https://www.nature.com/articles/s41598-019-47803-3
[“If less than 1% of agricultural land was converted to solar panels it
would be sufficient to fulfill global electric energy demand,” the
scientists claimed. Opposition to new solar capacity in some nations has
hinged on fears such developments will mean extensive losses of
productive farmland.
images in online article]
Solar PV Power Potential is Greatest Over Croplands
Elnaz H. Adeh, Stephen P. Good, M. Calaf & Chad W. Higgins
Scientific Reports volume 9, Article number: 11442 (2019)
Abstract
Solar energy has the potential to offset a significant fraction of
non-renewable electricity demands globally, yet it may occupy extensive
areas when deployed at this level. There is growing concern that large
renewable energy installations will displace other land uses. Where
should future solar power installations be placed to achieve the highest
energy production and best use the limited land resource? The premise of
this work is that the solar panel efficiency is a function of the
location’s microclimate within which it is immersed. Current studies
largely ignore many of the environmental factors that influence
Photovoltaic (PV) panel function. A model for solar panel efficiency
that incorporates the influence of the panel’s microclimate was derived
from first principles and validated with field observations. Results
confirm that the PV panel efficiency is influenced by the insolation,
air temperature, wind speed and relative humidity. The model was applied
globally using bias-corrected reanalysis datasets to map solar panel
efficiency and the potential for solar power production given local
conditions. Solar power production potential was classified based on
local land cover classification, with croplands having the greatest
median solar potential of approximately 28 W/m2. The potential for
dual-use, agrivoltaic systems may alleviate land competition or other
spatial constraints for solar power development, creating a significant
opportunity for future energy sustainability. Global energy demand would
be offset by solar production if even less than 1% of cropland were
converted to an agrivoltaic system.
Introduction
The goal of the United States Department of Energy is to reach a
levelized cost of energy for solar PV of $0.03 per kilowatt hour at
utility scale by 20301. This objective will strengthen the U.S. economy,
help the country reposition in the international energy market2,3, and
reduce CO2 gas emissions4,5,6. Solar energy represents a 1% share of the
energy share in the U.S and is set to expand its share to as much as 30%
by 20507. Potential land competition between energy and food
production8,9 necessitates a deeper understanding of the available solar
resource and the overlapping agricultural or ecosystem land use
services10. The global expansion of solar energy will require that both
the most sustainable energy infrastructure developments10 as well as the
locations of these developments are identified. The aim of this study is
to augment the scientific grounds for this discussion by ranking land
cover classes according to their solar energy production potential.
Solar PV potential fundamentally depends on the incoming solar
radiation, which is strongly dependent on geographic location, but it is
also well-known that the system’s efficiency depends on the temperature
of the solar cells, and the temperature of the solar cells is a function
of the local microclimate. Each potential location has an associated
microclimate; therefore, the influence of local climatology on PV
conversion efficiency must be addressed. The thermal processes that
connect a solar panel to its surroundings are modulated by four primary
environmental variables: insolation, air temperature, wind speed and
relative humidity. A first order description of the influence of these
factors can be cast in a simple energy balance model of the PV panel
where wind speed and air temperature influence convective heating or
cooling of the panel, water vapor alters the long wave radiation budget,
and solar radiation is the primary energy source. Here, this new
microclimate-informed PV efficiency model is validated using field
data11 from a 1.5 MW solar array located at Oregon State University in
Corvallis, Oregon12. The first order model is used to map global solar
power potential in order to assess the overlap between solar potential
and underlying land use.
Results
Modeled PV efficiency as a function of air temperature, wind speed and
relative humidity are consistent with measured values in the Corvallis
solar array (Fig. 1). A full description of the field measurements and
the reduced-order model is provided in the material section. Solar PV
efficiency diminishes as a function of air temperature at a rate of
approximately 0.5% per 10 °C. This is consistent with literature
observations of decreased efficiency with increasing ambient
temperature13,14. Light winds lead to increased energy efficiency
relative to quiescent conditions with a 0.5% increase in efficiency from
0.5 m/s to 1.5 m/s. This result is consistent with Dupré et al.8, who
show that small changes in the convective heat transfer coefficient can
lead to significant changes in the solar PV efficiency. Increased vapor
pressure is associated with a reduction in median efficiency that is not
fully captured with the reduced order model.
