Frequency-Dynamics-Aware Economic Dispatch with Optimal Grid-Forming Inverter Allocation and Reserved Power Headroom

TL;DR

This paper introduces a deep learning-based frequency-constrained optimal power flow framework that accurately predicts frequency dynamics and optimally allocates grid-forming IBRs to enhance system stability and economic efficiency.

Contribution

It develops a novel DL model integrated into FCOPF for precise frequency support estimation, addressing limitations of existing simplified models.

Findings

DL-FCOPF achieves minimum cost and desired frequency response.

The approach outperforms traditional OPF and linear FCOPF benchmarks.

Sensitivity analysis identifies optimal DL predictor structures.

Abstract

The high penetration of inverter-based resources (IBRs) reduces system inertia, leading to frequency stability concerns, especially during synchronous generator (SG) outages. To maintain frequency dynamics within secure limits while ensuring economic efficiency, frequency-constrained optimal power flow (FCOPF) is employed. However, existing studies either neglect the frequency support capability and allocation of grid-forming (GFM) IBRs or suffer from limited accuracy in representing frequency dynamics due to model simplifications. To address this issue, this paper proposes a deep learning (DL)-based FCOPF (DL-FCOPF) framework. A DL model is first developed as a predictor to accurately estimate frequency-related metrics: the required reserved headroom and allocation of GFM IBRs, the rate of change of frequency and frequency nadir. After being trained with data obtained from electromagnetic transient simulations, the DL model is reformulated and incorporated into FCOPF. Case studies conducted on two test systems demonstrate the effectiveness of the proposed approach. Compared with the traditional OPF and linear FCOPF benchmarks, the DL-FCOPF can optimally coordinate SGs and IBRs with minimum cost, achieving desired frequency response, within an acceptable computing time. Further-more, sensitivity analyses are conducted to identify the most suit-able structure and linearization approach of the DL-based frequency predictor.