Metaheuristic optimization algorithms for multi-area economic dispatch of power systems: part II-a comparative study

Multi-Area Economic Dispatch (MAED) plays an important role in the operation and planning of power systems. In Part I of this series, we have summarized various optimization techniques to the MAED problem comprehensively, showing clearly that metaheuristic optimization algorithms (MOAs) have become the dominant approach for solving this problem due to their ease of application and powerful search capability. Although many different types of MOAs have been proposed, there is no study on the comprehensive evaluation, comparison and recommendation of different MOAs for the MAED problem. In this part, we selected 32 algorithms including differential evolution, particle swarm optimization, teaching-learning based algorithm, JAYA algorithm, and their advanced variants to evaluate and compare their performance on the eleven reported MAED cases summarized in Part I of this series. The comparative study was comprehensively conducted based on various performance criteria including solution quality, convergence, robustness, computational efficiency, and statistical analysis. The comparisons reveal that the DE series is the most competitive overall. Nevertheless, there is no single algorithm that ranks in the top three on all cases. This study can provide a practical reference and applicability recommendation for the selection of MOAs for solving the MAED problem.