A technological review on electric vehicle DC charging stations using photovoltaic sources

Cheddadi, Youssef; Errahimi, Fatima; Es-Sbai, Najia; Alaoui, Chakib

doi:10.1088/1757-899x/353/1/012014

Cited by 27 publications

(19 citation statements)

References 46 publications

(45 reference statements)

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“…The bus is connected to the EV supply equipment (EVSE) via an appropriate power conversion apparatus. More details on the EV charger architectures and design considerations in grid-tied EV CSs can be found in [5], [33], [34] and [35].…”

Section: Mathematical Formulationsmentioning

confidence: 99%

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Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Arwa

Folly

2020

IEEE Access

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The utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES generations get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control. INDEX TERMS Electric vehicle charging station, energy management, Markov Decision Process, microgrid, reinforcement learning.

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Section: Mathematical Formulationsmentioning

confidence: 99%

“…This negates the environmental benefits of electric mobility [2]. The use of renewable energy sources (RES) to charge EVs would significantly reduce greenhouse gas emissions [3], [4], [5]. Nevertheless, RES are intermittent and non-dispatchable.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Arwa

Folly

2020

IEEE Access

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“…In [15] is proposed an optimal EV fast charging infrastructure, where the EVs are connected to a DC-Bus, employing an individual control for the charging process in order to optimize the power transfer from the AC PG to the DC-Bus. Other studies propose the integration of renewables connected to DC FCS [16], [17].…”

Section: Eai Endorsed Transactions On Energy Webmentioning

confidence: 99%

Power Electronics Converters for an Electric Vehicle Fast Charging Station with Energy Storage System and Renewable Energy Sources

Pinto¹,

Monteiro²,

Exposto³

et al. 2020

EAI Endorsed Transactions on Energy Web

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Fast Charging Stations (FCS) are a key element for the wide spreading of Electric Vehicles (EVs), by reducing the charging time to a range between 20 to 40 minutes. However, the integration of FCS causes some adverse impacts on the Power Grid (PG), namely the huge increase in the peak demand during short periods of time. This paper addresses the design of power electronics converters for an EV DC FCS with local storage capability and easy interface of renewables. In the proposed architecture, the energy storage capability is used to smooth the peak power demand and contributes to stabilize the PG. When integrated in a smart grid, the proposed architecture may even return some of the stored energy back to the PG. The accomplishment of the aforementioned objectives requires a set of different power electronics converters, described and discussed along the paper. In order to demonstrate the potentialities of the proposed EV DC FCS architecture, four different case studies were analysed.

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“…In order to integrate the PV module with the EV battery charging system, power electronic converters are employed. These interfaces are also known as battery chargers [8][9].…”

Section: Introductionmentioning

confidence: 99%

PV and Grid interfaced Plug-in EV Battery Charger operating in P-VG, P-V and V-G Modes

Subudhi¹,

Krithiga²

2019

IJRTE

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 Abstract: The proposed system facilitates uninterruptable charging of a photovoltaic (PV) fed plug-in electric vehicle (EV) battery charging system irrespective of solar irradiation conditions by integrating utility grid to the battery charging system. The system employs bidirectional cycloconverter (BDCC) in order to use utility grid as source or sink during different modes of operation which depends on the availability of solar power. During low irradiation condition, the utility grid acts as a backup source in order to facilitate uninterruptable charging of the EV battery. When surplus power is generated from the PV panel, it is fed to the utility grid, which acts as sink in this mode. For uninterruptable EV battery charging, the controller operates the switches and relays in the proposed system corresponding to solar irradiation level. The available literatures define complex control strategies which are solved in this proposed system by adopting a simple dynamic control algorithm. The simulation of the proposed system has been carried out using PSIM simulation software and experimental prototype has been designed, developed and tested for different modes of operations to validate the efficacy of the proposed system.

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A technological review on electric vehicle DC charging stations using photovoltaic sources

Cited by 27 publications

References 46 publications

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Power Electronics Converters for an Electric Vehicle Fast Charging Station with Energy Storage System and Renewable Energy Sources

PV and Grid interfaced Plug-in EV Battery Charger operating in P-VG, P-V and V-G Modes

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