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

IEEE Transactions on Industry Applications, vol. 56, pp. 1086–1097

Abstract

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.

Authors 7

1. Fei Wang

   North China Electric Power University · State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources

   Affiliation as printed

   Department of Electrical Engineering and the Hebei Key Laboratory of Distributed Energy Storage and Microgrid, North China Electric Power University, Baoding, China

   State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, China

2. Biao Xiang

   North China Electric Power University

   Affiliation as printed

   Department of Electrical Engineering, North China Electric Power University, Baoding, China

3. Kangping Li

   North China Electric Power University

   Affiliation as printed

   Department of Electrical Engineering, North China Electric Power University, Baoding, China

4. Xinxin Ge

   North China Electric Power University

   Affiliation as printed

   Department of Electrical Engineering, North China Electric Power University, Baoding, China

5. Hai Feng Lu

   Yunnan Power Grid Co., Ltd. (China)

   Affiliation as printed

   Yunnan Power Grid Company, Ltd., Kunming, China

6. Jingang Lai Aachen E.ON Energy Research Center

   RWTH Aachen University

   Affiliation as printed

   E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany

7. Payman Dehghanian

   George Washington University

   Affiliation as printed

   Department of Electrical and Computer Engineering, George Washington University, Washington, USA