ArticlePDF Available

Offshore floating photovoltaics system assessment in worldwide perspective

June 2023
Progress in Photovoltaics Research and Applications 31(23)

June 2023
31(23)

DOI:10.1002/pip.3723

License
CC BY 4.0

Authors:

Sara Golroodbari

Utrecht University

Wilfried van Sark

Utrecht University

Floating solar photovoltaics (FPV), whether placed on freshwater bodies such as lakes or on the open seas, are an attractive solution for the deployment of photovoltaic (PV) panels that avoid competition for land with other uses, including other forms of renewable energy generation. While the vast majority of FPV deployments have been on freshwater bodies, in this paper, we chose to focus on offshore FPV, a mode of deployment that may be particularly attractive to nations where the landmass is constricted, such as is the case in small islands. There is a wide perception that seawater cooling is the main reason for the enhanced performance of offshore FPV panels. In this paper, a worldwide assessment is made to validate this perception. To this end, a technology‐specific heat transfer model is used to calculate PV system performance for a data set of 20 locations consisting of one system located on land and another one offshore. The analysis assumes that the floating offshore panels are placed on metal pontoons and that all panels are based on monocrystalline silicon technology. Our analysis shows that the energy yield difference, between land‐based and offshore systems, for the time period of 2008 and 2018, varies between 20% and −4% showing that offshore FPV yield advantages are site‐specific. In addition, the effect of other environmental factors, namely, irradiation level difference, ambient temperature, wind speed, precipitation, and sea surface temperature, is studied in this paper, which leads to the formulation of two different regression models. These can be used as a first step in predicting yield advantages for other locations.

Content uploaded by Sara Golroodbari

Content may be subject to copyright.

Content uploaded by Sara Golroodbari

Content may be subject to copyright.

RESEARCH ARTICLE

Offshore floating photovoltaics system assessment in

worldwide perspective

S. Zahra Golroodbari | Abdulhadi W.A. Ayyad | Wilfried van Sark

Copernicus Institute of Sustainable Institute,

Utrecht University, Utrecht, The Netherlands

Correspondence

S. Zahra Golroodbari, Copernicus Institute of

Sustainable Institute, Utrecht University,

Princetonlaan 8a, 3584CB Utrecht, The

Netherlands.

Email: s.z.mirbagherigolroodbari@uu.nl

Funding information

Rijksdienst voor Ondernemend Nederland

Abstract

Floating solar photovoltaics (FPV), whether placed on freshwater bodies such as lakes

or on the open seas, are an attractive solution for the deployment of photovoltaic

(PV) panels that avoid competition for land with other uses, including other forms of

renewable energy generation. While the vast majority of FPV deployments have

been on freshwater bodies, in this paper, we chose to focus on offshore FPV, a mode

of deployment that may be particularly attractive to nations where the landmass is

constricted, such as is the case in small islands. There is a wide perception that sea-

water cooling is the main reason for the enhanced performance of offshore FPV

panels. In this paper, a worldwide assessment is made to validate this perception. To

this end, a technology-specific heat transfer model is used to calculate PV system

performance for a data set of 20 locations consisting of one system located on land

and another one offshore. The analysis assumes that the floating offshore panels are

placed on metal pontoons and that all panels are based on monocrystalline silicon

technology. Our analysis shows that the energy yield difference, between land-based

and offshore systems, for the time period of 2008 and 2018, varies between 20%

and 4% showing that offshore FPV yield advantages are site-specific. In addition,

the effect of other environmental factors, namely, irradiation level difference, ambi-

ent temperature, wind speed, precipitation, and sea surface temperature, is studied in

this paper, which leads to the formulation of two different regression models. These

can be used as a first step in predicting yield advantages for other locations.

KEYWORDS

climate zones, floating PV, offshore, photovoltaics, yield assessment

1|INTRODUCTION

Solar photovoltaics (PV) presently account for roughly 28% of the

total of 3.07 TW of installed renewable energy technologies,

a fact

which reflects rapid levels of technological growth, as well as

increased economic confidence with investors increasingly choosing

to invest in PV installations. This is also highlighted by, among others,

the World Energy Outlook 2020 published by the International

Energy Agency, which dubbed solar PV as “the new king of electricity

supply”within their Net Zero Emissions by 2050 scenario

(NZE2050),

an outlook that envisages an annual installation of

500 GWp of solar PV capacity.

The annual energy yield of PV panels is determined by a number

of factors. Most obviously, these include the conversion efficiency of

Wilfried van Sark, ISES member

Received: 19 October 2022 Revised: 23 February 2023 Accepted: 31 May 2023

DOI: 10.1002/pip.3723

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,

provided the original work is properly cited.

Prog Photovolt Res Appl. 2023;31:1061–1077. wileyonlinelibrary.com/journal/pip 1061

the PV panel, which in turn is the result of the specific cell technology

used (e.g., monocrystalline/polycrystalline silicon [Si], thin films based

on cadmium indium gallium selenide [CIGS] or cadmium telluride

[CdTe], a number of climatological/meteorological factors [irradiance,

temperature], and installation details [orientation, tilt, mounting struc-

ture]). There is well-established literature covering the interplay

between some climatological/meteorological factors, such as solar

irradiance and temperature, and the performance of photovoltaic

panels (see, e.g., Pearsall

). Performance variation across the globe has

been correlated with Köppen–Geiger (KG) climate zones

for Si, CdTe,

GaAs, and perovskites,

while recently a PV-specific KG climate classi-

fication has been suggested, which divides the globe into 12 zones

based on the performance of PV panels.

The KG climate classification

system divides the world into various regions into three separate

levels. Differences across the zones are based on temperature; the

amount and pattern of precipitation; and, indirectly, irradiation.

Other various local effects such as dust and humidity

pose an

additional challenge to understanding which locations across the

globe are likely to have the best-performing solar PV installations. This

is obviously also a question of economic importance, as the economic

feasibility of any solar PV installation will be due in part to climatologi-

cal considerations. A recent detailed exploration of this has been per-

formed for mainland China, a large and ecologically/climatologically

diverse landmass.

The authors of that paper in addition suggest that

it would be most advantageous for a given electricity grid that PV

installations connected to it were divided between land-based and

offshore floating PV.

As a further example of increased interest in FPV, in the

Netherlands, a recent roadmap for future PV deployment has shown

that half of the potential can be found offshore, on the North Sea

9,10

in addition to considerable inland FPV potential as well.

While most deployments of FPV to date have been on freshwater

bodies,

we focus in this paper on offshore FPV (OFPV). For our pur-

poses, we include sites that are rough 56-km offshore from the

selected port site. OFPV will be particularly attractive for small island

nations, for example, Malta,

the Maldives,

and Singapore.

Like-

wise, OFPV is a particularly good option for nations with compara-

tively large coastal areas, such as the Netherlands.

In such

situations, there may be no choice but to consider OFPV in order to

achieve the two aims of reducing carbon emissions while maintaining

energy security.

In other situations, land-based PV systems might compete with

other essential demands on land use, such as agriculture, nature

reserves, and recreation. Our choice of distance to offshore sites does

not directly take into account factors such as wave height or the tem-

perature differences between sites, but it is a choice aimed at ensur-

ing a number of factors. First, based on several constraints, for

example, the energy losses in electricity transmission lines as well as

the possibility of building infrastructure and underwater cabling to

connect an offshore PV site to an onshore connection near the port

site, 56 km is estimated as the maximum possible distance. Second,

offshore sites that are much less than 50 km away from the port

would be too close to the port to give a reliable indication of

differences, based on the spatial resolution of our basic dataset.

Finally, once a distance of 50 km is decided for the offshore site, it

seemed intuitively correct to ensure that the inland sites we studied

were equidistant from the port, thus ensuring consistency across our

set of sites.

It is straightforward to see that deploying OFPV will offer advan-

tages in certain contexts. While there has been immense progress

made in this field in recent years,

economic obstacles may hamper

the further deployment of OFPV.

18,19

To put this into a quantitative

context, a recent review paper

suggests that the total cumulative

capacity of FPV amounts to a marginal 2.6 GW and is concentrated in

three countries (cf. the widely reported 270 GW of solar PV, which

was installed worldwide in 2022). Consequently, it is important to

understand exactly which sites globally can expect to have the most

pronounced advantage for the use of OFPV, and these would then

become the sites at which further exploration becomes feasible at

first.

More recently, detailed performance data on FPV comes from the

Singapore Tengah Reservoir,

in which the performance of eight dif-

ferent commercially available FPV technologies are tested. Rigorous

testing from this site has allowed for a comparison of the performance

of different panel technologies on FPV structures, and in particular

how different PV technologies perform when used in FPV installa-

tions. Some of the main findings from this test bed include that ambi-

ent temperatures over water are typically 2–3C lower than on land,

and wind speeds over the water body are also generally higher.

humidity above water is also relatively higher, the heat flux on the

floating system location is decreased.

Some literature exists on the performance benefits of OFPV com-

pared with conventional, land-based PV systems. An early overview

of FPV designs provided estimates of 10% larger energy yields com-

pared with similar installations on land,

which is explained by the

cooling effect of the underlying water body leading to lower PV mod-

ule temperatures. There is also another thermal modeling based on

fish farm floater technology which concluded relatively different

results than the earlier studies. This research conducted by a group of

researchers in Norway

demonstrates that in order for any apprecia-

ble improvements to PV yield, one would need to take the site-

specific water temperature into account. In this study, two adjacent

PV strings were compared: One in direct contact with water and the

other installed above water and there is an air gap between the back

sheet and the water surface. The comparison results showed that a

string directly in contact with the water body on average exhibits a

5%–7% higher energy yield than the string that was cooled by air, the

results are based on experiments between the months of May

and July.

