Research on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent game. Issue 4 (15th May 2020)

• Record Type: Journal Article
• Authors: Liu, Hong; Li, Jifeng; Ge, Shaoyun
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 479
• Page End: 489
• Publication Date: 2020-05-15
• Subjects: Petri nets -- energy conservation -- multi‐agent systems -- decision making -- distributed power generation -- game theory -- learning (artificial intelligence) -- Internet -- optimisation -- scheduling -- artificial intelligence; multidimensional interests -- multienergy microgrid system -- artificial intelligence technology -- integrated energy scheduling -- hierarchical control optimisation -- multiagent game -- microgrid system architecture -- multiagent partition -- integrated energy regulation -- artificial intelligence method -- energy Internet energy management -- optimisation learning method -- traditional fossil energy -- energy efficiency -- cost‐effective integrated energy system -- traditional centralised scheduling method; B0260 Optimisation techniques -- C1180 Optimisation techniques -- C6170K Knowledge engineering techniques -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0268
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Due to the depletion of traditional fossil energy, to improve energy efficiency and build a cost‐effective integrated energy system has become an inevitable choice. Aiming at the problems that the traditional centralised scheduling method is difficult to reflect the multi‐dimensional interests of different agents in the multi‐energy microgrid system, and the application of artificial intelligence technology in integrated energy scheduling still needs further exploration, this manuscript proposed a hierarchical control optimisation learning method with consideration of multi‐agent game. Firstly, the multi‐energy microgrid was taken as the research object, the microgrid system architecture was analysed, and the multi‐agent partition in the system was pursued based on different economic interests. Secondly, for the technical aspects involved in the integrated energy regulation and management, the management layers of the multi‐energy microgrid were divided, and the functions of different management layers were analysed. Based on this, the regulation functions were realised by considering the Nash Q‐learning and the artificial intelligence method of Petri‐net. Finally, the learning and decision‐making ability of the method through practical cases were analysed, and the effectiveness and applicability of the proposed method were explained. This study explores the application of artificial intelligence technology in energy Internet energy management.