An Adaptive Q-Learning Algorithm Developed for Agent-Based Computational Modeling of Electricity Market

Balancing between exploration and exploitation with adaptation of the Q -learning (QL) parameters to the condition of dynamic uncertain environment has always been a significant subject of interest in the context of reinforcement learning. The peculiarities of the electricity market have provided su...

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Vydáno v:IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Ročník 40; číslo 5; s. 547 - 556
Hlavní autoři: Rahimiyan, Morteza, Mashhadi, Habib Rajabi
Médium: Journal Article
Jazyk:angličtina
Vydáno: New-York, NY IEEE 01.09.2010
Institute of Electrical and Electronics Engineers
Témata:
Adaptation
Agent-based computational modeling
Algorithms
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Decision making
Dynamics
Economics
Electric power generation
Electrical engineering. Electrical power engineering
Electrical power engineering
Electricity
electricity market
Electricity supply industry
Environmental economics
Exact sciences and technology
Fuzzy systems
Learning and adaptive systems
Learning systems
Markets
Mathematical models
Power generation economics
Power networks and lines
Power supplies
Power system economics
Power system modeling
Power system simulation
Q -learning (QL)
risk strategy
Simulation
Software
Parameter estimation
Adaptive algorithm
Methodology
Expert system
Economic sciences
Modeling
Dynamic conditions
Economic market
Uncertain system
Agent-based computational modeling
Learning algorithm
Human
Q-learning (QL)
Bidding
electricity market
Computer simulation
Decision support system
Decision making
risk strategy
Reinforcement learning
Intelligent agent
Fuzzy logic
Multiagent system
Electrical network
Fuzzy decision
Artificial intelligence
Power
ISSN:1094-6977, 1558-2442
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Popis
Shrnutí:Balancing between exploration and exploitation with adaptation of the Q -learning (QL) parameters to the condition of dynamic uncertain environment has always been a significant subject of interest in the context of reinforcement learning. The peculiarities of the electricity market have provided such complex dynamic economic environment, and consequently have increased the requirement for advancement of the learning methods. In this economic system, the agent's market power plays a vital role in bidding decision-making problem. In order to improve the QL method, as main idea, adaptation of its parameters to the market power is proposed for making a good balance between exploration and exploitation. To implement this adaptation process, due to the fuzzy nature of human's decision-making process, a fuzzy system is designed to map each agent's market power into the QL parameters. Therefore, a fuzzy QL method is developed to model the power supplier's strategic bidding behavior in a computational electricity market. In the simulation framework, the QL algorithm selects the power supplier's bidding strategy according to the past experiences and the values of the parameters, which show the human's risk characteristic. The application of the proposed methodology for the power supplier in a multiarea power system shows the performance improvement in comparison to the QL with fixed parameters.
Bibliografie:ObjectType-Article-2
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ISSN:1094-6977
1558-2442
DOI:10.1109/TSMCC.2010.2044174