A Distributionally Robust Control Strategy for Frequency Safety based on Koopman Operator Described System Model

TL;DR

This paper proposes a distributionally robust control framework using Koopman operator models to enhance frequency safety in power systems with high renewable energy, addressing uncertainties in data-driven predictions.

Contribution

It introduces a novel distributionally robust emergency frequency control method based on Koopman operator models and Wasserstein ambiguity sets, improving safety and efficiency.

Findings

Ensures frequency safety under uncertain prediction errors.

Achieves low control costs and computational efficiency.

Demonstrates effectiveness through simulations.

Abstract

As the proportion of renewable energy and power electronics in the power system increases, modeling frequency dynamics under power deficits becomes more challenging. Although data-driven methods help mitigate these challenges, they are exposed to data noise and training errors, leading to uncertain prediction errors. To address uncertain and limited statistical information of prediction errors, we introduce a distributionally robust data-enabled emergency frequency control (DREFC) framework. It aims to ensure a high probability of frequency safety and allows for adjustable control conservativeness for decision makers. Specifically, DREFC solves a min-max optimization problem to find the optimal control that is robust to distribution of prediction errors within a Wasserstein-distance-based ambiguity set. With an analytical approximation for VaR constraints, we achieve a computationally efficient reformulations. Simulations demonstrate that DREFC ensures frequency safety, low control costs and low computation time.