Smart Households' Aggregated Capacity Forecasting for Load Aggregators Under Incentive-Based Demand Response Programs

• Published in: IEEE transactions on industry applications Vol. 56; no. 2; pp. 1086 - 1097
• Main Authors: Wang, Fei, Xiang, Biao, Li, Kangping, Ge, Xinxin, Lu, Hai, Lai, Jingang, Dehghanian, Payman
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-03-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregated capacity; Customers; Direct reduction; Economic forecasting; Energy management; home energy management system (HEMS); Households; incentive-based demand response (IBDR); load aggregator (LA); Mathematical models; Principal components analysis; Residential energy; smart household (SH); Support vector machines
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2020.2966426

The technological advancement in the communication and control infrastructure helps those smart households (SHs) that 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 before trading in the day-ahead market. However, there are few studies that forecast the available aggregated DR capacity from LAs' perspective. Therefore, this article proposes a forecasting model aiming to aid LAs forecast the available aggregated SHs' DR capacity in the day-ahead market. First, a home energy management system is implemented to perform optimal scheduling for SHs and to model the customers' responsive behavior in the IBDR program; second, a customer baseline load estimation method is applied to quantify the SHs' aggregated DR capacity during DR days; third, several features which may have significant impacts on the aggregated DR capacity are extracted and they are processed by principal component analysis; and finally, a support vector machine based forecasting model is proposed to forecast the aggregated SHs' DR capacity in the day-ahead market. The case study indicates that the proposed forecasting framework could provide good performance in terms of stability and accuracy.