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Verbrugge, B, Abdel Monem, M, El Baghdadi, M, Geury, T, Hegazy, O & Van Mierlo, J 2019, Development of an Energy Management Strategy and Sizing Algorithm for a Nanogrid Parking Lot for Electric Vehicles. in EVS32 Proceedings. EVS32, 32nd Electric Vehicle Symposium (EVS32) Lyon, France, May 19 - 22, 2019, Lyon, France, 19/05/19.

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@inproceedings{4c4445b62e754a2599f67dcee108cda7,
title = "Development of an Energy Management Strategy and Sizing Algorithm for a Nanogrid Parking Lot for Electric Vehicles",
abstract = "This paper presents an energy management strategy (EMS) and infrastructure-sizing algorithm for charging electric vehicles (EVs) from a nanogrid, comprising a photovoltaic array and a stationary battery pack. This to the utility grid connected system can be a solution for the integration of EVs in the future. The EMS is developed with a fuzzy logic controller that controls the power flow within the nanogrid with the objective to satisfy a weekly load demand of 250 kWh per charging station. The system’s components are optimally sized with a genetic algorithm that minimizes a cost function including the capital and operating costs. The results prove the correct functioning of the EMS, but it was found that a nanogrid parking is not yet attractive from the economical point of view.",
keywords = "Energy storage, Electric vehicle (EV), Optimization, Photovoltaic, Power Management",
author = "Boud Verbrugge and {Abdel Monem}, Mohamed and {El Baghdadi}, Mohamed and Thomas Geury and Omar Hegazy and {Van Mierlo}, Joeri",
year = "2019",
month = "5",
language = "English",
booktitle = "EVS32 Proceedings",
publisher = "EVS32",

}

RIS

TY - GEN

T1 - Development of an Energy Management Strategy and Sizing Algorithm for a Nanogrid Parking Lot for Electric Vehicles

AU - Verbrugge, Boud

AU - Abdel Monem, Mohamed

AU - El Baghdadi, Mohamed

AU - Geury, Thomas

AU - Hegazy, Omar

AU - Van Mierlo, Joeri

PY - 2019/5

Y1 - 2019/5

N2 - This paper presents an energy management strategy (EMS) and infrastructure-sizing algorithm for charging electric vehicles (EVs) from a nanogrid, comprising a photovoltaic array and a stationary battery pack. This to the utility grid connected system can be a solution for the integration of EVs in the future. The EMS is developed with a fuzzy logic controller that controls the power flow within the nanogrid with the objective to satisfy a weekly load demand of 250 kWh per charging station. The system’s components are optimally sized with a genetic algorithm that minimizes a cost function including the capital and operating costs. The results prove the correct functioning of the EMS, but it was found that a nanogrid parking is not yet attractive from the economical point of view.

AB - This paper presents an energy management strategy (EMS) and infrastructure-sizing algorithm for charging electric vehicles (EVs) from a nanogrid, comprising a photovoltaic array and a stationary battery pack. This to the utility grid connected system can be a solution for the integration of EVs in the future. The EMS is developed with a fuzzy logic controller that controls the power flow within the nanogrid with the objective to satisfy a weekly load demand of 250 kWh per charging station. The system’s components are optimally sized with a genetic algorithm that minimizes a cost function including the capital and operating costs. The results prove the correct functioning of the EMS, but it was found that a nanogrid parking is not yet attractive from the economical point of view.

KW - Energy storage

KW - Electric vehicle (EV)

KW - Optimization

KW - Photovoltaic

KW - Power Management

M3 - Conference paper

BT - EVS32 Proceedings

PB - EVS32

ER -

ID: 46251120