Energy Management System with PV Power Forecast to Optimally Charge EVs at the Workplace

Journal Article (2018)
Author(s)

Dennis van der Meer (Uppsala University)

Gautham Ram Chandra Mouli (TU Delft - DC systems, Energy conversion & Storage)

Germán Morales-España (TU Delft - Algorithmics)

Laura Ramirez Elizondo (TU Delft - DC systems, Energy conversion & Storage)

Pavol Bauer (TU Delft - DC systems, Energy conversion & Storage)

Research Group
DC systems, Energy conversion & Storage
Copyright
© 2018 Dennis van der Meer, G.R. Chandra Mouli, G. Morales-Espana, L.M. Ramirez Elizondo, P. Bauer
DOI related publication
https://doi.org/10.1109/TII.2016.2634624
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Dennis van der Meer, G.R. Chandra Mouli, G. Morales-Espana, L.M. Ramirez Elizondo, P. Bauer
Related content
Research Group
DC systems, Energy conversion & Storage
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Issue number
1
Volume number
14
Pages (from-to)
311-320
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Abstract

This paper presents the design of an energy management system (EMS) capable of forecasting photovoltaic (PV) power production and optimizing power flows between PV system, grid, and battery electric vehicles (BEVs) at the workplace. The aim is to minimize charging cost while reducing energy demand from the grid by increasing PV self-consumption and consequently increasing sustainability of the BEV fleet. The developed EMS consists of two components: An autoregressive integrated moving average model to predict PV power production and a mixed-integer linear programming framework that optimally allocates power to minimize charging cost. The results show that the developed EMS is able to reduce charging cost significantly, while increasing PV self-consumption and reducing energy consumption from the grid. Furthermore, during a case study analogous to one repeatedly considered in the literature, i.e., dynamic purchase tariff and dynamic feed-in tariff, the EMS reduces charging cost by 118.44 % and 427.45% in case of one and two charging points, respectively, when compared to an uncontrolled charging policy.

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