Statistical estimation of operating reserve requirements using rolling horizon stochastic optimization

We develop a multi-period stochastic optimization framework for identifying operating reserve requirements in power systems with significant penetration of renewable energy resources. Our model captures different types of operating reserves, uncertainty in renewable energy generation and demand, and differences in generator operation time scales. Along with planning for reserve capacity, our model is designed to provide recommendations about base-load generation in a non-anticipative manner, while power network and reserve utilization decisions are made in an adaptive manner. We propose a rolling horizon framework with look-ahead approximation in which the optimization problem can be written as a two-stage stochastic linear program (2-SLP) in each time period. Our 2-SLPs are solved using a sequential sampling method, stochastic decomposition, which has been shown to be effective for power system optimization. Further, as market operations impose strict time requirements for providing dispatch decisions, we propose a warm-starting mechanism to speed up this algorithm. Our experimental results, based on IEEE test systems, establish the value of our stochastic approach when compared both to deterministic rules from the literature and to current practice. The resulting computational improvements demonstrate the applicability of our approach to real power systems.