Harvesting Solar Power in India

Gulati, Ashok; Manchanda, Stuti; Kacker, Rakesh

doi:10.2139/ssrn.2851743

Cited by 25 publications

(11 citation statements)

References 51 publications

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“…The current solar generation capacity in India as of October 2019 is 29.42 GWp from solar power that is ground mounted, 2.28 GWp from solar rooftop grid-interactive system, and 936.47 MW EQ from off-grid solar photovoltaic (SPV) systems [10]. In order to achieve this ambitious target of installing 100 GW of solar power capacity by 2022, an addition of 15 GW per year is needed [11]. Cumulative solar rooftop installation is 4.37 GW as of March 2019, which has to increase 10-fold to achieve the target of 40 GW by 2022 [12].…”

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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“…AgroCenta assumed that "it is easier to provide services through cooperatives as these serve as a means of accountability in itself". 152 Furthermore, the company assumed that cooperatives had better access to government subsidized fertilizers. Following this approach AgroCenta recruited agents with good contacts to respective community leaders.…”

Section: Solving the Chicken-and-egg Problemmentioning

confidence: 99%

“…Obirikorang, entry in LinkedIn: https://www.linkedin.com/in/swapchief/detail/recent-activity151 According to Michael K. Ocansey, CTO, in an interview https://www.youtube.com/watch?v=pb3p-68OCe4152 Response to questionnaire by Michael K. Ocansey, CTO, received 18 th January 2021153 Response to questionnaire by Michael K. Ocansey, CTO, received 18 th January 2021154 Quoted from a telephone interview with Michael K. Ocansey, 8 th December 2020…”

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confidence: 99%

Understanding Strategic Decisions of Digital Agricultural Platform Companies: Six Case Studies of Sub-Saharan African Platforms

Bismarck-Osten¹

2021

SSRN Journal

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“…The shade of panels can also reduce irrigation requirements [30]. The electricity thus generated can be used to meet the farm needs and the surplus power can be sold.…”

Section: Solar Farmingmentioning

confidence: 99%

Solar Energy Applications in Agriculture with Special Reference to North Western Himalayan Region

Mir

Bahar

Mehdi

et al. 2021

CJAST

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The UN Sustainability Goals emphasise on use of renewable sources of energy viz wind, solar, hydro power, biomass etc which are increasingly becoming important in the global energy mix. India with a 900 GW potential, aims to have 175 GW by 2022 and about 40% of total power production from renewable sources by 2030 with solar source contributing the most (83 %). Solar energy is the most fundamental renewable energy resource with many agricultural applications. The abundance of solar energy makes it suitable for electricity and thermal applications and hence can be used in agriculture in photovoltaic electricity generation, powering irrigation, crop and grain drying, pesticide application, green house heating and ventilation, cold storages etc. North western Himalayan regions are energy-poor with high energy requirements. Low ambient temperature, high Global Horizontal Irradiance (GHI) and Direct Net Irradiance (DNI) of 4.8-6.43 kWh per square metre per day indicate huge solar potential, higher solar photovoltaic electricity and solar thermal production efficiency. Solar energy can replace or supplement conventional sources used for domestic and agricultural applications in the region. However, the use of solar energy is limited by policy and regulatory obstacles, financial obstacles, land availability constraints and low PV conversion efficiency. Hence a robust policy, financial measures and technological refinement are needed to remove the bottlenecks. In this paper, attempts have been made to discuss solar energy use in agriculture, scope in the north western Himalayan region of India and future recommended strategies.

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Harvesting Solar Power in India

Cited by 25 publications

References 51 publications

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

Understanding Strategic Decisions of Digital Agricultural Platform Companies: Six Case Studies of Sub-Saharan African Platforms

Solar Energy Applications in Agriculture with Special Reference to North Western Himalayan Region

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