Low-carbon operation method of the building based on dynamic carbon emission factor of power system

被引:3
作者
Bu, Le [1 ]
Chen, Xingying [1 ]
Gan, Lei [1 ]
Yu, Kun [1 ]
Zhou, Yue [1 ]
Cao, Jiawei [1 ]
Cao, Yuan [1 ]
机构
[1] Hohai Univ, Coll Energy & Elect Engn, Nanjing 210098, Peoples R China
关键词
Carbon - Energy conservation - Energy utilization - Historic preservation;
D O I
10.1049/stg2.12085
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Energy conservation and carbon reduction in building energy is an important way to achieve the global goal of 'carbon neutrality'. Common low-carbon operation strategies of buildings rely on price incentives to guide users' behaviour, which is difficult to make users aware of the impact of their energy consumption behaviour on carbon emissions. In this paper, the power system's dynamic carbon emission factors (CEF) were used to release information on energy consumption and carbon emission to building users. At the same time, the differential effects of building envelope and external temperature in the Building Information Modelling were considered. An optimisation method of building low-carbon energy consumption strategy considering both the building and power carbon emission was established to improve the comprehensive carbon reduction ability of the building and power system. The simulation results show that the proposed method effectively coordinates the building virtual energy storage and demand response. By incorporating the dynamic energy carbon transaction cost into the objective function, the target signal of carbon reduction is transmitted to users so that the volatility of the renewable Energy and other random energy behaviours can be considered in the dynamic CEF.
引用
收藏
页码:67 / 85
页数:19
相关论文
共 32 条
[1]   A review of data-driven building energy consumption prediction studies [J].
Amasyali, Kadir ;
El-Gohary, Nora M. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 :1192-1205
[2]  
[Anonymous], 2019, Standard for Calculation of Building Carbon Emission
[3]   Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques [J].
Cai, Mengmeng ;
Pipattanasomporn, Manisa ;
Rahman, Saifur .
APPLIED ENERGY, 2019, 236 :1078-1088
[4]  
Chen S, 2015, 2015 6 INT C MAN SCI, P443
[5]   Modeling Carbon Emission Flow in Multiple Energy Systems [J].
Cheng, Yaohua ;
Zhang, Ning ;
Wang, Yi ;
Yang, Jingwei ;
Kang, Chongqing ;
Xia, Qing .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (04) :3562-3574
[6]   Peer-to-Peer Energy Sharing Among Smart Energy Buildings by Distributed Transaction [J].
Cui, Shichang ;
Wang, Yan-Wu ;
Xiao, Jiang-Wen .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (06) :6491-6501
[7]   Modeling of Heating and Cooling Energy Needs in Different Types of Smart Buildings [J].
Efkarpidis, Nikolaos A. ;
Christoforidis, Georgios C. ;
Papagiannis, Grigoris K. .
IEEE ACCESS, 2020, 8 :29711-29728
[8]  
[Eggleston S. IPCC. IPCC.], 2006, IPCC Guidelines for National Greenhouse Gas Inventories, V4
[9]   Retrofitting strategy for building envelopes to achieve energy efficiency [J].
El-Darwish, Ingy ;
Gomaa, Mohamed .
ALEXANDRIA ENGINEERING JOURNAL, 2017, 56 (04) :579-589
[10]  
[侯立强 Hou Liqiang], 2017, [土木建筑与环境工程, Journal of Civil, Architectural & Environmental Engineering], V39, P56