期刊
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 56, 期 2, 页码 1086-1097出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2020.2966426
关键词
Aggregated capacity; home energy management system (HEMS); incentive-based demand response (IBDR); load aggregator (LA); smart household (SH)
资金
- National Key R&D Program of China [2018YFE0122200]
- National Natural Science Foundation of China [51577067]
- Major Science and Technology Achievements Conversion Project of Hebei Province [19012112Z]
- State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources [LAPS19016]
- Fundamental Research Funds for the Central Universities [2018QN077]
- Science and Technology Project of State Grid Corporation of China [SGHE0000KXJS1800163, kjgw2018-014]
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.
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