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Collective effects and performance of algorithmic electric vehicle charging strategies.

Gardlo, Miroslav and Buzna, Ľuboš and Carvalho, Rui and Gibbens, Richard and Kelly, Frank (2018) 'Collective effects and performance of algorithmic electric vehicle charging strategies.', in 2018 IEEE Workshop on Complexity in Engineering (COMPENG), 10-12 October 2018, Florence. Piscataway, NJ: IEEE, pp. 1-7.


We combine the power flow model with the proportionally fair optimization criterion to study the control of congestion within a distribution electric grid network. The form of the mathematical optimization problem is a convex second order cone that can be solved by modern non-linear interior point methods and constitutes the core of a dynamic simulation of electric vehicles (EV) joining and leaving the charging network. The preferences of EV drivers, represented by simple algorithmic strategies, are conveyed to the optimizing component by realtime adjustments to user-specific weighting parameters that are then directly incorporated into the objective function. The algorithmic strategies utilize a small number of parameters that characterize the user's budgets, expectations on the availability of vehicles and the charging process. We investigate the collective behaviour emerging from individual strategies and evaluate their performance by means of computer simulation.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:18 September 2018
Date deposited:05 October 2018
Date of first online publication:15 November 2018
Date first made open access:No date available

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