• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (11): 142-150.doi: 10.12204/j.issn.1000-7229.2022.11.014

• Power Economic Research • Previous Articles     Next Articles

Optimization of Microgrid Trading Strategy Based on MADDPG and Smart Contracts

XIE Xinyi(), YING Liming(), TIAN Shusheng(), ZHU Guiqi()   

  1. School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
  • Received:2022-03-30 Online:2022-11-01 Published:2022-11-03
  • Contact: XIE Xinyi E-mail:1114378452@qq.com;513251626@qq.com;2331961156@qq.com;1462913922@qq.com
  • Supported by:
    National Natural Science Foundation of China(72174151)

Abstract:

In order to solve the problems of long decision-making time, high cost of trust and privacy security faced by microgrid in the traditional centralized trading, a decentralized market trading system based on multi-agent deep deterministic policy gradient (MADDPG) algorithm and smart contract is proposed for microgrid. Firstly, after dividing the multiple agents in the microgrid market, a decentralized transaction mechanism for microgrids that is suitable for all entities to participate in distributed transactions is designed to protect the interests of market entities. Secondly, in order to realize the optimization of the transaction strategy of the microgrid market entities in the transaction confirmation stage, the multi-agent deep deterministic policy gradient algorithm is used to solve the bidding model that each entity pursues the most benefits. Finally, the feasibility and economy of the MADDPG algorithm in the optimization process of the transaction strategy of the microgrid market entities under the smart contract is verified by example simulation.

Key words: smart contract, microgrid, multi-agent, deep reinforcement learning, game theory

CLC Number: