Simone Paoletti (Università di Siena)
New Issues in Electric Load Forecasting for Smart Grids
Electric load forecasting is a well-established topic and a rich variety of approaches has been proposed in the literature. However, the advent of smart grids opens new issues in load forecasting related to new factors affecting the electricity demand in the smart grid environment. Among these, Active Demand (or Demand Response) represents a scenario in which households and small commercial consumers “participate” in the grid management through appropriate modifications of their consumption profiles during certain time periods in return of a monetary reward. The participation is mediated by a new player, called aggregator, who designs the consumption profile modifications to make up standardized products to be sold on the energy market. The presence of this new “input” generated by aggregators modifies the consumers’ behavior, asking for load forecasting algorithms which explicitly take into account the Active Demand effect. This talk illustrates an approach to load forecasting in the presence of Active Demand based on grey-box models, where the seasonal component of the load is extracted through a suitable pre-processing and the Active Demand is considered as an exogenous input to a linear transfer function model. The approach is thought for a distribution system operator which performs technical validation of Active Demand products, and therefore possesses full information about Active Demand in the network. A comparison of the performance of the proposed approach with techniques not using the information on Active Demand and with approaches based on nonlinear black-box models is performed using real measurements, representing the aggregated load of about 60 consumers from an Italian LV network.