Agent-Based Simulation of Power Markets under Uniform and Pay-as-Bid Pricing Rules using Reinforcement Learning
In this paper agent-based simulation is employed to study the power market operation under two alternative pricing systems: uniform and discriminatory (pay-as-bid). Power suppliers are modeled as adaptive agents capable of learning through the interaction with their environment, following a reinforc...
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| creator | Bakirtzis, A.G. Tellidou, A.C. |
| description | In this paper agent-based simulation is employed to study the power market operation under two alternative pricing systems: uniform and discriminatory (pay-as-bid). Power suppliers are modeled as adaptive agents capable of learning through the interaction with their environment, following a reinforcement learning algorithm. The SA-Q-learning algorithm, a slightly changed version of the popular Q-Learning, is used in this paper; it proposes a solution to the difficult problem of the balance between exploration and exploitation and it has been chosen for its quick convergence. A test system with five supplier-agents is used to study the suppliers' behavior under the uniform and the pay-as-bid pricing systems |
| doi_str_mv | 10.1109/PSCE.2006.296473 |
| format | Conference Proceeding |
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| ispartof | 2006 IEEE PES Power Systems Conference and Exposition, 2006, p.1168-1173 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Costs Electricity supply industry Electricity supply industry deregulation Learning Power markets Power supplies Power system modeling Predictive models Pricing System testing |
| title | Agent-Based Simulation of Power Markets under Uniform and Pay-as-Bid Pricing Rules using Reinforcement Learning |
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