Machine Learning-assisted Dynamics-Constrained Day-Ahead Energy Scheduling

TL;DR

This paper develops a machine learning-assisted energy scheduling method that incorporates dynamic grid stability constraints, specifically RoCoF, to improve the stability and efficiency of future renewable-rich power grids.

Contribution

It introduces a novel ML-based RoCoF predictor integrated into a unit commitment model to enforce dynamic stability constraints during energy scheduling.

Findings

Ensures RoCoF stability post-contingency with minimal conservativeness.

Integrates a GINN-based RoCoF predictor into unit commitment optimization.

Validated solutions maintain dynamic stability through case studies.

Abstract

TThe rapid expansion of inverter-based resources, such as wind and solar power plants, will significantly diminish the presence of conventional synchronous generators in fu-ture power grids with rich renewable energy sources. This transition introduces in-creased 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 so-lutions 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 commit-ment (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 loca-tional RoCoF stability with minimum conservativeness.