Improving profitability of EV chargepoints through integrated location optimization（通过集成选址优化提升电动汽车充电桩盈利能力）¶

Industry Sector: Other Infrastructure

Business Function: Production

Foundry was used at a Charge Point Operator (CPO) to prioritize where to build a network of EV Charging Stations and model the financial performance of these investments, allowing network planning analysts to optimize the uptake and profitability of the stations as well as significantly speed up the evaluation time.

Challenge¶

Prior to Foundry, evaluations on where to build charging infrastructure for EV have been made in Excel by pulling information from several different applications manually. This process was very labor intensive even after leveraging multiple scripts to automate it as much as possible. Furthermore, due to technical limitations, tradeoffs had to be made between execution speed and the level of detail / number of factors included into the analysis. Evaluations take a long time and critical factors (such as existence of nearby planned locations) have not been taken into account.

Solution¶

Using Foundry, connections to all important applications and data sources were established, syncing the data automatically and frequently. The workload required to rerun the analysis went down to zero, enabling analysts to continuously test and iterate. Beyond existing data, Foundry enabled the users to add additional, 3rd party data ad-hoc / directly into the data asset.

These workflows let users build and simulate entire networks, modeling costs, revenue, customer, utilization, and even take the electrical grid's capacity into account. Today, the decision is based on a broader picture, accounting for more factors without sacrificing execution speed. Moreover, the CPO increased the speed at which they could perform evaluations, as decisions are more straight forward and understandable when looking at the complete picture.

The same solution and data assets are also used by maintenance engineers, ensuring decisions can be made on a transparent data foundation across different parts of the organization.

Improving Profitability of EV Chargepoints Through Integrated Location Optimization

Users and stakeholders¶

Network Planning
Location Acquisitions

Impact¶

Reduced evaluation time by a factor of 5.
Reduced number of applications consulted during evaluation from seven to just Foundry.
8-digit dollar amount expected increase in profitability.

Implement a similar use case¶

This use case implements the following Pattern. Follow the link below to read more about a particular Pattern and learn how it is implemented within Foundry.

Resource allocation & optimization (used for 3 other use cases)

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中文翻译¶

通过集成选址优化提升电动汽车充电桩盈利能力¶

行业领域：其他基础设施

业务职能：生产

某充电桩运营商（Charge Point Operator, CPO）利用 Foundry 平台，优先确定电动汽车充电站网络的选址，并对这些投资的财务表现进行建模，使网络规划分析师能够优化充电站的利用率与盈利能力，同时显著缩短评估时间。

挑战¶

在使用 Foundry 之前，该运营商通过手动从多个不同应用程序中提取信息，在 Excel 中完成电动汽车充电基础设施的选址评估。即使借助多个脚本尽可能实现自动化，这一流程仍然非常耗费人力。此外，由于技术限制，不得不在执行速度与分析所包含的细节程度/因素数量之间进行权衡。评估耗时较长，且关键因素（例如附近是否有已规划站点）未被纳入考量。

解决方案¶

借助 Foundry，该运营商建立了与所有重要应用程序和数据源的连接，实现了数据的自动高频同步。重新运行分析所需的工作量降至零，使分析师能够持续进行测试与迭代。除现有数据外，Foundry 还允许用户临时/直接将额外的第三方数据添加到数据资产中。

这些工作流使用户能够构建并模拟整个网络，对成本、收入、客户、利用率进行建模，甚至将电网容量纳入考量。如今，决策基于更全面的视角，在兼顾更多因素的同时不牺牲执行速度。此外，该 CPO 的评估速度得以提升，因为当看到全局图景时，决策变得更加直接且易于理解。

同一解决方案和数据资产也被维护工程师使用，确保组织内不同部门都能在透明的数据基础上做出决策。

通过集成选址优化提升电动汽车充电桩盈利能力

用户与利益相关方¶

网络规划部门
选址收购部门

影响¶

评估时间缩短至原来的五分之一。
评估过程中需要查阅的应用程序数量从七个减少到仅使用 Foundry。
预期盈利能力提升达八位数美元级别。

实施类似用例¶

本用例实现了以下模式。点击下方链接可了解更多关于特定模式的信息及其在 Foundry 中的实现方式。

资源分配与优化（另用于 3 个其他用例）

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