A stochastic integer programming approach to the optimal
thermal and wind generator scheduling problem

Prof. Michael Chen, York University

时间：11月16日（本周五）下午3：00

地点：海韵教学楼302教室

简介：Professor Michael Chen graduated from Northwestern University Industrial
Engineering department in 2008. He conducted the renewable energy study during his
post-doc at IBM T.J. Watson Research Center in New York. Since 2009, he has been an
assistant professor at York University Math and Stats department. His research work in
stochastic optimization and its application in renewable energy has been published in
various journals including the SIAM J. of Optimization etc.

Abstract: In recent years, the increasing capacity of wind energy, together with solar and
other renewable energy, brings in a new challenge to the electricity generator scheduling
problem: the renewable energy is stochastic by its nature and it is a daunting task to meet
a stochastic demand by a stochastic supply year around. In the near future, as we
approach the goal of 25% percent renewable energy by 2025, this challenge will be more
and more prominent. Sufficient reserve has been used to achieve this goal in the past
when the supply is fully controllable. Will the same approach work with 25% stochastic
supply? Do we need to increase the reserve level? We will first model the complicated
electricity grid, physics of generators, stochastic demand and wind power, and the dayahead
decision process. Our model aims at a good balance of the reality and
computational complexity. Based on this stochastic integer model, we develop an
effective scenario-crossing deep cut, which accelerates the state-of-art CPLEX solver
significantly.