Electric Power Bidding Models for Competitive Markets

Abstract

Profit maximization for power companies is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. Therefore, models that include price uncertainty are needed to analyze the market and to make better bidding decisions. In this dissertation, the main objective is to develop bidding models for electric power generators and wholesale power suppliers under price uncertainty. A quadratic programming model and a nonlinear programming model were developed to find a solution to the bidding problem. However, in these models the computational time increases exponentially as the size of the problem increases. To overcome this limitation, two particle swarm optimization models are developed. The first method uses a conventional particle swarm optimization technique to find bid prices and quantities under the rules of a competitive power market. The second method uses a decomposition technique in conjunction with the particle swarm optimization approach. In addition, a spreadsheet based simulation algorithm is developed to evaluate a bid offer under given price samples. It is shown that for nonlinear cost functions particle swarm optimization solutions provide higher expected profits than marginal cost based bidding. A model to find an equilibrium solution in competitive power markets for power suppliers bidding into day-ahead market under forecasted demand is also developed.