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Multi-state operating reserve model of aggregate thermostatically-controlled-loads for power system short-term reliability evaluation

Author

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  • Ding, Yi
  • Cui, Wenqi
  • Zhang, Shujun
  • Hui, Hongxun
  • Qiu, Yiwei
  • Song, Yonghua

Abstract

Thermostatically-controlled-loads (TCLs) have been regarded as a good candidate for maintaining the power system reliability by providing operating reserve. The short-term reliability evaluation of power systems, which is essential for power system operators in decision making to secure the system real time balancing, calls for the accurate modelling of operating reserve provided by TCLs. However, the particular characteristics of TCLs make their dynamic response different from the traditional generating units, resulting in difficulties to accurately represent the reliability of operating reserve provided by TCLs with conventional reliability model. This paper proposes a novel multi-state reliability model of operating reserve provided by TCLs considering their dynamic response during the reserve deployment process. An analytical model for characterizing dynamics of operating reserve provided by TCLs is firstly developed based on the migration of TCLs’ room temperature within the temperature hysteresis band. Then, considering the stochastic consumers’ behaviour and ambient temperature, the probability distribution functions of reserve capacity provided by TCLs are obtained by cumulants. On this basis, the states of reserve capacity and the corresponding probabilities at each time instant are obtained for representing the reliability of operating reserve provided by TCLs in the LZ-transform approach. Case studies are conducted to validate the proposed technique.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:241:y:2019:i:c:p:46-58
    DOI: 10.1016/j.apenergy.2019.02.018
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    1. Anatoly Lisnianski, 2012. "L z -Transform for a Discrete-State Continuous-Time Markov Process and its Applications to Multi-State System Reliability," Springer Series in Reliability Engineering, in: Anatoly Lisnianski & Ilia Frenkel (ed.), Recent Advances in System Reliability, chapter 0, pages 79-95, Springer.
    2. 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.
    3. Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
    4. Georges, Emeline & Cornélusse, Bertrand & Ernst, Damien & Lemort, Vincent & Mathieu, Sébastien, 2017. "Residential heat pump as flexible load for direct control service with parametrized duration and rebound effect," Applied Energy, Elsevier, vol. 187(C), pages 140-153.
    5. Afram, Abdul & Janabi-Sharifi, Farrokh, 2015. "Gray-box modeling and validation of residential HVAC system for control system design," Applied Energy, Elsevier, vol. 137(C), pages 134-150.
    6. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, September.
    7. 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.
    8. Hui, Hongxun & Ding, Yi & Liu, Weidong & Lin, You & Song, Yonghua, 2017. "Operating reserve evaluation of aggregated air conditioners," Applied Energy, Elsevier, vol. 196(C), pages 218-228.
    9. Adhikari, Rajendra & Pipattanasomporn, M. & Rahman, S., 2018. "An algorithm for optimal management of aggregated HVAC power demand using smart thermostats," Applied Energy, Elsevier, vol. 217(C), pages 166-177.
    10. 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.
    11. Xie, Dunjian & Hui, Hongxun & Ding, Yi & Lin, Zhenzhi, 2018. "Operating reserve capacity evaluation of aggregated heterogeneous TCLs with price signals," Applied Energy, Elsevier, vol. 216(C), pages 338-347.
    12. Fiorentini, Massimo & Wall, Josh & Ma, Zhenjun & Braslavsky, Julio H. & Cooper, Paul, 2017. "Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage," Applied Energy, Elsevier, vol. 187(C), pages 465-479.
    13. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    14. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    15. Lakshmanan, Venkatachalam & Marinelli, Mattia & Hu, Junjie & Bindner, Henrik W., 2016. "Provision of secondary frequency control via demand response activation on thermostatically controlled loads: Solutions and experiences from Denmark," Applied Energy, Elsevier, vol. 173(C), pages 470-480.
    16. Razmara, M. & Maasoumy, M. & Shahbakhti, M. & Robinett, R.D., 2015. "Optimal exergy control of building HVAC system," Applied Energy, Elsevier, vol. 156(C), pages 555-565.
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