Orderly charging strategy of electric vehicle based on improved PSO algorithm

Du, Wenyi; Yin, WanJun; Yin, Wenjun

doi:10.1016/j.energy.2023.127088

Cited by 48 publications

(20 citation statements)

References 12 publications

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“…On the basis of taking into account user preferences and comprehensive evaluation indicators on the grid side, the proposed IDPGMFO, the traditional MFO,the improved PSO (IPSO) proposed in reference [11] and the improved Honey-badger algorithm (IHBA) proposed in reference [12] are used as optimization algorithms for the practical EV OCP optimization example for Chinese economic and technological development zones.…”

Section: Simulation Verification Of An Ev Ocp Optimization Example Fo...mentioning

confidence: 99%

“…Therefore, intelligent optimization algorithms have received significant attention from researchers in the EV OCP field, and various improved intelligent optimization algorithms have been proposed. An improved particle swarm optimization (PSO) algorithm was proposed to model the orderly charging strategy; however, the effectiveness verification of large-scale example was lacking [11]. An improved honey-badger algorithm (IHBA) was proposed to compose the elite OBL, spiral update, and wild dog survival strategies to enhance the order of the charging of electric vehicles; however, the design is relatively complex, and it is far too dependent on the parameters [12].…”

Section: Introductionmentioning

confidence: 99%

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Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization

Che,

Chen,

Wang

et al. 2024

Algorithms

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This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO.

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Section: Simulation Verification Of An Ev Ocp Optimization Example Fo...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization

Che,

Chen,

Wang

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…Considering the current time-of-use electricity pricing policy in Jiangsu Province, it's worth highlighting that this pricing policy leads to an increase in residents' electricity demand during the off-peak price period. This indicates that residential EV charging is sensitive to electricity prices, and the peak-valley electricity pricing policy plays a valuable role in guiding residents to charge their EVs in an organized manner [4].…”

Section: The Impact Of Large-scale Evs On Grid Loadmentioning

confidence: 99%

The impact of electric vehicle scale on grid load: analysis based on the load peak-valley difference minimization model

Li,

Yuan,

et al. 2024

Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023)

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Electric vehicles have been experiencing rapid growth in China in recent years, and this trend is expected to impact the grid load. Using Jiangsu Province in China as an example, this paper predicts that the market penetration rate of new energy vehicles will reach 50% in China, and the number of electric vehicle owners in Jiangsu Province will exceed 6 million by the year 2030. Further analysis reveals that the increasing adoption of new energy sources and electric vehicles leads to a rise in centralized EV charging, which amplifies the disparity between peak and off-peak electricity demand. Additionally, this paper introduces an objective function centered around peak-value electricity demand and a peak-valley time-sharing tariff strategy with the aim of minimizing the peak-to-off-peak load difference in the grid. It is found that, in order to reduce the peak-to-off-peak load difference in the grid, there is a need to increase the electricity price during peak hours to some extent.

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“…Other scholars [21][22][23][24] also conducted similar studies using PSO in combination with other optimization algorithms. Among these algorithms, PSO is most commonly used, and it has the advantages of fewer parameters, high accuracy, fast convergence and the ability to search collaboratively [25]. Most of existing studies using optimization methods and inverse modeling to determine thermodynamics of solids employed constant heat flux to achieve simplicity, such as a cone calorimeter [26] and CAPA II [27].…”

Section: Introductionmentioning

confidence: 99%

Application of Particle Swarm Optimization (PSO) Algorithm in Determining Thermodynamics of Solid Combustibles

Pan

Gong

2023

Energies

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The thermodynamics of a solid are crucial in predicting thermal responses and fire behaviors, and they are commonly determined by inverse modeling and optimization algorithms at constant heat flux. However, in practical scenarios, the imposed heat flux frequently varies with time, and related thermodynamics determination methods are rarely reported. In this study, the particle swarm optimization (PSO) algorithm and a 1D numerical model were utilized to determine temperature-dependent thermal conductivity and specific heat of beech wood and polymethyl methacrylate (PMMA). Surface, 3 and 6 mm in-depth temperatures were measured in three sets of ignition tests where constant and time-dependent heat fluxes (HFs) were applied. In each set, PSO was implemented at individual HFs, and the average value was deemed as the final outcome. Reliability of the optimized thermodynamics was verified by comparing with the reported values in the literature and predicting the experimental measurements that were not employed during parameterization. The results showed that wood thermodynamics attained under constant and time-dependent HFs in agreement with previously reported ones. Similar optimization procedures were conducted for PMMA, and good agreement with literature values was found. Using the obtained thermodynamics of wood under constant HF, the numerical model successfully captured the surface temperature at time-dependent HFs. Meanwhile, comparisons using wood temperatures at constant HFs and PMMA temperatures at linear HFs also verified the feasibility of PSO.

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Orderly charging strategy of electric vehicle based on improved PSO algorithm

Cited by 48 publications

References 12 publications

Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization

Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization

The impact of electric vehicle scale on grid load: analysis based on the load peak-valley difference minimization model

Application of Particle Swarm Optimization (PSO) Algorithm in Determining Thermodynamics of Solid Combustibles

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