IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v196y2017icp218-228.html
   My bibliography  Save this article

Operating reserve evaluation of aggregated air conditioners

Author

Listed:
  • Hui, Hongxun
  • Ding, Yi
  • Liu, Weidong
  • Lin, You
  • Song, Yonghua

Abstract

The penetration of renewable energy sources (RES) in power system is increasing around the world. However, the severe intermittency and variability characteristics of RES make the operating reserve become more and more important for the electric power system to maintain balance between supply and demand. Moreover, the flexible loads, especially for air conditioners (AC), are growing so rapidly that they account for an increasingly large share in power consumption. With the development of information and communication technologies (ICT), ACs can be monitored and controlled remotely to provide operating reserve and respond actively when needed by the electric power system operation. In this paper, a novel control strategy for the aggregation model of ACs based on the thermal model of the room is proposed. By resetting the temperature of each AC, the operation state is adjusted temporarily without affecting customers’ satisfaction. The operation characteristics of both individual AC and the aggregation model of ACs are analysed. Furthermore, several indexes are put forward to evaluate the operating reserve performance, including reserve capacity, response time, duration time and ramp rate. The effectiveness of the proposed control strategy is illustrated in the numerical studies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:196:y:2017:i:c:p:218-228
    DOI: 10.1016/j.apenergy.2016.12.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916317676
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.12.004?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. Qv, Dehu & Dong, Bingbing & Cao, Lin & Ni, Long & Wang, Jijin & Shang, Runxin & Yao, Yang, 2017. "An experimental and theoretical study on an injection-assisted air-conditioner using R32 in the refrigeration cycle," Applied Energy, Elsevier, vol. 185(P1), pages 791-804.
    2. Wang, Weimin & Katipamula, Srinivas & Ngo, Hung & Underhill, Ronald & Taasevigen, Danny & Lutes, Robert, 2015. "Field evaluation of advanced controls for the retrofit of packaged air conditioners and heat pumps," Applied Energy, Elsevier, vol. 154(C), pages 344-351.
    3. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    4. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
    5. 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.
    6. Chassin, David P. & Rondeau, Daniel, 2016. "Aggregate modeling of fast-acting demand response and control under real-time pricing," Applied Energy, Elsevier, vol. 181(C), pages 288-298.
    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. Yongzhu Hua & Qiangqiang Xie & Liang Zheng & Jiadong Cui & Lihuan Shao & Weiwei Hu, 2022. "Coordinated Voltage Control Strategy by Optimizing the Limited Regulation Capacity of Air Conditioners," Energies, MDPI, vol. 15(9), pages 1-14, April.
    2. 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.
    3. Ferenc Szodrai, 2020. "Heat Sink Shape and Topology Optimization with Pareto-Vector Length Optimization for Air Cooling," Energies, MDPI, vol. 13(7), pages 1-15, April.
    4. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    5. Hui, Hengyu & Bao, Minglei & Ding, Yi & Song, Yonghua, 2022. "Exploring the integrated flexible region of distributed multi-energy systems with process industry," Applied Energy, Elsevier, vol. 311(C).
    6. Hui, Hongxun & Ding, Yi & Song, Yonghua, 2020. "Adaptive time-delay control of flexible loads in power systems facing accidental outages," Applied Energy, Elsevier, vol. 275(C).
    7. Shao, Junqiang & Huang, Zhiyuan & Chen, Yugui & Li, Depeng & Xu, Xiangguo, 2023. "A practical application-oriented model predictive control algorithm for direct expansion (DX) air-conditioning (A/C) systems that balances thermal comfort and energy consumption," Energy, Elsevier, vol. 269(C).
    8. Shi, Long, 2018. "Theoretical models for wall solar chimney under cooling and heating modes considering room configuration," Energy, Elsevier, vol. 165(PB), pages 925-938.
    9. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    10. Shan, Kui & Wang, Shengwei, 2017. "Energy efficient design and control of cleanroom environment control systems in subtropical regions – A comparative analysis and on-site validation," Applied Energy, Elsevier, vol. 204(C), pages 582-595.
    11. 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).
    12. Jonglak Pahasa & Potejanasak Potejana & Issarachai Ngamroo, 2021. "Multi-Objective Decentralized Model Predictive Control for Inverter Air Conditioner Control of Indoor Temperature and Frequency Stabilization in Microgrid," Energies, MDPI, vol. 14(21), pages 1-22, October.
    13. 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.
    14. 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.
    15. 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).
    16. 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).
    17. 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.
    18. 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.
    19. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
    20. 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.
    21. 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).
    22. Hongbo Shao & Yubin Mao & Yongmin Liu & Wanxun Liu & Sipei Sun & Peng Jia & Fufeng Miao & Li Yang & Chang Han & Bo Zhang, 2018. "A Three-Stage Procedure for Controlled Islanding to Prevent Wide-Area Blackouts," Energies, MDPI, vol. 11(11), pages 1-15, November.

