论文标题
可部署的在线优化框架，用于使用现实世界测试案例的电动汽车智能充电
A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases
论文作者
论文摘要
我们提出了一个可自定义的在线优化框架，用于实时EV智能充电，可以在实际的大规模充电设施中实施。值得注意的是，由于现实世界的约束，我们围绕3个主要要求设计了框架。首先，智能充电策略很容易可部署，并且可以为设施，基础架构，目标和约束的广泛阵列定制。其次，可以轻松地修改在线优化框架，以便在有或没有用户输入的情况下操作，以获取能源请求金额和/或出发时间估计，这允许我们的框架在具有1条通信的标准充电器上实现，或具有2条通信的较新充电器。第三，我们的在线优化框架在具有多种目标的多个现实世界测试案例中，在具有各种目标的多个现实世界测试案例中，在其他实时策略中的表现（包括先到先得的首次服务，最不安全优先，最早的第一至上等）。我们通过两个现实世界的测试案例展示了我们的框架，并提供了来自SLAC和Google校园的收费会话数据。
We present a customizable online optimization framework for real-time EV smart charging to be readily implemented at real large-scale charging facilities. Notably, due to real-world constraints, we designed our framework around 3 main requirements. First, the smart charging strategy is readily deployable and customizable for a wide-array of facilities, infrastructure, objectives, and constraints. Second, the online optimization framework can be easily modified to operate with or without user input for energy request amounts and/or departure time estimates which allows our framework to be implemented on standard chargers with 1-way communication or newer chargers with 2-way communication. Third, our online optimization framework outperforms other real-time strategies (including first-come-first-serve, least-laxity-first, earliest-deadline-first, etc.) in multiple real-world test cases with various objectives. We showcase our framework with two real-world test cases with charging session data sourced from SLAC and Google campuses in the Bay Area.
