Estimation of percentage of controllable load in total demand at bulk supply point

Abstract

This paper presents an Artificial Neural Network (ANN) based approach to estimate percentage of controllable load in overall demand at bulk supply point at any given time based on standard voltage, real and reactive power measurements at the substation. Monte Carlo Simulation (MCS) is used to generate the training and validation data. The estimated controllable and uncontrollable load percentages are compared with the targets in the validation process, and the probability distribution and the confidence levels of load participation estimation errors are obtained. When all inputs are available, the most probable absolute error of estimation of controllable and uncontrollable load percentage is approximately 4.3%, with about 60% of all estimations having absolute errors below 10%. The robustness of the methodology with respect to missing input data is also evaluated. It demonstrates that the absence of an input, especially the absence of the reactive power, can reduce the confidence level of estimation with the same estimation error.