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A Novel SW Tool for the Evaluation of Expected Benefits of V2H Charging Devices Utilization in V2B Building Contexts

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  • Carlo Villante

    (Industrial Engineering, Information and Economics Department, University of L’Aquila, 67040 L’Aquila, Italy)

Abstract

Energy systems need a complete decarbonization within the next 20–30 years, calling for the introduction of CO 2 -free renewable energy sources (RES). All final uses must face this challenge, now finally including the transportation sector which should mostly be electrified. This option could constitute both a challenge and an opportunity for the electric grid. In fact, connection to the grid of all electric vehicles (EVs) together with their electricity storage systems (ESSs) could reduce issues due to the nonprogrammable use of RES in electricity production; to this aim, sufficiently smart bi-directional vehicle-to-grid technologies (V2G) have to be designed and widely installed. Parallelly, electric grid capabilities must become fully bidirectional in all nodes, both physically and in terms of ICT capabilities (so-called smart grid paradigm). In the meanwhile, some of those V2G technologies may already be locally implemented in individual home contexts. Following previous research activity about the identification of potential users of the most promising V2H technologies and on the evaluation of their expected benefits in terms of local renewable energy auto-consumption and/or local consumption auto-feeding performance, the author aims his attention to the numerical evaluation of the further benefits obtainable through the combined utilization of a number of V2H technologies all acting on the same “building” energy node; this approach is normally referred to in the literature as a vehicle-to-building (V2B) application. The SW tool which was developed to this aim is fully physically consistent, scalable, modular, open-source, and user-friendly, and it can be distributed under request to other research groups. In the simulations performed, V2H devices all used the same controlling approach, but offered their services to a “building” energy community, defined by the instantaneous sum of the energy behaviors of all the individual users. The simulation results show that building environments make it possible to intersect energy fluxes far beyond single user expectation, leading to very energy grid performances. In particular, renewable energy auto-consumption ratios become higher than 50%, and almost all local electric final uses may be fed through grid-connected vehicular ESSs (100% home auto-feeding ratio). This limits building–grid interactions to much more predictable residual ESS charging phases, as well as the sale of PV panel overproduction. The performance obtainable through the simulated V2B approach proved to be much higher than that obtainable through the same V2H technologies acting on single individual grids (which were estimated in a previous study by the same research group), ranging from 25% to 69% in terms of PV auto-consumption ratios (with higher values only obtainable for “nocturnal workers”, living in their home mostly during the daytime); moreover, a poor performance was recorded in terms of local consumption auto-feeding, ranging from 27% to 81% (with higher values only obtainable for those users mostly inhabiting their home during the night-time).

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:2969-:d:1106383
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    References listed on IDEAS

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    1. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Franck Le Gall & Ehsan Sedgh Gooya, 2022. "Home Energy Management Considering Renewable Resources, Energy Storage, and an Electric Vehicle as a Backup," Energies, MDPI, vol. 15(8), pages 1-20, April.
    2. Sai Sudharshan Ravi & Muhammad Aziz, 2022. "Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives," Energies, MDPI, vol. 15(2), pages 1-27, January.
    3. 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).
    4. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
    5. Wei, Wu & Skye, Harrison M., 2021. "Residential net-zero energy buildings: Review and perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    6. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    7. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
    8. Lazzeroni, Paolo & Olivero, Sergio & Repetto, Maurizio & Stirano, Federico & Vallet, Marc, 2019. "Optimal battery management for vehicle-to-home and vehicle-to-grid operations in a residential case study," Energy, Elsevier, vol. 175(C), pages 704-721.
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    Keywords

    BEV; ESS; V2G; V2H; V2B;
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