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<p>The purpose of this study is to develop a novel hybrid microgrid energy management strategy using the dual predator optimization (DPO) algorithm, which integrates gray wolf optimization (GWO) and whale optimization algorithm (WOA) based on predator-prey dynamics. The objective is to enhance the efficiency of resource allocation in hybrid microgrids consisting of solar PV, wind, and battery storage systems. The methodology involves testing the DPO algorithm on hybrid microgrids in Ottawa (Canada) and Rangpur (Bangladesh) to assess its performance under diverse environmental conditions. The results of this study demonstrate that DPO achieves a multi-criteria function (MCF) value of 0.03825 in Rangpur, surpassing GWO, WOA, and particle swarm optimization (PSO). The algorithm also reduces the levelized cost of energy (LCOE) to $0.0350/kWh in Rangpur and $0.0356/kWh in Ottawa while maintaining low loss of power supply probability (LPSP) values of 1.20% and 2.40%, respectively. Furthermore, the study confirms that DPO enables a 100% renewable fraction (RF), eliminating carbon emissions and energy waste. The significance of this study lies in its ability to establish DPO as a benchmark optimization algorithm for hybrid microgrid management, demonstrating superior efficiency and robustness compared to existing methods.</p>

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An Innovative Method for Enhanced Energy Management in Hybrid Power Systems: Dual Predator Optimization (DPO)

  • 期刊论文

  • 发表日期：

    2025年5月21日

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  • Md. Nimul Hasan,
  • Md. Fatin Ishraque,
  • Md. Shatez Ahmed,
  • Farhan Tanvir Imam,
  • Sk. A. Shezan,
  • Naveed Ahmad

摘要

The purpose of this study is to develop a novel hybrid microgrid energy management strategy using the dual predator optimization (DPO) algorithm, which integrates gray wolf optimization (GWO) and whale optimization algorithm (WOA) based on predator-prey dynamics. The objective is to enhance the efficiency of resource allocation in hybrid microgrids consisting of solar PV, wind, and battery storage systems. The methodology involves testing the DPO algorithm on hybrid microgrids in Ottawa (Canada) and Rangpur (Bangladesh) to assess its performance under diverse environmental conditions. The results of this study demonstrate that DPO achieves a multi-criteria function (MCF) value of 0.03825 in Rangpur, surpassing GWO, WOA, and particle swarm optimization (PSO). The algorithm also reduces the levelized cost of energy (LCOE) to $0.0350/kWh in Rangpur and $0.0356/kWh in Ottawa while maintaining low loss of power supply probability (LPSP) values of 1.20% and 2.40%, respectively. Furthermore, the study confirms that DPO enables a 100% renewable fraction (RF), eliminating carbon emissions and energy waste. The significance of this study lies in its ability to establish DPO as a benchmark optimization algorithm for hybrid microgrid management, demonstrating superior efficiency and robustness compared to existing methods.