APSO (Adaptive Particle Swarm Optimization) algorithm-based electric vehicle fast charging station optimized siting and sizing method

A technology for electric vehicles and fast charging, applied in computing, data processing applications, forecasting, etc., can solve the problems of large charging current and high requirements for charging facilities, and achieve the effect of high computing efficiency

Inactive Publication Date: 2018-08-28
JIANGSU ELECTRIC POWER CO
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Problems solved by technology

The charging current in the fast charging mode is very large, and the requirements for charging facilities are also higher, so special fast charging stations need to be built

Method used

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  • APSO (Adaptive Particle Swarm Optimization) algorithm-based electric vehicle fast charging station optimized siting and sizing method
  • APSO (Adaptive Particle Swarm Optimization) algorithm-based electric vehicle fast charging station optimized siting and sizing method
  • APSO (Adaptive Particle Swarm Optimization) algorithm-based electric vehicle fast charging station optimized siting and sizing method

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Embodiment Construction

[0023] The present invention will be further described below in conjunction with specific drawings and embodiments.

[0024] The invention provides a method for optimizing the site selection and capacity determination of an electric vehicle fast charging station based on the APSO algorithm, comprising the following steps:

[0025] Step S1, obtain the relevant information of electric vehicles and electric vehicle users in the planning area, including: the average power consumption of electric vehicles per 100 kilometers, the average daily mileage of electric vehicles, the number of electric vehicles in the planning area, and the proportion of electric vehicles that choose fast charging mode Ratio, average electric vehicle capacity, average electric vehicle speed, local electricity price;

[0026] Step S2, according to the information in step S1, calculate the maximum capacity limit S of the charging station max and the minimum capacity limit S min ;Number of planned construct...

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Abstract

The invention provides an APSO (Adaptive Particle Swarm Optimization) algorithm-based electric vehicle fast charging station optimized siting and sizing method. According to the method of the invention, the range of the quantity of charging stations to be constructed in a planning area is obtained according to the estimated charging demand of the planning area and the maximum and minimum capacitylimit of single fast charging stations; whole society cost is taken as the objective function of a current scheme with a charging station service radius and the charging station maximum charging capacity adopted as constraint conditions; and the APSO algorithm is adopted to solve the siting models of different number of charging stations, and a scheme enabling the lowest whole society cost is adopted as an optimized siting scheme; and optimized sizing is performed on the scheme which has completed siting through adopting a queuing theory, so that the optimized siting and sizing of the fast charging stations can be realized. The computational efficiency of the method provided by the invention is higher than that of a traditional PSO (Particle Swarm Optimization) algorithm, and the optimization result of the method is also significantly better than that of the PSO algorithm.

Description

technical field [0001] The invention relates to the field of electric vehicle charging station planning, in particular to a method for optimal location selection and capacity determination of electric vehicle fast charging stations based on the APSO algorithm. Background technique [0002] In recent years, due to energy shortage and environmental pollution problems, electric vehicles (hereinafter referred to as EV) have attracted more and more attention. At the same time, with the growth of my country's economy and the improvement of EV technology, the number of EVs is continuously rising, and the charging infrastructure is an important basis for the promotion of EVs. Experts confirmed that among the many influencing factors of EV development, the importance of charging infrastructure construction is second only to battery technology. At the same time, both at home and abroad believe that the construction of charging infrastructure should be moderately ahead of EV. [0003...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/30
CPCG06Q10/04G06Q50/30
Inventor 陈铭陈黎军缪立恒沈海平傅雨婷孙国强臧海祥刘志仁乔臻
Owner JIANGSU ELECTRIC POWER CO
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