期刊
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 54, 期 2, 页码 1017-1028出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2017.2781639
关键词
Building energy management system (BEMS); demand response (DR); indoor environmental modeling; multiobjective optimization; synergetic dispatch
资金
- National Natural Science Foundation of China [51577067]
- Beijing Natural Science Foundation of China [3162033]
- Hebei Natural Science Foundation of China [E2015502060]
- State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources [LAPS16007, LAPS16015]
- Science and Technology Project of the State Grid Corporation of China
- State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems (China Electric Power Research Institute) [5242001600 FB]
- China Scholarship Council
- Fonds Europeen de Developpement Economique et Regional funds through COMPETE
- Portuguese funds through Fundacao para a Ciencia e a Tecnologia [SAICT-PAC/0004/2015-POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013]
- EU [309048]
The optimized operation of a building energy management system (BEMS) is of great significance to its operation security, economy, and efficiency. This paper proposes a day-ahead multiobjective optimization model for the BEMS under time-of-use price-based demand response (DR), which integrates building integrated photovoltaic with other generations to optimize the economy and occupants' comfort by the synergetic dispatch of source-load-storage. The occupants' comfort contains three aspects of the indoor environment: visual comfort; thermal comfort; and indoor air quality comfort. With the consideration of controllable load that could participate in DR programs, the balances among different energy styles, electric, thermal, and cooling loads are guaranteed during the optimized operation. YALMIP toolbox in MATLAB was applied to solve the optimization problem. Finally, a case study was conducted to validate the effectiveness and adaptability of the proposed model.
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