Damping Tuning Considering Random Disturbances Adopting Distributionally Robust Optimization

TL;DR

This paper introduces a distributionally robust damping optimization framework for power systems with high renewable energy penetration, effectively handling uncertainty in disturbances caused by RES fluctuations.

Contribution

It develops a novel control framework that incorporates distributional robustness and Polynomial Chaos Expansion to optimize damping in uncertain power system environments.

Findings

Enhanced stability under RES fluctuations

Robust damping control parameters derived

Effective handling of disturbance distribution uncertainty

Abstract

In scenarios where high penetration of renewable energy sources (RES) is connected to the grid over long distances, the output of RES exhibits significant fluctuations, making it difficult to accurately characterize. The intermittency and uncertainty of these fluctuations pose challenges to the stability of the power system. This paper proposes a distributionally robust damping optimization control framework (DRDOC) to address the uncertainty in the true distribution of random disturbances caused by RES. First, the installation location of damping controllers and key control parameters are determined through Sobol sensitivity indices and participation factors. Next, a nonlinear relationship between damping and random disturbances is established with Polynomial Chaos Expansion (PCE). The uncertainty in the distribution of disturbances is captured by ambiguity sets. The DRDOC is formulated as a convex optimization problem, which is further simplified for efficient computation. Finally, the optimal control parameters are derived through convex optimization techniques. Simulation results demonstrate the effectiveness and distribution robustness of the proposed DRDOC.