Distribution System State Estimation with AMI Based on Load Correction Method

State estimation for distribution systems (DSSE) is an important smart grid function for greater penetration of distributed renewable generation and energy storage, system resiliency, controllability, improved fault management, and optimal performance. The deployment of Advanced Metering Infrastructure (AMI) by the utilities provides useful near real-time data, which helps to overcome the historical limitation of measurement. Researches have proposed several estimation techniques; however, accuracy, observability and other issues still remain. This paper evaluates the Load-Calibration State Estimation method that compares the calculated and measured power (active and reactive) at source substation using forward backward load flow algorithm. Voltage and customer load demand data are processed into normalized daily load profile from AMI smart meters for state estimator. A digital radial distribution network database, modeler, and simulator system provides a testing and verification environment. The testing platform simulates various operating conditions and generates measurement data with additive noise. The state estimator is tested on the IEEE 13 and IEEE 37-bus test systems, and the estimator's output is compared with the known answers. Results indicate better speed and accuracy for the Load-Calibration State Estimator over the traditional transmission-level non-linear leastsquares estimator.