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Energy-optimal route planning for electric vehicle (EV) is highly required for the wide-spread use of EV, which is hindered by limited battery capacity and relative short cruising range. Obtaining the cost for each link (i.e., link energy consumption) in road networks plays a key role in energy-optimal route planning process. The link energy consumption depends mainly on energy consumption factor, which is related to not only vehicle speed but also road type. This study aims to analyze the difference of EV’s energy consumption factors for different road types. According to the floating car data (FCD) collected from the road network in Beijing, the vehicle specific power (VSP) distributions under different average travel speeds for different road types are analyzed firstly, and then the EV’s energy consumption rates under different VSP-Bins are calculated. By using VSP as an intermediate variable, EV’s energy consumption factor models for different road types are established and the difference of EV’s energy consumption factors is analyzed. The results show that road type-based energy consumption factor should be used in EV’s energy-optimal route planning process.

The economy of distribution networks largely depends on the utilization rate of distribution network equipment. Most of the emerging intelligent power consumption technologies have a positive effect on equipment utilization and their use can save investment of distribution networks. In this paper, the influence of intelligent power consumption technologies on the utilization rate of distribution network equipment is reviewed. The evaluation methods and indexes are assessed first and then intelligent power consumption equipment with energy storage function, vehicle-to-grid (V2G) technology and time-of-use (TOU) tariff are reviewed respectively. It is concluded that these intelligent power consumption technologies and measures have great potential to improve utilization rate of distribution network equipment because of their effective improvement to power load. Meanwhile, recommendations on how to utilize these intelligent power consumption technologies to improve utilization rate of distribution network equipment are proposed.

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