电动汽车充电场站选址建议数据

本数据在电动汽车充电基础设施建设和运营领域具有多维度应用价值。具体应用场景如下：
1、对平台的价值（助力资源优化配置）：通过网格级供需分析实现精准投资决策，优先建议商家在S/D＞1.5的网格布局充电场站；动态调整运营策略，建议商家在"低效冗余型"区域实施设备迁移或错峰定价；支撑投资风险预警机制建立，以自动拦截低效区域的建站申请。
2、对场站商家的价值（投资决策优化）：可充分利用本数据的选址分类建议结果，规避或降低充电场站的投资风险。
3、对城市管理的价值（基础设施规划）：为城市充电设施整体规划提供动态评估结果；结合充电场站选址建议，优化城市电网改造时序表，迎合市场充电需求。
4、对电力企业的价值：为构建区域用电负荷预测模型提供参考，优化变电资源布局。1.数据采集：原始数据经授权合法获取并使用，采集日期、城市名称、网格ID、网格面积、网格内直连场站枪数、网格内直连场站订单数、网格内互联预测订单数、网格内外部场站枪数、网格内外部场站数量等字段。
2.网格内供需指标计算：网格内预测充电订单数S=网格内直连场站订单数+网格内互联预测订单数+网格内外部场站枪数×网格内外部场站数量；网格内充电枪数D=网格内直连场站枪数+网格内外部场站枪数。
3.建立选址评估模型：（1）计算供需比（S/D）：供需比=网格内预测充电订单数÷网格内充电枪数。（2）选址评估分类和建议：若S/D≤0.5，则为“低效冗余型”，建议“暂停新建”，若0.5＜S/D≤1.0，则为“份额领先型”，建议“维持现状”，若1.0＜S/D≤1.5，则为“潜力平衡型​”，建议“轻量补足”，若1.5＞S/D，则为“再建供给型”，建议“加大建设”。

This dataset has multi-dimensional application value in the construction and operation of electric vehicle (EV) charging infrastructure. The specific application scenarios are as follows:
1. Value for the platform (facilitating optimal resource allocation): Achieve precise investment decisions through grid-level supply and demand analysis, prioritizing recommendations for merchants to layout charging stations in grids where S/D > 1.5; dynamically adjust operational strategies, advising merchants to implement equipment relocation or peak-staggered pricing in "low-efficiency redundant" zones; support the establishment of an investment risk early warning mechanism to automatically block station construction applications in low-efficiency zones.
2. Value for charging station operators (optimizing investment decisions): Fully utilize the site selection classification recommendation results of this dataset to avoid or reduce the investment risks of charging stations.
3. Value for urban management (infrastructure planning): Provide dynamic evaluation results for the overall planning of urban charging facilities; combine the charging station site selection recommendations to optimize the urban power grid renovation timeline to meet market charging demand.
4. Value for power enterprises: Provide references for constructing regional power load forecasting models and optimize the layout of substation resources.

1. Data collection: The original data is legally obtained and used with authorization. The included fields are: collection date, city name, grid ID, grid area, number of charging ports of directly connected stations within the grid, number of orders of directly connected stations within the grid, predicted number of orders from inter-connected stations within the grid, number of charging ports of internal and external stations within the grid, and number of internal and external stations within the grid.
2. Calculation of supply and demand indicators within the grid: The predicted number of charging orders S within the grid = number of orders of directly connected stations within the grid + predicted number of orders from inter-connected stations within the grid + number of charging ports of internal and external stations within the grid × number of internal and external stations within the grid; The number of charging ports D within the grid = number of charging ports of directly connected stations within the grid + number of charging ports of internal and external stations within the grid.
3. Establishment of site selection evaluation model:
(1) Calculate the supply-demand ratio (S/D): Supply-demand ratio = predicted number of charging orders within the grid ÷ number of charging ports within the grid.
(2) Site selection evaluation classification and recommendations: If S/D ≤ 0.5, it is classified as "low-efficiency redundant type", and the recommendation is "suspend new construction"; if 0.5 < S/D ≤ 1.0, it is classified as "share leading type", and the recommendation is "maintain status quo"; if 1.0 < S/D ≤ 1.5, it is classified as "potential balance type", and the recommendation is "light supplementation"; if S/D > 1.5, it is classified as "supply-increasing type", and the recommendation is "increase construction".