Charging Station Planning for Electric Vehicles

Kalakanti, Arun Kumar; Rao, Shrisha

doi:10.3390/systems10010006

Cited by 15 publications

(6 citation statements)

References 50 publications

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“…The primary assumption behind the modelling of Electric Vehicle Charging Stations (EVCS) [2] is that they provide the EV battery with precisely the necessary amount of actual current. There are three types of charging station for EVs which are three phase AC charging station, DC slow charging station and DC fast charging station [7].…”

Section: Evcs Modellingmentioning

confidence: 99%

“…With further study, EVs can perform on level with conventional cars, but EV charging infrastructure is a significant obstacle for the ecosystem of electric transportation. The impact of charging patterns on the distribution network might vary significantly depending on the location of the charging station [2], particularly in the context of a large fleet of electric vehicles. This phenomenon has the potential to lead to excessive load and power dissipation.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

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Optimal Location of Electric Vehicle Charging Station in Reconfigured Radial Distribution Network

Srinivas,

Reddy

2023

IJEER

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Electric vehicles are becoming increasingly popular because they are cleaner-burning and more efficient than combustion-engine automobiles. Due to the diminishing availability of fossil fuels and the carbon emissions produced by cars, electric vehicles (EV) have become a need for mobility in the near future. Electric car charging stations were established as a consequence of the increase in EVs. Electric car charging lowers voltage and increases real power loss in the radial distribution network. In order to mitigate real power loss and provide a stable voltage profile, the charging station has to be placed as efficiently as possible. The current research examines and elucidates the influence of electric vehicle charging stations (EVCS) on the balanced radial distribution network (BRDN) using particle swarm optimization (PSO). There are two steps involved in the planning process. The optimal placement for EVCS [1] in BRDN is identified in the first stage using PSO. The second stage uses reconfiguration to ensure higher EVCS stability. This two-step technique's main goal is to decrease the real power loss due to the placement of charging stations in the radial distribution network (RDN). The correctness of the suggested approach is expounded upon in four different cases. The second, third, and fourth cases use a two-step procedure, which is then compared to the base case. In the base case EVCS is ideally located in BRDN using PSO. The fourth case employs a concurrent two-step optimization approach and demonstrates superior performance compared to other cases. The suggested approach is implemented and evaluated on the IEEE 69 bus BRDN using the MATLAB software.

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Section: Evcs Modellingmentioning

confidence: 99%

Section: ░ 1 Introductionmentioning

confidence: 99%

Optimal Location of Electric Vehicle Charging Station in Reconfigured Radial Distribution Network

Srinivas,

Reddy

2023

IJEER

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“…Moreover, the most significant factor impacting profitability is the charging price. In [66], the authors present a machine learning approach based on different classes of clustering solutions. The work is tested on two large datasets, including spatial data on households with EVs.…”

Section: Charging Station Planningmentioning

confidence: 99%

“…In the GIS-based framework, the main approach seems to be evaluating certain criteria weight which can be used to develop suitability maps. As described in [65] and [66], the coupling of more traditional approaches with GISs seems to be very effective since it can take advantage of the data analysis available through GISs, which can effectively determine the optimal planning.…”

Section: Charging Station Planningmentioning

confidence: 99%

Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management

et al. 2022

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In this paper, a survey is presented on the use of optimization models for the integration of electric vehicles (EVs) and charging stations (CSs) in the energy system, paying particular attention both to planning problems (i.e., those problems related to long term decisions such as the siting and sizing of CSs), and operational management problems (i.e., the optimal scheduling of EVs in smart grids, microgrids and buildings taking into account vehicle-to-grid (V2G) capabilities). Moreover, specific attention was dedicated to decision problems that couple transportation and electrical networks, such as the energy demand assessment for a vehicle over a path and routing and charging decision problems for goods and people transportation. Finally, an effort was dedicated to highlighting the integration and the use of EVs in very recent regulation frameworks, with specific reference to the participation in the balancing market through the figure of an aggregator and the inclusion in the management of Energy Communities (ECs) and sustainable districts.

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“…Many researchers have stressed the impact and complexities of EVs on the distribution system [6][7][8]. Different models are developed [9][10][11][12] to reduce the uncertainties caused by EV mobility and to enhance environmental and economic benefits.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach

2022

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The usage of Electric Vehicles (EVs) for transportation is expected to continue growing, which opens up new possibilities for creating new smart grids. It offers a large-scale penetration of Fast Charging Stations (FCE) in a local utility network. A severe voltage fluctuation and increased active power loss might result from the inappropriate placement of the FCE as it penetrates the Distribution System (DST). This paper proposes a multi-objective optimisation for the simultaneous optimal allocation of FCEs, Distributed Generators (DGs), and Shunted Capacitors (SCs). The proposed Pareto dominance-based hybrid methodology incorporates the advantages of the Grey Wolf Optimiser and Particle Swarm Optimisation algorithm to minimise the objectives on 118 bus radial distribution systems. The proposed method outperforms some other existing algorithms in terms of minimising (a) active power loss costs of the distribution system, (b) voltage deviations, (c) FCE development costs, (d) EV energy consumption costs, and (e) DG costs, as well as satisfying the number of FCEs and EVs in all zones based on transportation and the electrical network. The simulation results demonstrate that the simultaneous deployment technique yields better outcomes, such as the active power loss costs of the distribution system being reduced to 53.21%, voltage deviations being reduced to 68.99%, FCE development costs being reduced to 22.56%, EV energy consumption costs being reduced to 19.8%, and DG costs being reduced to 5.1%.

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Charging Station Planning for Electric Vehicles

Cited by 15 publications

References 50 publications

Optimal Location of Electric Vehicle Charging Station in Reconfigured Radial Distribution Network

Optimal Location of Electric Vehicle Charging Station in Reconfigured Radial Distribution Network

Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management

Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach

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