Investigation of AIMD based charging strategies for EVs connected to a low-voltage distribution network

Mingming Liu; Seán McLoone

doi:10.1007/978-3-642-37105-9_48

Investigation of AIMD based charging strategies for EVs connected to a low-voltage distribution network

Mingming Liu
, Seán McLoone

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

In this paper we consider charging strategies that mitigate the impact of domestic charging of EVs on low-voltage distribution networks and which seek to reduce peak power by responding to time-ofday pricing. The strategies are based on the distributed Additive Increase and Multiplicative Decrease (AIMD) charging algorithms proposed in [5]. The strategies are evaluated using simulations conducted on a custom OpenDSS-Matlab platform for a typical low voltage residential feeder network. Results show that by using AIMD based smart charging 50% EV penetration can be accommodated on our test network, compared to only 10% with uncontrolled charging, without needing to reinforce existing network infrastructure.

Original language	English
Pages (from-to)	433-441
Number of pages	9
Journal	Communications in Computer and Information Science
Volume	355
DOIs	https://doi.org/10.1007/978-3-642-37105-9_48
Publication status	Published - 01 Sept 2013

Keywords

AIMD
Distributed algorithm
EV charging
Smart Grid

ASJC Scopus subject areas

General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-642-37105-9_48

Cite this

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abstract = "In this paper we consider charging strategies that mitigate the impact of domestic charging of EVs on low-voltage distribution networks and which seek to reduce peak power by responding to time-ofday pricing. The strategies are based on the distributed Additive Increase and Multiplicative Decrease (AIMD) charging algorithms proposed in [5]. The strategies are evaluated using simulations conducted on a custom OpenDSS-Matlab platform for a typical low voltage residential feeder network. Results show that by using AIMD based smart charging 50\% EV penetration can be accommodated on our test network, compared to only 10\% with uncontrolled charging, without needing to reinforce existing network infrastructure.",

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AU - McLoone, Seán

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N2 - In this paper we consider charging strategies that mitigate the impact of domestic charging of EVs on low-voltage distribution networks and which seek to reduce peak power by responding to time-ofday pricing. The strategies are based on the distributed Additive Increase and Multiplicative Decrease (AIMD) charging algorithms proposed in [5]. The strategies are evaluated using simulations conducted on a custom OpenDSS-Matlab platform for a typical low voltage residential feeder network. Results show that by using AIMD based smart charging 50% EV penetration can be accommodated on our test network, compared to only 10% with uncontrolled charging, without needing to reinforce existing network infrastructure.

AB - In this paper we consider charging strategies that mitigate the impact of domestic charging of EVs on low-voltage distribution networks and which seek to reduce peak power by responding to time-ofday pricing. The strategies are based on the distributed Additive Increase and Multiplicative Decrease (AIMD) charging algorithms proposed in [5]. The strategies are evaluated using simulations conducted on a custom OpenDSS-Matlab platform for a typical low voltage residential feeder network. Results show that by using AIMD based smart charging 50% EV penetration can be accommodated on our test network, compared to only 10% with uncontrolled charging, without needing to reinforce existing network infrastructure.

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KW - Distributed algorithm

KW - EV charging

KW - Smart Grid

UR - https://www.scopus.com/pages/publications/84883762488

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ER -

Investigation of AIMD based charging strategies for EVs connected to a low-voltage distribution network

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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