Multiobjective Heuristic Optimization in Combined Heat and Power Dispatch Problem

Abstract

The electrical power system forms the backbone of national development, and with increasing electricity demand, ensuring optimal generation and dispatch has become more complex and critical. Traditional thermal generation contributes significantly to environmental pollution, prompting the need for optimization techniques that consider both economic and environmental objectives. Multi-objective optimization has emerged as a powerful tool to address these challenges by minimizing fuel cost and emissions simultaneously. In this context, Combined Heat and Power Economic Dispatch (CHPED), Economic Emission Dispatch (EED), and Dynamic Economic Dispatch (DED) problems have garnered significant research attention due to their nonlinear, nonconvex, and constrained nature. newlineThis study proposes advanced variants of the Slime Mould Algorithm (SMA) to address these complex dispatch problems. Five hybrid algorithms Oppositional SMA (OSMA), OSMA with Wavelet Mutation (OSMAW), Social Cognition OSMA (SCOSMA), Comprehensive Learning Wavelet-Mutated SMA (CLWSMA), and Blended SMA (BSMA) are developed and tested. These variants integrate wavelet mutation, social cognition, and comprehensive learning strategies to improve convergence, exploration, and constraint handling. The algorithms are validated on CEC benchmark functions and various power system test cases, including systems with prohibited operating zones, valve-point loading, and plug-in electric vehicle (PEV) charging profiles. newlineThe results show that the proposed methods offer improved performance in cost reduction, emission control, and operational feasibility. Statistical tests, such as the Wilcoxon signed-rank test, confirm their superiority over conventional methods, making them suitable for real-world power dispatch applications. newline newline