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

Journal article (2018)

Authors

Dennis van der Meer Uppsala University
G.R. Chandra Mouli DC systems, Energy conversion & Storage - EEMCS
G.A. Morales Espana Algorithmics - EEMCS
L.M. Ramirez Elizondo DC systems, Energy conversion & Storage - EEMCS
P. Bauer DC systems, Energy conversion & Storage - EEMCS
Research Group
DC systems, Energy conversion & Storage (EEMCS) (TU Delft)
Copyright: © 2018 Dennis van der Meer, G.R. Chandra Mouli, G. Morales-Espana, L.M. Ramirez Elizondo, P. Bauer
DOI: https://doi.org/10.1109/TII.2016.2634624
Electric vehicles Solar carport Forecast Autoregressive integrated moving average (ARIMA) Energy management system (EMS) Mixed-integer linear programming (MILP)
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

DC systems, Energy conversion & Storage

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.