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Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks

Author

Listed:
  • Reza Ahmadi Kordkheili

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, Aalborg 9220, Denmark)

  • Seyyed Ali Pourmousavi

    (NEC Laboratories America Incorporations, Cupertino, CA 95014, USA)

  • Mehdi Savaghebi

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, Aalborg 9220, Denmark)

  • Josep M. Guerrero

    (Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, Aalborg 9220, Denmark)

  • Mohammad Hashem Nehrir

    (Electrical and computer engineering department, Montana State University, Bozeman, MT 59717, USA)

Abstract

A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs) in distribution networks by intelligent management of plug-in electric vehicle (PEV) storage. The proposed algorithm is defined to manage the reverse power flow (PF) from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner’s quality of service (QoS). The method uses genetic algorithm (GA) to increase photovoltaic (PV) penetration without jeopardizing PEV owners’ (QoS) and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV) grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up to 50%, depending on the PEV penetration level and the freedom of the system operator in managing the available PEV storage.

Suggested Citation

  • Reza Ahmadi Kordkheili & Seyyed Ali Pourmousavi & Mehdi Savaghebi & Josep M. Guerrero & Mohammad Hashem Nehrir, 2016. "Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks," Energies, MDPI, vol. 9(1), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:1:p:34-:d:61858
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    References listed on IDEAS

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    1. Ying-Yi Hong & Yuan-Ming Lai & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2015. "Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables," Energies, MDPI, vol. 8(4), pages 1-20, March.
    2. Cristina Rottondi & Simone Fontana & Giacomo Verticale, 2014. "Enabling Privacy in Vehicle-to-Grid Interactions for Battery Recharging," Energies, MDPI, vol. 7(5), pages 1-19, April.
    3. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
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    Cited by:

    1. Morsy Nour & José Pablo Chaves-Ávila & Gaber Magdy & Álvaro Sánchez-Miralles, 2020. "Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems," Energies, MDPI, vol. 13(18), pages 1-34, September.
    2. Kang Miao Tan & Vigna K. Ramachandaramurthy & Jia Ying Yong & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg, 2017. "Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling," Energies, MDPI, vol. 10(11), pages 1-21, November.
    3. Jean-Michel Clairand & Javier Rodríguez-García & Carlos Álvarez-Bel, 2018. "Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation," Energies, MDPI, vol. 11(11), pages 1-21, November.

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