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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jianhui Wang |
| Copyright Year | 2009 |
| Abstract | A novel conjectural variation-based bidding strategy combined with a Q-learning algorithm is presented in this paper. Generation companies are modeled as adaptive agents in the electricity markets. Conjectural variation is introduced to model rivals' reaction to one generation company's bidding strategy. Q-learning is used to model the bidding behavior of generation companies that can learn and adjust their strategies over time. SA-Q-learning algorithm with Metropolis criterion is applied to balance exploitation and exploration in the reinforcement learning process. A series of stage games represented by short-term market clearings form a repeated game for generation companies. Interactions among market participants can be studied accordingly by the proposed agent-based simulation approach. Case studies demonstrate the learning process and corresponding equilibriums. |
| Starting Page | 1 |
| Ending Page | 10 |
| File Size | 286274 |
| Page Count | 10 |
| File Format | |
| ISBN | 9780769534503 |
| ISSN | 15301605 |
| DOI | 10.1109/HICSS.2009.128 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-01-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electricity supply industry Power generation Learning Game theory Economic forecasting Feedback Power generation economics Costs Weather forecasting Laboratories |
| Content Type | Text |
| Resource Type | Article |
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