Eid Ahmad, Alsafrani Abdulrahman
Department of Electrical Engineering, College of Engineering, Qassim University, Buraidah, 52571, Saudi Arabia.
Sci Rep. 2025 Oct 17;15(1):36438. doi: 10.1038/s41598-025-20339-5.
Numerous optimization techniques have recently been employed in the literature to enhance various electric power systems. Optimization algorithms help system operators determine the optimal location and capacity of any renewable energy source (RES) connected to a system, enabling them to achieve a specific goal and improve its performance. This study presents a novel statistical evaluation of 20 famous metaheuristic optimization techniques based on 10 performance measures. The performance measures comprise five power loss indices, three voltage profile indices, load flow calling frequency, and execution time. The evaluation involves 10 distribution systems of varying sizes to ensure an equitable comparison of the algorithm. The Friedman Ranking method evaluates algorithms based on performance metrics, yielding a specific score. Upon modeling all distribution systems, a composite ranking methodology is employed to categorize the algorithms into only four categories: excellent, very good, good, and fair. The study finalizes the ranking of all algorithms according to their overall assessment. The AEO, GWO, JS, PSO, MVO, BO, and GNDO algorithms attain ranks below 25%, thereby placing them in the highest category. The ALO, DA, FPA, SSA, YAYA, and SPO algorithms fall into the second category, with rankings ranging from 25 to 50%. The SMA and CGO algorithms are classified in the third group, with rankings between 50 and 75%. The analysis ultimately reveals that the algorithms CStA, HHO, AOA, GOA, and AOS are positioned in the lowest group, each achieving rankings beyond 75%. As comparison case studies, the proposed algorithms achieved a power loss of 87.164 kW for the 33-bus system, which is less than or equal to the published work. The same result is achieved with the 69-bus system, which has a power loss of 71.644 kW for most of the studied algorithms. Using the appropriate algorithms with distribution systems saves time and effort for the system operator, enhances performance, and increases the usability of optimization algorithms.
近年来，文献中采用了众多优化技术来改进各种电力系统。优化算法可帮助系统运营商确定连接到系统的任何可再生能源（RES）的最佳位置和容量，使他们能够实现特定目标并改善系统性能。本研究基于10个性能指标，对20种著名的元启发式优化技术进行了新颖的统计评估。这些性能指标包括五个功率损耗指标、三个电压分布指标、潮流调用频率和执行时间。评估涉及10个不同规模的配电系统，以确保对算法进行公平比较。弗里德曼排名方法根据性能指标对算法进行评估，得出特定分数。在对所有配电系统进行建模后，采用综合排名方法将算法仅分为四类：优秀、非常好、良好和一般。该研究根据总体评估确定了所有算法的排名。AEO、GWO、JS、PSO、MVO、BO和GNDO算法的排名低于25%，因此属于最高类别。ALO、DA、FPA、SSA、YAYA和SPO算法属于第二类，排名在25%至50%之间。SMA和CGO算法被归为第三组，排名在50%至75%之间。分析最终表明，CStA、HHO、AOA、GOA和AOS算法处于最低组，每个算法的排名都超过75%。作为比较案例研究，对于33节点系统，所提出的算法实现了87.164千瓦的功率损耗，小于或等于已发表的研究成果。对于69节点系统，大多数研究算法也得到了相同的结果，其功率损耗为71.644千瓦。在配电系统中使用合适的算法可为系统运营商节省时间和精力，提高性能，并增加优化算法的可用性。
