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A novel methodology for optimal sizing photovoltaic-battery systems in smart homes considering grid outages and demand response

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  • Tostado-Véliz, Marcos
  • Icaza-Alvarez, Daniel
  • Jurado, Francisco

Abstract

–This paper deals with the optimal sizing of a hybrid photovoltaic-battery storage system for home energy management considering reliability against grid outages and demand response. To that end, a novel optimization framework is developed which aims at minimizing the electricity bill while the reliability of the system is ensured for certain common outages. In order to ensure the accuracy of the results, a large amount of characteristic outages along with demand, solar irradiance and temperature profiles are generated from real data. Clustering techniques are used for reducing this data to those most characteristics profiles and manage with the unpredictable behaviour of the outage events. Demand response is incorporated via different incentives like tariffs based on time of use and real time pricing, along with the optimal scheduling of different typical deferrable appliances. A case study on a smart-prosumer environment serves to illustrate the capabilities of the developed approach as providing sufficient guidelines for its universal applicability. Different cases studies are simulated considering different battery technologies and electricity tariffs for comparison. Various aspects related with the reliability against grid outages are also analysed like its impact on the project cost or the influence of demand response strategies.

Suggested Citation

  • Tostado-Véliz, Marcos & Icaza-Alvarez, Daniel & Jurado, Francisco, 2021. "A novel methodology for optimal sizing photovoltaic-battery systems in smart homes considering grid outages and demand response," Renewable Energy, Elsevier, vol. 170(C), pages 884-896.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:884-896
    DOI: 10.1016/j.renene.2021.02.006
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    Citations

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    Cited by:

    1. Tostado-Véliz, Marcos & León-Japa, Rogelio S. & Jurado, Francisco, 2021. "Optimal electrification of off-grid smart homes considering flexible demand and vehicle-to-home capabilities," Applied Energy, Elsevier, vol. 298(C).
    2. Khezri, Rahmat & Mahmoudi, Amin & Aki, Hirohisa, 2022. "Optimal planning of solar photovoltaic and battery storage systems for grid-connected residential sector: Review, challenges and new perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    3. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2022. "A Stochastic-IGDT model for energy management in isolated microgrids considering failures and demand response," Applied Energy, Elsevier, vol. 317(C).
    4. Wu, Yaling & Liu, Zhongbing & Li, Benjia & Liu, Jiangyang & Zhang, Ling, 2022. "Energy management strategy and optimal battery capacity for flexible PV-battery system under time-of-use tariff," Renewable Energy, Elsevier, vol. 200(C), pages 558-570.
    5. D'Adamo, Idiano & Gastaldi, Massimo & Morone, Piergiuseppe & Ozturk, Ilhan, 2022. "Economics and policy implications of residential photovoltaic systems in Italy's developed market," Utilities Policy, Elsevier, vol. 79(C).
    6. Lamnatou, Chr. & Chemisana, D. & Cristofari, C., 2022. "Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment," Renewable Energy, Elsevier, vol. 185(C), pages 1376-1391.
    7. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    8. Wang, Guotao & Zhou, Yifan & Lin, Zhenjia & Zhu, Shibo & Qiu, Rui & Chen, Yuntian & Yan, Jinyue, 2024. "Robust energy management through aggregation of flexible resources in multi-home micro energy hub," Applied Energy, Elsevier, vol. 357(C).
    9. Rafik Nafkha & Tomasz Ząbkowski & Krzysztof Gajowniczek, 2021. "Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers," Energies, MDPI, vol. 14(8), pages 1-17, April.
    10. Bryam Paúl Lojano-Riera & Carlos Flores-Vázquez & Juan-Carlos Cobos-Torres & David Vallejo-Ramírez & Daniel Icaza, 2023. "Electromobility with Photovoltaic Generation in an Andean City," Energies, MDPI, vol. 16(15), pages 1-16, July.
    11. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    12. Alexander Lavrik & Yuri Zhukovskiy & Pavel Tcvetkov, 2021. "Optimizing the Size of Autonomous Hybrid Microgrids with Regard to Load Shifting," Energies, MDPI, vol. 14(16), pages 1-19, August.
    13. Fawad Azeem & Ashfaq Ahmad & Taimoor Muzaffar Gondal & Jehangir Arshad & Ateeq Ur Rehman & Elsayed M. Tag Eldin & Muhammad Shafiq & Habib Hamam, 2022. "Load Management and Optimal Sizing of Special-Purpose Microgrids Using Two Stage PSO-Fuzzy Based Hybrid Approach," Energies, MDPI, vol. 15(17), pages 1-19, September.
    14. Wu, Yaling & Liu, Zhongbing & Liu, Jiangyang & Xiao, Hui & Liu, Ruimiao & Zhang, Ling, 2022. "Optimal battery capacity of grid-connected PV-battery systems considering battery degradation," Renewable Energy, Elsevier, vol. 181(C), pages 10-23.

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