8,151 research outputs found
Planning of Regional Urban Bus Charging Facility:A Case Study of Fengxian, Shanghai
The electrification of public transport is of great significance to alleviating environmental pollution and energy problems. The construction of charging stations for electric buses (EBs) is the key step for the electrification of public transport and receives more and more attention. This paper proposes a new urban electric bus charging station planning algorithm which consists of two parts, park-maintaining (PM) charging station planning and midway supply (MS) charging station planning. Firstly, bus routes are classified based on charging demands. Accordingly, the PM charging station planning model is divided into full slow charging (FSC) model, Bus Rapid Transit (BRT) model and Hybrid model. Secondly, the improved grid AP algorithm is applied to plan MS charging stations to enhance the EB operation reliability. Then by multi-terminal charging pile optimization model, the economics of charging facilities construction is enhanced. Finally, via an ordered control charging algorithm, the economic profits of overall planning schemes are enhanced. The bus system in Fengxian, Shanghai is taken as an example to demonstrate the proposed method. Results prove that the proposed method can effectively meet the charging demands of EBs and improve the operating reliability of the EB system. </p
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
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
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