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Optimal collaborative demand-response planner for smart residential buildings

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  • Gomez-Herrera, Juan A.
  • Anjos, Miguel F.

Abstract

This work presents a collaborative scheme for the end-users in a smart building with multiple housing units. This approach determines a day-ahead operational plan that provides demand-response services by taking into account the amount of energy consumed per household, the use of shared storage and solar panels, and the amount of shifted load. We use a biobjective optimization model to trade off total user satisfaction versus total cost of energy consumption. The optimization works in combination with a price structure based on time and level of use that encourages load shifting and benefits the participants. Computational experiments and an extensive sensitivity analysis validate the performance of the proposed approach and help to clarify its strengths, its limits, and the requirements for ensuring the desired outcome.

Suggested Citation

  • Gomez-Herrera, Juan A. & Anjos, Miguel F., 2018. "Optimal collaborative demand-response planner for smart residential buildings," Energy, Elsevier, vol. 161(C), pages 370-380.
    • Handle: RePEc:eee:energy:v:161:y:2018:i:c:p:370-380
    DOI: 10.1016/j.energy.2018.07.132
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    References listed on IDEAS

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    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
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    Citations

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    Cited by:

    1. Roldán-Blay, Carlos & Escrivá-Escrivá, Guillermo & Roldán-Porta, Carlos, 2019. "Improving the benefits of demand response participation in facilities with distributed energy resources," Energy, Elsevier, vol. 169(C), pages 710-718.
    2. José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
    3. Anjos, Miguel F. & Brotcorne, Luce & Gomez-Herrera, Juan A., 2021. "Optimal setting of time-and-level-of-use prices for an electricity supplier," Energy, Elsevier, vol. 225(C).
    4. Canales, Fausto A. & Jurasz, Jakub & Beluco, Alexandre & Kies, Alexander, 2020. "Assessing temporal complementarity between three variable energy sources through correlation and compromise programming," Energy, Elsevier, vol. 192(C).
    5. Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    6. Xu, Fangyuan & Wu, Wanli & Zhao, Fei & Zhou, Ya & Wang, Yongjian & Wu, Runji & Zhang, Tao & Wen, Yongchen & Fan, Yiliang & Jiang, Shengli, 2019. "A micro-market module design for university demand-side management using self-crossover genetic algorithms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Kuznetsova, Elizaveta & Anjos, Miguel F., 2020. "Challenges in energy policies for the economic integration of prosumers in electric energy systems: A critical survey with a focus on Ontario (Canada)," Energy Policy, Elsevier, vol. 142(C).
    8. Sadiq Ahmad & Ayaz Ahmad & Muhammad Naeem & Waleed Ejaz & Hyung Seok Kim, 2018. "A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid," Energies, MDPI, vol. 11(10), pages 1-33, October.

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