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Combined vehicle to building (V2B) and vehicle to home (V2H) strategy to increase electric vehicle market share

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  • Borge-Diez, David
  • Icaza, Daniel
  • Açıkkalp, Emin
  • Amaris, Hortensia

Abstract

Buildings are one of the most important energy consumers in modern economy countries. The massive use of electrical vehicles could help decarbonizing the economy by using electricity produced using renewable energy. Combined use of Vehicle to Grid (V2G), Vehicle to Home (V2H) and Vehicle to Building (V2B) is one of the strategies to increase the number of electrical vehicles, ensure a better coupling between energy generation and consumption, reducing peak demand and increasing global energy efficiency. This research presents a novel approach of combined use of V2H and V2B that can be applied in different scenarios such as when the building workers own EVs, company shared car fleets or leasing, among others. Recharged energy at workers homes during night hours is delivered in the building during daily working hours lowering peak demand, reducing carbon intensity and energy cost savings. The results show that the methodology is feasible and can be extended to other cases and greatly contribute to better energy efficiency, reduces peak demand in buildings and increase electric vehicles penetration in transport to workplaces.

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  • Borge-Diez, David & Icaza, Daniel & Açıkkalp, Emin & Amaris, Hortensia, 2021. "Combined vehicle to building (V2B) and vehicle to home (V2H) strategy to increase electric vehicle market share," Energy, Elsevier, vol. 237(C).
  • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221018569
    DOI: 10.1016/j.energy.2021.121608
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    1. Rosales-Asensio, Enrique & de Simón-Martín, Miguel & Borge-Diez, David & Blanes-Peiró, Jorge Juan & Colmenar-Santos, Antonio, 2019. "Microgrids with energy storage systems as a means to increase power resilience: An application to office buildings," Energy, Elsevier, vol. 172(C), pages 1005-1015.
    2. Jian, Liu & Zechun, Hu & Banister, David & Yongqiang, Zhao & Zhongying, Wang, 2018. "The future of energy storage shaped by electric vehicles: A perspective from China," Energy, Elsevier, vol. 154(C), pages 249-257.
    3. Miao, Hongzhi & Jia, Hongfei & Li, Jiangchen & Qiu, Tony Z., 2019. "Autonomous connected electric vehicle (ACEV)-based car-sharing system modeling and optimal planning: A unified two-stage multi-objective optimization methodology," Energy, Elsevier, vol. 169(C), pages 797-818.
    4. Tian, Man-Wen & Talebizadehsardari, Pouyan, 2021. "Energy cost and efficiency analysis of building resilience against power outage by shared parking station for electric vehicles and demand response program," Energy, Elsevier, vol. 215(PB).
    5. Miktha Farid Alkadri & Francesco De Luca & Michela Turrin & Sevil Sariyildiz, 2020. "Understanding Computational Methods for Solar Envelopes Based on Design Parameters, Tools, and Case Studies: A Review," Energies, MDPI, vol. 13(13), pages 1-24, June.
    6. Chen, T. Donna & Kockelman, Kara M. & Hanna, Josiah P., 2016. "Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 243-254.
    7. Sheng, Lei & Zhang, Hengyun & Su, Lin & Zhang, Zhendong & Zhang, Hua & Li, Kang & Fang, Yidong & Ye, Wen, 2021. "Effect analysis on thermal profile management of a cylindrical lithium-ion battery utilizing a cellular liquid cooling jacket," Energy, Elsevier, vol. 220(C).
    8. Oh, Simon & Seshadri, Ravi & Azevedo, Carlos Lima & Kumar, Nishant & Basak, Kakali & Ben-Akiva, Moshe, 2020. "Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 367-388.
    9. Uddin, Kotub & Jackson, Tim & Widanage, Widanalage D. & Chouchelamane, Gael & Jennings, Paul A. & Marco, James, 2017. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system," Energy, Elsevier, vol. 133(C), pages 710-722.
    10. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    11. Yiding, Li & Wenwei, Wang & Cheng, Lin & Xiaoguang, Yang & Fenghao, Zuo, 2021. "A safety performance estimation model of lithium-ion batteries for electric vehicles under dynamic compression," Energy, Elsevier, vol. 215(PA).
    12. Buonomano, Annamaria, 2020. "Building to Vehicle to Building concept: A comprehensive parametric and sensitivity analysis for decision making aims," Applied Energy, Elsevier, vol. 261(C).
    Full references (including those not matched with items on IDEAS)

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    3. Wu, Yan & Aziz, Syed Mahfuzul & Haque, Mohammed H., 2024. "Vehicle-to-home operation and multi-location charging of electric vehicles for energy cost optimisation of households with photovoltaic system and battery energy storage," Renewable Energy, Elsevier, vol. 221(C).
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    5. Georgios Yiasoumas & Lazar Berbakov & Valentina Janev & Alessandro Asmundo & Eneko Olabarrieta & Andrea Vinci & Giovanni Baglietto & George E. Georghiou, 2023. "Key Aspects and Challenges in the Implementation of Energy Communities," Energies, MDPI, vol. 16(12), pages 1-24, June.
    6. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
    7. Zhou, Yuekuan, 2023. "A dynamic self-learning grid-responsive strategy for battery sharing economy—multi-objective optimisation and posteriori multi-criteria decision making," Energy, Elsevier, vol. 266(C).
    8. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    9. Sun, Dexi & Xia, Jianjun, 2023. "Research on road transport planning aiming at near zero carbon emissions: Taking Ruicheng County as an example," Energy, Elsevier, vol. 263(PB).
    10. Zhou, Yuekuan, 2022. "A regression learner-based approach for battery cycling ageing prediction―advances in energy management strategy and techno-economic analysis," Energy, Elsevier, vol. 256(C).
    11. Daniel Icaza & David Borge-Diez & Santiago Pulla Galindo & Carlos Flores-Vázquez, 2023. "Analysis of Smart Energy Systems and High Participation of V2G Impact for the Ecuadorian 100% Renewable Energy System by 2050," Energies, MDPI, vol. 16(10), pages 1-24, May.
    12. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Li, Shaojie & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2023. "Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design," Energy, Elsevier, vol. 271(C).
    13. Carlo Villante, 2023. "A Novel SW Tool for the Evaluation of Expected Benefits of V2H Charging Devices Utilization in V2B Building Contexts," Energies, MDPI, vol. 16(7), pages 1-25, March.
    14. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo & Russo, Giuseppe, 2022. "Energy virtual networks based on electric vehicles for sustainable buildings: System modelling for comparative energy and economic analyses," Energy, Elsevier, vol. 242(C).
    15. Gajanan B. Haldankar & Swati Bhat & Kavir Kashinath Shirodkar & Amit Subramanyam, 2024. "Electric Vehicle Revolution in India: A Comprehensive and Comparative Study of Ev Business in India," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 150-159, March.
    16. Yannick Pohlmann & Carl-Friedrich Klinck, 2023. "Techno-Economic Potential of V2B in a Neighborhood, Considering Tariff Models and Battery Cycle Limits," Energies, MDPI, vol. 16(11), pages 1-24, May.

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