Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems

Domínguez-Navarro, José A.; Dufo-López, Rodolfo; Yusta, José M.; Sevil, Jesús Sergio Artal; Bernal-Agustín, José L.

doi:10.1016/j.ijepes.2018.08.001

Cited by 330 publications

(178 citation statements)

References 44 publications

Supporting

Mentioning

172

Contrasting

Unclassified

Order By: Relevance

“…However, the trend toward clean and renewable energy sources (RESs), opens up a new field where a hybrid energy source will support the quick changes in power demand with less reliance on the fossil fuels. Several publications have appeared in recent years demonstrating and documenting the feasibility of novel approaches, which are based on a multisource system gathered RESs with storage buffers [3][4][5][6]. In order to store the renewable energy and/or to overcome the power peak situation when superfast charging scenarios are set, numerous considerations must be taken into account as reported in [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Control Strategies of Electric Vehicles Charging Station based on Grid Tied PV/Battery System

Hassoune¹,

Khafallah²,

Mesbahi³

et al. 2020

IJACSA

View full text Add to dashboard Cite

In this paper, improved control strategies of a smart topology of EVs charging station (CS) based on grid tied PV/Battery system are designed and analyzed. The proposed strategies consist of three operating modes i.e. Pv2B; charging a battery storage buffer (BSB) of the CS from solar energy, V2G; discharging an EV battery via grid, and Pv2G; injecting the produced power from PV system into the energy distribution system. However, the BSB is connected to the PV system through a single ended primary inductor converter, the V2G operating mode is emulated by an EV lithium-ion battery tied to the grid via a high frequency full bridge inverter and a bidirectional dc/dc converter. The aim of this work is to improve the energy efficiency of the CS by using a hybrid energy system. Simulation studies are performed in Matlab/Simulink in order to operate the proposed solar CS with multiple control strategies of each case scenario based on a CS management algorithm (CSMA). To provide credible findings of this research, a low power prototype is developed in order to validate the proposed CSMA and its associated controls.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved Control Strategies of Electric Vehicles Charging Station based on Grid Tied PV/Battery System

Hassoune¹,

Khafallah²,

Mesbahi³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…In this work, EVs are modeled as non-controllable loads without V2G capability. Authors in [26] introduced a cost-effective operating framework to design a fast-charging station equipped with RESs and energy storage systems. Results show that optimal collaboration of RESs and ESSs is the best solution from the economic point of view.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

et al. 2020

View full text Add to dashboard Cite

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.Energies 2020, 13, 1706 2 of 25 microgrids is evolving quickly [1]. The advanced structure of the microgrids allows the distribution network operators (DSOs) to consider the EV battery, either as a load or distributed generator [2]. However, deployment of RESs and EVs creates some challenges, e.g., poor reliability and power quality problems. One of the most effective ways to mitigate these challenges is the deployment of energy storage systems (ESSs) in distribution grids. ESSs can alleviate the negative effects of uncertainties in RESs production [3]. On the other hand, despite the remarkable advantages of these units, there are some challenges in terms of adequate DERs capacity requirement, system configuration, and energy management and control. One of the most challenging issues is the optimal coordination of both supply and demand sides in microgrids with the main grid, while satisfying system constraints.Currently, extensive studies have been done on the impacts of the RESs, EVs, and ESSs on the network operation . In [4], a smart home energy management strategy is proposed to realize cost-effective energy systems for customers and provide reactive power compensation for home appliances using EV and ESS. In this paper, network-level studies are limited to investigating the power factor indicator, while the potential role of EVs and ESSs in distribution networks has not been considered. Researchers in [5] presented the centralized control strategy to manag...

show abstract

“…The results are presented for a 1 day simulation. A third example of power demand curve derivation is provided by Dominguez-Navarro et al [13]. In this study, they ran a Monte Carlo simulation, considering an average time between two arrivals at the station as a function of the hour of the day.…”

Section: Introductionmentioning

confidence: 99%

Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations

2019

View full text Add to dashboard Cite

Multi-stall fast charging stations are often thought to require megawatt-range grid connections. The power consumption profile of such stations results in high cost penalties due to monthly power peaks and expensive linkage fees. A local energy storage system (ESS) can be used to address peak power demands. However, no appropriate sizing method is available to match specific constraints, such as the contracted power available from the grid and the projected recharging demand. A stochastic distribution of charging events was used in this paper to model power demand profiles at the station, with a one minute resolution. Based on 100 simulated months, we propose an optimum number of charging points, and we developed an algorithm to return the required local ESS capacity as a function of the available grid connection. The role of ESSs in the range of 100 kWh to 1 MWh was studied for all stations with up to 2000 charging events per week. The relevance of ESS implementation was assessed along three parameters: the number of charging points, the available grid connection, and the ESS capacity. This work opens new possibilities for multi-stall charging station deployment in locations with limited access to the medium voltage grid, and provides sizing guidelines for effective ESSs implementation. In addition, it helps build business cases for charging station operators in regions with high demand charges.

show abstract

Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems

Cited by 330 publications

References 44 publications

Improved Control Strategies of Electric Vehicles Charging Station based on Grid Tied PV/Battery System

Improved Control Strategies of Electric Vehicles Charging Station based on Grid Tied PV/Battery System

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations

Contact Info

Product

Resources

About