Distribution System State Estimation in the Presence of High Solar Penetration
It addresses the challenge of state estimation in distribution networks with high solar penetration, which is important for enabling better control strategies.
The paper formulates distribution system state estimation as a nonlinear weighted least squares problem using sensor measurements and forecast data, and investigates sensitivity to forecast uncertainties, sensor accuracy, and coverage levels.
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.