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

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 f...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 14; no. 1; pp. 311 - 320
Main Authors: van der Meer, Dennis, Mouli, Gautham Ram Chandra, Mouli, German Morales-Espana, Ramirez Elizondo, Laura, Bauer, Pavol
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autoregressive integrated moving average (ARIMA)
Autoregressive models
Autoregressive processes
Batteries
Charging
Cost engineering
Economic forecasting
Electric vehicles
Energy conservation
Energy consumption
Energy management
energy management system (EMS)
forecast
Integer programming
Linear programming
Load forecasting
Mixed integer linear programming
mixed-integer linear programming (MILP)
Optimization
Photovoltaic cells
Photovoltaic systems
solar carport
Solar cells
Tariffs
ISSN:1551-3203, 1941-0050
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2016.2634624