A fast dimension reduction framework for large-scale topology optimization of grid-layout offshore wind farm collector systems. (July 2023)

• Record Type: Journal Article
• Authors: Wang, Bangyan; Wang, Xiuli; Qian, Tao; Ning, Lianhui; Lin, Jintian
• Is Part Of: International journal of electrical power & energy systems. Volume 149(2023)
• Publication Date: 2023-07
• Subjects: Offshore wind farms -- Collector system planning -- Greedy algorithms -- Heuristics -- Bi-level model; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2023.109066
• Languages: English
• ISSNs: 0142-0615

Abstract

Abstract: The construction of large-scale offshore wind farms faces the difficulty of solving the optimization problem of collector systems. Due to its NP-hard feature, this problem cannot be solved straightforwardly. This paper proposes an efficient and precise solution framework suitable for large-scale collector system optimization and raises the main influencing factors of time complexity. First, according to a heuristic idea, an arc selection algorithm is established to tighten the feasible region. Second, an optimization framework based on the greedy algorithm and mixed integer quadratic programming (MIQP) is established with excellent efficiency and accuracy. The overall problem is decomposed into the master problem of topology optimization and the sub-problem of cable selection. A bi-level model that adopts both greedy algorithm and global optimization is proposed, iterating through the master problem and the sub-problem by transferring parameters and updating constraints. Third, a large number of examples are analyzed, and the advantages of the proposed algorithms and model are comprehensively verified. In the end, time complexity analysis is carried out, and the empirical formula of solution time is obtained by fitting. Based on the proposed method, large-scale problems can be solved much faster than traditional methods and reach a balance of both speed and quality. Highlights: A heuristic arc selection technique is proposed to reduce the candidate arcs. The greedy algorithm and global optimization are combined. Cases of 120 turbines can finish in 100 s. Large-Scale problems are solved with a balance of both speed and quality. Time complexity is analyzed and an empirical formula is proposed.