Distributed Energy Storage Control for Dynamic Load Impact Mitigation

Maximilian J. Zangs, Peter B. E. Adams, Timur Yunusov, William Holderbaum and Ben A. Potter
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Maximilian J. Zangs: School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK
Peter B. E. Adams: School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK
Timur Yunusov: School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK
William Holderbaum: School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK
Ben A. Potter: School of Systems Engineering, University of Reading, Whiteknights Campus, Reading RG6 6AY, UK

Energies, 2016, vol. 9, issue 8, 1-20

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.

Keywords: battery storage; distributed control; electric vehicles; additive increase multiplicative decrease (AIMD); voltage control; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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