Circuit-Theoretic Joint Parameter-State Estimation of Utility-Scale Photovoltaic, Battery, and Grid Systems

TL;DR

This paper introduces a circuit-theoretic AC state estimation method that accurately models and estimates the states of grid, PV, and battery systems simultaneously, improving real-time power grid modeling.

Contribution

It develops an aggregated circuit model and a joint estimation algorithm that incorporates detailed physics and measurements of PV and battery systems.

Findings

Accurately estimates states of 10,000-node transmission networks.

Outperforms stand-alone estimation methods in accuracy.

Robust against erroneous parameters.

Abstract

Solar PV and battery storage systems have become integral to modern power grids. Therefore, bulk grid models in real-time operation must include their physical behavior accurately for analysis and optimization. AC state estimation is critical to building real-time bulk power systems models. However, current ACSE techniques do not include detailed physics and measurements for battery and PV systems. This results in sub-optimal estimation results and subsequent less accurate bulk grid models for real-time operation. To address these challenges, we formulate a circuit-theoretic AC state estimator with accurate PV and battery systems physics and corresponding measurements. First, we propose an aggregated equivalent circuit model of the solar PV, battery, and traditional grid components. Next, we add measurements from PV and battery systems to the traditional measurement set to facilitate accurate estimation of the overall grid model. Finally, we develop a circuit-theoretic joint parameter-state estimation algorithm that can accurately estimate grid, PV, and battery system states and is robust against erroneous parameters. To demonstrate the efficacy of the proposed framework, we estimate the states of 10k node transmission networks with hundreds of battery+PV-tied systems. We compare the accuracy against the estimation of stand-alone grid, battery, and PV systems.