Since any benefits or drawbacks from deployment on the water

are dependent on both the site and the technology to be used, there

is no single reference that can indicate a quantitative improvement in

PV yield. In Olivera-Pinto and Stokkermans,

for instance, two pon-

toon floaters technologies are chosen. The first technology is made

out of HDPE where the panels are in contact with air and the polymer

material from their back sheet. The second one is based on a meshed

1062 GOLROODBARI ET AL.

1099159x, 2023, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/pip.3723 by Utrecht University Library, Wiley Online Library on [18/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

network galvanized steel frame where the panels are mainly in contact

with air and the galvanized steel frames in a small ratio of the back

sheet area. The results based on simulations in PVSyst

have demon-

strated the energy yield advantage of 0.31%–0.46% for HDPE struc-

ture and 1.8%–2.59% for galvanized steel frame structure. This shows

the energy yield advantage is mainly technology and site-specific.

As another example, an OFPV system on the west coast of

Norway shows energy yield advantages of 6%–10% larger yield for a

system in contact with water.

A theoretical study for an OFPV sys-

tem with horizontally placed modules on the North Sea about 50 km

off the coast showed an annual yield enhancement of 13% com-

pared with a land-based system, which is due to increased offshore

irradiance as well as cooling of the modules.

Here, it was found that

the PR advantage is 4%. While weather and wind patterns have

been considered, the effect of waves is found to be of limited impor-

tance. This contrasts with the case for tilted panels, where it would be

expected that powerful waves could have led to dynamic variations in

POA. Another theoretical study shows that energy loss to moving

modules due to waves can range from 3% for medium wave intensity

up to 9% for extreme wave intensity.

Based on the mathematical

modeling of OFPV system on the North Sea, on an annual basis, we

would expect that waves and their impact on the pontoons on which

offshore FPV are sited would lead to variations of about 1%–2% in

yield.

The studies reported above show that offshore deployment of

FPV has resulted in improved PV yields and that benefits are corre-

lated with geography and meteorological conditions. While technol-

ogy and the mounting type are also important, our approach aims to

develop a model that neutralizes these impacts and isolates geography

as a determinant factor of PV performance and the differences

between offshore and onshore PV installations. Therefore, the aim of

this research is to study the effect of geographical characteristics of

the locations as well as the meteorological variables to find a mean-

ingful correlation for the performance of OFPV systems. To this end,

we consider 20 different locations across the globe from different

continents and climate zones to simulate expected offshore perfor-

mance advantages using irradiation, temperature, and relative humid-

ity data, based on the NASA POWER database.

The effect of salinity and bio-fouling on panel degradation and

system efficiency are studied in Setiawan et al

and Suzuki et al.

However, due to the fact that not all of the water bodies in this study

follow the same trend with respect to bio-fouling based on different

water characteristics, we did not consider this important effect to pre-

vent the complexity in this study. Moreover, for such large-scale FPV

systems, the waves could be relatively damped for the panels if the

modules are protected from water splashing based on waves. That is

the reason we also neglect the salinity effect. The humid environment,

as well as the above-mentioned parameters, could affect the speed of

aging on the FPV system as discussed in detail in Zaharia et al.

Nonetheless, in this study, we assume that aging has a similar impact

on all FPV systems in all locations which is the reason it does not have

an effect on the performance difference between LBPV and FPV for

each location.

The rest of the paper is organized as follows: Section 2discusses

the implemented methodology, and Section 3presents and discusses

the obtained results. Finally, in the last section, conclusions are

presented.

2|VARIABLES AND DATA ACQUISITION

Solar irradiation and ambient temperature are two key parameters in

the solar PV system performance calculations. In this research, we will

broaden our model by considering other variables as well, namely

Atmospheric variables, climate zone, and ocean stream currents.

•Atmospheric variables

Mekhilef et al

describe the multivariate interaction between solar

irradiance, dust levels, and relative humidity to impact the perfor-

mance of PV cells. Note that the influence of humidity can not be

addressed without considering ambient temperature and wind

speed. In Golroodbari and van Sark,

a so-called apparent temper-

ature is estimated for solar cell temperature calculations and also

the floating PV system performance analysis. In this research, first,

we calculated the heat index, which is also known as apparent tem-

perature. In the developed model the wind speed effect is consid-

ered in the cell temperature estimation. Both variables and their

effect will be discussed in detail in this section.

•Climate zones and site locations

The Köppen–Geiger climate zone typology

is by far the most

widely used system to classify climate systems globally. This sys-

tem divides the world map into different climate zones considering

precipitation and temperature. It is noteworthy to mention that lat-

itude and solar irradiance levels do not define climate. An illustra-

tion of that, the cities of Utrecht, the Netherlands, and Edmonton,

Canada, have about the same latitude. While the former has cli-

mate classification within the Köppen–Geiger system of “Cfb”

(an oceanic climate with warm summers), while the latter has a

“Dfb”classification (hemiboreal), and its climate is more similar to

Stockholm, Sweden, which is far (1450 km) to the north-east of

Utrecht.

•Ocean stream current

An ocean current is a continuous, directed movement of seawater

generated by a number of forces acting upon the water, including

wind, the Coriolis effect, breaking waves, temperature, and salinity

differences. Ocean currents can be classified into two main ones:

(i) Surface circulation depending on wind speed, and (ii) deep water

currents or thermohaline circulation, depending on both tempera-

ture and salinity.

They cannot be separated by oceanographic

measurements.

2.1 |Data acquisition

In this paper, we utilize a dataset based on 20 port sites across the

world, which covers a wide geographical range. Each of the port cities

GOLROODBARI ET AL.1063

was geocoded using an open street map API, giving a longitude and

latitude pair as well as a complete bounding box for each site (see

Table 1and Figure 1). Each coastal/port site is expanded into a site

twin by adding one corresponding offshore site and one correspond-

ing inland site. The offshore sites are generally chosen to be equidis-

tant (56 km) from the coastal/port site. Within the twins, the

bounding box for the coastal site was used to define the KG classifica-

tion for them, using the hddtools package.

One limitation of this

approach, in general, is that there is as of yet no readily available sur-

vey that classifies the open seas according to the KG system.

The geocoded coordinates (Table 1) were used as the starting

point from which the locations of the inland and offshore sites were

determined. The following approach was taken:

1. For sites connecting to the open water toward the North, the off-

shore site was found by adding 0.5to the latitude. The reverse

was done for sites with open water to the South: the offshore site

was found by subtracting 0.5from the latitude. Likewise, for each

triplet, the inland site was found by going in the opposite direction.

The choice of 0.5was deliberate with the intention to match the

TABLE 1 The offshore and inland

locations for a given port site are

equidistant (about 56 km apart) from

each of these sites. Country names relate

to the three-character ISO country code,

for example “PNG”is Papua New

Guinea. Ocean class (current) is defined

as Warm Stream; “WS,”Cold Stream:

“CS,”not applicable: “NA”.KG

classification definitions can be found in

Beck et al.

Country Latitude Longitude Ocean KG

No. Name code (degrees) (degrees) current class

1 Bandar Penawar MYS 1.56N 104.23E WS Af

2 Ciudad del Carmen MEX 18.65N 91.81 W WS Aw

3 DaNang Port VNM 16.08N 108.22E NA Am

4 El Emir URY 34.96S 54.94 W CS Cfb

5 Hengsha Island CHN 31.32N 121.85E NA Cfa

6 Katsuura JPN 35.16N 140.32E CS Cfa

7 Kwala Tanjung IDN 3.35N 99.45E WS Af

8 Limassol Port CYP 34.65N 33.016E NA Csa

9 Port Antonio JAM 18.18N 76.45 W WS Am

10 Port Coquitlam CAN 48.55N 124.43 W WS Cfb

11 Port Moresby PNG 9.47S 147.16E WS Aw

12 Port of Rotterdam NLD 51.98N 4.13E WS Cfb

13 Port Shepstone ZAF 30.73S 30.45E WS Cfa

14 Port Vell ESP 41.38N 2.18E NA Csa

15 Puerto Belgrano ARG 38.89S 62.10 W CS Cfa

16 Puerto Colombia COL 10.99N 74.96 W WS Aw

17 Puerto La Cruz VEN 10.21N 64.63 W WS BSh

18 Ras Laffan QAT 25.92N 51.58E NA BWh

19 South Golden Beach AUS 28.50S 153.55E CS Cfa

20 Tanzania Port TZA 6.82S 39.29E WS Aw

FIGURE 1 The locations of the

twenty sites for this study.

1064 GOLROODBARI ET AL.

level of accuracy in the downloaded data from NASA POWER

(see below).

2. For sites facing east or west, the longitude was modified by the

equivalent of 56 km. Note that the amount of longitude variation

varies with latitude: 1longitude represents about 111 km at the

Equator, but the same amount of longitudinal distance shrinks for

increasing latitude when measured in kilometers to the North and

South. For the data set used here, the largest (absolute) value of

latitude is 51.98(corresponding to the Dutch site), for which

56 km along the longitudinal direction is equivalent to about

0.82longitude.

The above selection rules ensure that there is no more than

56 km between a coastal site and the OFPV site, chosen as an upper

limit for the distances to ensure the feasibility of the siting.

2.2 |Meteorological data

Historical data based on satellite imagery of meteorological data for a

given set of longitude and latitude coordinates are available via the

NASA POWER service.

