Machine Learning Enhanced Formulation Tightening of Energy Storage Resource Constraints in Unit Commitment

Recent years have seen an increased penetration of utility-scale energy storage resources (ESRs) into the power grid. To incorporate ESRs into the unit commitment (UC) problem, binary variables are required to prevent simultaneous charging and discharging. This further increases the problem’s complexity, prompting computationally efficient modeling of ESRs. A systematic formulation tightening approach based on constraint-to-vertex conversion, developed in our previous work, has shown effectiveness in tightening formulations of conventional generators. However, it requires extensive manual analysis and expert knowledge during the parameterization step (expressing numerical coefficients as combinations of generator parameters). In this paper, the systematic formulation tightening approach is enhanced via machine learning for ESRs. To address the challenge caused by the manual analysis for parameterization, a novel machine learning model is developed by taking ESR parameters as inputs and numerical coefficients of the tight constraints as outputs to identify the mathematical relationship between them. Furthermore, the entire tightening process is automated for flexibility and applicability. Results based on the IEEE 118-bus system show a significant reduction in solving time (up to 64%) while maintaining the solution quality of UC with ESRs, demonstrating the effectiveness of the approach. The approach is general and has great potential for tightening complicated Mixed Binary Linear Programming (MBLP) problems in power systems and beyond.