A Variable Reduction Method for Large-Scale Security Constrained Unit Commitment

TL;DR

This paper introduces a variable reduction approach for large-scale security constrained unit commitment problems, combining relaxation, linear programming, and binary variable fixing to improve computational efficiency.

Contribution

A novel variable reduction method that relaxes constraints, solves simplified subproblems, and fixes variables to reduce binary variables in large-scale SCUC problems.

Findings

Method is efficient on IEEE 118-bus system

Method is effective on 6484-bus system

Reduces computational burden significantly

Abstract

Efficient methods for large-scale security constrained unit commitment (SCUC) problems have long been an important research topic and a challenge especially in market clearing computation. For large-scale SCUC, the Lagrangian relaxation methods (LR) and the mixed integer programming methods (MIP) are most widely adopted. However, LR usually suffers from slow convergence; and the computational burden of MIP is heavy when the binary variable number is large. In this paper, a variable reduction method is proposed: First, the time-coupled constraints in the original SCUC problem are relaxed and many single-period SCUC problems (s-UC) are obtained. Second, LR is used to solve the s-UCs. Different from traditional LR with iterative subgradient method, it is found that the optimal multipliers and the approximate UC solutions of s-UCs can be obtained by solving linear programs. Third, a criterion for choosing and fixing the UC variables in the SCUC problem is established, hence the number of binary variables is reduced. Last, the SCUC with reduced binary variables is solved by MIP solver to obtain the final UC solution. The proposed method is tested on the IEEE 118-bus system and a 6484-bus system. The results show the method is very efficient and effective.