ArticlePublisher preview available

Enhancement of the distribution network in the presence of EV charging stations augmented by distributed generation

Authors:
Jitendra Singh Bhadoriya at Renew Power
Jitendra Singh Bhadoriya
  • Renew Power
Atma Ram Gupta at National Institute of Technology Kurukshetra
Atma Ram Gupta
Ashwani Kumar at National Institute of Technology Kurukshetra
Ashwani Kumar
Rohit Ray
Rohit Ray
Rohit Ray
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Show all 5 authorsHide
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

With the exponential growth of electric vehicles worldwide, integrating fast electric vehicle charging stations into the distribution system has become crucial. However, this integration can lead to adverse effects such as high power loss and poor voltage profiles. To address these challenges, this research focuses on two strategies: optimal placement of charging stations and allocation of distributed generation within the distribution system. The study investigates the negative impact of charging stations and the positive effects of distributed generation on an IEEE-25 unbalanced radial distribution system. The objective is to reduce active power loss, enhance voltage profile and improve the voltage stability index. The research employs a transient search optimization algorithm to optimize a multi-objective function. MATLAB simulations validate the proposed algorithm, showcasing its convergence characteristics across various scenarios. By exploring the effects of charging stations and distributed generation, this research contributes insights into mitigating negative impacts and utilizing distributed generation for enhanced distribution system performance.
Figures - available from: Electrical Engineering
This content is subject to copyright. Terms and conditions apply.
A preview of this full-text is provided by Springer Nature.
Learn more
Content available from Electrical Engineering
This content is subject to copyright. Terms and conditions apply.
Electrical Engineering (2023) 105:3703–3717
https://doi.org/10.1007/s00202-023-01901-8
ORIGINAL PAPER
Enhancement of the distribution network in the presence of EV
charging stations augmented by distributed generation
Jitendra Singh Bhadoriya1·Atma Ram Gupta2·Ashwani Kumar1·Rohit Ray3·Surita Maini4
Received: 31 January 2023 / Accepted: 14 June 2023 / Published online: 7 July 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
Abstract
With the exponential growth of electric vehicles worldwide, integrating fast electric vehicle charging stations into the dis-
tribution system has become crucial. However, this integration can lead to adverse effects such as high power loss and poor
voltage profiles. To address these challenges, this research focuses on two strategies: optimal placement of charging stations
and allocation of distributed generation within the distribution system. The study investigates the negative impact of charging
stations and the positive effects of distributed generation on an IEEE-25 unbalanced radial distribution system. The objective is
to reduce active power loss, enhance voltage profile and improve the voltage stability index. The research employs a transient
search optimization algorithm to optimize a multi-objective function. MATLAB simulations validate the proposed algorithm,
showcasing its convergence characteristics across various scenarios. By exploring the effects of charging stations and dis-
tributed generation, this research contributes insights into mitigating negative impacts and utilizing distributed generation for
enhanced distribution system performance.
Keywords Unbalanced radial distribution system ·Electric vehicle charging station ·Distributed generation ·Voltage profile ·
Power loss ·Voltage stability index
BJitendra Singh Bhadoriya
jitendra_61900077@nitkkr.ac.in
Atma Ram Gupta
argupta@nitkkr.ac.in
Ashwani Kumar
ashwaks@gmail.com
Rohit Ray
rray54957@gmail.com
Surita Maini
suritamaini@gmail.com
1Electrical Engineering Department, National Institute of
Technology, Kurukshetra, Kurukshetra, India
2Department of Electrical Engineering, Shri Phanishwar Nath
Renu Engineering College Araria, Bihar Engineering
University Patna, DSTTE, Patna, Bihar, India
3Department of Electrical Engineering, Indian Institute of
Technology, Patna, Patna, India
4Department of Electrical Instrumentation Engineering, Sant
Longowal Institute of Engineering Technology, Longowal,
Punjab, India
1 Introduction
In recent years, high oil costs have more impeded efforts in
several countries for cheap energy availability and height-
ened the importance of a diverse energy arrangement. The
transportation sector is the leading energy consumption from
fossil fuels and causes an oversized ratio to greenhouse gases.
