IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v130y2014icp428-436.html
   My bibliography  Save this article

Community-scale residential air conditioning control for effective grid management

Author

Listed:
  • Cole, Wesley J.
  • Rhodes, Joshua D.
  • Gorman, William
  • Perez, Krystian X.
  • Webber, Michael E.
  • Edgar, Thomas F.

Abstract

This paper investigates the potential for coordinated control of a large number of residential air conditioning systems to achieve substantial reductions in peak electricity demand. To do so, an extensive data set including home energy audits, homeowner surveys, and electricity meter measurements from homes in Austin, Texas, USA, was used to build a simulated community of 900 homes. Based on a reduced-order modeling strategy and an economic model predictive control approach, we analyze the effects of the community of homes responding optimally to variations in wholesale market electricity prices. We find that when exposed to dynamic pricing, peak demand from residential electricity consumption is shifted to earlier in the day, and is lower than the peak where no intervention is made. We also consider centralized and decentralized strategies for minimizing the peak demand of the community. For this simulated community, we find that centralized, coordinated control of residential air conditioning systems reduces overall peak by 8.8% but increases total energy consumption by 13.3%. Decentralized control reduces overall peak by 5.7%, demonstrating that the value of information sharing for peak reduction is 3.1%. It is also shown that properly tuned penalty terms allow a penalty-based decentralized controller to approach the optimal solution obtained by a centralized controller without the requirement of information sharing.

Suggested Citation

  • Cole, Wesley J. & Rhodes, Joshua D. & Gorman, William & Perez, Krystian X. & Webber, Michael E. & Edgar, Thomas F., 2014. "Community-scale residential air conditioning control for effective grid management," Applied Energy, Elsevier, vol. 130(C), pages 428-436.
    • Handle: RePEc:eee:appene:v:130:y:2014:i:c:p:428-436
    DOI: 10.1016/j.apenergy.2014.05.067
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914005728
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2014.05.067?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. Bode, Josh L. & Sullivan, Michael J. & Berghman, Dries & Eto, Joseph H., 2013. "Incorporating residential AC load control into ancillary service markets: Measurement and settlement," Energy Policy, Elsevier, vol. 56(C), pages 175-185.
    2. Chan, A.L.S., 2012. "Effect of adjacent shading on the thermal performance of residential buildings in a subtropical region," Applied Energy, Elsevier, vol. 92(C), pages 516-522.
    3. Arteconi, A. & Hewitt, N.J. & Polonara, F., 2012. "State of the art of thermal storage for demand-side management," Applied Energy, Elsevier, vol. 93(C), pages 371-389.
    4. Broeer, Torsten & Fuller, Jason & Tuffner, Francis & Chassin, David & Djilali, Ned, 2014. "Modeling framework and validation of a smart grid and demand response system for wind power integration," Applied Energy, Elsevier, vol. 113(C), pages 199-207.
    5. Huang, Yu & Niu, Jian-lei & Chung, Tse-ming, 2013. "Study on performance of energy-efficient retrofitting measures on commercial building external walls in cooling-dominant cities," Applied Energy, Elsevier, vol. 103(C), pages 97-108.
    6. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    7. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    8. Hong, Tianzhen & Chang, Wen-Kuei & Lin, Hung-Wen, 2013. "A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data," Applied Energy, Elsevier, vol. 111(C), pages 333-350.
    9. Bartusch, Cajsa & Wallin, Fredrik & Odlare, Monica & Vassileva, Iana & Wester, Lars, 2011. "Introducing a demand-based electricity distribution tariff in the residential sector: Demand response and customer perception," Energy Policy, Elsevier, vol. 39(9), pages 5008-5025, September.
    10. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    11. Rahman, M.M. & Rasul, M.G. & Khan, M.M.K., 2010. "Energy conservation measures in an institutional building in sub-tropical climate in Australia," Applied Energy, Elsevier, vol. 87(10), pages 2994-3004, October.
    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. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    2. Hirvonen, Janne & Kayo, Genku & Hasan, Ala & Sirén, Kai, 2014. "Local sharing of cogeneration energy through individually prioritized controls for increased on-site energy utilization," Applied Energy, Elsevier, vol. 135(C), pages 350-363.
    3. Shiljkut, Vladimir M. & Rajakovic, Nikola Lj., 2015. "Demand response capacity estimation in various supply areas," Energy, Elsevier, vol. 92(P3), pages 476-486.
    4. Tabares-Velasco, Paulo Cesar & Speake, Andrew & Harris, Maxwell & Newman, Alexandra & Vincent, Tyrone & Lanahan, Michael, 2019. "A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing," Applied Energy, Elsevier, vol. 242(C), pages 1346-1357.
    5. Ahmad Murtaza Ershad & Robert Pietzcker & Falko Ueckerdt & Gunnar Luderer, 2020. "Managing Power Demand from Air Conditioning Benefits Solar PV in India Scenarios for 2040," Energies, MDPI, vol. 13(9), pages 1-19, May.
    6. 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.
    7. Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2023. "Optimizing mixed cool thermal storage systems across a connected community," Energy, Elsevier, vol. 285(C).
    8. Touretzky, Cara R. & McGuffin, Dana L. & Ziesmer, Jena C. & Baldea, Michael, 2016. "The effect of distributed electricity generation using natural gas on the electric and natural gas grids," Applied Energy, Elsevier, vol. 177(C), pages 500-514.
    9. 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.
    10. Upshaw, Charles R. & Rhodes, Joshua D. & Webber, Michael E., 2017. "Modeling electric load and water consumption impacts from an integrated thermal energy and rainwater storage system for residential buildings in Texas," Applied Energy, Elsevier, vol. 186(P3), pages 492-508.
    11. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    12. 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.
    13. 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.
    14. Tina, Giuseppe Marco & Aneli, Stefano & Gagliano, Antonio, 2022. "Technical and economic analysis of the provision of ancillary services through the flexibility of HVAC system in shopping centers," Energy, Elsevier, vol. 258(C).
    15. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).
    16. Ren, Haoshan & Sun, Yongjun & Albdoor, Ahmed K. & Tyagi, V.V. & Pandey, A.K. & Ma, Zhenjun, 2021. "Improving energy flexibility of a net-zero energy house using a solar-assisted air conditioning system with thermal energy storage and demand-side management," Applied Energy, Elsevier, vol. 285(C).
    17. Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2021. "Design and dispatch optimization of packaged ice storage systems within a connected community," Applied Energy, Elsevier, vol. 298(C).
    18. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
    19. Haosen Qin & Zhen Yu & Tailu Li & Xueliang Liu & Li Li, 2022. "Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction," IJERPH, MDPI, vol. 19(21), pages 1-27, October.

