Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference

Alessandro Di Giorgio, Emanuele De Santis, Lucia Frettoni, Stefano Felli and Francesco Liberati
Additional contact information
Alessandro Di Giorgio: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy
Emanuele De Santis: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy
Lucia Frettoni: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy
Stefano Felli: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy
Francesco Liberati: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy

Energies, 2023, vol. 16, issue 3, 1-16

Abstract: This paper presents a control strategy aimed at efficiently operating a service area equipped with stations for plug-in electric vehicles’ fast charging, renewable energy sources, and an electric energy storage unit. The control requirements here considered are in line with the perspective of a service area operator, who aims at avoiding peaks in the power flow at the point of connection with the distribution grid, while providing the charging service in the minimum time. Key aspects of the work include the management of uncertainty in the charging power demand and generation, the design of congestion and state-dependent weights for the cost function, and the comparison of control performances in two different hardware configurations of the plant, namely BUS and UPS connection schemes. All of the above leads to the design of a stochastic model predictive controller aimed at tracking an uncertain power reference, under the effect of an uncertain disturbance affecting the output and the state of the plant in the BUS and UPS schemes respectively. Simulation results show the relevance of the proposed control strategy, according to an incremental validation plan focused on the tracking of selected references, the mitigation of congestion, the stability of storage operation over time, and the mitigation of the effect of uncertainty.

Keywords: plug-in electric vehicles; energy storage system; smart charging control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1348/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1348/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1348-:d:1048210

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().