Fractional Memory-Driven Marine Predator Optimization for Nonconvex Economic Load Dispatch in Renewable-Integrated Power Systems

The increasing integration of renewable energy sources (RESs) and the nonlinear nature of economic load dispatch (ELD) have significantly intensified the complexity of modern power systems, demanding efficient optimization techniques. This study proposes a Fractional Marine Predators Algorithm (FMPA) to address the ELD problem under uncertain and stochastic operating conditions. The proposed approach enhances the conventional MPA by incorporating fractional calculus (FC), which introduces memory-dependent search dynamics and improves the balance between exploration and exploitation. This modification enables the algorithm to utilize historical search information, resulting in smoother convergence behavior and reduced susceptibility to premature convergence. The performance of the proposed method is assessed using multiple benchmark systems, including 3-unit, 13-unit, and 40-unit configurations with integrated wind power. The optimization framework considers realistic system constraints such as generator limits, valve-point loading effects, and stochastic wind power modeled using probabilistic functions. Simulation results demonstrate that FMPA provides competitive and near-optimal generation costs with stable convergence behavior compared to several existing metaheuristic techniques. Statistical analysis over multiple independent runs confirms the robustness, stability, and reliability of the proposed method. The results indicate that FMPA provides a scalable and efficient solution for solving complex, nonlinear, and constrained ELD problems in modern power systems.