Optimal placement of electric vehicle charging station for unbalanced radial distribution systems

Reddy, M. Satish Kumar; Selvajyothi, K.

doi:10.1080/15567036.2020.1731017

Cited by 54 publications

(11 citation statements)

References 16 publications

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“…The proposed Bayesian Network is an optimization model from the perspective of social, environmental, and economic criteria. 39 Reddy et al 40 analyze multiple elements connected with the CSs such as their power, uncertainty of electricity price, and load uncertainty. The proposed method uses an unbalanced radial distribution system (URDS) by using a particle swarm optimization (PSO) algorithm.…”

Section: Charging Stations Topologies and Architecturesmentioning

confidence: 99%

An overview and performance evaluation of open charge point protocol from an electromobility concept perspective

Răboaca

Meheden

Musat

et al. 2021

Intl J of Energy Research

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Section: Charging Stations Topologies and Architecturesmentioning

confidence: 99%

An overview and performance evaluation of open charge point protocol from an electromobility concept perspective

Răboaca

Meheden

Musat

et al. 2021

Intl J of Energy Research

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“…Simulation results demonstrated the ability of the proposed algorithm in reducing power losses as well as achieving economic benefits for the EVCSs. The researchers in [17] optimally sized and placed EVCSs in an unbalanced distribution network using PSO. To increase the sustainability and reliability of their test networks, type one DGs which compensate only for reactive power were incorporated into the distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Integration of Electric Vehicle Charging Stations and Compensating Photovoltaic Systems in a Distribution Network Segregated into Communities

Fokui¹,

Ngoo

Saulo

2022

J. ADV. ENG. COMPUT.

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This paper proposes a method of optimally utilizing electric vehicles (EVs) in the distribution network. The method is firstly based on segregating the distribution network into communities and then optimally placing an EV charging station (EVCS) in each community using the backward forward sweep (BFS) technique. The Second phase uses particle swarm optimization (PSO) to size and allocates photovoltaic systems in the network for power loss minimization and voltage improvement. The proposed method is tested on an IEEE 33 node test feeder and simulation results showed the effectiveness of the BFS in finding the best nodes for the placement of EVCS in each community as well as the effectiveness of the PSO in allocating the photovoltaic systems. To validate the effectiveness of the BFS technique, its results obtained are compared with those obtained when the EVCSs are placed on some nodes other than those chosen by the BFS technique.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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“…Authors in Reference 23 analysed the economic parameters that affect the PV‐fed EV charging station. A charging strategy is proposed to maximize the usage of renewable energy with a reduction in grid power purchase.…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence‐based energy management of electric vehicle charging station integrated with renewable energy sources

Ray

Bhattacharjee

Raju

2021

Intl J of Energy Research

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Summary This research paper proposes a detailed design problem of electrical vehicle (EV) fast‐charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte‐Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA‐PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA‐PSO provides the best‐optimized results in profit maximization. The cost‐benefit analysis is also performed to estimate the financial feasibility of both RES and BESS.

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Optimal placement of electric vehicle charging station for unbalanced radial distribution systems

Cited by 54 publications

References 16 publications

An overview and performance evaluation of open charge point protocol from an electromobility concept perspective

An overview and performance evaluation of open charge point protocol from an electromobility concept perspective

Optimal Integration of Electric Vehicle Charging Stations and Compensating Photovoltaic Systems in a Distribution Network Segregated into Communities

Swarm intelligence‐based energy management of electric vehicle charging station integrated with renewable energy sources

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