User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response [electronic resource] : Preprint
- Washington, D.C. : United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, 2017.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- 1.4 MB : digital, PDF file
- Additional Creators:
- National Renewable Energy Laboratory (U.S.), United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.
- Report Numbers:
- E 1.99:nrel/cp-5500-68037
- Published through SciTech Connect.
Presented at the 2017 American Control Conference (ACC), 24-26 May 2017, Seattle, Washington.
Jin, Xin; Baker, Kyri; Christensen, Dane; Isley, Steven.
- Funding Information:
View MARC record | catkey: 23761132