Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23907
Title: A genetic algorithm based method for bidding strategy coordination in energy and spinning reserve markets
Authors: Wen, F
David, AK
Keywords: Ancillary service
Bidding strategies
Electricity market
Genetic algorithm
Monte Carlo method
Spinning reserve
Issue Date: 2001
Publisher: Elsevier Sci Ltd
Source: Artificial intelligence in engineering, 2001, v. 15, no. 1, p. 71-79 How to cite?
Journal: Artificial Intelligence in Engineering 
Abstract: The problem of building optimally coordinated bidding strategies for competitive suppliers in energy and spinning reserve markets is addressed based on the Monte Carlo simulation and a refined genetic algorithm (RGA). It is assumed that each supplier bids a linear energy supply function and a linear spinning reserve supply function into the energy and spinning reserve markets, respectively, and the two markets are dispatched separately to minimize customer payments. Each supplier chooses the coefficients in the linear energy and spinning reserve supply functions to maximize total benefits, subject to expectations about how rival suppliers will bid. A stochastic optimization model is first developed to describe this problem and a Monte Carlo and genetic algorithm based method is then presented to solve it. A numerical example is utilized to illustrate the essential features of the method.
URI: http://hdl.handle.net/10397/23907
ISSN: 0954-1810
DOI: 10.1016/S0954-1810(01)00002-4
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