    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. 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.
    2. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    3. Behboodi, Sahand & Chassin, David P. & Djilali, Ned & Crawford, Curran, 2018. "Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets," Applied Energy, Elsevier, vol. 210(C), pages 1310-1320.
    4. Pedro Faria & João Spínola & Zita Vale, 2018. "Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs," Energies, MDPI, vol. 11(8), pages 1-17, July.
    5. Alimohammadisagvand, Behrang & Jokisalo, Juha & Kilpeläinen, Simo & Ali, Mubbashir & Sirén, Kai, 2016. "Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control," Applied Energy, Elsevier, vol. 174(C), pages 275-287.
    6. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    7. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    8. Qi, Wei & Shen, Bo & Zhang, Hongcai & Shen, Zuo-Jun Max, 2017. "Sharing demand-side energy resources - A conceptual design," Energy, Elsevier, vol. 135(C), pages 455-465.
    9. Behboodi, Sahand & Chassin, David P. & Djilali, Ned & Crawford, Curran, 2017. "Interconnection-wide hour-ahead scheduling in the presence of intermittent renewables and demand response: A surplus maximizing approach," Applied Energy, Elsevier, vol. 189(C), pages 336-351.
    10. Elma, Onur & Taşcıkaraoğlu, Akın & Tahir İnce, A. & Selamoğulları, Uğur S., 2017. "Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts," Energy, Elsevier, vol. 134(C), pages 206-220.
    11. Fang, Debin & Wang, Pengyu, 2023. "Optimal real-time pricing and electricity package by retail electric providers based on social learning," Energy Economics, Elsevier, vol. 117(C).
    12. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    13. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    14. Kim, Donghun & Braun, James E., 2020. "Model predictive control for supervising multiple rooftop unit economizers to fully leverage free cooling energy resource," Applied Energy, Elsevier, vol. 275(C).
    15. Anilkumar, T.T. & Simon, Sishaj P. & Padhy, Narayana Prasad, 2017. "Residential electricity cost minimization model through open well-pico turbine pumped storage system," Applied Energy, Elsevier, vol. 195(C), pages 23-35.
    16. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    17. 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).
    18. Tejeda De La Cruz, Alberto & Riviere, Philippe & Marchio, Dominique & Cauret, Odile & Milu, Anamaria, 2017. "Hardware in the loop test bench using Modelica: A platform to test and improve the control of heating systems," Applied Energy, Elsevier, vol. 188(C), pages 107-120.
    19. Sadeghianpourhamami, N. & Demeester, T. & Benoit, D.F. & Strobbe, M. & Develder, C., 2016. "Modeling and analysis of residential flexibility: Timing of white good usage," Applied Energy, Elsevier, vol. 179(C), pages 790-805.
    20. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.

    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:196:y:2017:i:c:p:218-228. 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.