👉👉👉👉Adaptive Multi-Objective Energy Management System Using Polar Fox Optimization for Hybrid Renewable Energy Systems
🔴Abstract- The increasing penetration of renewable energy resources has introduced significant operational challenges to hybrid energy systems due to the intermittent nature of photovoltaic and wind generation. Conventional energy management systems (EMSs) are often limited by their inability to simultaneously optimize multiple conflicting objectives under highly dynamic operating conditions. This study proposes a novel adaptive multi-objective EMS based on the Polar Fox Optimization (PFO) algorithm for grid-connected hybrid renewable energy systems incorporating photovoltaic arrays, wind turbines, battery energy storage systems, and utility grids. The proposed framework dynamically adjusts its optimization strategy according to variations in renewable generation, electricity prices, battery state-of-charge, and load demand. Unlike conventional optimization methods, the proposed EMS simultaneously minimizes operational cost, carbon emissions, battery degradation, and grid power fluctuations while maintaining system reliability. Comprehensive simulations will be conducted under various weather conditions and load profiles to evaluate system performance. The proposed approach will be benchmarked against several state-of-the-art optimization algorithms, including PSO, GWO, DBO, and MPA. The anticipated results are expected to demonstrate significant improvements in economic performance, renewable energy utilization, operational stability, and computational efficiency, making the proposed EMS a promising solution for future smart energy systems.
