Emerging energy sources for electric vehicle charging station

Singh, Arshdeep; Letha, Shimi Sudha

doi:10.1007/s10668-018-0151-x

Cited by 26 publications

(15 citation statements)

References 103 publications

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“…where δ refers road slope function, F r implies rolling resistance, M implies weight of vehicle, and g * denotes gravity constant. The grade force of the road is calibrated using Equation (15).…”

Section: Vehicle Dynamics Modelmentioning

confidence: 99%

“…14 Besides, a coordinated management strategy among DS and EVCS attains the peak demand shaving, improving the profiles of the voltage and dropping the losses of the power. 15 Furthermore, under the DS environment, the EVCSs smart energy achieves the smallest amount of operation cost. 16 For achieving the entire system finest operation, an optimal online load approach is presented for multiple EVCS on DSs.…”

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confidence: 99%

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A hybrid optimization based energy management between electric vehicle and electricity distribution system

Rajani

Sekhar

2021

Int Trans Electr Energ Syst

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Section: Vehicle Dynamics Modelmentioning

confidence: 99%

mentioning

confidence: 99%

A hybrid optimization based energy management between electric vehicle and electricity distribution system

Rajani

Sekhar

2021

Int Trans Electr Energ Syst

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“…According to the market share of IES to manipulate the electricity price, the price is considered to be the price taken to buy or sell electricity with EEM at the default price. 40 The transaction cost among EEM and IES is demonstrated on Equation (1).…”

Section: Trading Model With External Energy Market (Eem)mentioning

confidence: 99%

“…In many countries, the implementation of harmful programs in ozone is being reduced and transport electrification is one of the most promising arrangements 1 . The main goal of this electric vehicle (EV) is to diminish fossil fuel use and greenhouse gas emissions 2 .…”

Section: Introductionmentioning

confidence: 99%

Optimally manage the energy between electric vehicle charging stations and electricity distribution system: A hybrid technique

Venkatakrishnan

Rengaraj

Prakash³

2021

Int J Numerical Modelling

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In this manuscript, the energy trading model is proposed for investigating the cooperative benefits between several electric vehicle charging stations (EVCSs) and integrated energy systems (IES) based on hybrid system. The proposed hybrid system is combination of Radial‐Basis Function Neural Network (RBFNN) and artificial transgender longicorn algorithm (ATLA), hence it is called RBFNN‐ATLA technique. Initially, the RBFNN approach manages the energy between IES and EVCSs. After that, the original issue breaks down into the main energy trade and payment negotiation issue. The energy trading issue and payment negotiation problem can be solved using ATLA approach. This proposed structure may not only diminish the IES cost, however also enlarge the EVCS profit. The uncertainties in electricity and renewable energy prices are modeled using a robust optimization technique. Additionally, the integrated demand response is modeled for maximizing operational performance. The distributed algorithm depends on the proposed technique is evolved for solving the issue of energy trading, ensuring the privacy of players. The proposed algorithm may obtain the global optimal solutions devoid of adjusting the parameter compared with existing algorithm. The proposed system is performed by the matrix laboratory (MATLAB)/Simulink and the performance is evaluated with other existing methods. The RBFNN‐ATLA method reduces the cost up to 3.76%, 7.793%, 8.210% and 9.01% for independent and cooperative mode. The experimental outcomes demonstrate that the proposed system can accurately detect that optimal global solution.

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“…Currently, based on the growth of fossil fuel dehydration and climate change around the world, the consolidation of renewable energy sources (RESs) and electric vehicles (EVs) under distribution systems (DSs) has stimulated deep interest in the previous year. 1 In the DS, EVs with vehicle-togrid (V2G) functions are considered as optimally adaptable resources and offer associated operations, like, maximum power reduction, voltage regulation, and stability improvement. [2][3][4][5] Such associated operations efficiently deliver the negative impacts produced with renewable energy insolation, and also it provides the DS run profitably.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA ² B approach

Thangaraju¹

2021

Int J of Intelligent Sys

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Demand response acts as an effectual tool to better balance electricity demand and supply in the smart grid. The response to prices and incentives is described as "changes on electricity usage from conventional consumption patterns to end-use customers." In this paper, a hybrid approach is proposed for Electric Vehicle Based Grid connected to the distribution generation (DG). The proposed system is joined performance of student psychology optimization algorithm (SPOA) and AdaBoost algorithm, thus it known as SPOA 2 B approach. The major aspire of this study is "to minimize the peak power cutoff, voltage regulation, spin reserve for making the optimization mode ideally convex, and accurate second-order conic relaxations." Additionally, the SPOA 2 B method is a proficient deal by standard business solvers with allocation models, and then acquires least annualized costs. In this study, the electric vehicle charging station (EVCS) is linked to DG, and then it gives charging service to electric vehicles (EVs). When the EV appears at the charging station, it informs the EVCS operator of their own energy requirement with anticipated departure time. Every EVCS can capture the information of all EVs using an SPOA 2 B technique. By then, the proposed system is performed on MATLAB/ Simulink site and the efficiency is compared with other processes. The number of iteration, like, 100, 250, 500,

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Emerging energy sources for electric vehicle charging station

Cited by 26 publications

References 103 publications

A hybrid optimization based energy management between electric vehicle and electricity distribution system

A hybrid optimization based energy management between electric vehicle and electricity distribution system

Optimally manage the energy between electric vehicle charging stations and electricity distribution system: A hybrid technique

Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA ² B approach

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Emerging energy sources for electric vehicle charging station

Cited by 26 publications

References 103 publications

A hybrid optimization based energy management between electric vehicle and electricity distribution system

A hybrid optimization based energy management between electric vehicle and electricity distribution system

Optimally manage the energy between electric vehicle charging stations and electricity distribution system: A hybrid technique

Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA 2 B approach

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Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA ² B approach