IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v290y2021ics0306261921002944.html
   My bibliography  Save this article

Forecasting the price-response of a pool of buildings via homothetic inverse optimization

Author

Listed:
  • Fernández-Blanco, Ricardo
  • Morales, Juan Miguel
  • Pineda, Salvador

Abstract

This paper focuses on the day-ahead forecasting of the aggregate power of a pool of smart buildings equipped with thermostatically-controlled loads. We first propose the modeling of the aggregate behavior of its power trajectory by using a geometric approach. Specifically, we assume that the aggregate power is a homothet of a prototype building, whose physical and technical parameters are chosen to be the mean of those in the pool. This allows us to preserve the building thermal dynamics of the pool. We then apply inverse optimization to estimate the homothetic parameters with bilevel programming. The lower level characterizes the price-response of the ensemble by a set of marginal utility curves and a homothet of the prototype building, which, in turn, are inferred in the upper-level problem. The upper level minimizes the mean absolute error over a training sample. This bilevel program is transformed into a regularized nonlinear problem that is initialized with the solution given by an efficient heuristic procedure. This heuristic consists in solving two linear programs and its solution is deemed a suitable proxy for the original bilevel problem. The results have been compared to state-of-the-art methodologies.

Suggested Citation

  • Fernández-Blanco, Ricardo & Morales, Juan Miguel & Pineda, Salvador, 2021. "Forecasting the price-response of a pool of buildings via homothetic inverse optimization," Applied Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:appene:v:290:y:2021:i:c:s0306261921002944
    DOI: 10.1016/j.apenergy.2021.116791
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921002944
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.116791?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. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    2. Chan, Timothy C.Y. & Kaw, Neal, 2020. "Inverse optimization for the recovery of constraint parameters," European Journal of Operational Research, Elsevier, vol. 282(2), pages 415-427.
    3. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    4. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    5. de Souza Dutra, Michael David & Alguacil, Natalia, 2020. "Optimal residential users coordination via demand response: An exact distributed framework," Applied Energy, Elsevier, vol. 279(C).
    6. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    7. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    8. Cornélusse, Bertrand & Savelli, Iacopo & Paoletti, Simone & Giannitrapani, Antonio & Vicino, Antonio, 2019. "A community microgrid architecture with an internal local market," Applied Energy, Elsevier, vol. 242(C), pages 547-560.
    9. Bertrand Corn'elusse & Iacopo Savelli & Simone Paoletti & Antonio Giannitrapani & Antonio Vicino, 2018. "A Community Microgrid Architecture with an Internal Local Market," Papers 1810.09803, arXiv.org, revised Feb 2019.
    10. Zhou, Ying & Wang, Lizhi & McCalley, James D., 2011. "Designing effective and efficient incentive policies for renewable energy in generation expansion planning," Applied Energy, Elsevier, vol. 88(6), pages 2201-2209, June.
    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. Sun, Fangyuan & Kong, Xiangyu & Wu, Jianzhong & Gao, Bixuan & Chen, Ke & Lu, Ning, 2022. "DSM pricing method based on A3C and LSTM under cloud-edge environment," Applied Energy, Elsevier, vol. 315(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. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    2. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Savelli, Iacopo & Morstyn, Thomas, 2021. "Electricity prices and tariffs to keep everyone happy: A framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Omega, Elsevier, vol. 103(C).
    4. Gayo-Abeleira, Miguel & Santos, Carlos & Javier Rodríguez Sánchez, Francisco & Martín, Pedro & Antonio Jiménez, José & Santiso, Enrique, 2022. "Aperiodic two-layer energy management system for community microgrids based on blockchain strategy," Applied Energy, Elsevier, vol. 324(C).
    5. Chen, Yang & Park, Byungkwon & Kou, Xiao & Hu, Mengqi & Dong, Jin & Li, Fangxing & Amasyali, Kadir & Olama, Mohammed, 2020. "A comparison study on trading behavior and profit distribution in local energy transaction games," Applied Energy, Elsevier, vol. 280(C).
    6. Fioriti, Davide & Frangioni, Antonio & Poli, Davide, 2021. "Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users," Applied Energy, Elsevier, vol. 299(C).
    7. Thilker, Christian Ankerstjerne & Madsen, Henrik & Jørgensen, John Bagterp, 2021. "Advanced forecasting and disturbance modelling for model predictive control of smart energy systems," Applied Energy, Elsevier, vol. 292(C).
    8. Xiong, Linyun & Li, Penghan & Wang, Ziqiang & Wang, Jie, 2020. "Multi-agent based multi objective renewable energy management for diversified community power consumers," Applied Energy, Elsevier, vol. 259(C).
    9. Giovanni Gino Zanvettor & Marco Casini & Antonio Vicino, 2024. "Optimal Operation of Energy Storage Facilities in Incentive-Based Energy Communities," Energies, MDPI, vol. 17(11), pages 1-20, May.
    10. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    11. Matthew Gough & Sérgio F. Santos & Mohammed Javadi & Rui Castro & João P. S. Catalão, 2020. "Prosumer Flexibility: A Comprehensive State-of-the-Art Review and Scientometric Analysis," Energies, MDPI, vol. 13(11), pages 1-32, May.
    12. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & José Matas, 2021. "Individual vs. Community: Economic Assessment of Energy Management Systems under Different Regulatory Frameworks," Energies, MDPI, vol. 14(3), pages 1-27, January.
    13. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    14. Guido Cavraro & Tommaso Caldognetto & Ruggero Carli & Paolo Tenti, 2019. "A Master/Slave Approach to Power Flow and Overvoltage Control in Low-Voltage Microgrids," Energies, MDPI, vol. 12(14), pages 1-22, July.
    15. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    16. Alexandros-Georgios Chronis & Foivos Palaiogiannis & Iasonas Kouveliotis-Lysikatos & Panos Kotsampopoulos & Nikos Hatziargyriou, 2021. "Photovoltaics Enabling Sustainable Energy Communities: Technological Drivers and Emerging Markets," Energies, MDPI, vol. 14(7), pages 1-21, March.
    17. Wenting Zhao & Jun Lv & Xilong Yao & Juanjuan Zhao & Zhixin Jin & Yan Qiang & Zheng Che & Chunwu Wei, 2019. "Consortium Blockchain-Based Microgrid Market Transaction Research," Energies, MDPI, vol. 12(20), pages 1-22, October.
    18. Savelli, Iacopo & Hepburn, Cameron & Morstyn, Thomas, 2024. "A blueprint for energy systems in the era of central bank digital currencies," Technological Forecasting and Social Change, Elsevier, vol. 207(C).
    19. Ishizaki, Takayuki & Koike, Masakazu & Yamaguchi, Nobuyuki & Ueda, Yuzuru & Imura, Jun-ichi, 2020. "Day-ahead energy market as adjustable robust optimization: Spatio-temporal pricing of dispatchable generators, storage batteries, and uncertain renewable resources," Energy Economics, Elsevier, vol. 91(C).
    20. Denis Sidorov & Daniil Panasetsky & Nikita Tomin & Dmitriy Karamov & Aleksei Zhukov & Ildar Muftahov & Aliona Dreglea & Fang Liu & Yong Li, 2020. "Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region," Energies, MDPI, vol. 13(5), pages 1-18, March.

    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:290:y:2021:i:c:s0306261921002944. 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.