Smart grid demand response management using internet of things for load shedding and smart-direct load control

Abstract

This paper proposes the use of a novel algorithm for smart direct load control and load shedding to minimize the power outage in sudden grid load changes, as well as reduce the Peak-to-Average Ratio (PAR). The algorithm uses forecasting, shedding, and smart direct load control. The algorithm also uses the Internet of Things and stream analytics to provide real-time load control, and generates a daily schedule for customers' equipped with IEDs, based on their demands, comfort, and the forecasted load model. The demand response techniques are utilized for real time load control and optimization. To test the algorithm, a simulation system was developed that takes into account one hundred customers owning randomly selected appliances. The results indicated that load shedding using ARIMA time series prediction model and applying smart direct load control (S-DLC) and Internet of Things can remarkably reduce customers' power outage.