Agent-based model of short-term and long-term allocation of electric vehicle charging resources in Netlogo

The REVCID (Residential Electric Vehicle Charging Infrastructure Development) model is an agent-based model, built in NetLogo 6.4.0. It’s goal is to identify strengths and weaknesses of various roll-out strategies, taking into account residential demand, growth projections, equity and grid limitations.Parameters from a real case study were used to initialize the model (identify-neighbourhoods.nls)  and parameters should be adjusted to the area of interest when using the model. The netlogo procedures can be found in different files:<br>globals.nls contains the global variables (parameters) used in the modelchargepoints-own.nls, transformators-own.nls and admins-own.nls is a list of the parameters within the chargepoint, transformer and policy-maker agents.identify-neighborhoods.nls contains the statistics as derived from external data (such as EVdata and CBS, see references) for each of the 9 selected case study neighborhoodsset-parameters-grid.nls sets the charging speed of various charging modesdetermine-peak.nls adjusts the occupancy rates based on whether the hour of the day is a peak hour, and adds a random chance for higher occupancygrow-demand.nls sets the growth factor of the occupancy rateset-values-for-bs.nls turns the output into reporters that can be saved as csv or table output when running the simulations in BehaviorspaceThe nlogo file contains the entire model, interface and procedures. The nls files should be imported for the model to work.<br><br><br><br><br><br>

REVCID（Residential Electric Vehicle Charging Infrastructure Development，住宅电动汽车充电基础设施发展模型）是一款基于智能体的模型，基于NetLogo 6.4.0构建。其核心目标为识别各类充电基础设施推广策略的优劣，同时综合考量住宅充电需求、增长预测、公平性与电网限制等关键维度。本模型采用真实案例研究的参数完成初始化，对应配置文件为identify-neighbourhoods.nls；在实际使用该模型时，需根据目标应用区域对参数进行针对性调整。模型的NetLogo程序代码分散于多个功能文件中：globals.nls包含模型所使用的全部全局变量（参数）；chargepoints-own.nls、transformators-own.nls与admins-own.nls分别列出了充电桩智能体、变压器智能体与政策制定者智能体的参数配置；identify-neighborhoods.nls包含来自外部数据源（如EVdata与CBS，详见参考文献）的9个选定案例研究街区的统计数据；set-parameters-grid.nls用于设置各类充电模式的充电速度参数；determine-peak.nls可根据当日时段是否为高峰时段调整充电桩占用率，并为更高占用率场景添加随机概率；grow-demand.nls用于设置充电桩占用率的增长因子；set-values-for-bs.nls将模型输出转换为可被读取的报告器，以便在Behaviorspace中运行模拟时，将结果导出为CSV格式或表格形式。本模型的完整代码、界面与所有程序逻辑均封装在nlogo文件中，需导入所有配套的nls文件后方可正常运行该模型。