Models and Techniques for Electric Load Forecasting in the Presence of Demand Response

Abstract

Demand-side management has been recently recognized as a strategic concept in smart electricity grids. In this context, active demand (AD) represents a demand response scenario in which households and small commercial consumers participate in grid management through appropriate modifications of their consumption patterns during certain time periods in return of a monetary reward. The participation is mediated by a new player, called aggregator, who designs the consumption pattern modifications to make up standardized products to be sold on the energy market. The presence of this new input to consumers generated by aggregators modifies the load behavior, asking for load forecasting algorithms that explicitly consider the AD effect. In this paper, we propose an approach to load forecasting in the presence of AD, based on gray-box models where the seasonal component of the load is extracted by a suitable preprocessing and AD is considered as an exogenous input to a linear transfer function model. The approach is thought for a distribution system operator that performs technical validation of AD products, and therefore possesses full information about the AD schedule in the network. A comparison of the performance of the proposed approach with techniques not using the information on AD and with approaches based on nonlinear black-box models is performed on a real load time series recorded in an area of the Italian low voltage network.