Smoothing control of wind power fluctuations with battery energy storage system of electric vehicles

Abstract

With the rapid development of wind industry, its integration causes inevitable and rapid power fluctuations in the power grid. To promote the integration of wind power and investigate the energy storage capability of electric vehicles (EVs), a smoothing control strategy is proposed to smooth the wind power fluctuations considering the battery energy storage system of EVs (EESS). Based on a proposed maximum operation area of an individual EV considering its traveling behaviors and traveling comfort level, the response parameters are defined to describe the response characteristics of the EV aggregator (EVA). The EESS can operate as an energy storage system composed of several EVAs with different response parameters. A smoothing control strategy is then developed to smooth the wind power fluctuations with the EESS. Considering the power fluctuation rate and the response capability of the EESS, the required power change of each EVA in the EESS is determined in the smoothing control. Then the required power output change is allocated to individual EVs in the EVA with the developed SOC-adaptive method and the power output limits constrained by the maximum operation area of each EV. Comparative results are provided to validate the effectiveness of the proposed smoothing control strategy. The EESS is able to smooth the wind power fluctuations, and the power output limits and the traveling comfort levels of individual EVs are ensured with the SOC-adaptive method and the constraints from the maximum operation areas. K E Y W O R D Selectric vehicle (EV), energy storage system of EVs (EESS), power fluctuation, smoothing control, SOC-adaptive method, wind power

Frequency Regulation of Electric Vehicle Aggregator Considering User Requirements with Limited Data Collection

“…The available regulation capacity of the EV aggregator in the down-direction with C2I is given by Equation (23). The available regulation capacity of the EV aggregator in the down-direction with C2I and I2D is given by Equation (24).…”

“…Existing studies were conducted to coordinate the power outputs of individual EVs with different methods. In the literature [18,[23][24][25], the state-of-charge (SOC) sorting method [23], the least-laxity-first method [18], the optimal control method [24], and the proportional-integral (PI) control method [25] were used to regulate the real-time power control for individual EVs. These existing methods for regulating individual EVs are based on the assumption that the acquisition of real-time state measurement for the terminal EVs and the accurate power control for terminal EVs, such as the direct control for individual EVs [18,[23][24][25], have been realized in actual application.…”

“…In the literature [18,[23][24][25], the state-of-charge (SOC) sorting method [23], the least-laxity-first method [18], the optimal control method [24], and the proportional-integral (PI) control method [25] were used to regulate the real-time power control for individual EVs. These existing methods for regulating individual EVs are based on the assumption that the acquisition of real-time state measurement for the terminal EVs and the accurate power control for terminal EVs, such as the direct control for individual EVs [18,[23][24][25], have been realized in actual application. However, the limited data collection, especially the data deficiency of EV states, adds difficulty to simultaneously realizing the frequency regulation performance and the EV regulation requirement.…”