We used an hourly temporal resolution for

10 years of data, extending from January 2008 to January 2018. We

extracted hourly averages for irradiance H(in kWh/m2), clearness

index kT, temperature (ambient Taand sea surface Ts), wind speed vw,

and relative humidity RH. The ambient temperatures collected for the

offshore sites were the sea surface temperatures, while for the inland

and coastal sites, these were the air temperatures at 2 m height. Addi-

tionally, we downloaded solar azimuths and solar hour (cosines) values

in order to facilitate the transposition from direct normal irradiation

(DNI) to plane-of-array.

Further discussions about the uncertainty of the meteorological

data are provided in Section 4.

2.3 |Heat index

The heat index (HI)(

C) of a given combination of dry-bulb ambient

temperature and relative humidity (RH) is defined as the dry-bulb tem-

perature, which would feel the same if the water vapor pressure were

1.6 kPa.

The method of calculating heat index varies across environ-

mental studies, and many different methods for calculating this metric

are studied in research by Anderson et al.

In this work, we used Equation (1) subject to the correction factor

shown in Equation (2); the coefficients are tabulated at Table 2.

34,35

HI ¼a0þa1Tþa2RH þa3TRH þa4T2þa5ðRHÞ2

þa6T2RH þa7TðRHÞ2þa8T2ðRHÞ2ð1Þ

fHIjT<79

oFg¼Tð2Þ

where Tis the ambient temperature in F, RH is relative humidity in

[%] and HI is denoting the heat index. In other words, we set the heat

index to be the same as the temperature when the temperature value

is below 79 (C) Fahrenheit.

2.4 |Heat transfer

A one-dimensional heat transfer analysis model is developed in this

research to calculate the solar cell temperature considering the sea

surface temperature, and heat transfer in the system consisting of PV

module, pontoon, and ocean water. The pontoon is assumed to be

made out of steel, thus because of the good heat conductivity of steel

we assumed that the temperature of the pontoon is equal to the

water surface temperature at the beginning of the analysis.

As the solar panels during the day will have higher temperature

compared with the pontoon and sea surface temperature, we consider

the heat flux flow to go from the solar module toward the water. The

equilibrium equation of the one-dimensional heat transfer analysis is

defined as follows:

QPV ¼QPO ð3Þ

where Qi,iPV,PO is the heat flow rate (W/m2Þ, and PV and PO

denote the PV module and the pontoon, respectively. The heat flow

rates in Equation (3) are as follows:

Qi¼UiAiΔTið4Þ

where Uiis thermal transmittance, Aiis module/pontoon area, and

ΔTiis the temperature difference.

The Uvalue for the solar module is estimated using the same

model as discussed in Sánchez-Palencia et al.

In this model, the U

value for the module is a function of solar irradiation. After lineariza-

tion of this model, we could estimate the Uvalue for the solar mod-

ules in this model considering

U¼G

1500 þ2ð5Þ

TABLE 2 Coefficients for the HI calculations.

34,35

a0a1a2a3a4a5a6a7a8

42.4 2.05 10.14 0.22 6.84 1035.48 1021.23 1038.5310 41.99106

GOLROODBARI ET AL.1065

in which Gis solar irradiance.

2.5 |Cell temperature

The efficiency of photovoltaic cells depends on the cell tempera-

ture, which is usually described by temperature coefficients for

the current, the voltage, and the power. In Tina et al,

thermal

analysis has been done considering the cooling effect of evapora-

tion and the coefficient values introduced by Faiman

have been

optimized based on their panel's characteristics. A similar method

used in this work, which is shown in Equation (6), is a simple

empirical model implemented by Koehl et al

to estimate the cell

temperature TCell

TCell ¼Tamb þG

U0þðU1vwÞð6Þ

where Tamb is ambient temperature, U0and U1are correlation coeffi-

cients (Table 3), and vwis local wind speed near the modules.

2.6 |Energy yield

To calculate the energy yield Yfor any PV system, we use

Y¼ηðTPV ,GÞHð1LoÞð7Þ

with ηthe efficiency of the PV panels used, which depends on PV

temperature (TPV [C]) and irradiance (G[W/m2]), H(kWh/m2) the

solar irradiation, and Lorepresenting losses due to system losses other

than caused by temperature, and which we assume to be 10%

throughout, based on typical performance ratio values found in the lit-

erature.

Temperature losses are accounted for in the definition of

the efficiency of the PV panels, as follows

η¼ηSTC 1βTPV 25

ðÞ

þγlog10ðGÞ

ðÞ

ð8Þ

where ηSTC is the nameplate or standard test conditions (STC

) effi-

ciency of the PV panel, βand γare material-specific properties, TPV is

the operating cell or panel temperature (in C), and Gis the solar irra-

diance (in W/m2). We use ηSTC ¼0:1935

(or a 1.6 m2sized 310 Wp

module), β¼0:0045C1, and γ¼0:12, both for crystalline silicon,

and Lo¼0:1, based on Reich et al.

We further define the absolute and relative offshore yield advan-

tage as

Yadvantage ¼Yoff shore Yinland ð9Þ

Yadvantage,rel ¼Yoffshor e Yinland

Yinland

100 ð10Þ

which provides the advantage (or disadvantage) when comparing an

offshore site with an inland site with >50-km distance in between.

Figure 2shows the algorithm flowchart of the whole methodol-

ogy discussed in this section to calculate the energy yield for this

study. The energy yield calculated from this algorithm will be used to

compare different locations in their offshore PV advantage in terms of

performance.

2.7 |Regression analysis

For finding the correlation between the independent variables and

the offshore PV advantage in Equation (10), two different methodolo-

gies are used: (i) Multiple linear regression (MLR) and (ii) multivariate

polynomial regression (MPR). Both methods will be discussed in the

following.

2.7.1 | MLR

MLR is a statistical technique that can be used to analyze the relation-

ship between a single dependent variable and several independent

variables. The objective of multiple regression analysis is to use the

independent variables whose values are known to predict the value of

the single dependent value. The general form of the regression equa-

tion with multiple predictors is

y¼b0þX

i¼1

bixi,i½1,nð11Þ

where b0is the intercept, and biis coefficient number i,nis the num-

ber of independent variables, and xiis variable number i. The ordinary

least squares (OLS) regression can be used as a method to derive the

regression coefficients. This method is based on minimizing the sum

of the squares of the deviations of the observed and predicted values

of ^

TABLE 3 Koehl correlation coefficients for different technologies.

Monocrystalline Polycrystalline Microcrystalline Amorphous Cadmium

PV technology silicon (m-Si) silicon (p-Si) silicon (a-Si) silicon (c-Si) telluride (CdTe)

U030.02 25.73 23.37

U16.28 10.67 5.44

1066 GOLROODBARI ET AL.

2.7.2 | MPR

The MPR model provides an effective way to describe complex non-

linear input–output relationships since it is tractable for optimization,

sensitivity analysis and prediction of confidence intervals.

MPR for a

second-order polynomial is defined in Equation (12).

y¼b0þX

i¼1

bi,1xiþX

j¼1

i¼1

bi,jxixj,i,j½1,nð12Þ

where parameters are defined as in Equation (11), noting that an addi-

tional variable jis used. In this research, the collected data are used

to find a correlation between variables and the absolute and rela-

tive yield advantage. Thus, in addition to the methodology for

deriving the coefficients, it is essential to define the correct inde-

pendent variables. We will discuss these variables later in the fol-

lowing section.

3|RESULTS AND DISCUSSIONS

In this section, the solar cell operating temperature for both offshore

and land-based systems will be discussed, considering different vari-

ables, that is, wind speed, relative humidity, ambient temperature, and

FIGURE 2 The flowchart for the described methodology to calculate the energy yield.

FIGURE 3 (A) Ambient temperature and (B) heat index, for all locations at both land-based and offshore system sites.

GOLROODBARI ET AL.1067

sea surface temperature. Moreover, the energy yield for all 20 loca-

tions will be compared between offshore and land-based systems.

Figure 3shows the average ambient temperature and a heat

index of all locations between this research time interval. Listing is

done alphabetically, as in Table 1. Due to the fact that for computing

the heat index we only consider the effect of relative humidity, it is

clearly shown that the heat index is not necessarily lower for the off-

shore system. However, we should take into consideration that ambi-

ent temperature and heat index are not the only parameters relevant

to the performance of offshore systems.

3.1 |Operating temperature for modules

Figure 4A shows the average operating cell temperature for all loca-

tions for both offshore and land-based PV systems. The analysis of

temperature differences between offshore and inland sites for the

different locations showed that in almost all of the cases, the mod-

ule temperatures at offshore sites were lower compared with the

land-based sites which is the result of the water cooling effect for

this specific FPV structure with steel pontoons. The average cell

temperature difference is defined in Equation (13). The maximum

value for this variable is 0.32C which belongs to Port Coquitlam

located in British Columbia, Canada, and the minimum value is

14C belonging to Puerto Colombia located in Atlántico Depart-

ment, Colombia.

ΔTCell ¼TOff shore TLandbased ð13Þ

The positive value for the Port Coquitlam site is a result of the

effect of the Alaska Current. This is a southwestern shallow warm-

water current alongside the west coast of the North American conti-

nent beginning at about 48–50N, which is quite close to the location

of the site. It has been mentioned by Walsh et al

that due to the

high latitude marine heat wave in 2016, the Gulf of Alaska (GOA) and

the Bering Sea have been anomalously warm for several years with

the heat peaking in 2016.

These years are within the time range of our data set. These tem-

perature variations will have an effect on the sea surface temperature

and, as a result, will affect the equilibrium operating cell temperature

as well.

