A multi-time scale schedulable capacity evaluation method for stations considering user wishes

Abstract

As a flexible resource, electric vehicles (EVs) can realize various auxiliary services such as peak shaving, reactive power optimization, fault recovery, and emergency power supply. It is essential to quickly and accurately evaluate the EV schedulable capacity to realize auxiliary services. In this context, this paper first simulates EV charging demand using trip chain and Monte Carlo (MC) methods based on the 2017 National Household Travel Survey (NHTS2017) results to provide a multi-time scale estimation of EV charging station schedulable capacity to provide a data foundation. Then, considering incentive price, price sensitivity, and user credit, a demand response model is established to describe the uncertainty of user response and constraints such as differentiated demand on both sides of the grid user, battery charging and discharging state, battery loss, and response uncertainty are integrated to establish a multi-time scale evaluation model of the schedulable capacity of EV charging stations. Finally, the effectiveness of the proposed evaluation model is verified by simulation, and the effects of site area, incentive price, and dispatching time scale on the maximum schedulable capacity of EV charging stations are analyzed.

Keywords

• Electric vehicle
• Auxiliary service
• Trip chain
• Schedulable capacity
• Demand response
• Multi-time scale