Chance-Constrained Two-Stage Unit Commitment under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition

TL;DR

This paper introduces a bilinear Benders decomposition approach for solving chance-constrained two-stage unit commitment problems with uncertain load and wind power, improving computational efficiency and model strength.

Contribution

It presents a novel bilinear mixed integer formulation for chance-constrained UC and develops an efficient Benders decomposition algorithm for large-scale problems.

Findings

Bilinear formulation is stronger than conventional models.

Benders decomposition significantly speeds up computation.

Method effective on IEEE systems.

Abstract

In this paper, we study unit commitment (UC) problems considering the uncertainty of load and wind power generation. UC problem is formulated as a chance-constrained two-stage stochastic programming problem where the chance constraint is used to restrict the probability of load imbalance. In addition to the conventional mixed integer linear programming formulation using Big-M, we present the bilinear mixed integer formulation of chance constraint, and then derive its linear counterpart using McCormick linearization method. Then, we develop a bilinear variant of Benders decomposition method, which is an easy-to-implement algorithm, to solve the resulting large-scale linear counterpart. Our results on typical IEEE systems demonstrate that (i) the bilinear mixed integer programming formula-tion is stronger than the conventional one; (ii) the proposed Benders decomposition algorithm is generally an order of magnitude faster than using a professional solver to directly compute both linear and bilinear chance-constrained UC models.