Pareto‐optimal design of UHF antenna using modified non‐dominated sorting genetic algorithm II

Feng, Bin; Wang, Feng; Chen, She; Zhong, Liang; Lin, Sanbao

doi:10.1049/iet-map.2020.0121

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Five MOEAs are used to solve the four scenarios, and they are the NSGA‐II with parent inheritance and roulette wheel selection (i‐NSGA‐II‐RWS), 46 the NSGA‐II based on DE operator (NSDE), 49 modified nondominated sorting genetic algorithm II (MNSGA‐II), 56 improved nondominated sorting genetic algorithm‐II (INSGA‐II) 57 and our proposed NSGA‐II‐MCEI, respectively. The parameter settings of the first two MOEAs are the same as those of their original literature.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…It is clear that the standard deviation of loads of Scenario 4 is the smallest, and thus its load distribution is the smoothest. The data of the compromise solution of Scenario 4 is recorded in the appendix due to space limitations.Five MOEAs are used to solve the four scenarios, and they are the NSGA-II with parent inheritance and roulette wheel selection (i-NSGA-II-RWS),46 the NSGA-II based on DE operator (NSDE),49 modified nondominated sorting genetic algorithm II (MNSGA-II),56 improved nondominated sorting genetic algorithm-II (INSGA-II)57 and our proposed NSGA-II-MCEI, respectively. The parameter settings of the first two MOEAs are the same as those of their original literature.…”

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confidence: 99%

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An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

Wu,

Zou,

Zhang

et al. 2024

Energy Science & Engineering

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This paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day.

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