Multi-Layer Event-Based Vehicle-to-Grid (V2G) Scheduling With Short Term Predictive Capability Within a Modular Aggregator Control Structure

Abstract

In this work a novel method of event-based V2G scheduling is devised that is suitable for dynamic real time aggregator control in large scale V2G applications within centrally controlled EV car parks. The method is applicable in deterministic systems where a V2G network provides or receives electricity in reoccurring and predictable patterns (events). The scheduling strategy shown is based on a robust modular high-level aggregator control structure and a proposed communications and data management system. The scheduling consists of three algorithm layers, differentiating between predictive scheduling for in-event periods, smart charging for out-of-event periods and reactive scheduling for ongoing adjustments in real-time to account for uncertainty. The scheduling process is described in terms of its underlying rules for prioritising EVs to be either charged or discharged. It's behaviour is then analysed using a simulated car park of up to one thousand connected EVs for an example application in which a V2G network is used to support nearby electrified rail infrastructure, providing power for train acceleration and accepting power from regenerative braking. The departure or arrival of a train of known type and speed pattern can be regarded as a reoccurring event and its effect on the V2G network is therefore predictable due to train schedules and tracking.

Vehicle-to-Grid (V2G) as line-side energy storage for support of DC-powered electric railway systems

“…Neither is any potential overlap of train arrival and train departure events considered (which should not occur if the public train schedule is strictly adhered to). A V2G network supporting these variable power demands needs to be resilient and dynamically adjust to any such deviations (achieved here through V2G aggregator control combining predictive and reactive scheduling techniques, see section 4 and (Krueger and Cruden, 2020a)). The resulting 24-h power demand model used in the following discussion features 117 train departures (see Fig.…”

“…This discharging rate exceeds any of the assumed charging/discharging limits of the bi-directional EV chargers (also up to 50 kW, see section 4). The V2G aggregator control system used in this work operates in real-time and incorporates all aggregator-to-EV communications (as outlined in (Krueger and Cruden, 2020a)). While V2G communications are not the subject of this particular work, the simulated EV population complies with the existing communication scheme.…”

Application of Traction Supply System for Charging Electric Cars

“…This, in turn, leads to the increased demand for power and energy drawn from the power distribution system, which often requires substantial development of this system [1]. The use of modern smart grid technologies, such as Vehicle to Grid (V2G) [2,3], can be extremely useful for this purpose. Thanks to V2G technologies, electric vehicles can act as power-balancing prosumers, supporting the power system [4,5].…”

Renewable energy allocation based on maximum flow modelling within a microgrid

“…They are inherently embedding battery devices and can be charged even with the intermittency of the energy source [5]. Third, the energy Int J Elec & Comp Eng ISSN: 2088-8708  storage capability supported by battery devices makes it possible to shift peak load and allows diverse applications such as bidirectional energy trade [6]. However, EVs impose a complex and unprecedented load on the grid, and their simultaneous charging will sharply increase the energy demand.…”