We apply the reduced order model to obtain a global maps of solar PV
efficiency and annual mean solar power potential (Fig. 2a,b), using data
sets for the solar radiation, air temperature, wind speed and humidity,
obtained at a global scale from re-analysis products11,12. The reported
solar efficiency is the ratio of the solar power generated to the solar
irradiance incident on the PV panel.
The most efficient continental locations include western America,
southern Africa, and the Middle East. This pattern is generally
consistent with prior assessments of solar power’s potential which
emphasize other factors15,16,17 including transmission and economic
potential which are not considered in the present study18,19,20. The
solar power potential associated seventeen underlying land cover types
identified with NASA’s Moderate Resolution Imaging Spectrometer (MODIS)
data21 is ranked by its median value (Fig. 3). Here, we find that
croplands, grasslands, and wetlands were the top three land classes.
Barren terrains, traditionally prioritized for solar PV system
installation22, were ranked fifth.
Discussion
The top three land covers associated with greatest solar PV power
potential are croplands, grasslands and wetlands. Solar panels are most
productive with plentiful insolation, light winds, moderate temperatures
and low humidity. These are the same conditions that are best for
agricultural crops, and vegetation has been shown to be most efficient
at using available water under mesic conditions where atmospheric
evaporative demand is balanced by precipitation supply23. Estimates of
cropland expansion since 170024 suggest that much contemporary cropland
was previously savannas/grasslands/steppes and forest/woodlands, thus
similarity in the power potential of croplands with grasslands and mixed
forests (Fig. 3) is likely driven by the conversion to agriculture of
land with similar climates. Further, one could think of agriculture as a
form of solar harvesting where the sun’s energy is stored in the
chemical bonds of the plant matter, and agricultural activities already
occupy those places on earth most amenable to solar harvesting.
Our rankings of solar power potential by land cover type (Fig. 3) may be
interpreted to forecast increased land competition between dedicated
food production and dedicated energy production. It could also be
interpreted to forecast a significant increase in the adoption of
agrivoltaic systems. Agrivoltaic systems leverage the superposition of
energy and food production for mutual benefit12. Crops are grown in the
intermittent shade cast by the PV panels in agrivoltaic systems. The
shade does not necessarily diminish agricultural yield.
Researchers have successfully grown aloe vera25, tomatoes26, biogas
maize27, pasture grass12, and lettuce28 in agrivoltaic experiments. Some
varieties of lettuce produce greater yields in shade than under full
sunlight; other varieties produce essentially the same yield under an
open sky and under PV panels29. Semi-transparent PV panels open
additional opportunities for colocation and greenhouse production30. The
reduced order model was re-evaluated to assess the potential for
agrivoltaic globally, and the global energy demand31 (21 PWh) could be
offset by solar production if <1% of agricultural land at the median
power potential of 28 W/m2 were suitable candidates for agrivoltaic
systems and converted to dual use. Lack of energy storage and the
temporal variance in the availability of solar energy will restrict this
expansion.
Methods
Data sources
Field data used in this study were collected during a two-year study on
a six acre agrivoltaic solar farm and sheep pasture at Oregon State
University Campus (Corvallis, Oregon, US.)11,12. Climatic variables
(temperature, relative humidity, wind speed and incoming short-wave
radiation were collected at a height of two meters (as the solar panel
height) and one-minute intervals over two years. Wind speed was measured
with a DS-2 acoustic anemometer (Meter Group, WA); relative humidity and
air temperature were recorded with a VP-3 hygrometer (Meter Group, WA),
and incoming solar radiation was measured by a PYR sensor (meter Group,
WA) which integrated the solar spectrum between 300 and 1200 nm. The
arithmetic means of all data were calculated on 15-minute intervals that
coincided with the energy production data at the solar array (provided
by Solar City).
