A Sustainable Business Framework Using Solar and Bio-Energy to Instate Incessant Power in Rural India: Optimal Scheduling, Smart Metering, and Economic Viability

Kalkal, Pratik; Teja, A. V. Ravi

doi:10.1109/access.2022.3145710

Cited by 10 publications

(4 citation statements)

References 68 publications

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“…The technical and financial analysis carried out for two villages and 768 households showed that the system provides 24/7 power and is also cost-effective due to the relatively short payback period (less than 8 years). In addition, the implementation of the strategy provided benefits such as the reduction of greenhouse gas emissions (by 93%), the automatic control of electricity theft, biofertilizers and the improvement of the natural environment [43]. For Indonesia in the Merauke area, Regency assessed the use of renewable energy potential from biomass, solar and wind power to reduce dependence on fossil energy and offset the increase in electricity consumption at a time of economic and population growth.…”

Section: Review Of the Literaturementioning

confidence: 99%

The Economic Profitability of Photovoltaic Installations in Households in Poland from a New Policy Perspective

Augustowski,

Kułyk

2023

Energies

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Solar energy is a source of energy that can meet a significant part of the demand for heat and electricity. However, its acquisition is associated with high variability both during the year and on individual days. The construction of installations due to changes in tariff systems, investment costs and subsidy programs raises doubts as to their profitability. The aim of this study was to assess the profitability of photovoltaic investments for households, considering public financial support. This study used RetScreen Expert software 8.1.2.13 to perform the entire analysis and performance cost, risk and sensitivity simulations. Climate data were imported from ground monitoring stations or from NASA global satellite or analytical data. The conducted research showed the profitability of investments in photovoltaics, even without state support. Differences between regions were insignificant. However, the support system has changed, extending the period of return on investment.

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Section: Review Of the Literaturementioning

confidence: 99%

The Economic Profitability of Photovoltaic Installations in Households in Poland from a New Policy Perspective

Augustowski,

Kułyk

2023

Energies

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“…Solar energy studies have been conducted using software such as PVsyst, PV-Online, System Avisor Model-SAM, Solar-GIS, and RETScreen [21][22][23]. In addition, there are PVGIS and PVWatts software programs that have not been researched much in the literature.…”

Section: Introductionmentioning

confidence: 99%

Technoeconomic Analysis of 1 MWp Grid Connected Solar Power Plant in Konya (Türkiye)

2023

Teh. vjesn.

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Global warming is seen as one of the most important problems that trigger climate change in the world. The leading cause of global warming is the high amount of greenhouse gases released into the atmosphere. Countries are creating policies to encourage the use of renewable energy sources to control greenhouse gas emissions. Many universities in Türkiye have enough campus areas to generate electricity from renewable energy sources. The main purpose of this article is to analyze the feasibility of developing a solar power plant at Necmettin Erbakan University. This article proposes a 1MW solar power plant in Necmettin Erbakan University Köyceğiz Campus located in the city of Konya, which has the largest surface area in Türkiye. The technical feasibility of the proposed PV plant was analyzed using PVGIS, PVWatts, and PVsyst software. For this purpose, the economic and environmental aspects of the power plant to be established on campus were investigated through the production data. 1 MWp solar power plant can generate approximately 1685.78 MWh of electricity per year while reducing 820.21 tCO2 greenhouse gas emissions. The technical data obtained in the analyses made with PVGIS, PVWatts, and PVsyst software are similar.

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“…The daily generating energy of a photovoltaic power station affects the power consumption of the local area [1][2][3], while the photovoltaic power generation has a relationship with environmental factors, such as sunshine duration, temperature, etc. Thus, the prediction of the generating energy helps the local power grid system to improve foreseeability and to create a proper generating schedule [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Improved Generating Energy Prediction Method Based on Bi-LSTM and Attention Mechanism

Zhang

et al. 2022

Electronics

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The energy generated by a photovoltaic power station is affected by environmental factors, and the prediction of the generating energy would be helpful for power grid scheduling. Recently, many power generation prediction models (PGPM) based on machine learning have been proposed, but few existing methods use the attention mechanism to improve the prediction accuracy of generating energy. In the paper, a PGPM based on the Bi-LSTM model and attention mechanism was proposed. Firstly, the environmental factors with respect to the generating energy were selected through the Pearson coefficient, and then the principle and implementation of the proposed PGPM were detailed. Finally, the performance of the proposed PGPM was evaluated through an actual data set collected from a photovoltaic power station in Suzhou, China. The experimental results showed that the prediction error of proposed PGPM was only 8.6 kWh, and the fitting accuracy was more than 0.99, which is better than existing methods.

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A Sustainable Business Framework Using Solar and Bio-Energy to Instate Incessant Power in Rural India: Optimal Scheduling, Smart Metering, and Economic Viability

Cited by 10 publications

References 68 publications

The Economic Profitability of Photovoltaic Installations in Households in Poland from a New Policy Perspective

The Economic Profitability of Photovoltaic Installations in Households in Poland from a New Policy Perspective

Technoeconomic Analysis of 1 MWp Grid Connected Solar Power Plant in Konya (Türkiye)

An Improved Generating Energy Prediction Method Based on Bi-LSTM and Attention Mechanism

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