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<p>Energy plays a pivotal role in addressing the climate crisis, with fossil fuel combustion being a significant source of greenhouse gas emissions. To combat this, industrial nations are urged to transition to renewable energy sources like solar photovoltaic systems. However, optimizing photovoltaic systems requires consideration of technical, economic, and environmental factors. In this study, we devised a two-phase stochastic programming method for optimizing a photovoltaic system connected to a power grid. Our focus is on incorporating economic and environmental uncertainties into risk scenarios. The aim is to ascertain the optimal number of photovoltaic array installations, accounting for importing energy from the grid and exporting surplus energy to external utilities. Environmental factors and market change parameters are dynamically determined during decision-making, while model parameters are deterministic. The initial optimization stage determines the necessary number of photovoltaic modules. Given the scenario-driven nature of photovoltaic module selection, energy allocation to clients, external utilities, and grid interactions vary accordingly. Our proposed approach employs risk metrics to address environmental, economic, and reliability considerations in decision-making. A case study for sizing a photovoltaic system to power a housing area at King Fahd University of Petroleum and Minerals demonstrates the effectiveness of our approach. Sensitivity analysis highlights key trade-offs, showing the proposed system can break even at $1.79 million per year, generate 388.53 megawatt-hours of electricity annually, and reduce carbon dioxide emissions by 50.52&#xa0;kg per year. Our study underscores the importance of integrating renewable energy solutions into the global energy mix to mitigate climate change impacts.</p>

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Optimal Risk Management in Grid-Connected Photovoltaic System Design Under Environmental and Economic Changes: A Case Study in Saudi Arabia’s Climate

  • 期刊论文

  • 发表日期：

    2025年2月10日

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  • Ahmed M. Attia,
  • Mohamed N. Darghouth,
  • Awsan Mohammed,
  • Mohammed A. M. Abdel-Aal

摘要

Energy plays a pivotal role in addressing the climate crisis, with fossil fuel combustion being a significant source of greenhouse gas emissions. To combat this, industrial nations are urged to transition to renewable energy sources like solar photovoltaic systems. However, optimizing photovoltaic systems requires consideration of technical, economic, and environmental factors. In this study, we devised a two-phase stochastic programming method for optimizing a photovoltaic system connected to a power grid. Our focus is on incorporating economic and environmental uncertainties into risk scenarios. The aim is to ascertain the optimal number of photovoltaic array installations, accounting for importing energy from the grid and exporting surplus energy to external utilities. Environmental factors and market change parameters are dynamically determined during decision-making, while model parameters are deterministic. The initial optimization stage determines the necessary number of photovoltaic modules. Given the scenario-driven nature of photovoltaic module selection, energy allocation to clients, external utilities, and grid interactions vary accordingly. Our proposed approach employs risk metrics to address environmental, economic, and reliability considerations in decision-making. A case study for sizing a photovoltaic system to power a housing area at King Fahd University of Petroleum and Minerals demonstrates the effectiveness of our approach. Sensitivity analysis highlights key trade-offs, showing the proposed system can break even at $1.79 million per year, generate 388.53 megawatt-hours of electricity annually, and reduce carbon dioxide emissions by 50.52 kg per year. Our study underscores the importance of integrating renewable energy solutions into the global energy mix to mitigate climate change impacts.