Electric vehicles (EV) promise to switch historically burn-
ing engine vehicles, resulting in zero carbon emission drive
on the road. The growth in electric vehicles globally will
encourage green transportation, further resulting in a low
carbon footprint. Due to high prices and fluctuation in fossil
fuels, EVs are economical, reliable, and eco-friendly solu-
tions to reduce carbon emissions. In many countries, the
number of electric vehicle charging station (EVCS) increases
to fulfill the future demand raised due to growth in EV. The
government policies feature the installation of EVCS near
fuel stations, tourist places, and highways to promote the
EV. In India, 246,000 EVs were sold in FY2020, and just
1332 charging stations have introduced to the nation; the
two and three-wheelers EV deals represented 98% of the
complete EV deals (152,000 and 90,000 units separately),
in contrast to China and the U.S., where four-wheelers deals
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Real-time charging navigation for EVs is explored in ref. [22], which proposes a comprehensive optimization model for the joint optimal planning of DGs and EVCSs using a mixed integer second-order cone programming for efficient problem-solving. The challenges posed by integrating fast EVCSs were addressed in ref. [23] on an IEEE-25 unbalanced RDS system using a transient search optimization algorithm. The study demonstrates how DG mitigates the negative impacts of EVCSs by decreasing the power loss and enhancing the voltage profile of the system. ...
Optimal Integration of New Technologies and Energy Sources into Radial Distribution Systems Using Fuzzy African Vulture Algorithm
Article
Full-text available
  • Feb 2025
In the contemporary global context, excessive fossil fuel consumption remains a critical issue, particularly within the transportation sector. Electric vehicles offer a promising alternative due to their durability and reduced greenhouse gas emissions. However, their rapid adoption has introduced significant challenges, including increased network power losses, deteriorating voltage profiles, and declining substation power factors. This study proposes an approach that integrates fuzzy objective optimization with African Vulture Optimization (AVO) to determine the optimal sitting and sizing of distributed generations (DG), shunt capacitors (SC), and electric vehicle charging stations (EVCS) within radial distribution systems (RDS). The proposed methodology is evaluated on the standard IEEE-69 bus RDS. A detailed comparative analysis between the proposed simultaneous optimization approach for DGs, SCs, and EVCSs and with the traditional two-staged method is presented. The findings indicate that the proposed strategy not only matches but surpasses the performance of existing strategies for the reduction of power losses and enhancement of bus voltage profiles. Key findings include a significant reduction in active and reactive power line loss, with losses minimized by 85.90% and 82.15%, respectively. In addition, an improvement in the minimum bus voltage to 0.98 p.u. is also achieved. Thereafter, the proposed issue is solved in different loading scenarios to present the effectiveness of the approach under different operating conditions. This research effectively demonstrates the complexities introduced by EVCS integration and addresses the issue with simultaneous optimal sitting and sizing of DGs, SCs, and EVCSs that significantly enhance the sustainability and efficiency of RDS.
View
... Rene et al. [26] recommended using a hybrid genetic algorithm and particle swarm optimization (GA-PSO) to determine the best way to distribute EVCSs and RESs throughout the distribution network while minimizing losses and voltage drop. Bhadoriya et al. [27] suggested an innovative approach based on search optimization for the distribution network's ideal location of RESs and charging stations, seeking to minimize voltage drop and losses. ...
