Profit Maximization for Generating Companies Using Chaotic Whale Optimization with Inertia Weight

Optimization of power generation scheduling is critical for generating companies (GENCOs) operating in deregulated electricity markets, where maximizing profit under operational constraints is challenging. Traditional metaheuristic algorithms often suffer from premature convergence and poor exploration-exploitation balance. Here we propose an improved Whale Optimization Algorithm incorporating chaotic mapping and inertia weight (CWOA-IW) to enhance population diversity and convergence behaviour. The algorithm is applied to the profit maximization problem of GENCOs, considering market prices and system constraints. Validation on 23 benchmark functions and three power system test cases (3-unit 10-bus, IEEE 39-bus, IEEE 118-bus) demonstrates superior accuracy, stability, and faster convergence compared to standard WOA, Grey Wolf Optimizer, and Particle Swarm Optimization. The results indicate that CWOA-IW provides a robust and scalable optimization framework, offering practical benefits for GENCOs in competitive electricity markets.