Self Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets: A Stochastic Adaptive Robust Optimization Approach

This paper considers the self-scheduling problem of a virtual power plant trading in both energy and reserve electricity markets. The virtual power plant comprises conventional generation, wind power generation, and a flexible demand that participate in those markets as a single entity in order to optimize the use of energy resources. As a distinctive feature, the proposed model explicitly accounts for the uncertainty associated with the virtual power plant being called upon by the system operator to deploy reserves. This uncertainty and the uncertainty in available wind power generation levels are modeled using confidence bounds, while uncertain market prices are modeled using scenarios. Therefore, the proposed model is formulated as a stochastic adaptive robust optimization problem, which is solved using an effective column-and-constraint generation algorithm involving the iterative solution of a subproblem and a master problem. Results from a case study are provided to illustrate the performance of the proposed approach.