# Distribution System State Estimation in the Presence of High Solar Penetration

## TL;DR

This paper develops a state estimation method for distribution networks with high renewable energy penetration, addressing challenges like nonlinear equations, limited sensors, and forecast uncertainties to improve control strategies.

## Contribution

It formulates a nonlinear weighted least squares state estimation approach incorporating forecast data and analyzes its sensitivity to uncertainties and sensor coverage.

## Key findings

- Estimator accuracy is sensitive to forecast errors.
- Sensor coverage significantly impacts estimation quality.
- Inclusion of forecast data enhances state estimation robustness.

## Abstract

Low-to-medium voltage distribution networks are experiencing rising levels of distributed energy resources, including renewable generation, along with improved sensing, communication, and automation infrastructure. As such, state estimation methods for distribution systems are becoming increasingly relevant as a means to enable better control strategies that can both leverage the benefits and mitigate the risks associated with high penetration of variable and uncertain distributed generation resources. The primary challenges of this problem include modeling complexities (nonlinear, non-convex power-flow equations), limited availability of sensor measurements, and high penetration of uncertain renewable generation. This paper formulates the distribution system state estimation as a nonlinear, weighted, least squares problem, based on sensor measurements as well as forecast data (both load and generation). We investigate the sensitivity of state estimator accuracy to (load/generation) forecast uncertainties, sensor accuracy, and sensor coverage levels.