PV efficiency model definition
The low-order solar PV efficiency model is a simple energy balance of
the solar PV module.
The incoming energy is the sum of the shortwave radiation from the sun
and the incoming longwave radiation from the atmosphere and ground. The
outgoing energy is composed of a reflected shortwave component, the
black body radiation from the PV panel itself, the convective cooling of
the panel, and the electrical energy output. The imbalance between the
incoming and outgoing heat fluxes results in a gain or loss of stored
thermal energy expressed through a change of the panel’s temperature.
A schematic of the control volume and the associated energy fluxes is
presented in Fig. 4. Steady state is assumed, and the atmosphere is
modeled under a neutral stratification as a first order approximation.
The consequence is that the energy storage term is neglected and that
the ground temperature is equal to the air temperature. The resultant
energy balance of the panel is expressed as:
(1−α−ε)Rsun↓+Lsky↓+Lg↑−2Lp−2qconv=0,(1−α−ε)Rsun↓+Lsky↓+Lg↑−2Lp−2qconv=0,
(1)
where ε is the efficiency of the solar panel, α = 0.2 is the PV panel
surface albedo, Rsun is the measured incoming shortwave radiation from
the sun, and expressions for the remaining individual terms are
presented below. The integral longwave radiation reaching the solar
module from the sky (assuming clear sky conditions) is modeled according
to Brutsaert (1975)32.
Lsky↓=1.24σ(eaTa)17Ta4,
(2)
where ea is the measured vapor pressure of water (hPa), Ta, is the
measured air temperature (°K) and σ = 5.670367 × 10−8 kg s−1 K−4 is the
Stephan-Boltzmann constant. The incoming long wave radiation from the
ground is modeled as a simple black body:
Lg↑=Tg4,
(3)
where Tg is the ground surface temperature. The PV panel is modeled as a
black body for longwave emission:
Lp=σTP4,
(4)
where Tp is the panel temperature. The convective cooling of the panel
is modeled with the bulk transfer equation:
qconv=h(Tp−Ta),
(5)
where h is the convective heat transfer coefficient which has been
estimated as33:
h=0.036kairlpanel(ulpanelυ)4/5Pr1/3,
(6)
where kair=0.026WmK
is the thermal conductivity of dry air, υ = 1.57e-5 m2s−1 is the
kinematic viscosity of air, Pr = 0.707 is the Prandtl number of dry air,
and u is the measured wind speed at the panel height. PV panels are
typically arranged in rows that span a distance much greater the size of
an individual panel. Heat transfer is maximal when the flow is
perpendicular to the row. In this case, the relevant scale is the length
of an individual panel, lpanel = 1.5 m. The efficiency of the solar
panel is modeled based on a linear relationship with panel temperature,
according to34:
ε=εref[1−A(Tp−Tref)],
(7)
where εref = 0.135, is the reference efficiency of the panel at a
reference temperature, Tref = 298 K, and A = 0.0051/°K is the change in
panel efficiency associated with a change in panel temperature34. This
linear relationship is assumed valid when |Tp−Tref|≤20∘K
34.
Substitution of Equations 2–7 into Equation 1 yields an equilibrium
expression for the PV panel efficiency. This expression is a quartic
polynomial with only one unknown: the PV panel efficiency, ε, and four
input variables: Rsun↓
, Ta, u, and ea. This equation also has only one real root which can be
obtained numerically with any root finding algorithm. The field data
described above were used as inputs to generate the model outputs
plotted in Fig. 1. Night time periods and times of low sun angles (≤15°)
were excluded from the analysis. In the global scale analysis, the input
environmental data were provided for each 0.5° × 0.5° pixel. Monthly
reanalysis datasets were used to compute monthly maps which were
arithmetically averaged to produce Fig. 2a,b.
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