A Supplier Bidding Strategy Through Q-Learning Algorithm in Electricity Auction Markets
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概要
- 論文の詳細を見る
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 a 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. The dynamics and the incomplete information of the market are considered. The impacts of suppliers’ strategic bidding on the market price are analyzed under uniform pricing rule and discriminatory pricing rule. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.
- 社団法人 電気学会の論文
- 2003-06-01
著者
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Okuma Shigeru
Okuma Lab. Dept. Of Information Electronics Nagoya University
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Okuma Shigeru
School Of Engineering Nagoya University
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Okuma Shigeru
Department Of Electrical Engineering And Electronics Nagoya University
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Okuma Shigeru
Dept. Of Electrical Engineering Nagoya Univ.
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XIONG Gaofeng
Dept. of Information Electronics, Nagoya University
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HASHIYAMA Tomonori
Institute of Natural Science, Nagoya City University
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XIONG Gaofeng
Nagoya University
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HASHIYAMA Tomonori
Nagoya City University
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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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