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Kai PAN, the Hong Kong Polytechnic University:Strong Formulations for the Multistage Stochastic Unit Commitment
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发布日期:2017-09-01 点击数:

喻园管理论坛 2017年第59期 (总第315期)

演讲主题: Strong Formulations for the Multistage Stochastic Unit Commitment

主 讲 人: Kai PAN, the Hong Kong Polytechnic University

主 持 人: 秦虎副教授

活动时间: 2017年9月4日(周一)下午3:00-5:00

活动地点:管院110教室

演讲内容摘要:
With the increasing penetration of renewable energy into the power grid system, significant challenges arise for system operators in both regulated and deregulated electricity markets to solve the security-constrained unit commitment problem with intermittent generation (due to renewables) and uncertain load, in order to ensure system reliability and maintain cost effectiveness. In this talk, we present a security-constrained stochastic unit commitment model, which we use to enhance the reliability unit commitment process. Considering the correlations of renewable energy generation outputs among different time periods, we explore a multistage stochastic scenario tree to formulate the uncertainties. Accordingly, the derived multistage stochastic unit commitment problem is transformed as a deterministic equivalent mixed-integer linear programming formulation. To overcome the curse of dimensionality, we develop strong valid inequalities for the derived stochastic unit commitment polytope to speed up the algorithms to solve the problem. In particular, we derive strong valid inequalities that can provide the convex hull descriptions for the two-period case and a special class of the three-period cases with rigorous proofs provided. Furthermore, strong valid inequalities, including facet-defining proofs, for multistage cases are proposed to further strengthen the model. Finally, numerical experiments verify the effectiveness of our derived strong valid inequalities by incorporating them in a branch-and-cut framework.

主讲人简介
Kai Pan is an Assistant Professor in the Faculty of Business at the Hong Kong Polytechnic University. He received his Ph.D. and M.S. degrees from the University of Florida and his Bachelor’s degree from Zhejiang University. Previously he worked as a Research Scientist at Amazon (Seattle, Washington) on Supply Chain Optimization and a Power System Engineer at GE Grid Solutions (Redmond, Washington) on Electricity Market Operations. His research interests include stochastic integer programming, data-driven stochastic optimization, and dynamic programming with applications in energy systems, transportation, supply chain, and healthcare operations. He is the first place winner of the prestigious IISE Pritsker Doctoral Dissertation Award in 2017 and his works have been published in or accepted by Operations Research, IISE Transactions, Transportation Research Part E, IEEE Transactions on Power Systems, and IEEE Transactions on Industrial Informatics.

 

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