Machine Learning-Additional Decision Constraints for Improved MILP Day-Ahead Unit Commitment Method

doi:10.1109/ACCESS.2023.3323594

2023-01-01

Mohamed Ibrahim Abdelaziz Shekeew

Bala Venkatesh *

DOI：10.1109/ACCESS.2023.3323594

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摘要

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This paper introduces a two-stage (offline and online) artificial neural network (ANN) driven constraint creator model to improve the computational quality of day-ahead unit commitment (DAUC) in power systems. The DAUC is crucial for planning 24-hour operations and complex bid clearing through mixed-integer linear programs (MILP). However, slow convergence is common due to system complexity. Machine learning (ML) based methods have been used to enhance MILP-DAUC. Nonetheless, they can lead to sub-optimality and infeasibility. To overcome these challenges, (1) this paper proposes in the offline stage the ANN-generators subset (AGS) that can predict part of the optimal MILP-DAUC decisions using an ANN model. Online, only ML-generated decisions of AGS are used to form the ANN-driven constraints to enhance the main MILP-DAUC, forming the proposed ANN-MILP-DAUC method. (2) A feasibility handling process is proposed to retain the infeasible ML states to be optimized by the main MILP-DAUC formulation. (3) The proposed model issues an artificial factor that provides the percentage of generators accurately predicted and used as an ML training performance metric. The ANN model was trained using optimal MILP-DAUC solutions. Test results on IEEE 14-bus and 118-bus systems reported solution time reductions of 61.43% and 70.1%, respectively. Larger Polish 2383-bus, 3012-bus, and Ontario systems reported time reductions in the range of 33% compared with the main MILP-DAUC method using MOSEK (TM), a commercial solver. No degradation in the optimal solution was observed for all test systems, and the proposed method provides a lower-objective solution for the same running time, leading to better solutions.

Keyword:

ANN-driven constraints generator

ANN-constrained unit commitment

mixed-integer linear programming