Optimal integrated generation bidding and scheduling with risk management under a deregulated daily power market

In the deregulated power industry, a generation company (GenCo) sells energy and ancillary services primarily through bidding at a daily market. Developing effective strategies to optimize hourly bid curves for a hydrothermal power system to maximize profits becomes one of the most important tasks of a GenCo. This paper presents a unified framework for optimizing energy and reserve bidding strategies under a deregulated market. In view of high volatilities of market clearing prices (MCP), the hourly MCPs and reserve prices are modeled as discrete random variables, whose probability mass functions are predicted with a classification based neural network approach. The mean-variance method is applied to manage bidding risks, where a risk penalty term related to MCP and reserve price variances is added to the objective function. To avoid buying too much power from the market at high prices, a GenCo may also require covering at least a certain percentage of its own customer load. This self-scheduling requirement is modeled similar to the system demand in traditional unit commitment problems. The formulation is a stochastic mixed-integer optimization with a separable structure. An optimization based algorithm combining Lagrangian relaxation and stochastic dynamic programming is presented to optimize bids for both energy and reserve markets. Numerical testing based on an 11-unit system in New England market shows that the method can significantly reduce profit variances and thus reduce bidding risks. Near-optimal energy and reserve bid curves are obtained in 4-5 minutes on a 600 Hz Pentium III PC, efficient for daily use.