发展电动汽车充电设施是加快电动汽车普及使用的基础。科学的充电设施规划方法是减少电动汽车充电负面影响，充分促进电动汽车使用的重要手段。未来智能电网发展所面临的广泛不确定性和电力市场环境对充电设施的规划有着显著的影响，而对此方面的研究还很不深入。本项目将基于充电负荷预测方法，研究电力市场中的充电设施运营模式，综合充电设施的交通和电力属性，充分考虑发展中的不确定性，开展充电设施综合规划方法研究。本项目将用几何布朗运动随机过程和极限学习机方法研究充电负荷中长期和短期预测。对于充电设施运营问题，研究充电设施有限信息环境下的分布式调度方法和参与日前及实时电力市场最小化运行成本的运营模式。对于广泛存在的多种不确定性，将建立基于数据包络分析的不确定性综合评估方法，最终建立综合考虑交通和电力因素的多目标高效规划方法。本项目将为充电设施规划提供技术支持，并丰富和完善智能电网理论体系。

Developing charging facilities is among the key factors to the widespread use of electric vehicles (EV). Scientific charging facility planning is an important research issue to reduce the impacts of EV charging to power grids and promote EV application. The planning of charging facility will be significantly affected by a broad spectrum of uncertainties in context of electricity market and smart grid development. However, only limited research has been done on this subject by far. Thus this project will focus on the comprehensive research of charging facility planning method, where investigations on various issues including but not limited to the EV charging demand forecast methods, charging facility operation models within the electricity market, impacts on the traffic system and power systems by charging facilities, as well as uncertainties in the development of EVs, will be conducted. Geometrical Brownian motion process and extreme learning machine methods will be studied in the medium-term and short-term EV charging demand forecast respectively. The distributed dispatch in the imperfect information environment of charging facilities will be studied, and a novel operation model of charging facilities to reduce operation cost will be developed in both the day-ahead and real time electricity markets. The data envelopment analysis (DEA) method will be utilized to evaluate the planning candidates with uncertainties. In addition, a high-efficient multi-objective planning framework considering both traffic and power system conditions will be established. This project will technically support the charging facility planning and enrich the theories of smart grid.