Almalaq Abdulaziz, Alqunun Khalid, Abbassi Rabeh, Ali Ziad M, Refaat Mohamed M, Abdel Aleem Shady H E
Department of Electrical Engineering, College of Engineering, University of Hail, 55473, Hail, Saudi Arabia.
Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, 11991, Wadi Addawaser, Saudi Arabia.
Sci Rep. 2024 Jun 6;14(1):13046. doi: 10.1038/s41598-024-63331-1.
Transmission expansion planning (TEP) is a vital process of ensuring power systems' reliable and efficient operation. The optimization of TEP is a complex challenge, necessitating the application of mathematical programming techniques and meta-heuristics. However, selecting the right optimization algorithm is crucial, as each algorithm has its strengths and limitations. Therefore, testing new optimization algorithms is essential to enhance the toolbox of methods. This paper presents a comprehensive study on the application of ten recent meta-heuristic algorithms for solving the TEP problem across three distinct power networks varying in scale. The ten meta-heuristic algorithms considered in this study include Sinh Cosh Optimizer, Walrus Optimizer, Snow Geese Algorithm, Triangulation Topology Aggregation Optimizer, Electric Eel Foraging Optimization, Kepler Optimization Algorithm (KOA), Dung Beetle Optimizer, Sea-Horse Optimizer, Special Relativity Search, and White Shark Optimizer (WSO). Three TEP models incorporating fault current limiters and thyristor-controlled series compensation devices are utilized to evaluate the performance of the meta-heuristic algorithms, each representing a different scale and complexity level. Factors such as convergence speed, solution quality, and scalability are considered in evaluating the algorithms' performance. The results demonstrated that KOA achieved the best performance across all tested systems in terms of solution quality. KOA's average value was 6.8% lower than the second-best algorithm in some case studies. Additionally, the results indicated that WSO required approximately 2-3 times less time than the other algorithms. However, despite WSO's rapid convergence, its average solution value was comparatively higher than that of some other algorithms. In TEP, prioritizing solution quality is paramount over algorithm speed.
输电扩展规划（TEP）是确保电力系统可靠高效运行的关键过程。TEP的优化是一项复杂的挑战，需要应用数学规划技术和元启发式算法。然而，选择合适的优化算法至关重要，因为每种算法都有其优势和局限性。因此，测试新的优化算法对于扩充方法工具箱至关重要。本文对十种近期的元启发式算法在三个不同规模的电力网络中求解TEP问题的应用进行了全面研究。本研究中考虑的十种元启发式算法包括双曲正弦余弦优化器、海象优化器、雪雁算法、三角拓扑聚合优化器、电鳗觅食优化算法、开普勒优化算法（KOA）、蜣螂优化器、海马优化器、狭义相对论搜索算法和白鲨优化器（WSO）。利用三种包含故障电流限制器和晶闸管控制串联补偿装置的TEP模型来评估元启发式算法的性能，每个模型代表不同的规模和复杂度水平。在评估算法性能时考虑了收敛速度、解的质量和可扩展性等因素。结果表明，在解的质量方面，KOA在所有测试系统中表现最佳。在一些案例研究中，KOA的平均值比第二优算法低6.8%。此外，结果表明WSO所需时间比其他算法少大约2至3倍。然而，尽管WSO收敛速度快，但其平均解值相对高于其他一些算法。在TEP中，解的质量比算法速度更为重要。
