A Deep Reinforcement Learning Bidding Algorithm on Electricity Market

被引：0

作者：

JIA Shuai ^{[1
]}

GAN Zhongxue ^{[2
]}

XI Yugeng ^{[1
]}

LI Dewei ^{[1
]}

XUE Shibei ^{[1
]}

WANG Limin ^{[3
]}

机构：

[1] Department of Automation,Key Laboratory of System Control and Information Processing,Shanghai Jiao Tong University

[2] State Key Laboratory of Coal-based Low-carbon Energy,ENN Science and Technology Development Co.,Ltd.

[3] ENN Energy Power Technology Shanghai Co.,Ltd.

来源：

Journal of Thermal Science | 2020年 / 29卷 / 05期

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

F426.61 [];

学科分类号：

0202 ; 020205 ;

摘要：

In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90% as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.

引用

页码：1125 / 1134

页数：10

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