Research on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent game

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.

Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking

“…Primitive-based control has been researched for at least two decades, in various forms, by transforming the time scale [2,3], concatenation-based mechanisms [4][5][6], and decomposition/re-composition in time [7,8]. More recently, primitive-based control has been studied in [9][10][11][12][13], mostly as part of hierarchical learning frameworks [14][15][16][17][18]. However, the application of the Iterative Learning Control (ILC, a full list of the acronyms used in the paper is presented in abbreviations part) technique [19][20][21][22][23][24][25] over linearized feedback closed-loop control systems (CLSs) as a primer mechanism for primitive-based learning was proposed in [8].…”

Bidirectional consistency of multi-agent based on data-driven

“…Many effective and practical control strategies [5] , [6] , [7] have been put forward to solve this problem. But most researchers focus on the Multi-agent system that there is only cooperation between agents, but in practice there is not only cooperation between agents, but also competition between them.…”

A Deep Reinforcement Learning Method for Unit Commitment with Renewable Energy Generators

“…In recent years, many researchers have adopted data-driven approaches like reinforcement learning (RL) to overcome the limitations of traditional approaches in PSD [7]. In [8], a distributed Q-learning algorithm is adopted to improve robustness through local information communication; In [9], the policy gradient algorithm is adopted to achieve a fast and efficient search of the action space through a bootstrap tree search approach; In [10], multi-scene parallel optimal scheduling is implemented; In [11], better convergence and economy are achieved through multi-intelligence reinforcement learning.…”