Learning Automata Based Bidding Strategy for Power Suppliers in Incomplete Information Environment

被引：0

作者：

Jia Q. ^{[1
]}

Chen S. ^{[1
]}

Li Y. ^{[1
,2
]}

Yan Z. ^{[1
]}

Xu C. ^{[1
]}

机构：

[1] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai

[2] Department of Electrical and Computer Science, North Carolina State University, Raleigh

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Artificial intelligence; Electricity market; Incomplete information; Reinforcement learning automata; Repeated game;

D O I：

10.7500/AEPS20200701002

中图分类号：

学科分类号：

摘要：

The electricity market is a typical imperfectly competitive market where power suppliers can increase their profits through strategic bidding. Existing research on bidding strategies for power suppliers usually assumes that the power suppliers can use sufficient market information, which is often not true if the market is just launched. To solve the bidding problem of power suppliers in the incomplete information environment, this paper proposes an improved reinforcement learning automata algorithm. This method requires little external information, and is easy to implement. In addition, this paper models the process of power supplier bidding and market clearing as a repeated game rather than the widely used Markov game, which avoids the strong assumption that Markov game requires time correlation between system states. Finally, the example result verifies the effectiveness of the proposed algorithm. © 2021 Automation of Electric Power Systems Press.

引用

页码：133 / 139

页数：6

共 27 条

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