A Deep Neural Network-based Frequency Predictor for Frequency-Constrained Optimal Power Flow

TL;DR

This paper introduces a deep neural network-based frequency predictor integrated into a frequency-constrained optimal power flow model to enhance grid frequency stability, validated through simulations and benchmark comparisons.

Contribution

It develops a DNN-based frequency predictor for FCOPF, reformulates it with MILP, and demonstrates improved frequency stability in power system optimization.

Findings

DNN accurately predicts frequency metrics from simulation data.

The proposed DNN-FCOPF outperforms traditional models in stability.

Simulation results confirm effectiveness across load profiles.

Abstract

Rate of change of frequency (RoCoF) and frequency nadir should be considered in real-time frequency-constrained optimal power flow (FCOPF) to ensure frequency stability of the modern power systems. Since calculating the frequency response is complex, deep neural network (DNN) could be adopted to capture the nonlinearities and estimate those two metrics accurately. Therefore, in this paper, a DNN-based frequency predictor is developed with the training data obtained from time-domain simulations using PSCAD/EMTDC. Subsequently, it is reformulated using a set of mixed-integer linear programming formulations and then embedded into the FCOPF framework as constraints to ensure grid frequency stability, creating the proposed DNN-FCOPF model. Two benchmark models, a traditional OPF without any frequency constraints and a linear system-wide RoCoF-constrained FCOPF, are also implemented to gauge the proposed DNN-FCOPF. Finally, the solutions obtained with these three models are compared and evaluated with time-domain simulations using PSCAD under various load profiles, demonstrating the effectiveness of the proposed DNN-FCOPF.