Energy flexibility of commercial buildings for demand response applications in Australia

被引:4
|
作者
Afroz, Zakia [1 ]
Goldsworthy, Mark [1 ]
White, Stephen D. [1 ]
机构
[1] Commonwealth Sci & Ind Res Org CSIRO Energy Ctr, Newcastle, NSW 2304, Australia
关键词
Demand response; HVAC commercial buildings; Setpoint adjustment; Energy flexibility; SENSITIVITY-ANALYSIS; NEURAL-NETWORK; CONSUMPTION; MODEL; LOAD; SIMULATION; UNCERTAINTY; VALIDATION; APPLIANCES; SYSTEMS;
D O I
10.1016/j.enbuild.2023.113533
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Demand response (DR) is widely recognized as an important mechanism in the Australian electricity market, though large-scale uptake in commercial buildings is yet to occur, in part due to the difficulty of characterising the resource. This paper describes a bottom-up physics-based approach to characterise the DR potential of three types of commercial buildings (schools, offices, and data centres) under a global set-point temperature offset strategy. Representative models are calibrated with energy meter data and parametric analysis is used to assess sensitivity to different building, operating and system parameters. Parametric equations are provided for relative DR potentials as functions of temperature and time of day. School buildings were found to have the highest relative DR potential (-40-45%) for ambient temperatures over 30 degrees C, followed by data centres (-20-30%) and offices (-20%). Location has the strongest relative influence for school and office buildings and equipment energy intensity for data centres. The Australia-wide combined DR potential for school, office and data centres is estimated to be between 551 and 647 MW. Office buildings have the highest aggregate potential at between 1.5 and 1.7 times that of school buildings and between 9 and 11 times that of data centres.
引用
收藏
页数:36
相关论文
共 50 条
  • [21] Transactive Control Design for Commercial Buildings to Provide Demand Response
    Huang, Sen
    Lian, Jianming
    Hao, He
    Katipamula, Srinivas
    IFAC PAPERSONLINE, 2019, 51 (34): : 151 - 156
  • [22] Accuracy of hourly energy predictions for demand flexibility applications
    Granderson, Jessica
    Fernandes, Samuel
    Crowe, Eliot
    Sharma, Mrinalini
    Jump, David
    Johnson, Devan
    ENERGY AND BUILDINGS, 2023, 295
  • [23] Energy demand flexibility in buildings and district heating systems - a literature review
    Luc, Katarzyna M.
    Heller, Alfred
    Rode, Carsten
    ADVANCES IN BUILDING ENERGY RESEARCH, 2019, 13 (02) : 241 - 263
  • [24] Optimal demand charge reduction for commercial buildings through a combination of efficiency and flexibility measures
    Zhang, Yuna
    Augenbroe, Godfried
    APPLIED ENERGY, 2018, 221 : 180 - 194
  • [25] Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation
    Amin, Amin
    Kem, Oudom
    Gallegos, Pablo
    Chervet, Philipp
    Ksontini, Feirouz
    Mourshed, Monjur
    APPLIED ENERGY, 2022, 305
  • [26] Energy flexibility quantification of grid-responsive buildings: Energy flexibility index and assessment of their effectiveness for applications
    Tang, Hong
    Wang, Shengwei
    Energy, 2021, 221
  • [27] Energy flexibility quantification of grid-responsive buildings: Energy flexibility index and assessment of their effectiveness for applications
    Tang, Hong
    Wang, Shengwei
    ENERGY, 2021, 221
  • [28] Peak Demand Optimization of Commercial Buildings based on Energy Storage Systems
    Yang, Zezhang
    Rabinowitz, Jake
    Li, Jian
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 5299 - 5304
  • [29] REDUCING ENERGY DEMAND IN COMMERCIAL BUILDINGS: BALANCING CONVECTION AND RADIANT COOLING
    Chusak, Lee
    Harris, Andrew
    Agarwal, Ramesh
    ES2010: PROCEEDINGS OF ASME 4TH INTERNATIONAL CONFERENCE ON ENERGY SUSTAINABILITY, VOL 1, 2010, : 1047 - 1054
  • [30] Distributed Control of Multi-zone Commercial Buildings for Demand Response
    Tang, Suigu
    Xu, Yinliang
    2017 IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2), 2017,