Title :
Reinforcement Learning Based Supplier-Agents for Electricity Markets
Author :
Rahimi-Kian, Ashkan ; Tabarraei, Hamidreza ; Sadeghi, Behrooz
Author_Institution :
Dept. of ECE, Tehran Univ.
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;
Conference_Titel :
Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation
Conference_Location :
Limassol
Print_ISBN :
0-7803-8936-0
DOI :
10.1109/.2005.1467220
