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Energies 2016, 9(8), 647; doi:10.3390/en9080647

Distributed Energy Storage Control for Dynamic Load Impact Mitigation

School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Rui Xiong
Received: 31 January 2016 / Revised: 18 July 2016 / Accepted: 4 August 2016 / Published: 17 August 2016
(This article belongs to the Special Issue Control of Energy Storage)
View Full-Text   |   Download PDF [6414 KB, uploaded 17 August 2016]   |  

Abstract

The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation. View Full-Text
Keywords: battery storage; distributed control; electric vehicles; additive increase multiplicative decrease (AIMD); voltage control; smart grid battery storage; distributed control; electric vehicles; additive increase multiplicative decrease (AIMD); voltage control; smart grid
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MDPI and ACS Style

Zangs, M.J.; Adams, P.B.E.; Yunusov, T.; Holderbaum, W.; Potter, B.A. Distributed Energy Storage Control for Dynamic Load Impact Mitigation. Energies 2016, 9, 647.

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