Asset Details
Do you wish to reserve the book?
Research on hierarchical control and optimisation learning method of multi-energy microgrid considering multi-agent game
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Research on hierarchical control and optimisation learning method of multi-energy microgrid considering multi-agent game
Do you wish to request the book?
Research on hierarchical control and optimisation learning method of multi-energy microgrid considering multi-agent game
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Research on hierarchical control and optimisation learning method of multi-energy microgrid considering multi-agent game
2020
Overview
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.
Publisher
The Institution of Engineering and Technology,John Wiley & Sons, Inc,Wiley
Subject
Agents (artificial intelligence)
/ artificial intelligence method
/ artificial intelligence technology
/ B0260 Optimisation techniques
/ B8120K Distributed power generation
/ C1180 Optimisation techniques
/ C6170K Knowledge engineering techniques
/ Cold
/ cost-effective integrated energy system
/ distributed power generation
/ energy Internet energy management
/ hierarchical control optimisation
/ integrated energy regulation
/ integrated energy scheduling
/ Internet
/ Learning
/ learning (artificial intelligence)
/ microgrid system architecture
/ multienergy microgrid system
/ optimisation learning method
/ Special Issue: Machine Learning in Power Systems
