14,342 research outputs found
Electric Power Allocation in a Network of Fast Charging Stations
In order to increase the penetration of electric vehicles, a network of fast
charging stations that can provide drivers with a certain level of quality of
service (QoS) is needed. However, given the strain that such a network can
exert on the power grid, and the mobility of loads represented by electric
vehicles, operating it efficiently is a challenging problem. In this paper, we
examine a network of charging stations equipped with an energy storage device
and propose a scheme that allocates power to them from the grid, as well as
routes customers. We examine three scenarios, gradually increasing their
complexity. In the first one, all stations have identical charging capabilities
and energy storage devices, draw constant power from the grid and no routing
decisions of customers are considered. It represents the current state of
affairs and serves as a baseline for evaluating the performance of the proposed
scheme. In the second scenario, power to the stations is allocated in an
optimal manner from the grid and in addition a certain percentage of customers
can be routed to nearby stations. In the final scenario, optimal allocation of
both power from the grid and customers to stations is considered. The three
scenarios are evaluated using real traffic traces corresponding to weekday rush
hour from a large metropolitan area in the US. The results indicate that the
proposed scheme offers substantial improvements of performance compared to the
current mode of operation; namely, more customers can be served with the same
amount of power, thus enabling the station operators to increase their
profitability. Further, the scheme provides guarantees to customers in terms of
the probability of being blocked by the closest charging station. Overall, the
paper addresses key issues related to the efficient operation of a network of
charging stations.Comment: Published in IEEE Journal on Selected Areas in Communications July
201
On the Evaluation of Plug-in Electric Vehicle Data of a Campus Charging Network
The mass adoption of plug-in electric vehicles (PEVs) requires the deployment
of public charging stations. Such facilities are expected to employ distributed
generation and storage units to reduce the stress on the grid and boost
sustainable transportation. While prior work has made considerable progress in
deriving insights for understanding the adverse impacts of PEV chargings and
how to alleviate them, a critical issue that affects the accuracy is the lack
of real world PEV data. As the dynamics and pertinent design of such charging
stations heavily depend on actual customer demand profile, in this paper we
present and evaluate the data obtained from a node charging network
equipped with Level chargers at a major North American University campus.
The data is recorded for weeks starting from late . The result
indicates that the majority of the customers use charging lots to extend their
driving ranges. Also, the demand profile shows that there is a tremendous
opportunity to employ solar generation to fuel the vehicles as there is a
correlation between the peak customer demand and solar irradiation. Also, we
provided a more detailed data analysis and show how to use this information in
designing future sustainable charging facilities.Comment: Accepted by IEEE Energycon 201
D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems
The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems
Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions
To enhance environmental sustainability, many countries will electrify their
transportation systems in their future smart city plans. So the number of
electric vehicles (EVs) running in a city will grow significantly. There are
many ways to re-charge EVs' batteries and charging stations will be considered
as the main source of energy. The locations of charging stations are critical;
they should not only be pervasive enough such that an EV anywhere can easily
access a charging station within its driving range, but also widely spread so
that EVs can cruise around the whole city upon being re-charged. Based on these
new perspectives, we formulate the Electric Vehicle Charging Station Placement
Problem (EVCSPP) in this paper. We prove that the problem is non-deterministic
polynomial-time hard. We also propose four solution methods to tackle EVCSPP
and evaluate their performance on various artificial and practical cases. As
verified by the simulation results, the methods have their own characteristics
and they are suitable for different situations depending on the requirements
for solution quality, algorithmic efficiency, problem size, nature of the
algorithm, and existence of system prerequisite.Comment: Submitted to IEEE Transactions on Smart Grid, revise
Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids
Electric vehicle fleets and smart grids are two growing technologies. These technologies
provided new possibilities to reduce pollution and increase energy efficiency.
In this sense, electric vehicles are used as mobile loads in the power grid. A distributed
charging prioritization methodology is proposed in this paper. The solution is based
on the concept of virtual power plants and the usage of evolutionary computation
algorithms. Additionally, the comparison of several evolutionary algorithms, genetic
algorithm, genetic algorithm with evolution control, particle swarm optimization, and
hybrid solution are shown in order to evaluate the proposed architecture. The proposed
solution is presented to prevent the overload of the power grid
Energy efficiency and integration of urban electrical transport systems: EVS and metro-trains of two real European lines
Transport is a main source of pollutants in cities, where air quality is a major concern. New transport technologies, such as electric vehicles, and public transport modalities, such as urban railways, have arisen as solutions to this important problem. One of the main difficulties for the adoption of electric vehicles by consumers is the scarcity of a suitable charging infrastructure. The use of the railway power supplies to charge electric vehicle batteries could facilitate the deployment of charging infrastructure in cities. It would reduce the cost because of the use of an existing installation. Furthermore, electric vehicles can use braking energy from trains that was previously wasted in rheostats. This paper presents the results of a collaboration between research teams from University of Rome Sapienza and Comillas Pontifical University. In this work, two real European cases are studied: an Italian metro line and a Spanish metro line. The energy performance of these metro lines and their capacity to charge electric vehicles have been studied by means of detailed simulation tools. Their results have shown that the use of regenerated energy is 98% for short interval of trains in both cases. However, the use of regenerated energy decreases as the train intervals grow. In a daily operation, an important amount of regenerated energy is wasted in the Italian and Spanish case. Using this energy, a significant number of electric vehicles could be charged every day
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