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Multi-layer event-based Vehicle-to-Grid (V2G) scheduling with short term predictive capability within a modular aggregator control structure

Multi-layer event-based Vehicle-to-Grid (V2G) scheduling with short term predictive capability within a modular aggregator control structure
Multi-layer event-based Vehicle-to-Grid (V2G) scheduling with short term predictive capability within a modular aggregator control structure
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.
0018-9545
Krueger, Hannes
0e1a9bc5-c646-411a-b2e9-b7dc6b2acdea
Cruden, Andrew
ed709997-4402-49a7-9ad5-f4f3c62d29ab
Krueger, Hannes
0e1a9bc5-c646-411a-b2e9-b7dc6b2acdea
Cruden, Andrew
ed709997-4402-49a7-9ad5-f4f3c62d29ab

Krueger, Hannes and Cruden, Andrew (2020) Multi-layer event-based Vehicle-to-Grid (V2G) scheduling with short term predictive capability within a modular aggregator control structure. IEEE Transactions on Vehicular Technology. (In Press)

Record type: Article

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.

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Multi-layer event-based Vehicle-to-Grid (V2G) scheduling with short term predictive capability within a modular aggregator control structure
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Accepted/In Press date: 23 January 2020

Identifiers

Local EPrints ID: 437545
URI: http://eprints.soton.ac.uk/id/eprint/437545
ISSN: 0018-9545
PURE UUID: 4996ec86-6cbd-46ff-a842-27286707bd50
ORCID for Andrew Cruden: ORCID iD orcid.org/0000-0003-3236-2535

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Date deposited: 04 Feb 2020 18:02
Last modified: 05 Feb 2020 01:32

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