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An optimal integrated planning method for supporting growing penetration of electric vehicles in distribution systems

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  • Zeng, Bo
  • Feng, Jiahuan
  • Zhang, Jianhua
  • Liu, Zongqi

Abstract

This paper proposes a multi-year expansion planning method for enabling distribution systems to support growing penetrations of plug-in electric vehicles. As distinct from the existing studies, the temporal characteristics of charging loads and their reliability impacts are especially focused in our work. To achieve this, a novel dual-stage optimization framework is developed. The proposed method considers the capacity reinforcement of distribution systems in conjunction with their operation decisions and coordinates them under the same frame so as to minimize the total system costs for accommodating electric vehicles. The uncertainties associated with renewable energy generation, charging behaviors, and conventional load demand are represented by multiple probabilistic scenarios. To fully reveal the impacts of electric vehicle integration, both uncontrolled and coordinated charging schemes are considered in our analysis. Furthermore, as charging loads bring about extra demand to the grid, the reliability criteria is also taken into account in the proposed model. Using a heuristic algorithm combined with reliability analysis, the optimal solution for the concerned problem can be determined, which involves the best timing, locations, and capacities for installation of distributed generation units and network components. The effectiveness of the proposed framework is examined based on a 38-bus test system and the obtained results verify the performance of the approach.

Suggested Citation

  • Zeng, Bo & Feng, Jiahuan & Zhang, Jianhua & Liu, Zongqi, 2017. "An optimal integrated planning method for supporting growing penetration of electric vehicles in distribution systems," Energy, Elsevier, vol. 126(C), pages 273-284.
    • Handle: RePEc:eee:energy:v:126:y:2017:i:c:p:273-284
    DOI: 10.1016/j.energy.2017.03.014
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    References listed on IDEAS

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    Citations

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    1. Hajebrahimi, Ali & Kamwa, Innocent & Huneault, Maurice, 2018. "A novel approach for plug-in electric vehicle planning and electricity load management in presence of a clean disruptive technology," Energy, Elsevier, vol. 158(C), pages 975-985.
    2. Spyros Giannelos & Stefan Borozan & Marko Aunedi & Xi Zhang & Hossein Ameli & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2023. "Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids," Energies, MDPI, vol. 16(13), pages 1-15, June.
    3. Davidov, Sreten & Pantoš, Miloš, 2017. "Stochastic expansion planning of the electric-drive vehicle charging infrastructure," Energy, Elsevier, vol. 141(C), pages 189-201.
    4. Miguel Carrión & Rafael Zárate-Miñano & Ruth Domínguez, 2020. "Integration of Electric Vehicles in Low-Voltage Distribution Networks Considering Voltage Management," Energies, MDPI, vol. 13(16), pages 1-23, August.
    5. Nunes, Pedro & Brito, M.C., 2017. "Displacing natural gas with electric vehicles for grid stabilization," Energy, Elsevier, vol. 141(C), pages 87-96.
    6. Luo, Lizi & Gu, Wei & Wu, Zhi & Zhou, Suyang, 2019. "Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation," Applied Energy, Elsevier, vol. 242(C), pages 1274-1284.
    7. Olukorede Tijani Adenuga & Senthil Krishnamurthy, 2023. "Economic Power Dispatch of a Grid-Tied Photovoltaic-Based Energy Management System: Co-Optimization Approach," Mathematics, MDPI, vol. 11(15), pages 1-22, July.
    8. Moradijoz, M. & Moghaddam, M. Parsa & Haghifam, M.R., 2018. "A flexible active distribution system expansion planning model: A risk-based approach," Energy, Elsevier, vol. 145(C), pages 442-457.

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