Smart Management of Electric Vehicles

配电网络通常使用基于典型负荷消耗模式的假设针对特定电力负荷而设计。电动汽车（EV）的电池充电将增加配电网络的电力需求，大规模的电动交通将需要对充电基础设施进行智能管理。根据车辆插入的位置和时间，它们可能会对电网造成局部限制。需要分析工具来确定将大量移动的电动汽车添加到电网的影响，以及客户的位置，每天的充电时间和持续时间。电动汽车聚集建模的主要问题是不确定性的表示，包括：（i）住宅负载的类型，（ii）EV位置，（iii）EV充电器的额定值，（iv）EV充电发生和（v）电动汽车充电持续时间。本研究中提出的电动汽车聚合器将作为一方的消费者与市场和其他电力系统参与者之间的关键调解人。另一边EV聚合器可能必须预测：（i）其自己的客户的电力消耗，用于预测聚合器的电力平衡，以及（ii）电力系统中的消耗，用于预测电价。电动汽车的影响对于配电网络运营商（DNO）来说是重要的，因为需要管理拥塞和电压下降。由于预计电动汽车的大规模部署可能对电网产生重要影响，因此预计将尽可能使车辆适应现有基础设施，这可以通过集成智能电网控制技术来实现。智能电网的主要目标是最佳控制电力分配和电动汽车的充电可以控制，以减少高峰负荷。为了回答这些问题，本项目借鉴了跨越工程和信息学边界的方法和结果。这是一个令人兴奋的机会，可以将定性和定量研究方法结合起来，研究涵盖电动汽车负载预测和智能管理的复杂系统。该项目旨在（i）研究电动汽车智能管理的控制算法，考虑到整个网络中电动汽车的空间多样性和电动汽车充电模式的时间多样性，以及（ii）通过实验室验证，展示实施控制算法的实用方法，以促进电动汽车的未来部署。

Distribution networks are typically designed for specific electrical loads using assumptions based on typical load consumption patterns. Battery charging of Electric Vehicles (EVs) will increase the power demand in distribution networks and large scale electric transport will require smart management of the charging infrastructure. Depending on the location and times the vehicles are plugged in, they could cause local constraints on the grid. Analysis tools are required to determine the effects of adding a large number of mobile EVs to the grid, as well as the customers' location, charging time and duration on a daily basis. The main problem in the modelling of the aggregation of EVs is the representation of the uncertainties including: (i) type of residential load, (ii) EV location, (iii) rating of EV charger, (iv) EV charging occurrence and (v) EV charging duration.An EV aggregator proposed in this research will act as a key mediator between the consumers on one side and the markets and the other power system participants on the other side. The EV aggregator may have to forecast: (i) the electricity consumption of its own customers, for forecasting the aggregator's power balance and (ii) the consumption in the electricity system, for forecasting electricity prices. The impact of EVs is significant for the Distribution Network Operators (DNOs) as there is a need to manage congestion and voltage drops. As the predicted large deployment of EVs could have an important impact on the grid it is expected to adapt the vehicle as much as possible with the existing infrastructure and this can be achieved by the integration of smart grid control techniques. The primary goal of a Smart Grid is the optimal control of the electricity distribution and the charging of EVs can be controlled to reduce peak load.In order to answer these questions, this project draws on methodologies and results across the boundaries of engineering and informatics. This is an exciting opportunity to bring qualitative and quantitative research methods together to study a complex system covering load forecasting and smart management of EVs. This project aims to (i) investigate control algorithms for smart management of EVs considering the spatial diversity of EVs throughout the network and temporal diversity of EV charging patterns and (ii) demonstrate a practical way of implementing control algorithms to facilitate the future deployment of EVs by laboratory validation.