Day-ahead Probabilistic Forecasting of Achievable Incentive-based Demand Response Potential for Load Aggregator

Abstract

As an intermediary between residential customers and system operators, load aggregators (LA) are responsible for integrating the residential customers' demand response (DR) potential and enabling their transactions in the day-ahead market. Profit of the market trading process mainly relies on the pre-estimated achievable DR potential, which puts forwards the necessity of its accurate forecasting. Therefore, this paper proposes a probabilistic forecasting model to forecast the day-ahead achievable DR potential at the aggregated level under an incentive-based demand response (IBDR) program. Firstly, we attempt to establish the customers' DR potential, during which a home energy management system (HEMS) is introduced to implement load adjustment for electrical appliances. Secondly, several features that may affect the DR potential are extracted, among which the more relevant ones are selected through the support vector machine recursive feature elimination (SVM-RFE) method. Finally, based on these selected features, a support vector machine (SVM) method is adopted to establish the DR potential point forecasting model, and then the probabilistic forecasting model of the aggregated DR potential is established through the superimposition of the point forecasting results and the corresponding error distribution, the latter could be estimated by the non-parametric kernel density (NKDE) method. Case studies show that a good performance could be achieved by the proposed probabilistic forecasting model and the feature selection process could significantly improve the forecasting accuracy.