Open AccessResearch on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent game
Author(s) -
Liu Hong,
Li Jifeng,
Ge Shaoyun
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0268
Subject(s) - microgrid , computer science , energy management system , energy management , scheduling (production processes) , distributed computing , multi agent system , artificial intelligence , game theory , energy (signal processing) , control (management) , control engineering , engineering , operations management , statistics , mathematics , economics , microeconomics
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.