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 on 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.
Site Wang is currently a Ph.D. Candidate in Department of Industrial Engineering at Clemson University, under the advisement of Dr. Scott J. Mason. He received his M.S. degree from Clemson University in 2015 and B.S. degree from Beijing Institute of Technology in 2013. During his doctoral program, Site interned with Los Alamos National Lab on his dissertation topic: electrical infrastructure adaptation under the uncertainty of climate change, which couple the decision-making process with physical simulation models for a resilient future. Site is passionate about stochastic optimization for realistic problems under complex uncertainty. He is also an active developer of the Julia Language community with focus on mathematical modeling interfaces and global optimization solvers for mixed-integer nonlinear programs.