Electric Power Systems Research, ( ISI ), No (78), Year (2008-1) , Pages (165-175)

Title : ( Supplier’s optimal bidding strategy in electricity pay-as-bid auctionComparison of the Q-learning and a model-based approac )

Authors: Morteza Rahimiyan , Habib Rajabi Mashhadi ,

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In this paper, the bidding decision making problem in electricity pay-as-bid auction is studied from a supplier’s point of view. The bidding problem is a complicated task, because of suppliers’ uncertain behaviors and demand fluctuation. In a specific case, in which, the market clearing price (MCP) is considered as a continuous random variable with a known probability distribution function (PDF), an analytic solution is proposed. The suggested solution is generalized to consider the effect of supplier market power due to transmission congestion. As a result, an algebraic equation is developed to compute optimal offering price. The basic assumption in this approach is to take the known probabilistic model for the MCP. The above-mentioned method, called model-based approach, is not more applicable in a realistic situation. In order to overcome the drawback of this method, which needs information about the MCP and its PDF, the supplier learns from past experiences using the Q-learning algorithm to find out the optimal bid price. The simulation results of the model-based and Q-learning methods are compared on a studied system. It is shown that a supplier using the Q-learning algorithm is able to find the optimal bidding strategy similar to one obtained by the model-based approach. Furthermore, to analyze a more realistic situation, the suppliers’ behaviors are modeled using a multi-agent system. Simulation results illustrate that the studied supplier finds the optimal bidding strategy in power market using the Q-learning algorithm.

Keywords

, Bidding strategy; Q, learning; Pay, as, bid auction; Transmission congestion; Market power; Multi, agent
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@article{paperid:1006590,
author = {Rahimiyan, Morteza and Rajabi Mashhadi, Habib},
title = {Supplier’s optimal bidding strategy in electricity pay-as-bid auctionComparison of the Q-learning and a model-based approac},
journal = {Electric Power Systems Research},
year = {2008},
number = {78},
month = {January},
issn = {0378-7796},
pages = {165--175},
numpages = {10},
keywords = {Bidding strategy; Q-learning; Pay-as-bid auction; Transmission congestion; Market power; Multi-agent system},
}

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%0 Journal Article
%T Supplier’s optimal bidding strategy in electricity pay-as-bid auctionComparison of the Q-learning and a model-based approac
%A Rahimiyan, Morteza
%A Rajabi Mashhadi, Habib
%J Electric Power Systems Research
%@ 0378-7796
%D 2008

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