Smart Households' Available Aggregated Capacity Day-ahead Forecast Model for Load Aggregators under Incentive-based Demand Response Program

Abstract

The rapid development of smart grid and smart appliances helps those smart households (SHs) more actively participate in the incentive-based demand response (IBDR) programs. As the agent facilitating the SHs' participation in the IBDR program, load aggregators (LAs) need to comprehend the available SHs' demand response (DR) capacity when trading with the system operator in the day-ahead market. This paper proposes a forecasting model aiming to aid LAs forecast the available aggregated SHs' DR capacity in the day-ahead market. Firstly, a home energy management system (HEMS) is implemented to perform an optimal scheduling for SHs and to model the customers' responsive behavior in the IBDR program; secondly, a customer baseline load (CBL) estimation method is applied to quantify the SHs' aggregated DR capacity during DR days; thirdly, several features which may have significant impacts on the aggregated DR capacity are extracted; finally, a support vector machine (SVM) based forecast model is proposed to forecast the aggregated SHs' DR capacity in the day-ahead market.