Particle Swarm Optimization Algorithm with Multi-strategies for Delay Scheduling

Zhang, Lirong; Xu, Junjie; Liu, Yi; Zhao, Huimin

doi:10.1007/s11063-022-10821-w

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…The learning factors C 1 and C 2 determine the particle's ability to learn from itself and from the population. This article adopts asynchronous changing learning factors, where C 1 decreases linearly and C 2 increases linearly; therefore, the population in the early stage of evolution can quickly search for the optimal value in a short time and in the late stage of evolution, it can quickly and accurately converge to the optimal solution [31]. The flow of Energies 2023, 16, 5002 10 of 17 the improved particle swarm algorithm is shown in Figure 6.…”

Section: ) Learning Factors Of Asynchronous Changementioning

confidence: 99%

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Li,

Wang,

Zhang

et al. 2023

Energies

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Under the background of “peak carbon dioxide emissions by 2030 and carbon neutrality by 2060 strategies” and grid-connected large-scale renewables, the grid usually adopts a method of optimal scheduling to improve its ability to cope with the stochastic and volatile nature of renewable energy and to increase economic efficiency. This article proposes a short-term optimal scheduling model for wind–solar storage combined-power generation systems in high-penetration renewable energy areas. After the comprehensive consideration of battery life, energy storage units, and load characteristics, a hybrid energy storage operation strategy was developed. The model uses the remaining energy in the system after deducting wind PV and energy storage output as the “generalized load”. An improved particle swarm optimization (PSO) is used to solve the scheduling schemes of different running strategies under different objectives. The optimization strategy optimizes the battery life-loss coefficient from 0.073% to 0.055% under the target of minimizing the mean squared deviation of “generalized load”, which was optimized from 0.088% to 0.053% under the minimized fluctuation of combined system output and optimized from 0.092% to 0.081% under the minimized generation costs of the combined system. The results show that the model can ensure a stable operation of the combined system, and the operation strategy proposed in this article effectively reduces battery life loss while reducing the total power generation cost of the system. Finally, the superiority of the improved PSO algorithm was verified.

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Section: ) Learning Factors Of Asynchronous Changementioning

confidence: 99%

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Li,

Wang,

Zhang

et al. 2023

Energies

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A Novel Design of Epidermal Flexible Antenna on Supraorbital Nerve to Correlate Diabetes and Anemia

Kannagi¹,

Jawahar²,

Nath

2022

IETE Journal of Research

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Intelligent recommendation method for offline course resources tax law based on chaos particle swarm optimization algorithm

Jingjing,

2023

IFS

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In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better.

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Particle Swarm Optimization Algorithm with Multi-strategies for Delay Scheduling

Cited by 3 publications

References 55 publications

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

A Novel Design of Epidermal Flexible Antenna on Supraorbital Nerve to Correlate Diabetes and Anemia

Intelligent recommendation method for offline course resources tax law based on chaos particle swarm optimization algorithm

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