Title: Models for Power Grid Optimization Problems under Uncertainty Speaker: Dr. Yongpei Guan, University of Florida Date/Time: Monday, January 24, 2011, 10:00 am MST - 9:00 am Pacific time Location: CSRI/90 in SNL/NM & 915/W133 in SNL/CA Brief Abstract: For both regulated and deregulated energy markets, due to the integration of renewable energy generation and uncertain demands, both supply and demand sides of a power grid system are volatile and under uncertainty. Accordingly, a large amount of spinning reserve is required at each bus to maintain the reliability of the power grid system in traditional approaches. In this talk, we describe robust and stochastic optimization models to address the uncertainties, and develop efficient algorithms to solve these two models. For the robust optimization model, uncertain problem parameters are assumed to be within a given cardinality or polyhedral uncertainty set. We analyze solution schemes to solve each problem that include an exact approach, and an efficient heuristic approach that provides a tight lower bound for the general robust power grid optimization problem. The final computational experiments on a revised 118-bus system from a US energy market verify the effectiveness of our approach, as compared to the worst-case scenario generated by the nominal model without considering the uncertainty. For the stochastic optimization model, a multi-stage stochastic integer programming formulation is developed and polyhedral studies have been provided for the derived deterministic equivalent formulations. We also utilize the strong valid inequalities as cutting planes to speed up the branch-and-cut algorithm to solve the problem. The computational experiments also show the effectiveness of our proposed approach, as compared to the computational results obtained by the default CPLEX. BIO: Dr. Yongpei Guan is currently an Assistant Professor and the Director of the Computational and Stochastic Optimization Lab in the Department of Industrial and Systems Engineering at the University of Florida. Dr. Guan’s research focuses on multi-stage formulations and solution methods for mixed integer programming under uncertainty, and its applications in production planning, supply chain management, and power grid optimization. His research has been supported by the National Science Foundation, the Department of Defense, the Department of Transportation, and industrial companies. Detailed research activities are referred to the Computational and Stochastic Optimization (CSO) Lab (www.cso.ise.ufl.edu/), which facilitates theoretical research, super-computing, and applications in the areas of multi-stage decision making under uncertainty and computational optimization. Results from his research have been published or accepted in journals such as Operations Research, Mathematical Programming, Operations Research Letters, Discrete Optimization, and Annals of Operations Research. Dr. Guan serves on the editorial boards for the Journal of Global Optimization and the Computational Optimization and Applications. He also serves on the board of directors for the Computer & Information Systems Division of the IIE society. He is a member of MPS, INFORMS and IIE. Dr. Guan’s honors include the National Science Foundation CAREER Award 2008, and the Office of Naval Research Young Investigator Award 2010.CSRI POC: Jean-Paul Watson, 505-845-8887 |