However, the condition is different for the Puerto Colombia site.

The large difference between the land-based and offshore tempera-

ture is also discussed in Ortiz-Royero et al.

: Strong outbreak of cold

air from the north called “northers”not only brings the sea surface

and ambient temperature on the offshore side down but also may

cause gales and very strong waves toward the coastal areas.

Figure 5shows different temperatures, namely, apparent temper-

ature (HI), sea surface temperature, cell temperature only with wind

cooling effect (initial temperature), and final equilibrium operating cell

temperature. Although the average sea surface temperature in most

of the locations is higher than the HI, shown in Figure 5A, the water

cooling effect is clearly shown in Figure 5B, and for all of the loca-

tions, the average of the final cell temperature is lower than its initial

value.

3.2 |Yield advantage

In order to calculate the energy yield advantage, we use the equiva-

lent of 1 MWp of panels. For this, an area of about 5200 m2would be

needed all placed horizontally on various connected pontoons assum-

ing that we use the 310-Wp module as mentioned above. We present

our results in specific annual yields of kWh/kWp. Table 4provides the

calculated energy yields for the sites within each location as well as

the absolute and relative yield advantage.

The energy yields depend on irradiance and conversion efficiency,

which mainly depends on the cell operating temperature which itself

is a function of ambient temperature and sea surface temperature for

FPV systems, and also wind speed.

FIGURE 4 Average (A) cell temperature and (B) irradiation levels, for all locations for both offshore and inland sides.

1068 GOLROODBARI ET AL.

To have a better understanding of the energy yield tabulated in

Table 4, let us first discuss the irradiation level difference for the loca-

tions and between the sites. Figure 4B shows the average irradiation

during the period of this study for all locations and sites. In 70% of

the locations, the average value for irradiation at the offshore site is

higher than at the land-based site. The maximum and minimum irradi-

ation level difference between the land-based and offshore systems

are found for the DaNang Port and Port Shepstone sites, which is

13.94% and 5.42%, respectively.

Although 30% of the locations show a relatively lower average

irradiation level on the offshore sites, the temperate difference com-

pensation leads to an increase in energy yield of some of the sites:

Only 20% of the locations show a negative energy yield difference.

For instance, at the Port Vell site, although the irradiation level

FIGURE 5 (A) Heat Index and sea surface temperature; (B) initial and final cell temperature of the offshore side for all locations between the

years 2008 and 2018.

TABLE 4 Average annual yields in

kWh/kWp across all locations for

offshore and land-based PV systems.

Yield Offshore Relative

Offshore Inland advantage offshore advantage

No. Site (kWh/kWp) (kWh/kWp) (kWh/kWp) (%)

1 Bandar Penawar 1658.65 1514.35 144.30 9.53

2 Ciudad del Carmen 1899.62 1677.71 221.90 13.22

3 DaNang Port 1589.96 1328.50 261.45 19.68

4 El Emir 1639.97 1549.98 89.99 5.80

5 Hengsha Island 1259.64 1263.69 4.05 0.32

6 Katsuura 1304.28 1321.14 16.86 1.27

7 Kwala Tanjung 1472.17 1484.61 12.44 0.83

8 New Limassol Port 1818.66 1654.61 164.05 9.91

9 Port Antonio 1750.85 1699.01 51.84 3.05

10 Port Coquitlam 1255.03 1115.08 139.95 12.91

11 Port Moresby 1711.61 1469.96 241.64 16.43

12 Port of Rotterdam 1117.90 1037.93 79.97 7.70

13 Port Shepstone 1584.99 1646.81 61.81 3.84

14 Port Vell 1550.65 1521.49 29.15 1.91

15 Puerto Belgrano 1681.68 1632.73 48.94 2.99

16 Puerto Colombia 1932.02 1700.09 231.91 13.64

17 Puerto La Cruz 1943.69 1727.55 216.13 12.51

18 Ras Laffan 1811.22 1677.09 134.13 7.99

19 South Golden Beach 1752.98 1668.95 84.03 5.03

20 Tanzania Port 1889.43 1653.09 236.33 14.29

Note: The colored values in the table belong to the locations with negative advantage of the offshore

system.

GOLROODBARI ET AL.1069

difference is 1.71%, the energy yield difference is 1.91%, which is

indicating that the offshore PV system performs relatively better for

this location in comparison with the land-based PV system.

Considering the data in Table 4, we would like to compare the

two best and two worst case results for the energy yield advantage,

namely, DaNang port and Port Moresby as two higher values and

Port Shepstone and Katsuura as two lower cases. Figure 6shows

the scatter plots of energy yield versus irradiation for each location.

For the two best locations, not only the irradiation level is much

higher for the offshore side but also the slope of the scatter plot of

energy yield versus irradiation is bigger. For the other two locations,

the scatter plots for land-based and offshore sides are almost similar,

which means that in terms of energy yield, the offshore system does

not have a better performance compared with the land-based

system.

Figure 7shows the final cell temperature versus the fluid

temperature, which is considered as water for offshore and air for the

land-based system. As shown for the two best locations the dry-bulb

temperature for the land-based system is relatively higher compared

with the other two locations. This means that the land-based system

in the two worst-case scenarios performs better compared with the

land-based systems of best case scenarios, in terms of the effect of air

temperature on final cell temperature. However, the water cooling

effect is much more tangible for the environments where the mini-

mum temperature is higher.

It helps to focus on one specific location to illustrate some of the

main conclusions in this paper.

We consider the case of Qatar, with latitude and longitude

values as follows: coastal site 25.915N, 51.580E, offshore site

26.420N, 51.580E. Both Qatar sites have high levels of irradi-

ance,buttheoffshoresitereceives consistently higher irradiance

than the other site (see Figure 8A). Partial explanations for this

include a higher level of diffuse radiation on the open seas due to,

for example, cloud conditions or a relatively low level of localized

pollution.

Yet, as with many of the other sites, the offshore site for Qatar

shows higher apparent temperatures than the inland site. However,

the water cooling effect and the higher irradiation level cause the off-

shore floating system performing significantly higher, and as tabulated

in Table 4the energy yield advantage is 7.99%.

Finally, let us consider the two locations with maximum and mini-

mum temperature differences, which were named before, that is,

Puerto Colombia and Port Coquitlam. Figure 9shows some informa-

tion about these two locations. Although the final cell temperature of

the offshore site for Port Coquitlam is almost always a positive value,

it barely exceeds 20C. However, this value for the land-based side of

this location belongs to a very wide range from very low to very high

temperatures. In contrast, the final cell temperature for the offshore

site of Puerto Colombia is limited and is always above 20C. This

value for the land-based site in this location is also always above 20C

FIGURE 6 Scatter plots of energy yield versus irradiation level in hourly time resolution for (A) DaNang Port, (B) Port Moresby, (C) Port

Shepstone, (D) Katsuura.

1070 GOLROODBARI ET AL.

and PV cells could get very hot. This affects the cell efficiency, which

is clearly shown in Figures 9C,D. A wide range of the final cell temper-

ature due to the ambient condition is shown in the cell efficiency for

Port Coquitlam, and it shows that the cell efficiency for the offshore

system is not always better than the land-based for this location.

Energy yield for Port Coquitlam is 12.9% higher, as also shown in

Table 4, the average irradiation level is higher at the offshore site for

this location also and the effect of temperature difference is counted.

However, the energy yield difference for Puerto Colombia is 13.64%,

which is mainly the effect of temperature difference.

3.3 |Regression analysis

Having studied the performance difference between land-based and

offshore systems, we will discuss how we can find a reliable

FIGURE 7 Scatter plots of final cell temperature versus ambient temperature in hourly time resolution for (A) DaNang Port, (B) Port Moresby,

FIGURE 8 (A) Average Irradiation levels per annum and (B) annual energy yield, for Ras Laffan located at Qatar for both land-based and

offshore system sites.

GOLROODBARI ET AL.1071

correlation between the offshore yield advantage defined in

Equation (10) and the environmental variables. To this end, first, we

need to define the independent variables and thereafter develop the

two regression methods, that is, (i) MLR and (ii) MPR. We will ana-

lyze the accuracy of the model by calculating the metrics R2and root

mean squared error (RMSE).

In the below list, we summarize the five main environmental/

meteorological variables that we will examine in our regression model:

•ΔG(%)

Equation (14) represents the difference in irradiation level between

offshore (GS) and land-based (GL) sites.

ΔG¼GSGL

100 ð14Þ

•Pr (mm/h)

The precipitation is expressed in average rainfall thickness per hour

(mm/h). This metric is one of the important metrics in the Köppen–

Geiger climate zone typology.

•TS(C)

Average sea surface temperature during the study period repre-

senting either the system to be on the warm or cold stream. Due

to the fact that the cold and warm streams on the sea surface may

vary by the passage of time, this variable is more reliable than a

FIGURE 9 Scatter plots of (A,B) final cell temperature versus fluid temperatures, namely, water and air, (C,D) cell efficiency versus irradiation,

(E,F) energy yield versus irradiation in hourly time resolution for Port Coquitlam and Puetro Colombia.

1072 GOLROODBARI ET AL.

boolean variable which represents to have either a cold or warm

stream on the location.

•ΔvW(%)

Equation (15) represents the difference in wind speed between

offshore (vW,S) and land-based vW,Lsites.

ΔvW¼vW,SvW,L

vW,L

100 ð15Þ

Considering the above-mentioned independent variables, we are

able to define the MLR and MPR as shown in Equation (16) and

Equation (17), respectively.

