I-Corps: Integrating Electric Vehicles in the Smart Grid Through Smart Charging Software

I-Corps项目更广泛的影响和商业潜力是使插电式电动汽车（PEV）安全集成到更广泛的电力网络中，并降低驾驶员的充电成本。如果不加以管理，pev将给公用事业带来额外的主要负荷，这将增加能源峰值消耗，并需要大量投资来升级电网基础设施。这里开发的技术包括一种智能充电软件，它将实现电动汽车车队和公用事业之间的实时通信，并将远程控制电动汽车充电，以提供电网调节服务。该项目将弥合汽车行业与能源行业之间的差距，并将制定战略，围绕电动汽车充电创建一个智能互联的生态系统。智能充电软件的成功商业化可能会降低电动汽车的拥有成本，并提高清洁交通在美国的普及率。I-Corps项目基于插电式电动汽车（PEV）充电控制的先进、自适应优化框架。该技术可以在一般不确定条件下控制电动汽车充电，同时考虑驾驶员移动性约束和电网约束。该优化框架特别适用于大型pev车队，具有快速收敛速度和隐私保护特性。该方法使用过去的PEV数据来预测出行行为和能源需求。并对配电网约束和电力市场结构进行建模，设定相应的优化目标。该方法利用多种优化方法，在最优性、约束满足和计算时间之间进行权衡，选择最优算法。这些方法包括针对共享汽车车队的偏微分方程聚合技术、针对高拥塞电网的即插即用模型预测控制技术以及针对计算能力较低的大规模住宅车队的双分裂方法。

The broader impact/commercial potential of this I-Corps project is to enable safe integration of Plug-in Electric Vehicles (PEVs) into the broader power network, as well as to reduce PEV charging costs for drivers. If unmanaged, PEVs would represent major extra loads for utilities, which would increase energy peak consumption and require considerable investment to upgrade grid infrastructure. The technology developed here involves a smart charging software, which will enable real time communication between PEV fleets and utilities, and will remotely control PEV charging to provide grid regulation services. This project will bridge the gap between the automotive industry and the energy industry, and will develop strategies to create a smart and connected ecosystem around PEV charging. Successful commercialization of the smart charging software could result in lower PEV cost of ownership, and increased penetration of clean transportation in the US.This I-Corps project is based on an advanced, adaptive, optimization framework for Plug-in Electric Vehicle (PEV) charging control in power networks. The technique can control PEV charging under conditions of general uncertainty, and consider both driver mobility constraints and power network constraints. The optimization framework is particularly adapted to large fleets of PEVs and shows fast convergence rate and privacy preserving properties. The method uses past PEV data to predict travel behaviors and energy demand. It also models distribution grid constraints and electricity market structure to set relevant optimization objectives. The method utilizes a range of optimization methods and selects the best algorithm based on tradeoff between optimality, constraint satisfaction and computation time. The methods include partial differential equation aggregation techniques for car sharing fleets, plug-and-play model predictive control for power networks with high congestion, and dual-splitting methods for large scale residential fleets with lower computation capabilities.