Robust Dynamic State Estimation of Multi-Machine Power Networks with Solar Farms and Dynamics Loads

TL;DR

This paper introduces a robust state estimator for modern power networks integrating renewable energy sources and loads, utilizing Lyapunov stability and convex optimization to improve accuracy amid uncertainties.

Contribution

It develops a novel estimator based on nonlinear DAE models for renewable-rich power systems, addressing the limitations of traditional fossil fuel-based models.

Findings

Estimator is robust against load demand uncertainties

Effective in handling solar irradiance variability

Validated on IEEE-39 bus system simulations

Abstract

Conventional state estimation routines of electrical grids are mainly reliant on dynamic models of fossil fuel-based resources. These models commonly contain differential equations describing synchronous generator models and algebraic equations modeling power flow/balance equations. Fuel-free power systems that are driven by inertia-less renewable energy resources will hence require new models and upgraded estimation routines. To that end, in this paper we propose a robust estimator for an interconnected model of power networks comprised of a comprehensive ninth order synchronous generator model, advanced power electronics-based models for photovoltaic (PV) power plants, constant power loads, constant impedance loads, and motor loads. The presented state estimator design is based on Lyapunov stability criteria for nonlinear differential algebraic equation (DAE) models and is posed as a convex semi-definite optimization problem. Thorough simulations studies have been carried out on IEEE-39 bus test system to showcase the robustness of the proposed estimator against unknown uncertainty from load demand and solar irradiance.