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<p>Agriculture is a significant energy-intensive sector polluting the environment on using fossil fuels. Photovoltaic water pumping systems (PVWPS) provide a sustainable solution to reduce energy costs and greenhouse gas (GHG) emissions, especially in areas with abundant solar availability. Four PVWPS scenarios with different storage elements are presented, including water storage tanks, a battery bank, a mix of both, or a grid-connected PVWPS. A Multi-objective Genetic Algorithm is employed to simultaneously minimize the cost of energy (COE) and GHG emissions, while achieving a zero loss of power supply probability (LPSP), and validated through a case study in New Assiut, Egypt, representing an agricultural farm with energy demand for irrigation. The optimization results show that scenario 1, with water storage tanks, is the most environmentally friendly, reducing GHG emissions to 7,985 kg CO<sub>2</sub> (91.2% reduction compared to the existing scenario). In contrast, scenario 4, with grid connection, achieves the lowest COE of 0.036 $/kWh, making it the most economically viable. Scenarios 2 and 3 based on batteries or mixed storage provide intermediate outcomes. A sensitivity analysis further highlights the trade-off between system cost, reliability, and environmental impact under varying LPSP constraints. The study findings provide practical guidance for designing cost-effective and environmentally sustainable PVWPS, providing valuable insights into integrating renewable energy into irrigation practices in areas with high solar potential.</p>

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Optimization of solar PV water pumping system with different scenarios for storage elements: a case-study in Egypt

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Mohamed Osama,
  • Mazen Abdel-Salam,
  • Ahmed Elnozahy

摘要

Agriculture is a significant energy-intensive sector polluting the environment on using fossil fuels. Photovoltaic water pumping systems (PVWPS) provide a sustainable solution to reduce energy costs and greenhouse gas (GHG) emissions, especially in areas with abundant solar availability. Four PVWPS scenarios with different storage elements are presented, including water storage tanks, a battery bank, a mix of both, or a grid-connected PVWPS. A Multi-objective Genetic Algorithm is employed to simultaneously minimize the cost of energy (COE) and GHG emissions, while achieving a zero loss of power supply probability (LPSP), and validated through a case study in New Assiut, Egypt, representing an agricultural farm with energy demand for irrigation. The optimization results show that scenario 1, with water storage tanks, is the most environmentally friendly, reducing GHG emissions to 7,985 kg CO2 (91.2% reduction compared to the existing scenario). In contrast, scenario 4, with grid connection, achieves the lowest COE of 0.036 $/kWh, making it the most economically viable. Scenarios 2 and 3 based on batteries or mixed storage provide intermediate outcomes. A sensitivity analysis further highlights the trade-off between system cost, reliability, and environmental impact under varying LPSP constraints. The study findings provide practical guidance for designing cost-effective and environmentally sustainable PVWPS, providing valuable insights into integrating renewable energy into irrigation practices in areas with high solar potential.