Short-term Electric Load Forecasting Using Grey Models By Considering Demand Response

In this paper the modified grey models are proposed for short-term load forecasting in presence of demand response. The demand response is a valuable element of the smart grids. On the other hand, short-term load forecasting is very important for energy purchase planning and optimal operating of restructured power systems. Since the consumer participation is undetermined and inherently uncertain, the load forecasting will be a difficult task in presence of demand response. Numerous methods have been proposed to the load forecasting, which they have little ability to track demand side reaction. Therefore, requirement of a high precision method to model and predict the electric load in presence of demand response is appreciable. In this paper, grey models, which utilize low number of data to high precision prediction, have been modified by an iterative strategy to short-term load forecasting in presence of demand response. Since grey models are local predictors, they show better ability in modeling and forecasting of the load profiles with the unexpected and sudden changes. After applying demand response scenarios on Iran consumption load data, they have been utilized to verify the proposed method. Simulation results show high performance and accuracy of the proposed methods.