Deep Neural Network-Enhanced Frequency-Constrained Optimal Power Flow with Multi-Governor Dynamics

TL;DR

This paper introduces a deep neural network-enhanced optimal power flow method that explicitly incorporates frequency security constraints, improving real-time power system stability analysis.

Contribution

It develops a DNN-based frequency prediction model integrated into FCOPF, transforming complex nonlinear dynamics into a MILP form for efficient optimization.

Findings

DNN accurately predicts frequency metrics from system conditions.

The proposed method outperforms traditional models in maintaining frequency security.

Extensive simulations validate the effectiveness of the DNN-FCOPF approach.

Abstract

To ensure frequency security in power systems, both the rate of change of frequency (RoCoF) and the frequency nadir (FN) must be explicitly accounted for in real-time frequency-constrained optimal power flow (FCOPF). However, accurately modeling sys-tem frequency dynamics through analytical formulations is chal-lenging due to their inherent nonlinearity and complexity. To address this issue, deep neural networks (DNNs) are utilized to capture the nonlinear mapping between system operating condi-tions and key frequency performance metrics. In this paper, a DNN-based frequency prediction model is developed and trained using the high-fidelity time-domain simulation data generated in PSCAD/EMTDC. The trained DNN is subsequently transformed into an equivalent mixed-integer linear programming (MILP) form and embedded into the FCOPF problem as additional con-straints to explicitly enforce frequency security, leading to the proposed DNN-FCOPF formulation. For benchmarking, two alternative models are considered: a conventional optimal power flow without frequency constraints and a linearized FCOPF in-corporating system-level RoCoF and FN constraints. The effec-tiveness of the proposed method is demonstrated by comparing the solutions of these three models through extensive PSCAD/EMTDC time-domain simulations under various loading scenarios.