Optimal integrated bidding and hydrothermal scheduling with risk management and self-scheduling requirements

This paper presents a novel formulation for integrated bidding and hydrothermal scheduling, where the market clearing price (MCP) is modeled as a Markov chain and the risks are directly formulated in the objective function in terms of MCP variances. The self-scheduling requirements and interactions between the energy and reserve markets are considered. A Lagrangian relaxation based algorithm is then developed to solve this problem, and a stochastic dynamic programming approach is developed to solve the unit subproblems. Since the bidding of pumped-storage units plays important roles in both the energy and reserve markets, and significantly affects thermal biddings, it is the focus in this paper. Effects of self-scheduling constraints, risk management and reserve market on the energy bidding strategies are analyzed. Numerical testing based on an 11-unit system in New England shows that the method can generate energy and reserve bid curves in 4-5 minutes, and risks of buying power at high market price are effectively reduced.