Multi-source power converter system for EV charging station with integrated ESS

Abronzini, Umberto; Attaianese, C.; D’Arpino, Matilde; Monaco, Mauro Di; Genovese, Antonino; Pede, G.; Tomasso, G.

doi:10.1109/rtsi.2015.7325135

Cited by 19 publications

(12 citation statements)

References 12 publications

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“…For a battery behind meter systems, the essential variables to solve the problem include the DG power output, the load profile, the measured battery storage system (BSS) instantaneous state of charge (SoC) and the forecasted day-ahead grid tariff profile. The costs considered include the grid power purchase cost, the cost of degradation of the BSS as defined in [28], [29], [39] and the cost of power purchase from auxiliary sources such as vehicleto-microgrid (V2M) [6], [40], [41]. In some cases, the grid tariff is constant like in [41] and in others, a stochastic tariff is considered as in [42], [43].…”

Section: Mathematical Formulationsmentioning

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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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%

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

Arwa

Folly

2020

IEEE Access

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“…Based on this characteristic, the converter circuit can be simplified by removing the gate amplifier power supply of the multilevel topology, which uses multiple switching devices according to the circuit configuration. Thus, a field effect transistor of the multilevel converter, excluding the lowest level of buck converters configured in series in the multilevel converter, is configured in the negative terminal of the input power supply, and the gate amplifier voltage is used as the power supply voltage, thus simplifying the circuit configuration [17][18][19][20].…”

Section: Comparison Between Structures and Operational Characteristic...mentioning

confidence: 99%

Improvement of Multilevel DC/DC Converter for E-Mobility Charging Station

2020

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Considerable efforts are being made to reduce CO2 emissions and thereby solve the problems of environmental pollution and global warming. Technologies for environmentally friendly transportation are being developed using batteries. In particular, with the increase in urbanization and one-person households, e-mobility products are drawing increasing attention as short-distance transportation devices. Among these vehicles, personal mobility devices (PMDs) are receiving attention as new transportation devices that are simple to operate. This paper proposes a new multilevel charging system that is advantageous in responding to the charging voltage specifications of various mobile devices with a single charging system while ensuring a low charging current ripple. The proposed diode-parallel multilevel converter consists of an independent Buck converter in series. The switch of the buck converter is configured at the negative terminal of the input power source so that the gate amplifier voltage is used as the power supply voltage; it can therefore be simply configured without a separate gate amplifier power supply. In addition, it is improved so as to have a wider charging voltage range in a low output voltage region and a better efficiency than the existing diode series multilevel converter. To verify the feasibility of the proposed system, simulations were performed using the software PowerSIM(PSIM), and, in order to verify the validity, a prototype charging system was fabricated to compare and analyze losses according to operating conditions.

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“…Thus, the PV surplus during this interval is used to charge the S-ESS to be used at h = 18. A new optimal policy is obtained for the control horizon h = (10,24).…”

Section: Real-time Analysismentioning

confidence: 99%

“…The coordination between the available sources represents a degree of freedom for the MS-EVCS, but at the same time it increases the complexity of system sizing and Energy Management Control (EMC). The EMC can be designed to minimize the grid energy provision, the operative cost, and/or the grid impact [8][9][10]; while the degradation of the stationary ESSs due to repeated charge/discharge cycles cannot be neglected in the control design. In [11][12][13][14][15] non-linear models for ESS capacity fade are presented to describe calendar-life and life cycle aging using electrochemical, heuristic, or experimental approach.…”

Section: Introductionmentioning

confidence: 99%

Cost Minimization Energy Control Including Battery Aging for Multi-Source EV Charging Station

et al. 2019

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A Multi-Source Electric Vehicle Charging Station (MS-EVCS) is a local entity that combines the grid energy with Distributed Energy Resources (DERs) with the aim of reducing the grid impact due to electric vehicles (EVs) charging events. The integration of stationary and in-vehicle Energy Storage Systems (ESSs) in MS-EVCSs has gained increasing interest thanks to the possibility of storing energy at off-peak hours to be made available at peak-hours. However, the ESS technology and the vehicle-to-grid (V2G) concept show several issues due to cost, battery life cycle, reliability, and management. The design of the MS-EVCS energy management system is of primary importance to guarantee the optimal usage of the available resources and to enhance the system benefits. This study presents a novel energy management strategy for Real-Time (RT) control of MS-EVCS considering DERs, stationary ESS, and V2G. The proposed energy management control allows defining the MS-EVCS control policy solving several cascaded-problems with the aim of achieving the minimum operating cost when the battery degradation and the stochastic nature of the sources are considered. The key feature of the proposed methodology is the lower computational effort with respect to traditional optimal control methodologies while achieving the same optimal solution.

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Multi-source power converter system for EV charging station with integrated ESS

Cited by 19 publications

References 12 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

Improvement of Multilevel DC/DC Converter for E-Mobility Charging Station

Cost Minimization Energy Control Including Battery Aging for Multi-Source EV Charging Station

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