Abstract

The rapid expansion of inverter-based resources, such as wind and solar power plants, will significantly diminish the presence of conventional synchronous generators in future power grids with rich renewable energy sources. This transition introduces increased complexity and reduces dynamic stability in system operation and control, with low inertia being a widely recognized challenge. However, the literature has not thoroughly explored grid dynamic performance associated with energy scheduling solutions that traditionally only consider grid steady-state constraints. This paper will bridge the gap by enforcing grid dynamic constraints when conducting optimal energy scheduling; particularly, this paper explores locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate substantial inertia reductions. This paper introduces a machine learning-assisted RoCoF-constrained unit commitment (ML-RCUC) model designed to ensure RoCoF stability after the most severe generator outage while maintaining operational efficiency. A graph-informed NN (GINN)-based RoCoF predictor is first trained on a high-fidelity simulation dataset to track the highest locational RoCoF, which is then reformulated as mixed-integer linear programming constraints that are integrated into the unit commitment model. Case studies, by solving the optimization problem ML-RCUC and validating its solutions with time-domain simulations, demonstrate that the proposed method can ensure locational RoCoF stability with minimum conservativeness.

Index Terms

Frequency stability, Low-inertia power systems, Machine Learning, Mixed-integer linear programming, Optimal energy scheduling, Unit commitment.

Cite this paper:

Mingjian Tuo, Xingpeng Li, and Pascal Van Hentenryck, “Machine Learning-assisted Dynamics-Constrained Day-Ahead Energy Scheduling”, Electric Power Systems Research, vol. 252, Jan. 2026.