Reinforcement Learning Based Supplier-Agents for Electricity Markets

Author

Rahimi-Kian, Ashkan ; Tabarraei, Hamidreza ; Sadeghi, Behrooz

Author_Institution

Dept. of ECE, Tehran Univ.

fYear

2005

fDate

27-29 June 2005

Firstpage

1405

Lastpage

1410

Abstract

Bidding strategies play important roles in maximizing the profits of power suppliers in competitive electricity markets. Therefore, it will be an advantage for a supplier to search for optimal bidding strategies in the market. In this paper the problem of designing fuzzy reinforcement learning (FRL) supplier-agents that compete in forward electricity markets (e.g. Day-Ahead energy market) to maximize their revenues is studied. An IEEE 30-bus power system with 6 generators (supplier-agents) and three demand areas with stochastic loads are used for our simulation studies. This model is applicable to different types of commodity markets with numerous supply and demand agents

Keywords

electricity supply industry; financial management; learning (artificial intelligence); multi-agent systems; power markets; supply and demand; IEEE 30-bus power system; commodity market; day-ahead energy market; electricity market; forward electricity markets; fuzzy reinforcement learning; optimal bidding strategy; power generators; power suppliers; profit maximization; revenue maximization; stochastic load; supplier agents; supply and demand agents; Electricity supply industry; Learning; Power generation; Power markets; Power supplies; Power system modeling; Power system simulation; Pricing; Stochastic systems; Supply and demand;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation

Conference_Location

Limassol

ISSN

2158-9860

Print_ISBN

0-7803-8936-0

Type

conf

DOI

10.1109/.2005.1467220

Filename

1467220