Optimal location identification for aggregated charging of electric vehicles in solar photovoltaic powered microgrids with reduced distribution losses

Gupta, Khushboo; Narayanankutty, Ravishankar Achathuparambil; Kumaravel, S.; Ashok, S.

doi:10.1080/15567036.2020.1745335

Cited by 42 publications

(11 citation statements)

References 28 publications

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“…The battery capacity varies with the type of the vehicle: light EVs (motorbikes with 3.6 KWh), small EVs (cars with 16 KW), medium EVs (large private cars with 25 KWh), and large EVs (buses and trucks with 63 KWh) 44 . The SOC of an EV battery is given by Equation ().

SOC ((),, S : μ_{SOC}, σ_{SOC}) goodbreak= \frac{1}{s σ_{SOC} \sqrt{2 normalΠ}} e^{- {(\ln S - μ_{SOC})}^{2} / 2 σ_{SOC}^{2}}

where deviation (

σ_{SOC} = 0.8

) and average (

μ_{SOC} = 3

) with initial SOC as “ S ” varies from (0‐1) 41 and it is shown in Figure 5C.…”

Section: Detailed Calculations Of Ev Demandmentioning

confidence: 99%

“…Authors in Reference 40 propose a stochastic charge scheduling model for charging EV batteries and utilises a Markov Chain Poisson method to study the complexity of the time‐varying nature of EVs. A PSO‐based optimal power flow methodology has been developed to determine the optimal location to charge EVs in a micro‐grid 41 . Thus, the studies with optimisation techniques in the literature provide scopes for finding the optimal system parameters using a novel hybrid CSA along with the PSO (hCSA‐PSO) which is used for the first time in this area.…”

Section: Introductionmentioning

confidence: 99%

“…A PSO-based optimal power flow methodology has been developed to determine the optimal location to charge EVs in a micro-grid. 41 Thus, the studies with optimisation techniques in the literature provide scopes for finding the optimal system parameters using a novel hybrid CSA along with the PSO (hCSA-PSO) which is used for the first time in this area. The energy management system plays a crucial role in designing EV fast-charging stations and is to be adequately investigated.…”

Section: Introductionmentioning

confidence: 99%

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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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SOC ((),, S : μ_{SOC}, σ_{SOC}) goodbreak= \frac{1}{s σ_{SOC} \sqrt{2 normalΠ}} e^{- {(\ln S - μ_{SOC})}^{2} / 2 σ_{SOC}^{2}}

where deviation (

σ_{SOC} = 0.8

) and average (

μ_{SOC} = 3

) with initial SOC as “ S ” varies from (0‐1) 41 and it is shown in Figure 5C.…”

Section: Detailed Calculations Of Ev Demandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…De Pinto et al [11] investigated the allowance of the range of the EV via PV panels [11]. Kim et al [12] investigated issues related to battery savings of the electrical vehicles while utilizing the vehicle's surface areas in order to optimize charging time [12], which is a new area of research [13]. The autonomy of the car showed an increase in both studies.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

An Investigation into Conversion of a Fleet of Plug-in-Electric Golf Carts into Solar Powered Vehicles Using Fuzzy Logic Control

et al. 2021

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This paper presents an investigation factors that need to be considered in the design and selection of components for the conversion of a fleet of plug-in electric golf carts at Princess Nourah Bint Abdelrahman University, (PNU), Riyadh, Kingdom of Saudi Arabia (KSA), into solar power energy. Currently, the plug-in electric golf carts are powered by a set of deep-cycle lead-acid battery packs consisting of six units. Solar energy systems (photovoltaics and solar thermal) provide significant environmental benefits and opportunities over the traditional and conventional sources. Therefore, they can contribute positively to many aspects of the built environment and societies. There are many factors that affect the energy generated from the solar panel system. These include type and dimension of the solar panels, weight, speed, acceleration, and other characteristics of the used golf carts, and the energy efficiency of the solar energy system, as main factors that affect the green energy generated to operate the carts. The energy values needed to power the electric cart were calculated and optimized using traction energy calculation and optimized using a fuzzy logic analysis. The fuzzy logic system was developed to assess the impacts of varying dimensions of solar panel, vehicle speed, and weight on the energy generation. Initial calculations show that the replacement cost of the batteries can be up to approximately 75 percent of the operating cost. Together with the indirect cost benefits of achieving zero tail-pipe emission and the comfort of silent operation, the cost of operation using solar energy can be significant when compared with the cost of battery replacement. In order to achieve better efficiency, supercapacitors can be investigated to replace the conventional batteries. The use of fuzzy logic successfully facilitated the optimization of system operation conditions for best performance. In this study, fuzzy logic and calculated data were used as an optimization tool. Future work may be able to use fuzzy logic with experimental data to demonstrate feasibility of utilizing fuzzy logic systems to assess energy generation processes. Future investigations could also include investigation of other factors and methodologies, such as various types of batteries, supercapacitors, solar panels, and types of golf carts, together with different techniques of artificial intelligence to assess the optimum system specifications.

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“…Charging strategies for plug‐in hybrid EVs (PHEV) are outlined in [10–17]. An instantaneous energy management algorithm has been implemented for a grid‐connected charging park in industrial/commercial locations [18–21]. If a significant number of grid‐connected hybrid EVs are connected to the grid under the management and control of a gathering entity such as electric car parks, they can operate as a small virtual power plant with a very high start‐up cost and no start‐up cost.…”

Section: Introductionmentioning

confidence: 99%

Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

Zand

Nasab

Padmanaban

et al. 2020

IET Renewable Power Generation

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This study introduces a type of solid‐state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic‐assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real‐time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real‐time power allocation which improves the efficiency of the photovoltaic‐based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power.

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Optimal location identification for aggregated charging of electric vehicles in solar photovoltaic powered microgrids with reduced distribution losses

Cited by 42 publications

References 28 publications

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

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

An Investigation into Conversion of a Fleet of Plug-in-Electric Golf Carts into Solar Powered Vehicles Using Fuzzy Logic Control

Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

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