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This research explores the most effective approach for planning power flow in the upcoming day, taking into account the expenses related to storing energy in batteries. Dealing with challenges associated with incorporating renewable energy and managing fluctuations in electricity demand, having dynamic and optimized power flow planning is essential to maintain a reliable and efficient electrical power system. The primary focus of this investigation revolves around integrating battery energy storage costs within an optimum power flow model. The study employs a dynamic method to simulate variations in renewable resources and electricity demand over 24 h. By factoring in fluctuating battery energy storage costs, this research devises an optimal model for determining the best allocation of power as well as scheduling recharging activities. This model considers network operational conditions, limitations on power supply, and operational expenditures to achieve an economically viable solution. According to findings from this study, integrating battery energy storage costs into next-day power flow planning can optimize the distribution of electric power while simultaneously reducing overall operating expenses within the system. The use of battery energy storage aids in addressing fluctuations in electricity demand as well as variability linked to renewable resources; thereby enhancing reliability and performance within systems for delivering electric grid services. Results obtained from simulations indicate that employing an optimal strategy when it comes to managing batteries can yield substantial benefits by cutting down operational costs while also boosting efficiency across electric grids. This study utilizes LSTM to forecast solar irradiation for the next day. The predictive model yielded a \({R}^{2}\) -value of 0.9988 and an RMSE of 2.0552. These predictions are subsequently employed in determining the Optimal Power Flow on the microgrid, resulting in a cost-effective supply of all dynamic loads for a full day at an optimal cost of $698,431.

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One Day Ahead Dynamic DC Optimal Power Flow in Microgrid Incorporating Battery Energy Storage

  • 会议论文

  • 发表日期：

    2025年

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  • Eki Rovianto,
  • Catur Harsito,
  • Ari Prasetyo

摘要

This research explores the most effective approach for planning power flow in the upcoming day, taking into account the expenses related to storing energy in batteries. Dealing with challenges associated with incorporating renewable energy and managing fluctuations in electricity demand, having dynamic and optimized power flow planning is essential to maintain a reliable and efficient electrical power system. The primary focus of this investigation revolves around integrating battery energy storage costs within an optimum power flow model. The study employs a dynamic method to simulate variations in renewable resources and electricity demand over 24 h. By factoring in fluctuating battery energy storage costs, this research devises an optimal model for determining the best allocation of power as well as scheduling recharging activities. This model considers network operational conditions, limitations on power supply, and operational expenditures to achieve an economically viable solution. According to findings from this study, integrating battery energy storage costs into next-day power flow planning can optimize the distribution of electric power while simultaneously reducing overall operating expenses within the system. The use of battery energy storage aids in addressing fluctuations in electricity demand as well as variability linked to renewable resources; thereby enhancing reliability and performance within systems for delivering electric grid services. Results obtained from simulations indicate that employing an optimal strategy when it comes to managing batteries can yield substantial benefits by cutting down operational costs while also boosting efficiency across electric grids. This study utilizes LSTM to forecast solar irradiation for the next day. The predictive model yielded a \({R}^{2}\) -value of 0.9988 and an RMSE of 2.0552. These predictions are subsequently employed in determining the Optimal Power Flow on the microgrid, resulting in a cost-effective supply of all dynamic loads for a full day at an optimal cost of $698,431.