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DOI:10.35833/MPCE.2021.000382
A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems
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Author: Yuzhou Zhou1, Qiaozhu Zhai1, Lei Wu2, Moammad Shahidehpour3

Author Affiliation: 1.Systems Engineering Institute, MOEKLINNS Lab, Xi’an Jiaotong University, Xi’an 710049, China
2.Electrical and Computer Engineering Department, Stevens Institute of Technology, Hoboken, U.S.
3.Electrical and Computer Engineering Department, Illinois Institute of Technology, Chicago, U.S.

Foundation:
This work was supported in part by the National Natural Science Foundation of China (No. 61773309).
Abstract: This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment (TCUC). Lagrangian relaxation (LR) and mixed-integer linear programming (MILP) are popular approaches to solving TCUC. However, with many binary unit commitment variables, LR suffers from slow convergence and MILP presents heavy computation burden. The proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems. A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible (ASF) approaches, which is then leveraged to efficiently solve new TCUC instances online. On/off statuses of considerable units can be fixed in the online calculation according to the database, which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances. A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables, which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure changes. Numerical tests illustrate the efficiency of the proposed approach.

Keywords:
Unit commitment ; accelerated algorithm ; data driven ; variable reduction
Received:June 14, 2021               Online Time:2023/01/28
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