A Q-Learning-Based Supplier Bidding Strategy in Electricity Auction Market
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- Xiong Gaofeng
- Okuma Lab., Dept. of Information Electronics, Nagoya University
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- Hashiyama Tomonori
- Hashiyama Lab., Institute of Natural Science, Nagoya City University
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- Okuma Shigeru
- Okuma Lab., Dept. of Information Electronics, Nagoya University
Bibliographic Information
- Other Title
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- Q Learning Based Supplier Bidding Strategy in Electricity Auction Market
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Abstract
One of the most important issues for power suppliers in the deregulated electric industry is how to bid into the electricity auction market to satisfy their profit-maximizing goals. Based on the Q-Learning algorithm, this paper presents a novel supplier bidding strategy to maximize supplier’s profit in the long run. In this approach, the supplier bidding strategy is viewed as one kind of stochastic optimal control problem and each supplier can learn from experience. A competitive day-ahead electricity auction market with hourly bids is assumed here, where no supplier possesses the market power and all suppliers winning the market are paid based on their own bid prices. The dynamics and the incomplete information of the market are considered. The impact of suppliers’ strategic bidding on the market price is analyzed. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.
Journal
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- IEEJ Transactions on Power and Energy
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IEEJ Transactions on Power and Energy 123 (4), 550-556, 2003
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390282679575111552
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- NII Article ID
- 130000081208
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- NII Book ID
- AN10136334
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- ISSN
- 13488147
- 03854213
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- NDL BIB ID
- 6530224
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed