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

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  • 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.

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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.
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