Next Article in Journal
Seamless Grid Synchronization of a Proportional+Resonant Control-Based Voltage Controller Considering Non-Linear Loads under Islanded Mode
Previous Article in Journal
Development and Experimental Validation of a TRNSYS Dynamic Tool for Design and Energy Optimization of Ground Source Heat Pump Systems
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Energies 2017, 10(10), 1507; doi:10.3390/en10101507

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
*
Author to whom correspondence should be addressed.
Received: 30 August 2017 / Revised: 23 September 2017 / Accepted: 25 September 2017 / Published: 28 September 2017
View Full-Text   |   Download PDF [393 KB, uploaded 29 September 2017]   |  

Abstract

The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university’s PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%. View Full-Text
Keywords: plug-in electric vehicle; economic model predictive control (EMPC); vehicle-to-grid (V2G); grid-to-vehicle (G2V); optimisation; smart grid; vehicle-to-grid (V2G) plug-in electric vehicle; economic model predictive control (EMPC); vehicle-to-grid (V2G); grid-to-vehicle (G2V); optimisation; smart grid; vehicle-to-grid (V2G)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Sha’aban, Y.A.; Ikpehai, A.; Adebisi, B.; Rabie, K.M. Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control. Energies 2017, 10, 1507.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top