Simultaneous Optimal Allocation of EVCSs and RESs Using an Improved Genetic Method
Article
Full-text available
In the last decade, with the development of the electric vehicle industry and their acceptance in human societies, the participation plan of electric vehicles in supplying the load of the network has been taken into consideration. One of the requirements of this plan is the optimal location of the stations for these vehicles in the network so that they play an effective role in the operation of the network. In this regard, along with the construction of charging and discharging stations for electric vehicles, the construction of renewable sources in the network can play a complementary role for these stations. In this paper, the effect of using renewable resources as a supplement for smart charging stations and the placement of these stations to achieve technical and economic goals have been investigated. In order to manage the demand on the side of consumers and even out the load curve, the time of use mechanism as one of the demand response programs has been considered in this study. In this research, the improved nondominant sorting genetic algorithm is proposed to solve the problem, and the results of the proposed method are also compared with the conventional genetic and particle swarm optimization algorithms. All the simulations have been done in the MATLAB software and on the IEEE 33-bus network. Based on the obtained results, after the implementation of the proposed plan in the distribution network, the objective functions of the loss, voltage drop, and the total cost have been reduced by 13.6%, 58.7%, and 54.4%, respectively, compared to the base conditions of the network.
View
... Extremely quick charging stations and energy storage systems for EVs were distributed throughout a grid in [16] using a Markov decision process solved via a deep deterministic policy gradient with constraints. To assess the best locations for DGs and EV charging stations within the distribution network that minimize active power loss while enhancing the voltage profile and voltage stability index, a methodology that incorporates a transient search optimizer has been presented [17]. A demand and supply analysis was used to determine the best locations for EV infrastructure on a highway network, considering the psychological aspects of driving [18]. ...
Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant
Article
Full-text available
  • Apr 2024
Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.
View
... A scenario-based stochastic model has been proposed in Part I [24] and Part II [25] for the multi-stage coordinated planning of distribution systems and EVCSs. In another study, a two-layer optimization strategy was utilized to integrate EVCS into the IEEE 33-bus distribution network [26]. The placement of EVCS along with reconfiguration has been used in [27] for minimizing losses. ...
Optimal planning of power distribution system employing electric vehicle charging stations and distributed generators using metaheuristic algorithm
Article
Full-text available
  • Feb 2024
  • ELECTR ENG
The establishment of an electric vehicle charging station (EVCS) infrastructure plays a vital role in fostering the sustainable expansion of the electric vehicle sector. The unplanned placement of EVCS raises various technical and economic issues in the distribution network, and it can lead to increased energy losses in the distribution system. Installing EVCSs and DGs at strategically chosen positions is essential to minimize the impact of EV load on the distribution network. To address these issues, this work has proposed a whale optimization technique (WOT) for optimizing the placement and sizing of EVCS and DG. This novel approach of formulating the multi-objective problem of allocating electric vehicle (EV) charging stations, along with DGs and capacitors simultaneously improves the voltage stability, minimizes active and reactive power losses in the radial distribution system, and requires execution time. A forward–backward power flow method has been carried out for load flow calculations. The efficacy of the proposed approach has been applied on the standard IEEE-33 and 69-bus test systems and the obtained results have been compared with the other latest optimization techniques. The simulation results validate the performance and effectiveness of the Whale Optimization Technique, and it has been analyzed that the optimized sizes and allocation of EVCSs, DGs, and capacitors lead to an extensive reduction in power losses of 81.33% and 80% in the case of active power and 78.46% and 80.26% in the case of reactive power for the IEEE-33 and 69-bus test systems, respectively.
