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With the goal of electricity system decarbonization, conventional synchronousgenerators are gradually replaced by converter-interfaced renewablegenerations. Such transition is causing concerns over system frequency andrate-of-change-of-frequency (RoCoF) security due to significant reduction insystem inertia. Existing efforts are mostly derived from uniform systemfrequency response model which may fail to capture all characteristics of thesystems. To ensure the locational frequency security, this paper presents adeep neural network (DNN) based RoCoF-constrained unit commitment (DNN-RCUC)model. RoCoF predictor is trained to predict the highest locational RoCoF basedon a high-fidelity simulation dataset. Training samples are generated frommodels over various scenarios, which can avoid simulation divergence and systeminstability. The trained network is then reformulated into a set ofmixed-integer linear constraints representing the locational RoCoF-limitingconstraints in unit commitment. The proposed DNN-RCUC model is studied on theIEEE 24-bus system. Time domain simulation results on PSS/E demonstrate theeffectiveness of the proposed algorithm.

Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems