Marine Predators Algorithm for Optimizing GENCO Surplus in Power Markets: A Metaheuristic-Based Approach

Abstract Optimizing bidding strategies of generating companies (GENCOs) in competitive electricity markets is crucial for maximizing surplus while ensuring efficient market operation. This paper presents a novel metaheuristic framework based on the Marine Predators Algorithm (MPA), designed to address the complex optimization landscape of power bidding in deregulated markets. Inspired by the foraging behaviour of marine predators, MPA is employed to generate strategic bids that enhance GENCO profitability. The proposed method is benchmarked against state-of-the-art optimization techniques, including Genetic Algorithm (GA), Intelligent Programmed Genetic Algorithm (IPGA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Teaching–Learning-Based Optimization (TLBO), and Biogeography-Based Optimization (BBO). Simulation studies involving six GENCOs and two consumer agents confirm that MPA exhibits superior convergence speed and surplus maximization compared to traditional methods. These findings underscore the effectiveness of MPA in supporting decision-making under competitive energy trading scenarios.