A new approach for generator startup sequence online decision making with a heuristic search algorithm and graph theory. (August 2022)

• Record Type: Journal Article
• Authors: Wu, Zirui; Xu, Wenwen; Li, Changcheng; Meng, Xiangfei
• Is Part Of: Energy reports. Volume 8(2022)Supplement 5
• Page Start: 678
• Page End: 686
• Publication Date: 2022-08
• Subjects: Depth-First Search -- Generator startup sequence -- Monte Carlo tree search -- Online decision making -- Yen's Algorithm; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.02.239
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Most system restoration strategies following blackouts are obtained by offline methods, which are efficient in theory. But this may not be the case. A new method is proposed to decide the generator startup sequence during the system restoration. The Monte Carlo tree search algorithm is used to search for the generator to be recovered according to the real-time situation of the power system. All paths from the restored system to the chosen generator are obtained by Depth-First Search. And the K-shortest paths are obtained by Yen's Algorithm to decide the best restoration path. Then, key constraints are verified. Especially, the constraint on the hot start of generators is considered. Finally, the feasible scheme with the maximum power generation is selected for implementation. In addition, the cranking process of G33 to G32 in the IEEE39-bus test system is taken as an example to illustrate the efficiency of this method. Depth-First Search detects 42 restoration paths, and Yen's Algorithm selects three most reasonable paths from them, which greatly improves the efficiency of online decision making.