ΔEY¼α0þα1ΔGþα2Pr þα3TSþα4ΔvWð16Þ

ΔEY¼β0þβ1ΔGþβ2Pr þβ3TSþβ4ΔvWþβ5ΔG2þβ6Pr2

þβ7T2

Sþβ8Δv2

Wþβ9ΔGPr þβ10ΔGTSþβ11ΔGΔvWþβ12PrTS

þβ13PrΔvWþβ14 TSΔvW

ð17Þ

The intercept and coefficients of MLR and MPR methods are

shown in Tables 5and 6, respectively. In addition to the coefficients,

the R-squared shown as R2and root mean square error (RMSE) values

for both methods are also calculated and listed in the tables.

Although the MPR method is much more complex, it leads to a

higher R2compared with the MLR method. This means that the data

MPR method fitted the data better compared with the MLR method.

However, it should also be taken into consideration that the MPR

method is very sensitive to outliers, thus the presence of outliers

could affect the performance of the model. For RMSE, this is the

other way around, representing that the standard deviation of the

residuals is smaller for the MPR method. Residuals indicate how far

the estimated points are from the real data, and RMSE shows how

these residuals diverge.

One way to demonstrate the validity of the main results is that

different multivariable regression methods return relatively big

R-squared values. R-squared is a measure of how closely the data in a

regression line fit the data in the sample. The closer the R-squared

value is to 1, the better the fit. Both methods lead to good fits, as is

also evident from Figure 10.

One example that shows latitude cannot by itself play a key

role in this prediction is a simple comparison between Ciudad del

Carmen (18.65N, 91.80W) and Port Antonio (18.17N, 76.44W).

Thesetwoportsarelocatedwithmoreorlesssimilardistancesto

the Equator, but the average energy yield advantage for Ciudad del

Carmen is 13.22% while it is 3.05% for Port Antonio. This differ-

ence can be explained by investigating which variable plays the

most important role in this comparison. To this end, we first con-

sider the average sea surface temperature and the average wind

speed for both locations.

Although the presented regression model has a high level of accu-

racy, this should not be taken as the model for a person to decide

whether or not offshore FPV is definitely a feasible option for a given

location. Instead, the model could be one tool of many to help focus

attention on the correct geographic and climatological factors that

determine the viability of offshore FPV.

3.3.1 | Sensitivity of models

One way to study the sensitivity of a function is to use partial deriva-

tives with respect to the independent variables. Thus, model sensitiv-

ity called “MSen

”can be defined as in Equation (18).

MSenjθ¼∂d

ΔEY

∂θð18Þ

where θis the independent variable in the aforementioned models.

Considering Equation (18), sensitivity of the MLR model,

Equation (18) can be expressed in terms of its coefficients for each

variable, as MLR is a linear model. From the data in Table 5, it is clear

that MLR has the highest sensitivity to precipitation, representing

Köppen–Geiger climate classification and the second variable which

makes the highest sensitivity is the difference of irradiation level

between offshore and land-based systems.

TABLE 5 Coefficients and metrics for MLR model.

α0α1α2α3α4R2RMSE

2.4494 1.1271 5.4096 0.0496 0.3629 0.9792 0.8972

TABLE 6 Coefficients and metrics

for MPR model. β0β1β2β3β4β5

4.7643 0.9415 41.386 0.2562 0.4946 1.8310e3

β6β7β8β9β10 β11

18.9158 1.3257e2 2.4814e10.7400 2.1005e24.9547e2

β12 β13 β14 R2RMSE

1.7674 3.3123 3.6208e3 0.9927 0.5295

GOLROODBARI ET AL.1073

To have a fair comparison for the sensitivity, the sensitivity func-

tion for the MPR model with respect to precipitation and irradiation

level difference is derived and shown in Equations (19) and (20).

MSenjPr¼∂d

ΔEY

∂Pr

¼β2þ2β6Prþβ9ΔGþβ12TSþβ13 ΔvWð19Þ

MSenjΔG¼∂d

ΔEY

∂ΔG¼β1þ2β5ΔGþβ9ΔPrþβ10TSþβ11 ΔvWð20Þ

Considering the values for the coefficients in Table 6, we can con-

clude that this model similar to MLR is more sensitive to variation in

precipitation than would otherwise be expected. It is important to

interpret this result correctly: Specifically, it means that differences in

precipitation between offshore and onshore sites—which we take to

be a proxy for the KG climate classification—can suffice to explain a

large part of the difference in performance between land-based and

offshore PV systems. Equally important, this is also a predictive model,

which avoids the direct use of the KG climate classification system

but which instead can use another geography- and climate-related

metrics to determine where deploying PV panels offshore would

(or would not) be favorable.

4|DISCUSSION

4.1 |Locations and dataset

For each location, the offshore FPV pontoons are located 56 km

away from the associated coastal site. This decision was to overcom-

pensate for a potential lack of accuracy in the data: Since the data

provided by NASA POWER have an accuracy that does not go beyond

0.5latitude or longitude, the decision was made to ensure that all

sites are at least 0.5apart which translates to roughly 56 km.

For the sake of practicality, the number of locations is limited to

20, producing data for a total of 40 sites. Although these cover a rea-

sonable extent of the globe, it remains possible that expanding the

data set to include a greater number of locations could lead to as-yet

unclear trends/relationships becoming apparent.

4.2 |Validity of data

Ultimately, the validity of the results presented here is based on the

validity of NASA's Modern-Era Retrospective analysis for Research

and Applications Version 2, or MERRA-2. MERRA-2 dates back to

1980 and is based on microwave radiation images. Each image is

based on data collected over three hours and fitted onto a map with a

0.5-degree resolution, roughly equivalent to 50 km in the latitudinal

directions (hence, the choice of 50 km when deciding the distance

between offshore, coastal, and inland sites). The intensity of micro-

wave radiation levels allows for the interpolation of solar insolations

and temperatures; wind speeds are likewise calculated. A full descrip-

tion of the MERRA-2 project is available in review form in a paper by

Geralo and co-authors.

Notably, the NASA POWER dataset is freely

available for use and covers the entire globe, so a spatial resolution of

5˜0 km is a fairly reasonable compromise.

Throughout this paper, we sought to limit and understand the

sources of the inaccuracy of the data sources in question. NASA

POWER data purports also to give “optimal radiation”for tilted

panels, yet, at the time of writing, the reliability of the model which

produces optimal insolation could not be guaranteed for arbitrary

locations.

Our choice of GHI radiation was thus aimed at reducing

at least one important source of uncertainty. It also implies a limitation

on our paper, in that we can consider only horizontally tilted panels.

This excludes the studying of panels that are tilted to capture optimal

levels of solar insolation; allowing for optimization of tilt panels would

change the workings of this paper.

FIGURE 10 Comparison between prediction values versus actual values implementing (A) the MLR and (B) the MPR method.

1074 GOLROODBARI ET AL.

A small number of papers mostly focused on agricultural produc-

tion have attempted to define the limits of the accuracy of NASA

POWER's data, mostly by seeking to cross-validate the information

from ground-based satellites. For example, Sayago and co-authors val-

idated NASA POWER's solar radiation estimates against ground

weather measurements across central Spain.

The authors report

that, even in the worst cases, the value of R2, the coefficient of deter-

mination, was no less than 0.85. Likewise, the same study reports an

RMS error of 1:78 MJ

d∗m2on a daily basis, or roughly equivalent to about

180 kWh per year. This suggests that high-insolation sites would be

less affected than those which receive relatively less solar insolation.

Bai and co-authors

verify NASA satellite data in China and found

the solar radiation data derived from NASA POWER to be generally

reliable for use when modeling maize crop yields, while the same

authors reported that the NASA POWER data tended to underesti-

mate the air temperatures. Our own adjustment of the temperature

(see Section 2.2) accounts at least partly for this loss. Clearly, NASA

POWER comes with its set of shortcomings and is likely to impact dif-

ferent sites unequally. Nonetheless, it remains the only truly “world-

wide”

dataset, which provides all of the meteorological data which

we could have needed. As a preliminary effort before further research

is conducted, we judge NASA POWER to be sufficient for our needs.

4.3 |Economics

This paper deals with determining the most favorable locations for the

deployment of offshore FPV in terms of energy yield. It is also clearly

possible to state, based on the results, that there are some locations

on the globe for which offshore FPV is simply not the most viable

option. This conclusion may come from an analysis of many different

metrics. Generally, the investment and installation costs of OFPV will

be higher than land-based systems, while the cost of land will be

absent for floating systems. On the other hand, the open water

(ocean/sea) condition could be an important metric to estimate the

OFPV system CAPEX based on the necessity of specific design for the

structure, mounting, mooring, and anchoring systems. However, we

excluded this aspect from our analysis and it is left for future work.

Although more focused on meteorological and climate/geography

considerations, there is a clear economic motivation for this work as

well. An economically viable OFPV system will have a yield advantage

that will offset higher investment and installation costs. A recent

example of a hybrid offshore wind and PV system corroborates this.

Of particular economic interest to this project is the differences

in the lifetimes of the different solar panels. For example, a report by

Zaharia and co-authors has been able to quantify the decomposition

of solar panels as a consequence of corrosion caused by salinity in

(e.g.) seawater.

Over the course of several years, it is likely that the

contact between offshore solar panels and seawater would lead the

offshore solar panels to degrade more rapidly than land-based PV

panels; this, in addition to the infrastructure investment required to

deploy PV panels offshore will influence any possible decision to

deploy PV panels offshore. This again, however, is outside of the

scope of our paper as we are specifically interested in determining the

extent to which geography and meteorology will shape the decision

to build offshore solar PV at a specific location.

