IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v250y2022ics0360544222006995.html
   My bibliography  Save this article

A hierarchical control scheme for residential air-conditioning loads to provide real-time market services under uncertainties

Author

Listed:
  • Lankeshwara, Gayan
  • Sharma, Rahul
  • Yan, Ruifeng
  • Saha, Tapan K.

Abstract

Recently, there has been growing interest in the provision of market services from distributed energy resources (DERs). In pursuing this goal, demand response (DR) aggregators continue to face challenges in retaining privacy and comfort for end-users, mitigating scalability issues while controlling a large cohort of DERs and handling uncertainties which are inevitable in a practical setting. This paper presents an end-user privacy and comfort preserving, scalable, hierarchical control scheme for inverter-type air conditioners to provide real-time market services in the presence of uncertainties. Privacy and scalability are achieved thorough the adoption of the alternating direction method of multipliers (ADMM) framework which ensures minimal reliance on local information whilst ensuring desired reference tracking without compromising the end-user comfort. Benefiting from the proposed non-conservative robust MPC design, the local control is able to account for mismatches in outdoor temperature predictions. The overall scheme is validated using real data obtained from the Australian Energy Market operator. The results demonstrate that the proposed approach can achieve desired tracking of the reference signal while regulating indoor temperature within a narrow range (±1 °C) from the nominal set-point. Besides, the robustness to uncertainties is achieved without compromising computational performance and therefore the approach is scalable.

Suggested Citation

  • Lankeshwara, Gayan & Sharma, Rahul & Yan, Ruifeng & Saha, Tapan K., 2022. "A hierarchical control scheme for residential air-conditioning loads to provide real-time market services under uncertainties," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006995
    DOI: 10.1016/j.energy.2022.123796
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222006995
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123796?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. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    2. Wang, Huilong & Wang, Shengwei, 2021. "A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids," Energy, Elsevier, vol. 230(C).
    3. Wang, Huilong & Wang, Shengwei & Shan, Kui, 2020. "Experimental study on the dynamics, quality and impacts of using variable-speed pumps in buildings for frequency regulation of smart power grids," Energy, Elsevier, vol. 199(C).
    4. Dong, Zihang & Zhang, Xi & Strbac, Goran, 2021. "Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems," Energy, Elsevier, vol. 237(C).
    5. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    6. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    7. Dengiz, Thomas & Jochem, Patrick, 2020. "Decentralized optimization approaches for using the load flexibility of electric heating devices," Energy, Elsevier, vol. 193(C).
    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. de Araujo Passos, Luigi Antonio & Ceha, Thomas Joseph & Baldi, Simone & De Schutter, Bart, 2023. "Model predictive control of a thermal chimney and dynamic solar shades for an all-glass facades building," Energy, Elsevier, vol. 264(C).
    2. Wang, Jingjie & Qiu, Rujia & Xu, Bin & Wu, Hongbin & Tang, Longjiang & Zhang, Mingxing & Ding, Ming, 2023. "Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units," Energy, Elsevier, vol. 276(C).
    3. Li, Li & Dong, Mi & Song, Dongran & Yang, Jian & Wang, Qibing, 2022. "Distributed and real-time economic dispatch strategy for an islanded microgrid with fair participation of thermostatically controlled loads," Energy, Elsevier, vol. 261(PB).

    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. Huang, Sen & Ye, Yunyang & Wu, Di & Zuo, Wangda, 2021. "An assessment of power flexibility from commercial building cooling systems in the United States," Energy, Elsevier, vol. 221(C).
    2. Tang, Hong & Wang, Shengwei, 2021. "Energy flexibility quantification of grid-responsive buildings: Energy flexibility index and assessment of their effectiveness for applications," Energy, Elsevier, vol. 221(C).
    3. Tang, Hong & Wang, Shengwei, 2023. "Game-theoretic optimization of demand-side flexibility engagement considering the perspectives of different stakeholders and multiple flexibility services," Applied Energy, Elsevier, vol. 332(C).
    4. Wang, Huilong & Ding, Zhikun & Tang, Rui & Chen, Yongbao & Fan, Cheng & Wang, Jiayuan, 2022. "A machine learning-based control strategy for improved performance of HVAC systems in providing large capacity of frequency regulation service," Applied Energy, Elsevier, vol. 326(C).
    5. Jianwen Ren & Yingqiang Xu & Shiyuan Wang, 2018. "A Distributed Robust Dispatch Approach for Interconnected Systems with a High Proportion of Wind Power Penetration," Energies, MDPI, vol. 11(4), pages 1-18, April.
    6. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
    7. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    8. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    9. Stefan Mišković, 2017. "A VNS-LP algorithm for the robust dynamic maximal covering location problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1011-1033, October.
    10. Sarhadi, Hassan & Naoum-Sawaya, Joe & Verma, Manish, 2020. "A robust optimization approach to locating and stockpiling marine oil-spill response facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    11. Li, Shukai & Liu, Ronghui & Yang, Lixing & Gao, Ziyou, 2019. "Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 88-109.
    12. Chassein, André & Dokka, Trivikram & Goerigk, Marc, 2019. "Algorithms and uncertainty sets for data-driven robust shortest path problems," European Journal of Operational Research, Elsevier, vol. 274(2), pages 671-686.
    13. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2016. "Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties," Energies, MDPI, vol. 9(4), pages 1-21, April.
    14. M. J. Naderi & M. S. Pishvaee, 2017. "Robust bi-objective macroscopic municipal water supply network redesign and rehabilitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2689-2711, July.
    15. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    16. Jun-ya Gotoh & Michael Jong Kim & Andrew E. B. Lim, 2020. "Worst-case sensitivity," Papers 2010.10794, arXiv.org.
    17. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    18. Zhang, Hanxiao & Li, Yan-Fu, 2022. "Robust optimization on redundancy allocation problems in multi-state and continuous-state series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    19. Evers, L. & Dollevoet, T.A.B. & Barros, A.I. & Monsuur, H., 2011. "Robust UAV Mission Planning," Econometric Institute Research Papers EI 2011-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).

    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:energy:v:250:y:2022:i:c:s0360544222006995. 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.journals.elsevier.com/energy .

    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.