A chaotic chimp sine cosine algorithm for optimizing hydrothermal power scheduling

This study addresses the critical issue of optimizing the short-term hydrothermal scheduling (STHTS) problem, which is a significant concern in the context of rising energy demands. The need to reduce fuel costs while meeting operational constraints makes the STHTS a pressing problem in today's power generation context. This study provides a comprehensive analysis of various soft computing techniques employed to solve the STHTS problem, characterized by its nonlinear nature, encompassing both linear and nonlinear constraints, and a nonlinear objective function. The importance of this study lies in its relevance to the global push towards more efficient and sustainable energy solutions. This study introduces a novel approach using the chaotic-chimp sine cosine (C-CHOA-SC) algorithm, which stands out because of its ability to handle the complexities of the STHTS problem efficiently, including avoiding local optima and managing constraints. This is because of its innovative repair process for candidate solutions, which addresses the limitations of traditional penalty function approaches. The comparative analysis is conducted on multiple hydrothermal scheduling test systems including genetic algorithms, particle swarm optimization, gravitational search algorithm, biogeography-based optimization, krill herd algorithm, bacterial foraging optimization, ant colony optimization, chaotic ant swarm optimization, cuckoo search optimization, co-evolutionary differential evolution, salp swarm algorithm, lightning attachment procedure optimization, social group optimization, multi-objective salp swarm algorithm, grey wolf optimizer, and enhanced harris hawks optimization. The results demonstrated that the C-CHOA-SC algorithm effectively solved the STHTS problem by minimizing fuel costs and emissions. It outperformed 17 other metaheuristic techniques across the three test systems and demonstrated superior performance in terms of cost reduction, constraint satisfaction, and convergence speed. Notably, it achieved optimal costs of $922,324.08, $41,701.57, and $163,391.60 per day for the three test systems, respectively. Statistical analysis of 30 individual runs validated the robustness of the algorithm in terms of the best, worst, and average values of the objective function, and box plots proved its consistency, positioning it as a valuable tool for optimizing power generation in modern power systems.