Accelerating NCUC Via Binary Variable-Based Locally Ideal Formulation and Dynamic Global Cuts

This paper presents tighter formulations and dynamic global cutting plane solution approaches for accelerating network-constrained unit commitment (NCUC) problems, as the computational bottleneck of NCUC problems is always a concern to independent system operators (ISOs)/regional transmission owners...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on power systems 2016-09, Vol.31 (5), p.4097-4107
Main Author: Wu, Lei
Format: Article
Language:English
Subjects:
Acceleration
Branch-and-cut
Computation
Cost function
Cutting
Dynamical systems
Dynamics
Electric utilities
Generators
Independent system operators
Joining processes
Linear programming
locally ideal formulation
Mathematical models
NCUC
Planes
Radiation detectors
Search problems
Searching
Sensitivity
Trees
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents tighter formulations and dynamic global cutting plane solution approaches for accelerating network-constrained unit commitment (NCUC) problems, as the computational bottleneck of NCUC problems is always a concern to independent system operators (ISOs)/regional transmission owners (RTOs). Specifically, this paper explores tighter mixed-integer linear programming (MILP)-based NCUC formulations, via locally ideal reformulation for piece-wise linear cost functions of generators and valid cover inequalities by linking system load balance/power flow constraints with generation capacity limits. The paper also discusses an enhanced solution approach by dynamically generating global cutting planes at infeasible/suboptimal nodes of the branch-and-cut (BAC) tree, which would prevent similar infeasible/suboptimal nodes from being explored repeatedly. Since the major computational complexity of NCUC is caused by the large number of binary UC variables, the proposed strategies, being directly related to binary UC variables, could quickly identify infeasible/suboptimal binary solutions, reduce the search space in the BAC tree, and in turn improve the computational performance. Numerical case studies show that the proposed approach could significantly reduce the overall computational time as compared to the traditional NCUC approach.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2015.2502594