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<p>The integration of hybrid renewable energy sources (HRES) like PV panels, wind turbines (WT), fuel cells (FC), microturbines (MT), diesel generators (DG), and battery energy storage systems (ESS) in microgrids provides a sustainable solution where traditional grid expansion is unfeasible. While existing studies on optimal energy dispatch focus on single-objective optimization or simpler algorithms, this research proposes a comprehensive strategy for both grid-connected and standalone microgrids using a novel multi-objective optimization framework. A mixed-integer linear programming (MILP) model is formulated to minimize operating costs, power losses, and greenhouse gas emissions, incorporating demand response (DR) programs. Unlike conventional methods, the proposed approach utilizes a hybrid optimization algorithm combining Artificial Rabbits Optimization (ARO) and African Vultures Optimization Algorithm (AVOA), providing optimal trade-offs among cost, emissions, and power losses. An optimized fuzzy interface is also developed for precise scheduling of ESS. The model achieves superior accuracy with an MAE of 0.076, RMSE of 0.114, and MSE of 0.013, outperforming existing methods. An Index of Agreement (IOA) of 0.424 further confirms its effectiveness. Simulations optimize capacities for various energy sources, ESS scheduling, and power exchange, minimizing costs and emissions. Compared to current literature, this work advances multi-objective energy management in microgrids by effectively integrating DR programs and hybrid renewable energy systems, offering a robust and sustainable energy management strategy.</p>

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Multi-Objective Energy Management in Microgrids with Hybrid Renewable Energy Sources and Battery Energy Storage Systems Using Hybrid Optimization Algorithm

  • 期刊论文

  • 发表日期：

    2025年10月4日

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  • M. Sasikumar,
  • S Seenivasan,
  • P. Vijayakumar,
  • S. Manikandan

摘要

The integration of hybrid renewable energy sources (HRES) like PV panels, wind turbines (WT), fuel cells (FC), microturbines (MT), diesel generators (DG), and battery energy storage systems (ESS) in microgrids provides a sustainable solution where traditional grid expansion is unfeasible. While existing studies on optimal energy dispatch focus on single-objective optimization or simpler algorithms, this research proposes a comprehensive strategy for both grid-connected and standalone microgrids using a novel multi-objective optimization framework. A mixed-integer linear programming (MILP) model is formulated to minimize operating costs, power losses, and greenhouse gas emissions, incorporating demand response (DR) programs. Unlike conventional methods, the proposed approach utilizes a hybrid optimization algorithm combining Artificial Rabbits Optimization (ARO) and African Vultures Optimization Algorithm (AVOA), providing optimal trade-offs among cost, emissions, and power losses. An optimized fuzzy interface is also developed for precise scheduling of ESS. The model achieves superior accuracy with an MAE of 0.076, RMSE of 0.114, and MSE of 0.013, outperforming existing methods. An Index of Agreement (IOA) of 0.424 further confirms its effectiveness. Simulations optimize capacities for various energy sources, ESS scheduling, and power exchange, minimizing costs and emissions. Compared to current literature, this work advances multi-objective energy management in microgrids by effectively integrating DR programs and hybrid renewable energy systems, offering a robust and sustainable energy management strategy.