View
Effects and Optimal Integration of Electric Vehicle Charging Systems in the Tanzania Electrical Distribution Networks using Metaheuristic Algorithms
Article
Full-text available
  • Apr 2025
Electric vehicles (EVs) present a viable solution for reducing carbon emissions, environmental pollution, and the effects of climate change. The EV utilizes energy stored in its battery banks, which are charged by electric vehicle charging systems (EVCS), primarily integrated with the power grid. However, integrating EVCS into grid poses significant challenges, including increased power losses, voltage deviations, harmonic injection, and grid instability. This study examines the impacts of connecting EVCS to Tanzania's electrical distribution networks and proposes an optimization approach using metaheuristic algorithms to mitigate power loss and voltage deviation challenges. The study reveals that adding one EVCS raises power loss from 13.0357 kW to 17.1963 kW, while voltage deviation increases from 0.47 V to 0.63 V, with further deterioration in system performance as more EVCS units are introduced. An enhanced Symbiotic Organism Search algorithm was employed to determine the optimal allocation and size of EVCS and PV systems. The results show that integrating 1 PV in the power system with 3 EVCSs reduced power loss to 5.26 kW from 61.42 kW. This research reveals the effectiveness of optimal PV system placement in improving the stability of the electrical network and the feasibility of an efficient EV penetration in Tanzania.
View
View
View
View
View
A novel AI approach for optimal deployment of EV fast charging station and reliability analysis with solar based DGs in distribution network
Article
Full-text available
  • Sep 2022
The transportation sector is one of the most prevalent fossil fuel users worldwide. Therefore, to mitigate the impacts of carbon-dioxide emissions and reduce the use of non-environmentally friendly traditional energy resources, the electrification of the transportation system, such as the development of electric vehicles (EV), has become crucial. For impeccable EVs deployment, a well-developed charging infrastructure is required. However, the optimal placement of fast charging stations (FCSs) is a critical concern. Therefore, this article provides a functional approach for identifying the optimal location of FCSs using the east delta network (EDN). In addition, the electrical distribution network's infrastructure is susceptible to changes in electrifying the transportation sector. Therefore, actual power loss, reactive power loss, and investment cost are three areas of consideration in deploying FCSs. Furthermore, including FCSs in the electricity distribution network increases the energy demand from the electrical grid. Therefore, this research paper recommends integrating solar-based distributed generations (SDGs) at selected locations in the distribution network, to mitigate the burden of FCSs on the system. Hence, making the system self-sustaining and reliable. In addition, the reliability of the distribution system is also analyzed after deploying the FCSs and SDGs. Furthermore, six case studies (CS) have been proposed to deploy FCSs with or without DG integration. Consequently, the active power loss went from 1014.48 kW to 829.68 kW for the CS-6.
View
Placement of electric vehicle fast-charging stations in distribution network considering power loss, land cost, and electric vehicle population
Article
Full-text available
  • Mar 2022
  • ENERG SOURCE PART A
Recently, Electric vehicles (EVs) gained tremendous attention from government agencies and the automotive industry due to lower CO2 emissions, low maintenance, and operating costs. However, due to increasing EV penetration, the EV's load affects the distribution network parameters like power loss, voltage profile, and harmonic distortion. Therefore, the proper placement of EV fast-charging stations (FCSs) is required for the reliability of the distribution network. Further, this paper proposes two-stage processes for the placement of FCSs. In the first stage, the charging station owner decision index (CSODI) has been introduced considering the land cost index (LCI) and electric vehicle flow index (EVFI). The CSODI has been formulated to minimize the land cost and maximize the EVs flow for FCSs placement. In the next stage, an optimization problem is formulated for minimizing the total active power loss by considering the distribution system operator (DSO) constraints. In addition, the minimization problem has been solved using the hybrid grey wolf optimization-particle swarm optimization (GWOPSO) algorithm. Therefore, the best possible locations were obtained by the GWOPSO with 198.93 kW power loss. Furthermore, the average 2.02 percent power loss for the GWOPSO technique is lower when compared to the PSO technique
View
Optimal location of electric vehicle charging station and its impact on distribution network: A review
Article
Full-text available
  • Feb 2022
At present, the limited existence of fossil fuels and the environmental issues over greenhouse gas emissions have been directly affected to the transition from conventional vehicles to electric vehicles (EVs). In fact, the electrification of transportation system and the growing demand of EVs have prompted recent researchers to investigate the optimal location of electric vehicle charging stations (EVCSs). However, there are numerous challenges would face when implementing EVs at large scale. For instance, underdeveloped EVCSs infrastructure, optimal EVCS locations, and charge scheduling in EVCSs. In addition, the most fundamental EV questions, such as EV cost and range, could be partly answered only by a well-developed EVCS infrastructure. According to the literature, the researchers have been followed different types of approaches, objective functions, constraints for problem formulation. Moreover, according to the approaches, objective functions, constraints, EV load modeling, uncertainty, vehicle to grid strategy, integration of distributed generation, charging types, optimization techniques, and sensitivity analysis are reviewed for the recent research articles. Furthermore, optimization techniques for optimal solution are also reviewed in this article. In addition, the EV load impact on the distribution network, environmental impacts and economic impact are discussed.
