Events Calendar

Epstein ISE Department Seminar

Speaker: Yongpei Guan, Associate Professor, Department of Industrial and Systems Engineering and Director, Computational and Stochastic Optimization Lab, University of Florida

Talk Title: Renewable Energy Integration and Data-Driven Risk-Averse Stochastic Optimization

Abstract:
Renewable energy has been increasingly penetrating into the power grid systems recently. Due to its intermittent nature, new challenges arise for power system operators to provide reliable unit commitment decisions to incorporate intermittent renewable generation, with the objective of ensuring system reliability while maintaining cost effectiveness.

In practice, the distribution of renewable energy output is unknown, and instead, only a set of historical data is available. This motives a theoretical study on data-driven risk-averse stochastic optimization. Starting from the given historical data set, we introduce a set of probability metrics to construct the confidence set for the unknown probability distribution through nonparametric statistical estimation. We accordingly formulate a risk-averse stochastic program (RASP) from the perspective of distributional robustness by hedging against the worst-case distribution within the confidence set and considering the corresponding expected total cost. In our study, for a specific metric, we can derive an equivalent reformulation for RASP, which explicitly reflects its linkage with a full spectrum of coherent risk measures under various risk-averseness levels. This reformulation result can be further extended to other interesting models in the stochastic programming literature including chance and stochastic dominance constraints. In addition, we develop a solution algorithm for the reformulation based on the sample average approximation method. We also perform convergence analysis to show that the risk-averseness of RASP vanishes as the data sample size grows to infinity, in the sense that the optimal objective value of RTSP converges to that of the risk-neutral one. Furthermore, we can show the “value of data” by analyzing the convergence rate of our solution approach for a family of metrics.

Finally, we apply the proposed solution framework to solve the reliability unit commitment problem with renewable energy integration, and the computational results show the effectiveness of our proposed approach.

This is joint work with Ruiwei Jiang and Chaoyue Zhao


Biography:
Yongpei Guan currently serves as an Associate Professor and the Director of the Computational and Stochastic Optimization Lab at the University of Florida. His research interests include nonparametric statistical estimation and stochastic optimization, discrete optimization, and stochastic impulse control with their applications in supply chain management and power system analysis with renewable energy integration. His works in these areas have led to NSF Career Award 2008 and Office of Naval Research Young Investigator Award 2010, and have been published in IEEE Transactions on Power Systems, Mathematical Programming, and Operations Research. His Ph.D. students have won the Nicholson Best Student Paper Award (first place) from INFORMS and Pritsker Doctoral Dissertation Awards (second and third places) from IIE. He is currently the associate editor for Journal of Global Optimization and Computational Optimization and Applications, as well as the newsletter editor for the INFORMS Computing Society. He was also nominated and served as the chair of the 2014 IIE Annual Conference ISERC Program, and invited and served as the 2013 Guest Editor-in-Chief for the Special Issue on “Optimization Methods and Algorithms Applied to Smart Grid” for IEEE Transactions on Smart Grid. Yongpei Guan obtained his Ph.D. from Georgia Tech in 2005.


Host: Daniel J. Epstein Department of Industrial and Systems Engineering

More Information: SEMINAR-Guan.doc

Location: Ethel Percy Andrus Gerontology Center (GER) - 206

Audiences: Everyone Is Invited

Contact: Georgia Lum