A genetic algorithm based method for bidding strategy coordination in energy and spinning reserve markets

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.