2019/04/16 by Thiagarajan Ramachandran, Ramachandran, Thiagarajan, Andrew P. Reiman +7
Engineering · #Energy Load and Power Forecasting #FOS: Electrical engineering #FOS: Mathematics #Optimal Power Flow Distribution #Optimization and Control (math.OC) #Power System Optimization and Stability #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1904.08036
openalex publication_date 2019/04/16 · openalex created_date 2022/07/24 · openalex updated_date 2026/08/01
Low-to-medium voltage distribution networks are experiencing rising levels of\ndistributed energy resources, including renewable generation, along with\nimproved sensing, communication, and automation infrastructure. As such, state\nestimation methods for distribution systems are becoming increasingly relevant\nas a means to enable better control strategies that can both leverage the\nbenefits and mitigate the risks associated with high penetration of variable\nand uncertain distributed generation resources. The primary challenges of this\nproblem include modeling complexities (nonlinear, non-convex power-flow\nequations), limited availability of sensor measurements, and high penetration\nof uncertain renewable generation. This paper formulates the distribution\nsystem state estimation as a nonlinear, weighted, least squares problem, based\non sensor measurements as well as forecast data (both load and generation). We\ninvestigate the sensitivity of state estimator accuracy to (load/generation)\nforecast uncertainties, sensor accuracy, and sensor coverage levels.\n