Response Accuracy and Tracking Errors with Decentralized Control of Commercial V2G Chargers

Abstract

There is a growing interest in using the flexibility of electric vehicles (EVs) to provide power system services, such as fast frequency regulation. Decentralized control is advocated due to its reliability and much lower communication requirements. A commonly used linear droop characteristic results in low average efficiencies, whereas controllers with 3 modes (idle, fully charging, fully discharging) result in large reserve errors when the aggregation size is small. To address these issues, we propose a stochastic, decentralized controller with tunable response granularity which minimizes switching actions. The EV fleet operator can optimize the chargers' performance according to the fleet size, the service error requirements, the average switching rate and the average efficiency. We use real efficiency characteristics from EVs and chargers providing fast frequency regulation and we show that the proposed controller can significantly reduce reserve errors and increase efficiency for a given fleet size, while at the same time minimizing the switching actions.

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

“…3). However, an aggregation of EVs can achieve higher average efficiencies regardless of the power setpoints, by applying appropriate control methods [28]. As shown previously, even for a constant considered efficiency, the intra-hourly losses depend on the power setpoint, and in these simulations this effect is taken into account.…”

Test and Modelling of Commercial V2G CHAdeMO Chargers to Assess the Suitability for Grid Services

“…It is found that the finest response has a granularity of 400 W, which represents the 4% of the rated power, thus not fulfilling the requirement. However, as this is the linearity for only one single unit, when managing an EV fleet the fleet operator should then apply smart logics, e.g., based on stochastic logics aimed at reaching-as proposed in [27]-the required target on an aggregated level. As for the activation time, the latencies due to remote control communication amount to about 3 s, while the mere hardware is characterized by an activation time of 4 s. Ref.…”

“…This issue may be dealt with proper calibration of the internal power electronics that should be tuned to avoid such inaccuracies. Furthermore, as the requirements refer to the overall battery plant, smart fleet management solutions could be implemented, to reduce the reserve provision error via appropriate individual control of the single EVs, e.g., as proposed in [27].…”

Large-scale provision of frequency control via V2G: The Bornholm power system case

“…As the primary function of an EV is transportation, their components are not designed to offer power system services, and thus many technological barriers need to be overcome when they are aggregated and controlled [15]. Critical response times of the aggregated EV fleet, as well as the need for each EV to comply with the ISO 15118 technical standard requirement of charging/discharging rate granularity, play an important role when dynamically assessing the response characteristics [16]. In fact, relatively large discrete step responses may trigger frequency stability problems, as presented in the literature within the domain of demand response [17]- [21], and also experienced in an experimental microgrid with smart-charging EVs [22], [23].…”