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Energies 2016, 9(1), 34; doi:10.3390/en9010034

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

1
Department of Energy Technology, Aalborg University, Pontoppidanstraede 101, Aalborg 9220, Denmark
2
NEC Laboratories America Incorporations, Cupertino, CA 95014, USA
3
Electrical and computer engineering department, Montana State University, Bozeman, MT 59717, USA
*
Author to whom correspondence should be addressed.
Academic Editor: K. T. Chau
Received: 23 October 2015 / Revised: 23 December 2015 / Accepted: 29 December 2015 / Published: 7 January 2016
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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. View Full-Text
Keywords: optimization; plug-in electric vehicle (PEV); photovoltaic (PV) panels; state of charge (SoC); vehicle to grid (V2G) optimization; plug-in electric vehicle (PEV); photovoltaic (PV) panels; state of charge (SoC); vehicle to grid (V2G)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ahmadi Kordkheili, R.; Pourmousavi, S.A.; Savaghebi, M.; Guerrero, J.M.; Nehrir, M.H. Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks. Energies 2016, 9, 34.

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