Novel twin fang algorithm for advanced optimization of energy coordination in hybrid power systems

In this study, a hybrid microgrid approach to energy management is demonstrated using the newly introduced Twin Fang Optimization (TFO) algorithm, which imitates the key characteristics of natural predator–prey dynamics by integrating the Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). This novel metaheuristic methodology was specifically developed to overcome the limitations of conventional algorithms, aiming for more efficient resource distribution among solar PV, wind, and battery storage systems. Within this work, the proposed TFO algorithm was applied to optimize hybrid microgrids in two geographically distinct sites in Bangladesh and Canada having two unique climatic and operational conditions to test the algorithm’s versatility. The results show that TFO significantly improves system performance across multiple evaluation metrics. It achieved Multi-Criteria Function values of 0. 03825 in Bangladesh and 0. 03725 in Canada, outperforming GWO, WOA, and PSO. Additionally, the energy levelized costs were reduced to 0. 0354/kWh in Bangladesh and 0. 0361/kWh in Canada. In both locations, the system maintained the full Sustainable Energy Score (SES), ensuring zero carbon emission and energy loss. Furthermore, the Power Supply Reliability Index (PSRI) was minimized to 1. 25% in Bangladesh and 2. 45% in Canada, indicating a high system reliability. The results demonstrate that TFO significantly outperforms both GWO and WOA in three out of four test cases, with p-values consistently below the 0. 05 threshold, confirming the robustness and effectiveness of TFO. These findings suggest that TFO is a promising approach for optimizing energy systems in real-world hybrid microgrid applications. A comparative performance analysis underscores the robustness, faster convergence, and stability of the TFO algorithm against other well-established methods. Overall, this research presents TFO as a promising tool for smart energy systems, setting a new benchmark for efficient and resilient hybrid microgrid management under diverse regional conditions. • The Twin Fang Optimization (TFO) algorithm integrates Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA) to address limitations of existing methods for hybrid microgrid energy management. • The TFO algorithm optimizes energy distribution among solar PV, wind, and batteries, tested in Ottawa (Canada) and Rangpur (Bangladesh) under varying environmental conditions. • TFO minimized the Multi-Criteria Function (MCF) to 0. 03825 in Rangpur and achieved levelized costs of energy (LCOE) of 0. 0350/kWh in Rangpur and 0. 0356/kWh in Ottawa. • The algorithm ensured 100% renewable energy use, zero carbon emissions, and reduced loss of power supply probability to 1. 20% in Rangpur and 2. 40% in Ottawa. • TFO outperformed GWO, WOA, and PSO in energy management, demonstrating its robustness and efficiency as a benchmark for hybrid microgrids.