This paper discusses three variants of how e-mobility development will affect the Polish Power System. Multivariate forecasts of annual new registrations of electric vehicles for up to seven years are developed. The forecasts use the direct trend extrapolation methods, methods based on the deterministic chaos theory, multiple regression models, and the Grey model. The number of electric vehicles in use was determined for 2019‒2025 based on the forecast new registrations. The forecasts were conducted in three variants for the annual electric energy demand in 2019‒2025, using the forecast number of electric vehicles and the forecast annual demand for electric energy excluding e-mobility. Forecasts were conducted in three variants for the daily load profile of power system for winter and summer seasons in the Polish Power system in 2019‒2025 based on three variants of the forecast number of electric vehicles and forecast relative daily load profiles.

From the perspective of a virtual power plant (VPP) with electric vehicles (EVs), a self-scheduling strategy considering the response time margin (RTM) and state of charge margin (SOCM) is proposed. Firstly, considering the response state of the state of charge (SOC) and charge-discharge state of EVs, a VPP based response capacity determination model of EVs is established. Then, RTM and SOCM indexes are introduced on the basis of the power system scheduling target and the EV users’ traveling demands. The RTM and SOCM indices are calculated and then are used to generate a priority sequence of responsive EVs for the VPP. In the process of the scheduling period and rolling iteration, the scheduling schemes of the EVs in the VPP for multiple time periods are determined. Finally, the VPP self-scheduling strategy is validated by taking an VPP containing three kinds of EV users as an example. Simulation results show that with the proposed strategy, the VPP is able to respond to the scheduling power from the power system, while ensuring the traveling demands of the EV users at the same time.

The development of electric vehicles (EV) necessitates the search for new solutions for configuring powertrain systems to increase reliability and efficiency. The modularity of power supplies, converters, and electrical machines is one such solution. Among modular electric machines, dual three-phase (DTP) motors are the most common in high-power drives. To simplify low and medium power drives for EVs based on DTP PM motor, it is proposed to use a BLDC drive and machine of the simplest design – with concentrated windings and surface mounted PMs on the rotor. To study and create such drives, an improved mathematical model of DTP PM machine was developed in this work. It is based on the results of 2D FEM modeling of the magnetic field. According to the developed method, the dependences of the self and mutual inductances between all phase windings from the angle of rotor position and loads of different motor modulus were determined. Based on these inductances, the circuit computer model of DTP PM machine was created in the Matlab/Simulink. It has a high simulation speed and a high level of adequacy, which is confirmed by experimental studies with a mock-up sample of the electric drive system.