5|CONCLUSIONS

In this paper, a detailed model has been developed that allows deter-

mining the potential yield advantage that offshore floating PV systems

may have across the globe. For this model, we considered steel pon-

toons for all the OFPV systems and assumed that panels on land are

air-cooled. We implemented our model for 20 different locations across

the globe, at different climate zones. While existing literature shows

considerable yield advantages, we have found that the advantage may

also be negative: in specific locations, offshore floating PV has a lower

annual yield than a land-based system. Based on our model the average

energy yield difference considering the time period of 2008 and 2018

varies from almost 20% to 4% for different locations.

To study the effect of different variables on this energy yield

advantage, we have developed two regression models that can be

used to predict offshore yield differences compared with land-based

systems. The major finding of this study on the energy advantage

between offshore and land-based PV systems is that the energy

advantage is clearly site-specific. Further, we developed a meaningful

regression model which quantifies a very definite correlation between

a number of geographical and meteorological values and the energy

(dis)advantage of deploying PV panels offshore.

We conclude that there is no iron-clad guarantee, or any type of

general “rule of thumb,”that deploying PV panels on bodies of water

results in an improved yield of electrical energy. Yet in cases where

competition for land or the need to avoid shading from the built envi-

ronment necessitates moving offshore, then the approach developed

in this paper can be used to make site selections. In other words, there

will be some use cases where building offshore FPV might appear

promising, and in such situations, having access to a geography-based

regression model such as this model will help decision-makers better

understand their options.

ACKNOWLEDGMENTS

The authors would like to gratefully thank Dr. Pita Verweij for the

valuable insights she gave in the pathway of this research. This work

is partly financially supported by the Netherlands Enterprise Agency

(RVO) within the framework of the Dutch Topsector Energy (projects

Comparative assessment of PV at Sea versus PV on Land, CSEALAND,

and North Sea Two, NS2).

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in

NASA POWER at https://power.larc.nasa.gov/data-access-viewer/.

ORCID

S. Zahra Golroodbari https://orcid.org/0000-0002-5843-0463

Wilfried van Sark https://orcid.org/0000-0002-4738-1088

GOLROODBARI ET AL.1075

REFERENCES

1. Lebedys A, Akande D, Coënt N, Elhassan N, Escamilla G, Arkhipova I,

Whiteman A. Renewable capacity statistics 2022. IRENA; 2022.

2. International Energy Agency. World Energy Outlook 2020. Paris,

France: International Energy Agency; 2020.

3. Pearsall N, ed. The Performance of Photovoltaic (PV) Systems Modelling,

Measurement and Assessment. Elsevier; 2016.

4. Beck HE, Zimmermann NE, McVicar TR, Vergopolan1 N, Berg A,

Wood EF. Present and future Köppen-Geiger climate classification

maps at 1-km resolution. Sci Data. 2018;5:180214.

5. Peters IM, Buonassisi T. Energy yield limits for single junction solar

cells. Joule. 2018;2:1160-1170.

6. Ascencio-Vásquez J, Brecl K, Topiˇ

c M. Methodology of Köppen-

Geiger-photovoltaic climate classification and implications to worldwide

mapping of PV system performance. Solar Energy. 2019;191:672-685.

7. Mekhilef S, Saidur R, Kamalisarvestani M. Effect of dust, humidity and

air velocity on efficiency of photovoltaic cells. Renew Sustain Energy

Rev. 2012;16:2920-2925.

8. Hu J, Harmsen R, Crijns-Graus W, Worrell E. Geographical optimiza-

tion of variable renewable energy capacity in China using modern

portfolio theory. Appl Energy. 2019;253:113614.

9. Folkerts W, Van Sark W, De Keizer C, Van Hooff W, Van den

Donker M. Roadmap PV Systemen en Toepassingen. Technical

report, SEAC, Utrecht University, TKI Urban Energy; 2017.

10. Quax R, Londo M, van Hooff W, Kuijers T, Jaap W, van Sark W,

Sinke W. Assessment of spatial implications of photovoltaics deploy-

ment policies in the Netherlands. Solar Energy. 2022;243:381-392.

11. World Bank. Where Sun Meets Water: Floating Solar Handbook for

Practitioners. Technical report, The World Bank; 2019.

12. Trapani K, Millar DL. Proposing offshore photovoltaic

(PV) technology to the energy mix of the Maltese islands. Energy Con-

vers Manag. 2013;67:18-26.

13. Keiner D, Salcedo-Puerto O, Immonen E, et al. Powering an island

energy system by offshore floating technologies towards 100%

renewables: a case for the Maldives. Appl Energy. 2022;308:118360.

14. Liu H, Krishna V, Leung JL, Reindl T, Lu Z. Field experience and per-

formance analysis of floating PV technologies in the tropics. Prog Pho-

tovoltaics. 2018;26:957-967.

15. Golroodbari SZ, van Sark W. Simulation of performance differences

between offshore and land-based photovoltaic systems. Prog Photo-

voltaics. 2020;28:873-886.

16. NASA POWER. Prediction of worldwide energy resources (power)

project. https://power.larc.nasa.gov

17. Rosa-Clot M, Marco Tina G, eds. Floating PV Plants. Academic Press;

2020.

18. Gorjian S, Sharon H, Ebadi H, Kant K, Scavo FB, Tina GM. Recent

technical advancements, economics and environmental impacts of

floating photovoltaic solar energy conversion systems. J Cleaner Prod-

uct. 2021;278:124285.

19. Trapani K, Red 

on Santafé M. A review of floating photovoltaic instal-

lations: 2007–2013. Prog Photovoltaics: Res Appl. 2015;23:524-532.

20. Ghigo A, Faraggiana E, Sirigu M, Mattiazzo G, Bracco G. Design and

analysis of a floating photovoltaic system for offshore installation: the

case study of Lampedusa. Energies. 2022;15(23):8804.

21. Kjeldstad T, Lindholm D, Marstein E, Selj J. Cooling of floating photo-

voltaics and the importance of water temperature. Solar Energy. 2021;

218:544-551.

22. Olivera-Pinto S, Stokkermans J. Assessment of the potential of differ-

ent floating solar technologies: overview and analysis of different

case studies. Energy Convers Manag. 2020;211:112747.

23. PVSyst. PVSyst Photovoltaic Software. 2020. http://www.

pvsyst.com

24. Seij JH, Lereng IH, De Paoli P, Ogaard MB, Otnes G, Bragstad S,

Bjorneklett B, Marstein E. The performance of a floating PV plant at

the west coast of norway. In: Proc 36th European Photovoltaic Solar

Energy Conference (EUPVSEC). Marseille, France; 2019:1763-1767.

25. Dörenkämper M, Van den Werf D, Sinapis K, De Jong M, Folkerts W.

Influence of wave induced movements on the performance of float-

ing pv systems. In: Proceedings of 36th European Photovoltaic Solar

Energy Conference (EUPVSEC). Marseille, France; 2019:1759-1762.

26. Setiawan F, Dewi T, Yusi S. Sea salt deposition effect on output and

efficiency losses of the photovoltaic system; a case study in Palem-

bang, indonesia. In: J Phys: Confer Ser, Vol. 1167. IOP Publishing;

2019:12028.

27. Suzuki S, Nishiyama N, Yoshino S, et al. Acceleration of

potential-induced degradation by salt-mist preconditioning in crystal-

line silicon photovoltaic modules. Japanese J Appl Phys. 2015;54(8S1):

8KG08.

28. Zaharia SM, Pop MA, Chicos LA, Lancea C, Semenescu A, Florea B,

Chivu OR. An investigation on the reliability and degradation of poly-

crystalline silicon solar cells under accelerated corrosion test. MATE-

RIALE PLASTICE. 2017;54(3):466.

29. Rahmstorf S. Thermohaline circulation: the current climate. Nature.

2003;421(6924):699.

30. Vitolo C. hddtools: Hydrological data discovery tools. The J Open

Source Softw. 2017;2(9):56.

31. Vittolo C. Private correspondence; 2020.

32. Sparks AH. nasapower: A NASA POWER Global Meteorology, Sur-

face Solar Energy and Climatology Data Client for R. The J Open

Source Softw. 2018;3(30):1035.

33. Rothfusz LP, NWS Southern Region Headquarters. The heat index

equation (or, more than you ever wanted to know about heat index).

Fort Worth, Texas: National Oceanic and Atmospheric Administra-

tion, National Weather Service, Office of Meteorology, 9023; 1990.

34. Anderson GB, Bell ML, Peng RD. Methods to calculate the heat index

as an exposure metric in environmental health research. Environ

Health Perspect. 2013;121(10):1111-1119.

35. Patricola CM, Cook KH. Northern African climate at the end of the

twenty-first century: an integrated application of regional and global

climate models. Clim Dyn. 2010;35(1):193-212.

36. Sánchez-Palencia P, Martín-Chivelet N, Chenlo F. Modeling tempera-

ture and thermal transmittance of building integrated photovoltaic

modules. Solar Energy. 2019;184:153-161.

37. Tina GM, Bontempo Scavo F, Merlo L, Bizzarri F. Comparative analy-

sis of monofacial and bifacial photovoltaic modules for floating power

plants. Appl Energy. 2021;281:116084.

38. Faiman D. Assessing the outdoor operating temperature of photovol-

taic modules. Prog Photovoltaics: Res Appl. 2008;16(4):307-315. doi:

10.1002/pip.813

39. Koehl M, Heck M, Wiesmeier S, Wirth J. Modeling of the nominal

operating cell temperature based on outdoor weathering. Solar Energy

Mater Solar Cells. 2011;95(7):1638-1646.

