Electric vehicle charging station planning method and device
A technology for electric vehicles and charging stations, applied in neural learning methods, instruments, marketing, etc., can solve the problems of unstable planning accuracy and poor convergence of electric vehicle charging stations
Pending Publication Date: 2022-03-11
ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD +1
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Problems solved by technology
[0005] This application provides a method and device for planning an electric vehicle charging station, which is used to solve the problem of non-linearity and multi-objectives of the charging station and the complexity of the large power grid in the prior art, which is prone to poor convergence and falls into a local convergence state , leading to the technical problem of unstable planning accuracy of electric vehicle charging stations
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[0044] The embodiment of the present application provides a method and device for planning an electric vehicle charging station, which is used to solve the problem of poor convergence and localization in the existing technology in the face of the nonlinearity and multi-objectives of the charging station and the complexity of the large power grid. Convergence state, leading to technical problems of unstable planning accuracy of electric vehicle charging stations.
[0045] In order to make the purpose, features and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the following The described embodiments are only some of the embodiments of the present application, but not all of them. Based on the embodiments in this application, all other embodiments obt...
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The invention discloses an electric vehicle charging station planning method and device, and the method employs a charging station planning model constructed through an integrated learning algorithm, integrates a plurality of intelligent algorithms through a sub-optimizer in an integrated learning architecture, and applies the intelligent algorithms to the electric vehicle charging station planning. The characteristics of different intelligent algorithms in each sub optimizer are combined through the upper optimizer, so that the method has higher adaptability to nonlinearity and multi-target of the charging station and complexity of a large power grid, and the problems of nonlinearity and multi-target of the charging station and complexity of the large power grid in the prior art are solved; the technical problem that the planning accuracy of the electric vehicle charging station is unstable due to the fact that the electric vehicle charging station is prone to poor convergence and falls into a local convergence state in the prior art is solved.
Description
technical field [0001] The present application relates to the technical field of charging station planning, in particular to a method and device for planning an electric vehicle charging station. Background technique [0002] With the increasing air pollution and the shortage of traditional fossil energy, clean and efficient electric vehicles are of great significance to solve environmental and energy problems. Prospects. [0003] The charging station is an important support system in the electric vehicle industry chain and an important supporting facility for electric vehicles. The access location and capacity of the charging station are extremely important. Improper selection will lead to urban traffic congestion, inconvenience to users, and even excessive loss of distribution network lines, and serious degradation of voltage quality at the end of the line and even the entire system. [0004] At present, the existing research on the objective function of EV charging stat...
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IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06Q30/02G06Q50/06
CPCG06Q10/043G06Q30/0201G06Q50/06G06N3/08G06N3/045Y04S50/14
Inventor 唐建林钱斌肖勇王浩林林晓明罗鸿轩王吉
Owner ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD