LAPSE:2023.3609v1
Published Article

LAPSE:2023.3609v1
Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference
February 22, 2023
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.
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.
Record ID
Keywords
energy storage system, plug-in electric vehicles, smart charging control
Subject
Suggested Citation
Di Giorgio A, De Santis E, Frettoni L, Felli S, Liberati F. Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference. (2023). LAPSE:2023.3609v1
Author Affiliations
Di Giorgio A: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy; Consortium for the Research in Automation and Telecommunications (CRAT), Via Giovanni Nicote [ORCID]
De Santis E: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy; Consortium for the Research in Automation and Telecommunications (CRAT), Via Giovanni Nicote [ORCID]
Frettoni L: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy
Felli S: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy [ORCID]
Liberati F: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy; Consortium for the Research in Automation and Telecommunications (CRAT), Via Giovanni Nicote [ORCID]
De Santis E: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy; Consortium for the Research in Automation and Telecommunications (CRAT), Via Giovanni Nicote [ORCID]
Frettoni L: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy
Felli S: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy [ORCID]
Liberati F: Department of Computer, Control and Management Engineering “Antonio Ruberti” (DIAG), University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy; Consortium for the Research in Automation and Telecommunications (CRAT), Via Giovanni Nicote [ORCID]
Journal Name
Energies
Volume
16
Issue
3
First Page
1348
Year
2023
Publication Date
2023-01-27
ISSN
1996-1073
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Original Submission
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PII: en16031348, Publication Type: Journal Article
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LAPSE:2023.3609v1
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https://doi.org/10.3390/en16031348
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Feb 22, 2023
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