion efficiency of photovoltaic modules were investi-gated by introducing a wind-condition analysis developedwith a 3D topographic map. The following conclusionscan be drawn from this study.

(1) Distribution of the temperature of the solar cell mod-ular surface and the electrical conversion efficiencywere clarified for the cases of introducing a large-scalephotovoltaic power plant into geographical featuressimulated by the Kitami racetrack site and of introduc-ing the system on the flat ground. Since the tempera-ture of the solar cell modular surface remains low ifthe ambient temperature is low, electrical conversionefficiency increases. However, the difference betweenthe temperature distribution and electrical conversionefficiency of the photovoltaic module exhibits a com-plicated relationship, because wind velocity and winddirection exert a significant influence.

(2) When the large-scale photovoltaic power plant isinstalled on the flat ground, which does not interruptthe wind, the cooling effect of the wind flowing intothe photovoltaic module is large; thus, electrical con-version efficiency is maintained to a high degree.Therefore, the modular cooling effect becomes restric-tive for the case of a large-scale photovoltaic powerplant being installed on a mountain slope.

(3) When the large-scale photovoltaic power plant isinstalled at the geographical features on the Kitamiracetrack site, the annual production of electricityincreases about 16% compared with the windlessvalue. On the other hand, when the vicinity of thephotovoltaic module is the flat ground, the produc-tion of electricity increases about 23% every monthcompared to the windless situation. Therefore, it iseconomically more advantageous to install thelarge-scale photovoltaic power plant on the flatground, which has no mountain etc. in the vicinity.

(4) Introducing a wind-condition investigation for eachresult described in this study has proven dramaticallyeffective for the study of installation locations oflarge-scale photovoltaic power plants.

References

Haider, T., 2013. The potential for air-temperature impact from large-scale deployment of solar photovoltaic arrays in urban areas originalresearch article. Sol. Energy 91, 358–367.

Japan Meteorological Agency, 2013a. The past meteorological data.Information of the climatological statistics. <http://www.data.jma.-go.jp/obd/stats/etrn/>.

http://refhub.elsevier.com/S0038-092X(14)00141-8/h0005




Japan Meteorological Agency, 2013b. Digital-national-information down-load service. <http://nlftp.mlit.go.jp/ksj/index.html>.

Japan Meteorological Agency, 2013c. The past meteorological data.<http://www.data.jma.go.jp/obd/stats/etrn/>.

Japan Weather Association, Tenki.jp. 2013. <http://tenki.jp/past/?month=1&year=2012>.

Massi Pavana, A., Mellit, A., De Pierid, D., Kalogiroue, S.A., 2013. Acomparison between BNN and regression polynomial methods for theevaluation of the effect of soiling in large scale photovoltaic plants.Appl. Energy 108, 392–401.

Ministry of Land, Infrastructure, Transport and Tourism, Base mapinformation, Geospatial Information Authority of Japan, 2013.<http://fgd.gsi.go.jp/download/>.

National amount-of-insolation normal data map: MONSOLA05(801),Japan Weather Association, 2013. <http://www.jwa.or.jp/content/view/full/1947/>.

New Energy and Industrial Technology Development Organization(NEDO). The hand book and review support tool of large-scalephotovoltaics introduction, 2013a. <http://www.nedo.go.jp/library/mega-solar.html>.

New Energy and Industrial Technology Development Organization(NEDO). Annual amount-of-insolation data base (METPV-11),2013b. <http://www.nedo.go.jp/library/nissharyou.html>.

Pearce, M.J., 2008. Industrial symbiosis of very large-scale photovoltaicmanufacturing. Renew. Energy 33, 1101–1108.

Rakibuzzaman, S., Mithulananthan, N., Bansal, R.C., 2012. Dampingperformance analysis of battery energy storage system, ultracapacitorand shunt capacitor with large-scale photovoltaic plants. Appl. Energy96, 235–244.

Skoplaki, E., Palyvos, J.A., 2009. On the temperature dependence ofphotovoltaic module electrical performance: a review of efficiency/power correlations. Sol. Energy 83, 614–624.

SolidWorks Corporation, 2012. Technical Reference of the SolidWorksFlow Simulation 2012. Section 1. USA, 3–14.

Strzalka, A., Alam, N., Duminil, E., Coors, V., Eicker, U., 2012. Largescale integration of photovoltaics in cities. Appl. Energy 93,413–421.

Taha, H., 2013. The potential for air-temperature impact from large-scaledeployment of solar photovoltaic arrays in urban areas. Sol. Energy91, 358–367.

Wybo, J.L., 2013. Large-scale photovoltaic systems in airports areas:safety concerns. Renew. Sust. Energy Rev. 21, 402–410.

Zhang, D., Chai, Q., Zhang, X., He, J., Yue, L., Dong, X., Wu, S., 2012.Economical assessment of large-scale photovoltaic power developmentin China. Energy 40, 370–375.

























output prediction of large-scale photovoltaics by wind-condition analysis using 3d topographic maps

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