Residential load forecasting under a demand response program based on economic incentives

This paper describes a tool for an Aggregator to forecast the aggregated load demand response of a group of domestic customers subscribed to an indirect load control program based on price/volume signals. The tool employs a bottom-up approach based on physical end-use load models where the individual responses of a random sample of customers are combined in order to build the aggregated load demand response model. Simulation of the individual responses is carried out with an optimization algorithm based on mixed integer linear programming that minimizes the electricity bill whilst maintaining consumer's comfort level. To improve the performance of the model, a genetic algorithm for fitting the input parameters according to measured data is also provided. The tool is intended to allow the Aggregator rehearsing the impact of different control strategies and therefore choosing the most appropriate ones for market participation and portfolio optimization. To exemplify the methodological applicability of the developed algorithm, a case study based on an actual power system in eastern Spain is considered.