Research on Distributed Renewable Energy Transaction Decision-making Based on Multi-Agent Bilevel Cooperative Reinforcement Learning
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Paper number
1381
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Chen, Zhangyu, Shanghai Jiaotong University, China
Liu, Dong, Shanghai Jiao Tong University, China
WU, Xiaofei, State Grid Huai’an Power Supply Company, China
XU, Xiaochun, State Grid Huai’an Power Supply Company, China
Liu, Dong, Shanghai Jiao Tong University, China
WU, Xiaofei, State Grid Huai’an Power Supply Company, China
XU, Xiaochun, State Grid Huai’an Power Supply Company, China
Abstract
With more and more distributed renewable energy connecting to the distribution network, the issue of regional transaction of distributed renewable energy has attracted more and more attention. In order to adapt to the complex transaction decision-making problem, this paper proposes a multi-agent bilevel cooperative reinforcement learning algorithm under the framework of bilevel stochastic decision-making model. By constructing a bilevel stochastic decision-making optimization model for distributed renewable energy trading, the uncertainties and fluctuations of distributed generation output are effectively solved. The objective of upper level planning is to maximize the profits of distributed renewable energy generators. The lower level planning is to optimize the dispatch of the whole regional market. The two layers are continuously iterated until the lower level planning is optimal, that is, the comprehensive benefit is maximized.After introducing multi-agent bilevel cooperative reinforcement learning, the algorithm can effectively carry out learning training, and after completing the training, it can quickly and accurately calculate the optimal results. Through the simulation of the model project of Guizhou Hongfeng area, the bidding decision algorithm has been verified, which can improve the profit of the power producer while taking risks into consideration, and at the same time maximize the comprehensive benefits.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Published in
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/390
http://dx.doi.org/10.34890/618
http://dx.doi.org/10.34890/618
ISSN
2032-9644
ISBN
978-2-9602415-0-1