Integrated transmission expansion planning incorporating fault current limiting devices and thyristor-controlled series compensation using meta-heuristic optimization techniques.

Autor: Almalaq A; Department of Electrical Engineering, College of Engineering, University of Hail, 55473, Hail, Saudi Arabia., Alqunun K; Department of Electrical Engineering, College of Engineering, University of Hail, 55473, Hail, Saudi Arabia., Abbassi R; Department of Electrical Engineering, College of Engineering, University of Hail, 55473, Hail, Saudi Arabia., Ali ZM; Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, 11991, Wadi Addawaser, Saudi Arabia. dr.ziad.elhalwany@aswu.edu.eg.; Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Aswân, 81542, Egypt. dr.ziad.elhalwany@aswu.edu.eg., Refaat MM; Photovoltaic Cells Department, Electronics Research Institute, Cairo, 11843, Egypt., Abdel Aleem SHE; Department of Electrical Engineering, Institute of Aviation Engineering and Technology, Giza, 12658, Egypt.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jun 06; Vol. 14 (1), pp. 13046. Date of Electronic Publication: 2024 Jun 06.
DOI: 10.1038/s41598-024-63331-1
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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