View
Investment analysis for optimal planning of electric vehicle charging station on a reconfigured unbalanced radial distribution system
Article
Full-text available
  • Jun 2022
  • ELECTR ENG
The inclusion of electric vehicle charging stations (EVCS) on a practical radial distribution system (RDS) will increase the losses in the system. These losses can be minimized by introducing compensating devices like capacitors, distributed generators (DGs), or reconfiguring the RDS which increases the cost. This paper addresses an optimum placement of a DG on an optimally reconfigured unbalanced RDS (URDS) with reduced investments and losses. The objective function for optimizing the location and capacity of DG on a reconfigured URDS is chosen as the minimization of investments in DG installation and erection of tie-lines. The performance of the proposed strategy is validated for 19-bus and 25-bus URDS considering various scenarios. A comprehensive analysis of this study is provided under each scenario and is compared with the URDS having optimally placed EVCS and DG without reconfiguration.
View
A Comprehensive Review on Optimal Location and Sizing of Reactive Power Compensation Using Hybrid-Based Approaches for Power Loss Reduction, Voltage Stability Improvement, Voltage Profile Enhancement and Loadability Enhancement
Article
Full-text available
  • Dec 2020
With the modernization of power grids, the network optimal utilization is essential to ensure that voltage profile at each bus is maintained within an acceptable range, voltage stability of the system is enhanced, power losses in lines are minimized, reliability and security of system are improved and etc. These can be achieved by introducing reactive power compensation devices such as Flexible Alternating Current Transmission System (FACTS) devices, Custom Power (CP) devices, synchronous condenser, capacitor bank and etc in distribution or transmission networks. Optimal location and sizing of the reactive power compensation devices are significantly important to ensure sufficient investment onto this device. Recently, most of conducted studies had focused on the techniques for determining the optimal location and sizing of various reactive power compensation devices in the power system using various indices proposed in the literature to access the power loss, voltage stability, voltage profile and line loadability. However, no review paper had discussed on the application of the existing indices adopted in the available techniques for solving the optimal location and sizing problems for all types of reactive power compensation devices. In this paper, current literature survey on optimal location and sizing of reactive power compensations had been discussed which includes analytical, conventional, metaheuristic and hybrid based approaches. The main objectives are to reduce power losses, to mitigate voltage deviations, to increase voltage stability and to improve reliability and security of the system.
View
Optimal distributed generation allocation in unbalanced radial distribution networks via empirical discrete metaheuristic and steepest descent method
Article
Full-text available
  • Feb 2021
  • ELECTR ENG
Loss minimization and voltage improvement through distributed generation (DG) planning is a well-established problem. However, a careful review of the literature shows that there is still room for the development of efficient algorithms for this purpose. In special, hybridization between optimization techniques is suitable for this complex problem, as it allows taking advantage of the positive features of different approaches. In this work, a novel empirical discrete metaheuristic (EDM) is presented and merged with the steepest descent method to solve the DG allocation problem. The allocation is broken into two subproblems: sitting the DGs and sizing them. The EDM deals with the first subproblem, while the second one is solved by the steepest descent method in an interchangeable optimization structure. The EDM tackles some key limitations of metaheuristic family methods. Relatively, it shows: low results variability in different executions; low initial conditions dependency; and few number parameters to tune. All simulations are performed in a communication scheme using the softwares Matlab and OpenDSS. The obtained results with IEEE 34-bus and IEEE 123-bus distribution test systems were compared to the literature and other metaheuristics, attesting the quality of the proposed approach.
