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Optimal distribution feeder reconfiguration for increasing the penetration of plug-in electric vehicles and minimizing network costs

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  • Kavousi-Fard, Abdollah
  • Abbasi, Alireza
  • Rostami, Mohammad-Amin
  • Khosravi, Abbas

Abstract

Appearance of PEVs (Plug-in Electric Vehicles) in future transportation sector brings forward opportunities and challenges from grid perspective. Increased utilization of PEVs will result in problems such as greater total loss, unbalanced load factor, feeder congestion and voltage drop. PEVs are mobile energy storages dispersed all over the network with benefits to both owners and utilities in case of V2G (Vehicle-to-Grid) possibility. The intelligent bidirectional power flow between grid and large number of vehicles adds complexity to the system and requires operative tools to schedule V2G energy and subdue PEV impacts. In this paper, DFR (Distribution Feeder Reconfiguration) is utilized to optimally coordinate PEV operation in a stochastic framework. Uncertainty in PEVs characteristics can be due to several sources from location and time of grid connection to driving pattern and battery SoC (State-of-Charge). The proposed stochastic problem is solved with a self-adaptive evolutionary swarm algorithm based on SSO (Social Spider Optimization) algorithm. Numerical studies verify the efficacy of the proposed DFR to improve the system performance and optimal dispatch of V2G.

Suggested Citation

  • Kavousi-Fard, Abdollah & Abbasi, Alireza & Rostami, Mohammad-Amin & Khosravi, Abbas, 2015. "Optimal distribution feeder reconfiguration for increasing the penetration of plug-in electric vehicles and minimizing network costs," Energy, Elsevier, vol. 93(P2), pages 1693-1703.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1693-1703
    DOI: 10.1016/j.energy.2015.10.055
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    References listed on IDEAS

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    3. Fu, D.Z. & Zheng, Z.Y. & Shi, H.B. & Xiao, Rui & Huang, G.H. & Li, Y.P., 2017. "A multi-fuel management model for a community-level district heating system under multiple uncertainties," Energy, Elsevier, vol. 128(C), pages 337-356.
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    5. Tabatabaee, Sajad & Mortazavi, Seyed Saeedallah & Niknam, Taher, 2016. "Stochastic energy management of renewable micro-grids in the correlated environment using unscented transformation," Energy, Elsevier, vol. 109(C), pages 365-377.
    6. Azizivahed, Ali & Narimani, Hossein & Naderi, Ehsan & Fathi, Mehdi & Narimani, Mohammad Rasoul, 2017. "A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration," Energy, Elsevier, vol. 138(C), pages 355-373.
    7. Ramos Muñoz, Edgar & Razeghi, Ghazal & Zhang, Li & Jabbari, Faryar, 2016. "Electric vehicle charging algorithms for coordination of the grid and distribution transformer levels," Energy, Elsevier, vol. 113(C), pages 930-942.
    8. Changhong Deng & Ning Liang & Jin Tan & Gongchen Wang, 2016. "Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network," Sustainability, MDPI, vol. 8(12), pages 1-15, November.
    9. Colmenar-Santos, Antonio & Linares-Mena, Ana-Rosa & Borge-Diez, David & Quinto-Alemany, Carlos-Domingo, 2017. "Impact assessment of electric vehicles on islands grids: A case study for Tenerife (Spain)," Energy, Elsevier, vol. 120(C), pages 385-396.

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