<scp>S2NA‐GEO</scp>
            method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Ilango, R.; Rajesh, P.; Shajin, Francis H.

doi:10.1002/2050-7038.13125

Cited by 3 publications

(4 citation statements)

References 54 publications

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“…Table 9 provides a classification of all mathematical and heuristic algorithms used in the literature, respectively. Furthermore, a considerable proportion of the suggested endeavors have been executed utilizing MAT-LAB/Simulink [38,85,95,97,103,105,107,114,129,135,142,157,166,170,185,188,194,195]. MAT-LAB/Simulink is a software tool that provides a graphical block diagram interface for creating and analyzing complex systems that incorporate multiple domains.…”

Section: Algorithms and Implementation Detailsmentioning

confidence: 99%

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Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

et al. 2023

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Electric vehicles (EVs) represent a promising green technology for mitigating environmental impacts. However, their widespread adoption has significant implications for management, monitoring, and control of power systems. The integration of renewable energy sources (RESs), commonly referred to as green energy sources or alternative energy sources, into the network infrastructure is a sustainable and effective approach to addressing these matters. This paper provides a comprehensive review of the integration of RESs and EVs into power systems. The bibliographic analysis revealed that IEEE Access had the highest impact among journals. In order to enhance the classification of the reviewed literature, we have provided an analytical summary of the contributions made by each paper. The categorization facilitated the recognition of the primary objectives explored in the reviewed works, including the classification of EVs and RESs, the incorporation of RESs and EVs into power systems with an emphasis on emissions, the establishment of EV charging stations and parking facilities, EV batteries and battery energy storage systems, strategies for managing the integration of RESs with EVs, EV aggregators, and the financial implications. In order to provide researchers with a valuable synopsis of the implementation particulars, the papers were bifurcated into two primary classifications, namely mathematical algorithms and heuristic algorithms. The mixed integer linear programming algorithm and particle swarm optimization algorithm were commonly utilized formulations in optimization. MATLAB/Simulink was the primary platform used for executing a considerable portion of these algorithms, with CPLEX being the dominant optimization tool. Finally, this study offers avenues for further discourse and investigation regarding areas of research that remain unexplored.

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Section: Algorithms and Implementation Detailsmentioning

confidence: 99%

“…Water cycle algorithm [151] Artificial ecosystem optimization algorithm [157] Soft actor-critic deep reinforcment learning algorithm [169] Autoregressive moving average algorithm [178] Spike neural network learning algorithm [188] Golden eagle algorithm [188] Table 10. Optimization tools used in the literature.…”

Section: Formulation Algorithm Referencementioning

confidence: 99%

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

et al. 2023

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“…Both PELGEO and GEO_DLS were applied for the 3D path planning of a UAV during power inspection. Ilango R et al [43] proposed S2NA-GEO combined with a neural network learning algorithm. Later, a model for the uncertainty associated with renewable energy based on GEO was developed to relate the negative effects of variations in RES output for electric vehicles and intelligent charging [44].…”

Section: Related Work On Geomentioning

confidence: 99%

A Nonlinear Convex Decreasing Weights Golden Eagle Optimizer Technique Based on a Global Optimization Strategy

Deng,

Zhang,

et al. 2023

Applied Sciences

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A novel approach called the nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy is proposed to overcome the limitations of the original golden eagle algorithm, which include slow convergence and low search accuracy. To enhance the diversity of the golden eagle, the algorithm is initialized with the Arnold chaotic map. Furthermore, nonlinear convex weight reduction is incorporated into the position update formula of the golden eagle, improving the algorithm’s ability to perform both local and global searches. Additionally, a final global optimization strategy is introduced, allowing the golden eagle to position itself in the best possible location. The effectiveness of the enhanced algorithm is evaluated through simulations using 12 benchmark test functions, demonstrating improved optimization performance. The algorithm is also tested using the CEC2021 test set to assess its performance against other algorithms. Several statistical tests are conducted to compare the efficacy of each method, with the enhanced algorithm consistently outperforming the others. To further validate the algorithm, it is applied to the cognitive radio spectrum allocation problem after discretization, and the results are compared to those obtained using traditional methods. The results indicate the successful operation of the updated algorithm. The effectiveness of the algorithm is further evaluated through five engineering design tasks, which provide additional evidence of its efficacy.

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“…Shukla et al, 10 have provided a multi-objective synergistic planning model of the EV CS that assumes interaction among the distribution system and the transport networks, since the fast charging of EVs affects the operation of EVs. Ilango et al 48 have carried out an efficient hybrid approach to alleviate the negative impact of fluctuations in RES production and the smart charging process for EVs. The hybrid system presented the joint execution of the Spike Neural Network Learning Algorithm (S2NA) and the Golden Eagle Optimizer (GEO); hereinafter referred to as the S2NA-GEO approach.…”

Section: Related Workmentioning

confidence: 99%

A hybrid WFS-CGO based approach for optimal allocation of EV charging spots along with capacitors in smart distribution network for congestion management

Vijayalakshmi

Amudha

Dhayalini³

et al. 2022

Energy & Environment

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In this document, a new methodology for the optimal allocation of capacitor electric vehicle (EV) charging points on smart grid systems is introduced. To realize a better balance between exploring and exploiting Wing suit Flying Search (WFS), the Chaos Game Optimization (CGO) algorithm is used on WFS performance. Thus, it is named the WFS-CGO approach. Here, along with reactive compensation, allocation of parking and capacitors is presented for congestion management. The proposed approach is used to limit the amount of parking space. The performance of the proposed system is tested on a IEEE 34 bus distribution network. The outcome obtained by the QGDA system is compared to existing procedures like PSO, ALO, ACSO, WFSA, and WFS2ACSO techniques. Under cases 1, 2, and 3, after EV loss, the proposed technique achieves 216.3397, 232.9558, and 265.0174. Additionally, the proposed outcomes portray that an uneven EV charging scenario may cause an important voltage imbalance that goes beyond their allowable limit of 2%.

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S2NA‐GEO method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Cited by 3 publications

References 54 publications

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

Integration of Renewable Energy and Electric Vehicles in Power Systems: A Review

A Nonlinear Convex Decreasing Weights Golden Eagle Optimizer Technique Based on a Global Optimization Strategy

A hybrid WFS-CGO based approach for optimal allocation of EV charging spots along with capacitors in smart distribution network for congestion management

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