View
Transient search optimization: a new meta-heuristic optimization algorithm
Article
Full-text available
  • Nov 2020
  • APPL INTELL
This article offers a new physical-based meta-heuristic optimization algorithm, which is named Transient Search Optimization (TSO) algorithm. This algorithm is inspired by the transient behavior of switched electrical circuits that include storage elements such as inductance and capacitance. The exploration and exploitation of the TSO algorithm are verified by using twenty-three benchmark, where its statistical (average and standard deviation) results are compared with the most recent 15 optimization algorithms. Furthermore, the non-parametric sign test, p value test, execution time, and convergence curves proved the superiority of the TSO against other algorithms. Also, the TSO algorithm is applied for the optimal design of three well-known constrained engineering problems (coil spring, welded beam, and pressure vessel). In conclusion, the comparison revealed that the TSO is promising and very competitive algorithm for solving different engineering problems.
View
Electric vehicles: Battery capacity, charger power, access to charging and the impacts on distribution networks
Article
Full-text available
  • May 2020
The battery electric vehicle (EV) market is in a state of continuing rapid evolution, both in terms of the battery capacities and charger power ratings that vehicle manufacturers are bringing to the market and the increasingly widespread penetration of charging infrastructure. As a result, individuals’ charging behaviours may change substantially and be different from what has been expected or observed to date. This could have a significant effect on the resulting electrical demand from EV charging. Aside from these technical parameters, the demographics of the population served by any given network – and how that might affect their travel habits, particularly car use – must be considered. In this paper, statistical analysis of a large travel survey dataset is carried out to support the hypothesis that car use is likely to vary according to population demographics. Car-based travel diaries disaggregated on key demographic traits of the drivers are assigned to vehicles in the network according to Census data pertaining to those same demographic traits. Charging schedules are derived from these travel diaries for different battery capacities, charger power ratings and level of access to charging to investigate the likely effect of changing these parameters on the resulting charging behaviour and electricity demand. It is found that out of the key emerging patterns identified in the evolving EV market, increasing battery capacities and the establishment of more widespread charging opportunities may reduce the peak demand from EV charging or shift it to a time less likely to coincide with peak domestic demand, hence making it easier for the network to cope with increasing penetrations of EVs. On the other hand, increasing charging power may increase the peak and bring it closer to a time where it is more likely to coincide with peak domestic demand.
View
Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming
Article
Full-text available
  • Mar 2020
To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.
View
Optimal placement of electric vehicle charging station for unbalanced radial distribution systems
Article
Full-text available
  • Feb 2020
Transportation is one of the dominant applications where abundant fossil fuel usage can be experienced across the world. To save the earth from carbon emission and depletion of conventional energy resources, alternate energy sources are to be introduced in transportation which will lead to Electric Vehicles (EV) and Hybrid Electric Vehicles (HEV). The infrastructure of the power system is also adaptable to the changes of electrified transportation. In this research work, a methodology is proposed to find the optimal placement of Electric Vehicle Charging Station (EVCS) along with the optimal capacity of charging stations for Unbalanced Radial Distribution System (URDS) by using Particle Swarm Optimization (PSO). This paper also explains the effect of EVCS placement in RDS w.r.t voltage profile and real power losses. Also, the insertion of EVCS to the power system draws additional power from the electrical grid. To make the system more self-sustainable and reliable, this research work proposes additional distributed generators (DGs) at optimal locations to reduce the impact of EVCS on the system by incorporating it. The proposed methodology is validated with two standard unbalanced IEEE test cases such as IEEE 19 bus and IEEE 25 bus URDS.
View