An efficient approach for solving large stochastic unit commitment problems arising in a California ISO planning model

Abstract

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.

Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory

“…Deliberation of uncertainties in unit commitment (UC) problem results in stochastic UC. To determine the stochastic UC problem, many approaches have been recommended in the literatures [1][2][3]. The stochastic UC problem with Lagrangian relaxation method is presented in [4].…”

A new methodology for solving the unit commitment in insular grids including uncertainty of renewable energies

“…Recently in [7], the progressive heading (PH) algorithm was proposed for solving UC problem. PH is a method based on the combination of Lagrangian relaxation and decomposition techniques.…”