Evaluation of Resource Risk in Solar-Project Financing

McMahan, Andrew C.; Grover, Catherine N.; Vignola, Frank

doi:10.1016/b978-0-12-397177-7.00004-8

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…This work is motivated by the need to understand the solar energy potential and support the evolving solar-based technologies. Solar monitoring provides support to the planning of installations of solar farms and rooftop panels [2][3][4]. The estimation of solar radiation using remote sensing technologies can also help the stakeholders to locate areas suitable for solar power production, which is a fast developing industry with huge potential.…”

Section: Introductionmentioning

confidence: 99%

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

et al. 2020

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Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions.

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Section: Introductionmentioning

confidence: 99%

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

et al. 2020

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“…Solar-resource uncertainty and inherent seasonal variability represent a performance and revenue risk for an SPP that is tied primarily to the quality of the data (i.e. GHI) available and the commercial risks dictated by the contractual arrangements governing the sale of solar electricity [11]. Therefore, the choice of an SRDB is expected to have a considerable impact on the predicted performance of an SPP and consequently on its techno-commercial feasibility [12].…”

Section: Introductionmentioning

confidence: 99%

Performance assessment of grid-interactive solar photovoltaic projects under India’s national solar mission

Purohit¹,

Purohit

2018

Applied Energy

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Performance evaluation of solar photovoltaic projects under India's Solar Mission • Inter-comparability of solar radiation databases for solar photovoltaic projects • Mean percentage error analysis for capacity factor and levelized cost of electricity • Energy yield assessment of photovoltaic projects using solar radiation databases • Long-term measured or high-resolution time series databases should be preferred

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“…Information on the characteristics of the variation in power generation by PV systems is useful for the operation of power grid systems, assessment of renewable resources, decision-making for energy policy, and investment in solar power projects. Information on interannual variability and trend is essential for project financing, as it helps evaluate the resource risk posed by solar energy (McMahan et al 2013). Surface solar energy data over several decades are required to understand the characteristics of PV power supply variation (McMahan et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Information on interannual variability and trend is essential for project financing, as it helps evaluate the resource risk posed by solar energy (McMahan et al 2013). Surface solar energy data over several decades are required to understand the characteristics of PV power supply variation (McMahan et al 2013). When the trend is significant in the analysis period, the climatology of solar energy changes depending on the choice of the analysis period for the assessment, affecting the decision-making process for new solar power projects.…”

Section: Introductionmentioning

confidence: 99%

Quantification of the interannual variability of the nationwide electric power supply from photovoltaic systems in Japan

Watanabe,

Oka,

Hijioka

2023

Environ. Res. Commun.

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Information on the variation in photovoltaic (PV) power generation is essential for resource assessment. This work investigated the interannual variability of the nationwide electric power supply from PV systems in Japan. Objectives of this study were twofold: One was the quantification of the annual variability of the nationwide PV power supply. The other was identifying the causes of the variability. However, the time span of available observation data on the PV power supply is inadequate to evaluate its variability, as PV systems have been rapidly installed in recent years. We used simulation to bypass this limitation. Due to the lack of available information for modeling, a hybrid modeling approach, combining a parametric model, and estimating parameters by fitting the model to observations, was employed. Nationwide PV power supply simulations were performed using historical weather data for 30 years, from 1991–2020. The long-term simulation data enabled us to quantify the interannual variability of the nationwide PV power generation. The annual variability measured with the range from the minimum to the maximum was approximately 9% of the mean. The variability for each month was less than 30% of the monthly mean for every month except for July when it was approximately 40%. An increasing trend in the annual mean PV power supply was observed over the 30 years, with an increase of 0.16% per year of the mean over the whole period. We found that the variations in sea surface temperature (SST) in the Tropics are factors contributing to the variability of nationwide PV power supply. Specifically, the variation in SST in the tropical Indian Ocean is one of the possible driving factors of the annual variability. The framework proposed in this study can provide valuable information for assessing solar energy resources on an interannual scale.

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Evaluation of Resource Risk in Solar-Project Financing

Cited by 11 publications

References 1 publication

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

Performance assessment of grid-interactive solar photovoltaic projects under India’s national solar mission

Quantification of the interannual variability of the nationwide electric power supply from photovoltaic systems in Japan

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