An efficient energy management in smart grid based on IOT using ROAWFSA technique

Venkatakrishnan, G. R.; Rengaraj, R.; Mohandoss, Rajalakshmi

doi:10.1007/s00500-022-07266-7

Cited by 13 publications

(3 citation statements)

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“…1,2 However, this surge in demand poses challenges to the existing power grid, including concerns about grid stability due to sudden high-demand influx and potential strain on local grids. [3][4][5] Physical limitations of the current power distribution infrastructure, requiring costly upgrades, further complicate the integration of fast-charging stations. 6,7 To address these challenges, a comprehensive approach is essential, incorporating smart charging technologies to optimize demand, energy storage systems (ESSs) for buffering during peak times, and collaborative efforts among grid operators, 8 EV manufacturers, and charging station providers to develop coordinated plans for grid expansion and EV charging infrastructure deployment.…”

Section: Introductionmentioning

confidence: 99%

“…The increasing adoption of electric vehicles (EVs) has underscored the critical need for efficient charging infrastructure, with DC fast‐charging stations (CSs) gaining prominence for their ability to rapidly recharge EV batteries 1,2 . However, this surge in demand poses challenges to the existing power grid, including concerns about grid stability due to sudden high‐demand influx and potential strain on local grids 3–5 . Physical limitations of the current power distribution infrastructure, requiring costly upgrades, further complicate the integration of fast‐charging stations 6,7 .…”

Section: Introductionmentioning

confidence: 99%

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Impact of fast charging station for electric vehicles with grid integration: Forensic‐based investigation and Archimedes optimization algorithm approach

Singh,

Kumar

2024

Optim Control Appl Methods

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This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic‐based investigation (FBI) and Archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of fast charging station for electric vehicles with grid integration: Forensic‐based investigation and Archimedes optimization algorithm approach

Singh,

Kumar

2024

Optim Control Appl Methods

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“…Accurate reliability assessment with computational efficiency is crucial for recurring smart grid issues, such as reliability optimization. [ 6,7 ] A precise and rapid reliability framework informs short‐term decisions based on reliability considerations, but smart grid complexity can lead to extended computation times. [ 8 ] Reducing the states in modeling plug‐in hybrid EVs (PHEVs) can expedite calculations, and optimizing PHEV modeling in various charging scenarios is vital for reliability assessment.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

Ranjith Kumar,

Vallimurugan

2023

Energy Tech

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Proper planning for electric vehicle (EV) charging stations, as well as the necessary network development, is critical for the continued growth of EVs and conventional loads. This article proposes a hybrid method for the allocation of fast‐charging stations (FCSs) and photovoltaic (PV) with battery energy storage (BES) and scheduling. The proposed hybrid technique is the wrapper of golden jackal optimization (GJO) and random forest algorithms (RFA), commonly named the GJO–RFA technique. Using the GJO approach, the allocation and EV assignment problems are resolved. RFA is used to address EV, traffic flow, and PV‐related uncertainties. The objective of the proposed approach is to minimize the loss of energy, investments, index of voltage deviation, and operation and maintenance costs of FCS, PV, and BES. The associated important factors are also assessed, including the number of charging ports, FCS capacity, and EV flow caught using a FCS. As a test example, a 33‐node radial distribution network with the appropriate traffic network is used. For the final problem, the proposed approach to minimal energy loss is 2622.7270 kWh. By then, the performance of the proposed approach is executed in MATLAB, and it is contrasted with other existing techniques. The result shows that the proposed method‐related energy loss is less than existing approaches.

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A CryStAl-RDF technique-based integrated circuit topology for fast charging station of electric vehicle (EV)

Ravindran,

Nallathambi

2023

Electr Eng

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An efficient energy management in smart grid based on IOT using ROAWFSA technique

Cited by 13 publications

References 50 publications

Impact of fast charging station for electric vehicles with grid integration: Forensic‐based investigation and Archimedes optimization algorithm approach

Impact of fast charging station for electric vehicles with grid integration: Forensic‐based investigation and Archimedes optimization algorithm approach

A Hybrid Technique for Optimal Placement of Fast‐Charging Stations of Electric Vehicles for the Reliability of Distribution Network

A CryStAl-RDF technique-based integrated circuit topology for fast charging station of electric vehicle (EV)

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