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Evaluating energy supply service reliability for commercial air conditioning loads from the distribution network aspect

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  • Cheng, Lin
  • Wan, Yuxiang
  • Tian, Liting
  • Zhang, Fang

Abstract

The limited amount of dispatching flexibility has encouraged the development and implementation of demand response in the distribution system. Commercial air conditioning loads are promising demand response resources and tend to have greater potential for centralized regulation. From the perspective of distribution networks, evaluating the reliability of energy supply services for air conditioning loads should be of much concern because reliable services would directly affect the customers’ tendency to demand response. However, traditional load reliability evaluation does not fully explore the fact that the energy supply processes of air conditioning loads can be flexible. Modeling the energy demands as flexible loads, a reliability assessment algorithm for commercial air conditioning loads is proposed for distribution systems in this paper. The “loss of load” event is innovatively defined considering customer preferences. Novel systematic indices are established that are specialized for describing the reliability of energy supply services. Afterward, a two-stage control strategy is proposed considering the comfort requirements of customers, which would affect reliability performance. The first stage optimizes the operating state of the air conditioning system internal equipment collaboratively, while the second stage sacrifices some comfort for sufficient utilization of the thermal self-storage capacity and load rebound suppression. Finally, the proposed method is validated in a standard test system with voltage violation risks. The results demonstrate the impacts of different control strategies on the reliability performance of commercial air conditioning loads. Analysis drawn from the sensitivity of load reliability indices can assist distribution system operators in coordinating energy supply reliability and dispatching requirements.

Suggested Citation

  • Cheng, Lin & Wan, Yuxiang & Tian, Liting & Zhang, Fang, 2019. "Evaluating energy supply service reliability for commercial air conditioning loads from the distribution network aspect," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:36
    DOI: 10.1016/j.apenergy.2019.113547
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    1. Solano, J.C. & Olivieri, L. & Caamaño-Martín, E., 2017. "Assessing the potential of PV hybrid systems to cover HVAC loads in a grid-connected residential building through intelligent control," Applied Energy, Elsevier, vol. 206(C), pages 249-266.
    2. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    3. Kwag, Hyung-Geun & Kim, Jin-O, 2014. "Reliability modeling of demand response considering uncertainty of customer behavior," Applied Energy, Elsevier, vol. 122(C), pages 24-33.
    4. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    5. Ding, Yi & Cui, Wenqi & Zhang, Shujun & Hui, Hongxun & Qiu, Yiwei & Song, Yonghua, 2019. "Multi-state operating reserve model of aggregate thermostatically-controlled-loads for power system short-term reliability evaluation," Applied Energy, Elsevier, vol. 241(C), pages 46-58.
    6. Li, Gengfeng & Bie, Zhaohong & Xie, Haipeng & Lin, Yanling, 2016. "Customer satisfaction based reliability evaluation of active distribution networks," Applied Energy, Elsevier, vol. 162(C), pages 1571-1578.
    7. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    8. Mathieu, Johanna L. & Dyson, Mark E.H. & Callaway, Duncan S., 2015. "Resource and revenue potential of California residential load participation in ancillary services," Energy Policy, Elsevier, vol. 80(C), pages 76-87.
    9. Wang, D. & Parkinson, S. & Miao, W. & Jia, H. & Crawford, C. & Djilali, N., 2012. "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, Elsevier, vol. 96(C), pages 104-114.
    10. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    11. Wang, Jixiang & Chen, Xingying & Xie, Jun & Xu, Shuyang & Yu, Kun & Gan, Lei, 2019. "Dynamic control strategy of residential air conditionings considering environmental and behavioral uncertainties," Applied Energy, Elsevier, vol. 250(C), pages 1312-1320.
    12. Lei Zhou & Yang Li & Beibei Wang & Zhe Wang & Xiaoqing Hu, 2015. "Provision of Supplementary Load Frequency Control via Aggregation of Air Conditioning Loads," Energies, MDPI, vol. 8(12), pages 1-20, December.
    13. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Zhang, Zhen, 2018. "Coordination optimization of multiple thermostatically controlled load groups in distribution network with renewable energy," Applied Energy, Elsevier, vol. 231(C), pages 456-467.
    14. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
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