AC-aware Optimization Framework for Under-Frequency Load Shedding

TL;DR

This paper introduces an AC-aware optimization framework for under-frequency load shedding that uses a high-fidelity reduced-order model to adapt load shedding strategies dynamically, improving system stability during disturbances.

Contribution

It develops a novel AC-aware predictive optimization method utilizing model order reduction to enhance UFLS scheme adaptability and effectiveness.

Findings

Outperforms traditional static UFLS schemes.

Maintains system stability across various scenarios.

Validates effectiveness on a large 1,648-bus system.

Abstract

Under-frequency load shedding (UFLS) prevents system collapse during large disturbances. Increased penetration of distributed energy resources (DERs) and reduced system inertia makes it challenging to design a static UFLS scheme, which relies on preset frequency thresholds and load shed fractions to meet design criteria across all possible operating conditions. Due to non-linearity and traceability issues, previous adaptive UFLS schemes use simplified tractable frequency models that overlook AC network effects such as voltage-dependent load/generation. This paper leverages model order reduction techniques to obtain a higher fidelity low-order model of system frequency dynamics that captures AC network effects while incorporating turbine governor action and their associated limits. The model is then used in a new AC-aware predictive optimization framework to adapt UFLS setpoints periodically based on current operating conditions while minimizing load shed. Validated on a 1,648-bus system with PSS/E simulations, the proposed method meets design criteria under various operating conditions and disturbance scenarios. Furthermore, the framework outperforms conventional static UFLS schemes and adaptive UFLS schemes based on simplified dynamic models.