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https://doi.org/10.1109/med514...
Article . 2021 . Peer-reviewed
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Optimal Control of an Energy Storage System and Plug-in Electric Vehicles Fast Charging in a Grid-connected Service Area

Authors: Francesco Liberati; Roberto Germanà; Emanuele De Santis; Alessandro Di Giorgio;

Optimal Control of an Energy Storage System and Plug-in Electric Vehicles Fast Charging in a Grid-connected Service Area

Abstract

As a result of the increasing charging rate implemented by car manufacturers in the new generation of plug-in electric vehicles (PEVs), charging point operators are continuously adjusting the charging infrastructure accordingly. In order to maximize the charging operator’s return of investment and minimize the impact on the electricity grid, a key aspect is finding technical solutions which allow to downsize the nominal power flow at the point of connection between the charging station/charging area and the electricity grid, as the operating expenses are significantly affected by this parameter. In this regard, this study discusses the optimal control of an energy storage system (ESS) and PEVs fast charging for reducing the impact on the grid of the charging load in a charging area. A trade-off is achieved between the objectives of keeping limited the charging power withdrawal from the grid and the one of keeping limited the fluctuation of the state of charge of the ESS around a given reference, while keeping the charging power near to the nominal one. We present a deterministic solution, under the realistic assumption that the charging operator knows a piece-wise constant estimate of the aggregated charging power demand over the control period. Numeric simulations are provided to validate the proposed approach.

Country
Italy
Keywords

Energy Storage System, Optimal Control, optimal control; plug-in electric vehicles; fast charging, Fast Charging, Electric Vehicles

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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