State Estimation with Exogenous Information for Grids with Large Renewable Penetration

Abstract

State estimation is one of the fundamental power system tools used in the determination of system state variables based on measurements. Current approaches for state estimation problems rely only on direct measurement and pseudo-measurements related with electric parameters only. In this work, we introduce a methodology for the inclusion of weather data, linked with renewable production, as exogenous measured parameters into the state estimation problem. To test our proposed framework, a simulation environment was developed and validated in a 5-bus system. Statistical significance and stability of our proposed framework were investigated by running 100 simulation experiments. We also, explored statistical test analysis for detection of dishonest manipulation of renewable power injected. Our numerical test showed that state estimation with exogenous weather parameter measurement could enhance state estimation accuracy by up to 79%. We also showed that, with 99% of confidence, our framework is able to detect 81.25% of dishonest cases with small cases of false positives.