A charging station planning method based on an improved adaptive genetic algorithm

Abstract

The invention discloses a charging station planning method based on an improved adaptive genetic algorithm. The method is characterized in that firstly, an objective function, a constant volume modeland constraint conditions of a charging station planning model are determined; and then, an improved adaptive genetic algorithm is adopted to solve the charging station planning model, and a planningresult is obtained. The invention provides the improved adaptive genetic algorithm which is more suitable for solving the charging station planning problem, and the algorithm can effectively reduce the search space and improve the search efficiency and the search precision.

Description

technical field [0001] The invention belongs to the technical field of electric vehicle charging, and in particular relates to a charging station planning method based on an improved self-adaptive genetic algorithm. Background technique [0002] The transportation sector is a major source of greenhouse gas emissions and energy consumption. In the United States, 30% of carbon dioxide emissions come from the transportation sector, and one-third of energy consumption is in the transportation sector. With the increasingly prominent problems of energy shortage and environmental pollution, electric vehicles have attracted worldwide attention due to their good environmental and social benefits such as zero exhaust emissions and low noise pollution. At present, many countries have formulated relevant policies to stimulate the research and promotion of electric vehicles. France, the Netherlands, Germany and other countries have proposed plans to stop the sale of fuel vehicles. Howe...

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Problems solved by technology

[0003] In the research on charging station planning, intelligent algorithms are often used to solve the problem. However, intelligent algorithms are not completely suitable for solving charging station planning problems, and there is room for improvement in optimization performance.

In order to avoid the distribution of charging stations too close together, some studies have proposed the rotation partition method to improve the cloud adaptive particle swarm optimization algorithm to improve the speed and efficiency of the algorithm. However, this algorithm only considers the spatial distribution of charging stations, and does not consider EV. Distribution of charging demand: It will be difficult to search for the optimal solution by using the rotating partition method in the planning area where the distribution of EV charging demand is uneven

In addition, the algorithm is based on the geographical center partition of the planning area, and is not suitable for irregular situations such as narrow and long planning areas. When there are a large number of charging stations planned, strict and uniform partitioning according to the geographical center partition will also increase the calculation error and lose the optimal solution.

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