IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v241y2019icp46-58.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919303083
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.02.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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, February.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Liu, Yu & Wu, Chuanshen & Wang, Sicheng, 2021. "Congestion-aware robust security constrained unit commitment model for AC-DC grids," Applied Energy, Elsevier, vol. 304(C).
    2. Alejandro Martín-Crespo & Sergio Saludes-Rodil & Enrique Baeyens, 2021. "Flexibility Management with Virtual Batteries of Thermostatically Controlled Loads: Real-Time Control System and Potential in Spain," Energies, MDPI, vol. 14(6), pages 1-18, March.
    3. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    4. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
    5. Barbero, Mattia & Corchero, Cristina & Canals Casals, Lluc & Igualada, Lucia & Heredia, F.-Javier, 2020. "Critical evaluation of European balancing markets to enable the participation of Demand Aggregators," Applied Energy, Elsevier, vol. 264(C).
    6. Md Musabbir Hossain & Asatur Zh. Khurshudyan, 2019. "Controlling Power Consumption in a Heterogeneous Population Model of TCLs with Diffusion: The Green’s Function Approach," Mathematics, MDPI, vol. 7(6), pages 1-8, June.
    7. 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.
    8. Xie, Kang & Hui, Hongxun & Ding, Yi & Song, Yonghua & Ye, Chengjin & Zheng, Wandong & Ye, Shuiquan, 2022. "Modeling and control of central air conditionings for providing regulation services for power systems," Applied Energy, Elsevier, vol. 315(C).
    9. Hui, Hongxun & Ding, Yi & Shi, Qingxin & Li, Fangxing & Song, Yonghua & Yan, Jinyue, 2020. "5G network-based Internet of Things for demand response in smart grid: A survey on application potential," Applied Energy, Elsevier, vol. 257(C).
    10. Hui, Hongxun & Ding, Yi & Song, Yonghua & Rahman, Saifur, 2019. "Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error," Applied Energy, Elsevier, vol. 250(C), pages 161-174.
    11. Bo, Yimin & Bao, Minglei & Ding, Yi & Hu, Yishuang, 2024. "A DNN-based reliability evaluation method for multi-state series-parallel systems considering semi-Markov process," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hui, Hongxun & Ding, Yi & Song, Yonghua & Rahman, Saifur, 2019. "Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error," Applied Energy, Elsevier, vol. 250(C), pages 161-174.
    2. Dong, Zihang & Zhang, Xi & Li, Yijun & Strbac, Goran, 2023. "Values of coordinated residential space heating in demand response provision," Applied Energy, Elsevier, vol. 330(PB).
    3. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
    4. Xie, Kang & Hui, Hongxun & Ding, Yi & Song, Yonghua & Ye, Chengjin & Zheng, Wandong & Ye, Shuiquan, 2022. "Modeling and control of central air conditionings for providing regulation services for power systems," Applied Energy, Elsevier, vol. 315(C).
    5. Zeng, Yuan & Zhang, Ruiwen & Wang, Dong & Mu, Yunfei & Jia, Hongjie, 2019. "A regional power grid operation and planning method considering renewable energy generation and load control," Applied Energy, Elsevier, vol. 237(C), pages 304-313.
    6. 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.
    7. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    8. Hui, Hongxun & Ding, Yi & Shi, Qingxin & Li, Fangxing & Song, Yonghua & Yan, Jinyue, 2020. "5G network-based Internet of Things for demand response in smart grid: A survey on application potential," Applied Energy, Elsevier, vol. 257(C).
    9. 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.
    10. Lork, Clement & Li, Wen-Tai & Qin, Yan & Zhou, Yuren & Yuen, Chau & Tushar, Wayes & Saha, Tapan K., 2020. "An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management," Applied Energy, Elsevier, vol. 276(C).
    11. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    12. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    13. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    14. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.
    15. Wu, Xin & Yao, Lijuan & Pi, Tanxin & Liu, Yuhang & Li, Xiang & Gong, Gangjun, 2023. "Virtual-real interaction control of hybrid load system for low-carbon energy services," Applied Energy, Elsevier, vol. 330(PB).
    16. Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
    17. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    18. Oshnoei, Arman & Kheradmandi, Morteza & Blaabjerg, Frede & Hatziargyriou, Nikos D. & Muyeen, S.M. & Anvari-Moghaddam, Amjad, 2022. "Coordinated control scheme for provision of frequency regulation service by virtual power plants," Applied Energy, Elsevier, vol. 325(C).
    19. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:241:y:2019:i:c:p:46-58. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.