Bidding strategy for electric power suppliers in the daily energy market

Traditional unit commitment or hydrothermal scheduling problems have been integrated with generation resource bidding, but the development of optimization based bidding strategies is only at a very preliminary stage. This paper presents a bidding strategy based on the theory of ordinal optimization that the ordinal comparisons of performance measures are robust with respect to noise and modeling error, and the problems become much easier if the optimization goal is softened from asking for the `best' to `good enough' solution. The basic idea of our approach is to use an approximate model that describes the influence of bidding strategies on the market clearing prices (MCPs). A nominal bid curve is obtained by solving optimal power generation for a given set of MCPs via Lagrangian relaxation. Then N bids are generated by perturbing the nominal bid curve. The ordinal optimization method is applied to isolate a good enough set S that contains some good bids with high probability by performing rough evaluation. The best bid is then selected by solving full hydrothermal scheduling or unit commitment problems for each of the bids in S. Using ordinal optimization approach we are able to obtain a good enough bidding strategy with reasonable computational effort. Numerical results using historical MCPs from the California market and a generation company with 10 units show that the ordinal optimization based method is efficient and good bidding strategies are obtained.