    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. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    2. 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.
    3. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    4. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    5. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    6. Seo, Dong-yeon & Koo, Choongwan & Hong, Taehoon, 2015. "A Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design," Applied Energy, Elsevier, vol. 142(C), pages 66-79.
    7. Ihara, Takeshi & Gustavsen, Arild & Jelle, Bjørn Petter, 2015. "Effect of facade components on energy efficiency in office buildings," Applied Energy, Elsevier, vol. 158(C), pages 422-432.
    8. Jankowski, Nicholas R. & McCluskey, F. Patrick, 2014. "A review of phase change materials for vehicle component thermal buffering," Applied Energy, Elsevier, vol. 113(C), pages 1525-1561.
    9. 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.
    10. Fan, Yuling & Xia, Xiaohua, 2018. "Building retrofit optimization models using notch test data considering energy performance certificate compliance," Applied Energy, Elsevier, vol. 228(C), pages 2140-2152.
    11. Kuo-Liang Lin & Ming-Young Jan & Chien-Sen Liao, 2017. "Energy Consumption Analysis for Concrete Residences—A Baseline Study in Taiwan," Sustainability, MDPI, vol. 9(2), pages 1-13, February.
    12. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    13. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
    14. Yu, Xinqiao & Yan, Da & Sun, Kaiyu & Hong, Tianzhen & Zhu, Dandan, 2016. "Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings," Applied Energy, Elsevier, vol. 183(C), pages 725-736.
    15. Koo, Choongwan & Park, Sungki & Hong, Taehoon & Park, Hyo Seon, 2014. "An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method," Applied Energy, Elsevier, vol. 115(C), pages 205-215.
    16. Dong, Bing & Li, Zhaoxuan & Taha, Ahmad & Gatsis, Nikolaos, 2018. "Occupancy-based buildings-to-grid integration framework for smart and connected communities," Applied Energy, Elsevier, vol. 219(C), pages 123-137.
    17. Gencel, Osman & Danish, Aamar & Yilmaz, Mukremin & Erdogmus, Ertugrul & Sutcu, Mucahit & Sadak, Ferhat & Ozbakkaloglu, Togay, 2024. "Performance evaluation of phosphor-based luminescent bricks using different coating methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    18. Horowitz, Shira & Mauch, Brandon & Sowell, Fallaw, 2014. "Forecasting residential air conditioning loads," Applied Energy, Elsevier, vol. 132(C), pages 47-55.
    19. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    20. Comodi, Gabriele & Giantomassi, Andrea & Severini, Marco & Squartini, Stefano & Ferracuti, Francesco & Fonti, Alessandro & Nardi Cesarini, Davide & Morodo, Matteo & Polonara, Fabio, 2015. "Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies," Applied Energy, Elsevier, vol. 137(C), pages 854-866.

    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:130:y:2014:i:c:p:428-436. 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.