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

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

Energy, 2015, vol. 93, issue P2, 1693-1703

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.

Keywords: PEV (Plug-in electric vehicle); V2G (Vehicle-to-grid); DFR (Distribution feeder reconfiguration); SSO (Social spider optimization) (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544215014231
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:93:y:2015:i:p2:p:1693-1703

DOI: 10.1016/j.energy.2015.10.055

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().