Guojing Cong, David A. Bader
Journal of Parallel and Distributed Computing
We describe our experience in obtaining significant computational improvements in the solution of large stochastic unit commitment problems. The model we use is a stochastic version of a planning model used by the California Independent System Operator, covering the entire WECC western regional grid. We solve daily hour-timestep stochastic unit commitment problems using a new progressive hedging approach that features linear subproblems and guided solves for finding feasible solutions. For stochastic problems with 5 scenarios, the algorithm produces near-optimal solutions with a 6 times improvement in serial solution time, and over 20 times improvement when run in parallel; for previously unsolvable stochastic problems, we obtain near-optimal solutions within a couple of hours. We note that although this algorithm is demonstrated for stochastic unit commitment problems, the algorithm itself is suitable for application to generic stochastic optimization problems.
Guojing Cong, David A. Bader
Journal of Parallel and Distributed Computing
Guojing Cong, Konstantin Makarychev
IPDPS 2011
Fan Zhou, Guojing Cong
IJCAI 2018
Guojing Cong, Hanhong Xue
IPDPS 2008