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An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid

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  • Mortaz, Ebrahim
  • Vinel, Alexander
  • Dvorkin, Yury

Abstract

In this paper, we propose a new optimization model for investment planning for Vehicle-to-Grid (V2G) technology in a microgrid. The V2G technology allows the microgrid to take advantage of temporal arbitrage in the electricity market, and hence, improve economic viability of electric vehicles. We present both a mathematical model and a custom solution methodology for this problem. The proposed stochastic model is a very large-scale mixed-integer nonlinear programming problem, and thereby, we develop an algorithm that provides high-quality solutions. A case study for 14-node and 37-node microgrid test systems for a planning horizon of five years is presented, and a thorough sensitivity analysis for effects of uncertainty, investors preferred payback period, market price fluctuations and electric vehicles arrival and departure rates is discussed. The simulation results show that investing in the V2G technology can considerably improve the long-term economics of the microgrid, but the obtained profit and the payback period can vary from one plan to another.

Suggested Citation

  • Mortaz, Ebrahim & Vinel, Alexander & Dvorkin, Yury, 2019. "An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid," Applied Energy, Elsevier, vol. 242(C), pages 1649-1660.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:1649-1660
    DOI: 10.1016/j.apenergy.2019.03.131
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    4. Viviani Caroline Onishi & Carlos Henggeler Antunes & João Pedro Fernandes Trovão, 2020. "Optimal Energy and Reserve Market Management in Renewable Microgrid-PEVs Parking Lot Systems: V2G, Demand Response and Sustainability Costs," Energies, MDPI, vol. 13(8), pages 1-24, April.
    5. Yi, Tao & Cheng, Xiaobin & Peng, Peng, 2022. "Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO," Energy, Elsevier, vol. 239(PC).
    6. Lingling Hu & Junming Zhou & Feng Jiang & Guangming Xie & Jie Hu & Qinglie Mo, 2023. "Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization," Sustainability, MDPI, vol. 16(1), pages 1-25, December.
    7. Liu, Jia & Cao, Sunliang & Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2021. "Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage," Applied Energy, Elsevier, vol. 281(C).
    8. Solanke, Tirupati U. & Khatua, Pradeep K. & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao, 2021. "Control and management of a multilevel electric vehicles infrastructure integrated with distributed resources: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Mohseni, Soheil & Khalid, Roomana & Brent, Alan C., 2023. "Stochastic, resilience-oriented optimal sizing of off-grid microgrids considering EV-charging demand response: An efficiency comparison of state-of-the-art metaheuristics," Applied Energy, Elsevier, vol. 341(C).

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