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

Dennis van der Meer; Gautham Ram Chandra Mouli; Germán Morales-España Mouli; Laura Ramirez Elizondo; Pavol Bauer

doi:10.1109/TII.2016.2634624

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

Dennis van der Meer, Gautham Ram Chandra Mouli, Germán Morales-España Mouli, Laura Ramirez Elizondo, Pavol Bauer

Research output: Contribution to journal › Article › Scientific › peer-review

173 Citations (Scopus)

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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.

Original language	English
Article number	7763845
Pages (from-to)	311-320
Number of pages	10
Journal	IEEE Transactions on Industrial Informatics
Volume	14
Issue number	1
DOIs	https://doi.org/10.1109/TII.2016.2634624
Publication status	Published - 2018

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.

Keywords

Autoregressive integrated moving average (ARIMA)
electric vehicles
energy management system (EMS)
forecast
mixed-integer linear programming (MILP)
solar carport

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TII.2016.2634624

Energy_Management_System_With_PV_Power_Forecast_to_Optimally_Charge_EVs_at_the_WorkplaceFinal published version, 992 KB

Cite this

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title = "Energy Management System with PV Power Forecast to Optimally Charge EVs at the Workplace",

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

Abstract

Bibliographical note

Keywords

UN SDGs

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Erratum to: Energy management system with pv power forecast to optimally charge evs at the workplace (IEEE Transactions on Industrial Informatics (2018) 14:1 (311-320) DOI: 10.1109/TII.2016.2634624)

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