Enhanced AIMD based decentralized residential charging of EVs

Mingming Liu, Seán McLoone

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
295 Downloads (Pure)

Abstract

Moving from combustion engine to electric vehicle (EV)-based transport is recognized as having a major role to play in reducing pollution, combating climate change and improving energy security. However, the introduction of EVs poses major challenges for power system operation. With increasing penetration of EVs, uncontrolled coincident charging may overload the grid and substantially increase peak power requirements. Developing smart grid technologies and appropriate charging strategies to support the role out of EVs is therefore a high priority. In this paper, we investigate the effectiveness of distributed additive increase and multiplicative decrease (AIMD) charging algorithms, as proposed by Stu¨dli et al. in 2012, at mitigating the impact of domestic charging of EVs on low-voltage distribution networks. In particular, a number of enhancements to the basic AIMD implementation are introduced to enable local power system infrastructure and voltage level constraints to be taken into account and to reduce peak power requirements. The enhanced AIMD EV charging strategies are evaluated using power system simulations for a typical low-voltage residential feeder network in Ireland. Results show that by using the proposed AIMD-based smart charging algorithms, 50% EV penetration can be accommodated, compared with only 10% with uncontrolled charging, without exceeding network infrastructure constraints.
Original languageEnglish
Pages (from-to)853-867
Number of pages15
JournalTransactions of the Institute of Measurement and Control
Volume37
Issue number7
Early online date18 Jul 2013
DOIs
Publication statusPublished - Aug 2015

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