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.
Highlights
T HE desire to reduce our dependency on fossil fuels and resulting environmental impacts in individual transportation has led to significant increases in the number of electric vehicles (EVs) in recent years
In order to analyse the behaviour of the scheduling algorithms presented, the same combination of events are scheduled and simulated using A) only predictive scheduling and B) only reactive scheduling
All aggregator control algorithms were handled on a machine equipped with an Intel Core i5-2400 CPU (4x3.1 GHz) and 4 gigabyte DDR3 Random Access Memory (RAM)
Summary
T HE desire to reduce our dependency on fossil fuels and resulting environmental impacts in individual transportation has led to significant increases in the number of electric vehicles (EVs) in recent years. The V2G concept has the potential to provide system response times within a few seconds or even on a sub-second time-scale, but certain challenges of vehicle management, communication and decision making have to be addressed These challenges incrrludtthheee: scale of EV population on the network changing energy storage potential (connected capacity, r r power available, SOC of EV battery packs) predictability of EV availability and power grid demands the necessity to charge EVs over time (constraining r scheduling process) aggregator-to-EV communication delays (exacerbated by the need for encryption to address security and privacy r concerns [25]) complexity of underlying scheduling rules. V2G network for electric rail support could offer a number of advranltoawgeersi:ng grid connection upgrade requirements for new rail electrification projects (support from the V2G network could lower peaks in power demand from train acceleration r experienced by the grid) lowering grid connection upgrade requirements for new EV charging infrastructure (assuming an EV car park can r share the connection with the electrified rail infrastructure) enabling regenerative braking for electric trains where it has not been available before (leading to energy savings and potentially cost savings due to reduced wear on the mechanical brake systems [28])
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