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Mobile energy hub planning for complex urban networks: A robust optimization approach

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  • Khardenavis, Amaiya
  • Hewage, Kasun
  • Perera, Piyaruwan
  • Shotorbani, Amin Mohammadpour
  • Sadiq, Rehan

Abstract

The electricity grid with a high penetration of renewable energy can enable travelers to travel free of emissions using state-of-the-art electric vehicles (EVs). Extensive electric vehicle demands at the peak-times, and an increase in electricity consumption due to population growth, have led to higher utility infrastructure investments. Mobile energy hubs i.e. clustered EVs parked in a dedicated location, can be used as an innovative demand-side management solution to reduce long-term utility infrastructure investments. They can store and release electricity to the grid based on consumer demand. However, a scientific planning approach for grid integration has been overlooked. Accordingly, this study proposes a comprehensive framework required to plan and develop mobile energy hubs based on optimization of life cycle cost, access distance and parking duration considering the temporal variation of EV recharging demands. The results of the study show that the framework developed can minimize lifecycle costs, and improve infrastructure utilization by accounting for the interests of all stakeholders. The total cost with the proposed robust optimization model under uncertainties of 50% is lesser than the robust cost calculated from a scenario-based approach. Furthermore, the developed framework is useful for recharging infrastructure planners to devise the deployment schedules and attract investors based on the economic viability of the planned strategies.

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  • Khardenavis, Amaiya & Hewage, Kasun & Perera, Piyaruwan & Shotorbani, Amin Mohammadpour & Sadiq, Rehan, 2021. "Mobile energy hub planning for complex urban networks: A robust optimization approach," Energy, Elsevier, vol. 235(C).
  • Handle: RePEc:eee:energy:v:235:y:2021:i:c:s0360544221016728
    DOI: 10.1016/j.energy.2021.121424
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    2. 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).
    3. Anna Auza & Ehsan Asadi & Behrang Chenari & Manuel Gameiro da Silva, 2023. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles," Energies, MDPI, vol. 16(13), pages 1-25, June.
    4. Wang, Ning & Tian, Hangqi & Zhu, Shunbo & Li, Yuan, 2022. "Analysis of public acceptance of electric vehicle charging scheduling based on the technology acceptance model," Energy, Elsevier, vol. 258(C).
    5. Rahimi Sadegh, Ainollah & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal resilient allocation of mobile energy storages considering coordinated microgrids biddings," Applied Energy, Elsevier, vol. 328(C).
    6. Mehrdad Tarafdar-Hagh & Kamran Taghizad-Tavana & Mohsen Ghanbari-Ghalehjoughi & Sayyad Nojavan & Parisa Jafari & Amin Mohammadpour Shotorbani, 2023. "Optimizing Electric Vehicle Operations for a Smart Environment: A Comprehensive Review," Energies, MDPI, vol. 16(11), pages 1-21, May.

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