Linear-Extrapolation-Based Gray-Wolf Optimization Algorithm for Global Maximum Power Tracking of Thermoelectric Generators

Jang, Yohan; Lee, Chaeeun; Bae, Sungwoo

doi:10.1109/tec.2023.3303931

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…EVs have been efficiently discharged at an average power rate in accordance with the actual load in 24-hour schedule. The cost optimization strategy using Jaya algorithm for EV charging/discharging has resulted in an average reduction of 23.12% w.r.t PSO-GA [43], 15.18% w.r.t PLM-ESA [44,45] and 9.74% w.r.t GWO [19] described in figure 9. The MOjaya algorithm aligns the EVs SOCs as per customer expectations by providing an optimal solution that caters to customer interests.…”

Section: V2g Operationmentioning

confidence: 99%

“…Through this iterative process, the algorithm converges towards charging/discharging schedules that achieve the desired balance between cost minimization and power constraints. The MOJaya algorithm utilizes less energy i.e., 13.33% as compared to GWO [43], 16.66% w.r.t PLM-ESA [44,45] and 26.66% w.r.t PSO-GA [19]. Thus, the algorithm provides better efficiency by reducing the overall power consumption and costs as compared to other algorithms like GWO, PSO-GA and PLM-ESA as described in figure 14.…”

Section: Pareto Front Participants Benefitsmentioning

confidence: 99%

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Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

Sharma,

Ali

2024

Eng. Res. Express

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The rising demand for electric vehicle (EV) charging is spurring their increased integration into microgrids. With significant advancements, EVs have become widely adopted and integrated into various settings for charging/discharging. EVs integrated with the microgrids possess the capability to serve as variable loads and the various energy suppliers present it as a dual opportunity. However, a primary challenge in EV deployment lies in efficiently managing charging stations (CSs) to minimize waiting times for users and reduce charging costs for EV owners. In addressing these challenges require consideration of dynamic pricing mechanisms and the diverse characteristics of EVs to achieve optimal scheduling. A novel approach that combines dynamic pricing strategies with optimized scheduling for effective EV charging operations using multi-objective Jaya algorithm. To evaluate its performance, we conducted a numerical experiment using real-time data and the Nissan Leaf model EV. The results demonstrate that our multi-objective Jaya-based approach outperforms existing methods by achieving a remarkable cost saving rate of 16.013 % and an average profit of ₹ 243.6331 per kilowatt-hour with a network convergence time of 112 seconds. Also, our proposed algorithm provides a correlation between minimized EV charging costs and maximized EV aggregator profits. These findings validate the effectiveness and practical applicability of our proposed EV scheduling algorithm in real-world scenarios.

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Section: V2g Operationmentioning

confidence: 99%

Section: Pareto Front Participants Benefitsmentioning

confidence: 99%