40. Reich NH, Mueller B, Armbruster A, Van Sark WGJHM, Kiefer K,

Reise C. Performance ratio revisited: is PR > 90% realistic? Prog Pho-

tovoltaics: Res Appl. 2012;20:717-726.

41. Skoplaki E, Palyvos JA. On the temperature dependence of photovol-

taic module electrical performance: a review of efficiency/power cor-

relations. Solar Energy. 2009;83:614-624.

42. International Electrotechnical Committee. IEC 61215:2005, crystal-

line silicon terrestrial photovoltaic (PV) modules—Design qualification

and type approval. Technical report, Geneva, International Electro-

technical Committee; 2005.

43. Notton G, Cristofari C, Mattei M, Poggi P. Modelling of a double-glass

photovoltaic module using finite differences. Appl Thermal Eng. 2005;

25:2854-2877.

44. Pace L. Multiple regression. Beginning R. Springer; 2012:185-199.

45. Toh K-A, Yau W-Y, Jiang X. A reduced multivariate polynomial model

for multimodal biometrics and classifiers fusion. IEEE Trans Circ Syst

Video Technol. 2004;14(2):224-233.

1076 GOLROODBARI ET AL.

46. Walsh JE, Thoman RL, Bhatt US, et al. The high latitude marine heat

wave of 2016 and its impacts on alaska. Bull Am Meteorol Soc. 2018;

99(1):S39-S43.

47. Ortiz-Royero JC, Otero LJ, Restrepo JC, Ruiz J, Cadena M. Cold fronts

in the Colombian Caribbean Sea and their relationship to extreme

wave events. Nat Hazards Earth Syst Sci. 2013;13(11):2797-2804.

48. Devis-Morales A, Montoya-Sánchez RA, Bernal G, Osorio AF. Assess-

ment of extreme wind and waves in the Colombian Caribbean Sea for

offshore applications. Appl Ocean Res. 2017;69:10-26.

49. Gelaro R, Mccarty W, Surez MJ, et al. The modern-era retrospective

analysis for research and applications, version 2 (MERRA-2). J Clim.

2017;30(14):5419-5454.

50. Tindell J. Private correspondence with the NASA Langley facility;

2020.

51. Sayago S, Ovando G, Almorox J, Bocco M. Daily solar radiation from

NASA-POWER product: assessing its accuracy considering atmo-

spheric transparency. Int J Remote Sens. 2020;41(3):897-910.

52. Bai J, Chen X, Dobermann A, Yang H, Cassman KG, Zhang F. Evalua-

tion of NASA satellite- and model-derived weather data for simula-

tion of maize yield potential in China. Agronomy J. 2010;102(1):9-16.

53. Hall J, Hall J. Evaluating the accuracy of solar radiation data sources.

Solar Data Warehouse; 2010.

54. Golroodbari SZM, Vaartjes DF, Meit JBL, Van Hoeken AP,

Eberveld M, Jonker H, Van Sark WGJHM. Pooling the cable: a

techno-economic feasibility study of integrating offshore floating

photovoltaic solar technology within an offshore wind park. Solar

Energy. 2021;219:65-74.

55. Marian ZS, Alin PM, Antoneta CL, Camil L, Augustin S, Bogdan F,

Roxana CO. An investigation on the reliability and degradation of

polycrystalline silicon solar cells under accelerated corrosion test.

Materiale Plastice. 2017;54(3):466-472.

How to cite this article: Golroodbari SZ, Ayyad AWA, van

Sark W. Offshore floating photovoltaics system assessment in

worldwide perspective. Prog Photovolt Res Appl. 2023;31(11):

1061‐1077. doi:10.1002/pip.3723

GOLROODBARI ET AL.1077

Floating Offshore Photovoltaics across Geographies: An Enhanced Model of Water Cooling

Article

Full-text available

Feb 2024

Solar photovoltaics (PV) continues to grow rapidly across the world and now accounts for a very considerable proportion of all non-fossil-fuel electricity. With the continuing urgency of greenhouse gas abatement, the growth of solar PV is inevitable. Competition with other land uses and the desire to optimize the efficiency of the panels by making use of water cooling are compelling arguments for offshore floating PV (OFPV), a trend that could also benefit from the existing infrastructure recently built for offshore wind farms. Building on our earlier work, we present a larger dataset (n = 82) located around the globe to assess global yield (dis)advantages while also accounting for a modified form of water cooling of the offshore panels. Using our results regarding the Köppen–Geiger (KG) classification system and using a statistical learning method, we demonstrate that the KG climate classification system has limited validity in predicting the likely gains from OFPV. Finally, we also explore a small subset of sites to demonstrate that economics, alongside geography and technology, impacts the feasibility of locating PV panels offshore.

Discussion on the development of offshore floating photovoltaic plants, emphasizing marine environmental protection

Article

Full-text available

Mar 2024

The development of solar energy is one of the most effective means to deal with the environmental and energy crisis. The floating photovoltaic (PV) system is an attractive type because of its multiple advantages and has been well developed based on fresh water areas on land. This paper focuses on the expansion of this sector towards the ocean, offshore floating PV plants, which is the new growth point with huge potential for the future PV sector. For this new field, the technology readiness level is really low and research to understand the interaction between offshore floating PV plants and marine environment are proceeding. In this paper, we aim to discuss the technological feasibility of offshore floating PV plants as well as analyze potential impacts on the marine environment during the life cycle of PV from manufacturing until disposal.

Wave Basin Tests of a Multi-Body Floating PV System Sheltered by a Floating Breakwater

Article

Full-text available

Apr 2024

The development of floating photovoltaic systems (FPV) for coastal and offshore locations requires a solid understanding of a design’s hydrodynamic performance through reliable methods. This study aims to extend insights into the hydrodynamic behavior of a superficial multi-body FPV system in mild and harsh wave conditions through basin tests at scale 1:10, with specific interest in the performance of hinges that interconnect the PV panels. Particular effort is put into correctly scaling the elasticity of the flexible hinges that interconnect the PV modules. Tests of a 5 × 3 FPV matrix are performed, with and without shelter, by external floating breakwater (FBW). The results show that the PV modules move horizontally in the same phase when the wave length exceeds the length of the FPV system, but shorter waves result in relative motions between modules and, for harsh seas, in hinge buckling. Relative motions suggest that axial loads are highest for the hinges that connect the center modules in the system and for normal wave incidence, while shear loads are highest on the outward hinges and for oblique incidence. The FBW reduces hinge loads as it attenuates the high-frequency wave energy that largely drives relative motions between PV modules.

Floating PV Systems

Chapter

Jun 2024

A framework to identify guano on photovoltaic modules in offshore floating photovoltaic power plants

Article

May 2024
SOL ENERGY

The complementarity of offshore wind and floating photovoltaics in the Belgian North Sea, an analysis up to 2100

Article

Aug 2023
RENEW ENERG

Design and Analysis of a Floating Photovoltaic System for Offshore Installation: The Case Study of Lampedusa

Article

Full-text available

Nov 2022

In recent years, numerous projects for floating PV systems have been developed. These plants of various sizes have mainly been installed on enclosed lakes or basins characterised by the absence of external forcing related to waves and currents. However, offshore installation would allow the development of such plants in areas where land is not available, such as islands. This paper analyses the state of the art of floating PV, describes the design of a floating PV platform and the development of a numerical model to evaluate the system performance in an offshore environment. The case study of the island of Lampedusa is then analyzed: starting from a single floating foundation with its mooring system, a floating PV system is designed to meet the island’s electricity needs. In order to provide the competitiveness of the system, a techno-economic analysis is carried out, evaluating the main cost items of Capex, Opex and LCOE. Although the LCOE obtained is significantly higher than a traditional solar plant installed on land, this technology is competitive compared to other offshore marine technologies such as offshore wind and wave energy.

Powering an island energy system by offshore floating technologies towards 100% renewables: A case for the Maldives

Article

Full-text available

Feb 2022
APPL ENERG

Low-lying coastal areas and archipelago countries are particularly threatened by the impacts of climate change. Concurrently, many island states still rely on extensive use of imported fossil fuels, above all diesel for electricity generation, in addition to hydrocarbon-based fuels to supply aviation and marine transportation. Land area is usually scarce and conventional renewable energy solutions cannot be deployed in a sufficient way. This research highlights the possibility of floating offshore technologies being able to fulfil the task of replacing fossil fuels with renewable energy solutions in challenging topographical areas. On the case of the Maldives, floating offshore solar photovoltaics, wave power and offshore wind are modelled on a full hourly resolution in two different scenarios to deal with the need of transportation fuels: By importing the necessary, carbon neutral synthetic e-fuels from the world market, or by setting up local production capacities for e-fuels. Presented results show that a fully renewable energy system is technically feasible in 2030 with a relative cost per final energy of 120.3 €/MWh and 132.1 €/MWh, respectively, for the two scenarios in comparison to 105.7 €/MWh of the reference scenario in 2017. By 2050, cost per final energy can be reduced to 77.6 €/MWh and 92.6 €/MWh, respectively. It is concluded that floating solar photovoltaics and wave energy converters will play an important role in defossilisation of islands and countries with restricted land area.

