An Extended State Space Model of Aggregated Electric Vehicles for Flexibility Estimation and Power Control
Yiping Liu, Xiaozhe Wang, Geza Joos
TL;DR
The paper addresses the challenge of predicting and controlling the aggregated flexibility of electric vehicles (EVs) for grid services as EV penetration rises. It introduces an extended state space model (eSSM) that adds two boundary SOC intervals to the standard SSM, explicitly capturing fully charged and discharged EVs, and uses a Markov transition matrix $\bm{A}$ with input $\bm{u}(k)$ to model population dynamics. The output $\bm{y}(k)$ reflects the aggregated flexibility and power trajectory, enabling accurate real-time tracking of power references for ancillary services such as frequency regulation. Numerical results show that the eSSM yields more accurate flexibility predictions and power tracking than the SSM, closely matching a baseline IMM, and thereby enhances the practical deployment of V2G-enabled EV fleets for grid support.
Abstract
The increasing penetration of electric vehicles (EVs) can provide substantial electricity to the grid, supporting the grids' stability. The state space model (SSM) has been proposed as an effective modeling method for power prediction and centralized control of aggregated EVs, offering low communication requirements and computational complexity. However, the SSM may overlook specific scenarios, leading to significant prediction and control inaccuracies. This paper proposes an extended state space model (eSSM) for aggregated EVs and develops associated control strategies. By accounting for the limited flexibility of fully charged and discharged EVs, the eSSM more accurately captures the state transition dynamics of EVs in various states of charge (SOC). Comprehensive simulations show that the eSSM will provide more accurate predictions of the flexibility and power trajectories of aggregated EVs, and more effectively tracks real-time power references compared to the conventional SSM method.