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

The problem of building optimally coordinated bidding strategies for compet itive suppliers in energy and spinning reserve markets is addressed based o n the Monte Carlo simulation and a refined genetic algorithm (RGA). It is a ssumed that each supplier bids a linear energy supply function and a linear spinning reserve supply function into the energy and spinning reserve mark ets, respectively, and the two markets are dispatched separately to minimiz e customer payments. Each supplier chooses the coefficients in the linear e nergy and spinning reserve supply functions to maximize total benefits, sub ject to expectations about how rival suppliers will bid. A stochastic optim ization model is first developed to describe this problem and a Monte Carlo and genetic algorithm based method is then presented to solve it. A numeri cal example is utilized to illustrate the essential features of the method. (C) 2001 Elsevier Science Ltd. All rights reserved.