Cooling of floating photovoltaics and the importance of water temperature

Article

Full-text available

Apr 2021
SOL ENERGY

Enhanced performance of floating PV due to water cooling is widely claimed, but poorly quantified and documented in the scientific literature. In this work, we assess the effect of water cooling for a specific technology developed by Ocean Sun AS, consisting of a floating membrane with horizontally mounted PV modules allowing for thermal contact between the modules and the water. The impact of thermal contact with water on energy yield is quantified using production data from a well-instrumented 6.48 kW installation at Skaftå, Norway. In addition, we apply a thermal model that incorporates the effect of heat transport from the module to the water to estimate the module temperature. By comparing a module string in thermal contact with water with a module string with an air gap between the water and the modules, we find that the water-cooled string had on average 5–6% higher yield compared to the air-cooled string. Also, we find that the system in thermal contact with water has a U-value of approximately 70–80 W/m²K, and that it is necessary to consider the water temperature for a more accurate calculation of the module temperature.

Pooling the cable: A techno-economic feasibility study of integrating offshore floating photovoltaic solar technology within an offshore wind park

Article

Full-text available

Mar 2021
SOL ENERGY

In this paper, a techno-economic analysis is performed to assess the feasibility of adding an offshore floating solar farm to an existing Dutch offshore wind farm in the North Sea, under the constraint of a certain fixed cable capacity. The specific capacity of the cable that connects the offshore park to the onshore grid is not fully used due to the limited capacity factor of the wind farm. The principle of cable pooling allows to add floating solar capacity. Using weather data it is found that adding solar capacity leads to forced curtailment due to the cable capacity, but this is quite limited as result of the anti-correlation of the solar and wind resource. For the economic analysis, different scenarios regarding subsidy measures are considered for the calculation of net present value and levelized cost of electricity. Also the optimum additional PV capacity for each scenario is computed. The results show that with higher cost per Wp, optimum PV capacity decreases, but more favourable subsidies lead to higher optimized PV capacities. As the aim of the paper is not limited to a case study a methodology is developed for generalization of the techno-economic analysis of a hybrid solar/wind park. In this generalization, the initial investment, system degradation, cable capacity, number of hours when each system is active, and energy price, are implemented to compute the optimum PV capacity regarding the net present value as an indicator for economic analysis of the project.

Simulation of performance differences between offshore and land‐based photovoltaic systems

Article

Full-text available

May 2020

The purpose of this study is to model, simulate, and compare the performance of a photovoltaics system on land and at sea. To be able to have a fair comparison the effect of sea waves, wind speed and relative humidity are considered in this model. The sea waves are modeled in the frequency domain, using a wave spectrum. The irradiation on a tilted surface for a floating system is calculated considering the tilt angle that is affected by the sea waves. Moreover, the temperature is estimated based on heat transfer theory and the natural cooling system for both floating and land‐based photovoltaic systems. Actual measured weather data from two different locations, one located at Utrecht University campus and the other one on the North Sea, are used to simulate the systems, thus making the comparison possible. Energy yield is calculated for these weather conditions. The results show that the relative annual average output energy is about 12.96% higher at sea compared with land. However, in some months, this relative output energy increases up to 18% higher energy yield at sea. Modeling, simulation, and comparison for the performance of floating and land‐based PV systems. The effect of sea waves is considered to compute the irradiation on a varying tilted surface for the FPV system and wind speed is considered for computing the effect of the natural cooling system for both systems. The computed energy yields show that relative annual average output energy is about 12.96% higher at sea.

Assessment of the potential of different floating solar technologies – Overview and analysis of different case studies

Article

Full-text available

May 2020
ENERG CONVERS MANAGE

Floating solar technology (FPV) emergence is driven by lack of available land, loss of efficiency at high operating cell temperature, energy security and decarbonization targets. It is a promising area of solar photovoltaic application, with a large global market potential. A significant challenge when assessing FPV projects is the lack of a suitable simulation tools for estimating the electricity production. A clear understanding and quantification of the cooling effect, how it differs between different FPV technologies, is still unclear among the industry. As the sector grows, larger projects will emerge, which require financing. Ensuring bankability is fundamental, thus having confidence in the energy estimates is key. Using PVsyst® to estimate electricity production of FV plants ensures bankability but has limitations. This work aimed to close the gap between the expected enhanced performance highlighted in the literature for FPV systems and the results from numerical simulations of energy production performed with PVsyst®. Results show that the increase in production from FPV technologies is below the anticipated values of the literature (ca. 10%), ranging from 0.31% to 2.59%, depending on the floating solar technology. Based on the results presented, higher performance is strongly dependent on technology and the location. The values of the levelized cost of energy ranged between 96.2 €/MWh to 50.3 €/MWh, depending on the technology and the solar irradiation that reaches the site.

Assessment of spatial implications of photovoltaics deployment policies in the Netherlands

Article

Sep 2022
SOL ENERGY

Electricity generation with photovoltaic (PV) solar energy technology requires significant amounts of space; a particular point of discussion in a densely populated country like the Netherlands. Therefore, we developed a new analytical framework to analyse potential electricity generation for specific PV typologies on 43 different land utilisation and water types. Our results indicate that spatial potentials for PV are substantial compared to current Dutch scenarios and ambitions for its role in a long-term decarbonised energy economy. The spatial potential of PV on rooftop areas is sufficient to largely meet these ambitions; however spatial allocation depends on more factors than the potential alone, e.g. desired implementation speed. Therefore, we have sketched some variants with a more balanced allocation over the various land use types. Innovative options, such as PV within offshore wind parks and on infrastructure, parking spaces and façades offer considerable opportunities for additional generation. Furthermore, we illustrate the considerable impacts on PV potential of (i) current net metering policy, (ii) the upcoming societal preference for lower panel densities in land-based PV parks, and (iii) a focus on cost efficiency instead of spatial efficiency. Policy makers should bear these outcomes in mind when designing support schemes for PV.

Comparative analysis of monofacial and bifacial photovoltaic modules for floating power plants

Article

Jan 2021
APPL ENERG

The aim of this study is to create usable commercial simulation software tools for photovoltaic (PV) systems even in the case of floating applications. Using the experimental data of a plant installed at the “Enel Innovation Lab” in Catania (IT), adequate heat exchange coefficients of the software’s thermal models were found, which allowed us to take into account the thermal effects for these types of installations. An optimization of the sizes for mono- and bifacial floating systems was carried out. The simulated data for a ground system were compared with the floating data through performance indices. In addition, monofacial models were compared with bifacial. The optimized variables are the tilt angle and the pitch, whereas for the bifacial systems, the module elevation is also considered. The albedo is a sensitive factor mainly for the bifacial modules, so it was considered a parameter in the different design solutions. The simulations were performed by two specialized commercial software pro- grams, where different PV system models are implemented. The normalized annual energy yield was chosen as a meaningful parameter with which to compare the different solutions. As the analysis of PV systems is highly site dependent, the study was developed for two locations, characterized by different diffuse and albedo solar irra- diance components, specifically at high latitudes (Frankfurt, DE) and at intermediate latitudes (Catania, IT).

Recent Technical Advancements, Economics and Environmental Impacts of Floating Photovoltaic Solar Energy Conversion Systems

Article

Jan 2021
J CLEAN PROD

Floating Photovoltaic (FPV) is an emerging technology that has experienced significant growth in the renewable energy market since 2016. It is estimated that technical improvements along with governmental initiatives will promote the growth rate of this technology over 31% in 2024. This study comprehensively reviews the floating photovoltaic (FPV) solar energy conversion technology by deep investigating the technical advancements and presenting a deliberate discussion on the comparison between floating and ground-mounted photovoltaic (PV) systems. Also, the economics and environmental impacts of FPV plants are presented by introducing the main challenges and prospects. The FPV plants can be conventionally installed on water bodies/dam reservoirs or be implemented as multipurpose systems to produce simultaneous food and power. Installing FPV modules over water reservoirs can prevent evaporation but penetration of solar radiation still remains an issue that can be eliminated by employing bifacial PV modules. The salt deposition in off-shore plants and algae-bloom growth are other important issues that can degrade modules over time and adversely affect the aquatic ecosystem. The capital expenditure (CAPEX) for FPV systems is about 25% higher than ground-mounted plants, mainly due to the existence of floats, moorings, and anchors. It has been stated that the capacity increase of FPV plants (ranges from 52 kW to 2 MW) can intensely decrease the levelized cost of energy (LCOE) up to 85%. It is estimated that FPV technology can become more affordable in the future by further research, developments, and progress in both technology and materials.

Methodology of Köppen-Geiger-Photovoltaic climate classification and implications to worldwide mapping of PV system performance

Article

Sep 2019
SOL ENERGY

Photovoltaic (PV) already proves but even more promises to be massively deployed worldwide. To evaluate the performance of PV systems globally and assess risk due to different climate conditions, we propose a methodology for the global Köppen-Geiger-Photovoltaic (KGPV) climate classification that divides the globe into 12 zones with regard to the temperature, precipitation and irradiation, and standardizes the evaluations of the performance in regions with similar climatic conditions. Additionally, we present implications of KGPV to simulated PV performance using monthly data, for current and future operation of PV systems worldwide including climate change scenarios. A set of electrical and thermal performance indicators of crystalline silicon PV modules in different KGPV zones is analyzed and their evolution over time due to climate changes caused by high greenhouse gas emissions discussed. Results show that the KGPV scheme proves to be a convenient methodology to relate the KGPV climate zones with PV performance.

Offshore floating photovoltaics system assessment in worldwide perspective

Abstract

Recommended publications

A comprehensive model for the German electricity and heat sector in a future energy system with a do...

SOLAR POWER GENERATOR CUTS OFFSHORE OPERATING COST.

Probabilistic assessment of photovoltaic (PV) generation systems

Enhancement of a stand-alone photovoltaic system’s performance: Reduction of soft and hard shading