Distributed Hierarchical Control with Cost Optimization and Priority-Based Dispatch for Workplace EV Charging: A Field Study

Anna Malkova (), Simone Striani, Jan Martin Zepter and Mattia Marinelli ()
Additional contact information
Anna Malkova: DTU Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Simone Striani: DTU Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Jan Martin Zepter: DTU Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark
Mattia Marinelli: DTU Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark

Energies, 2025, vol. 18, issue 21, 1-21

Abstract: Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart EV charging. The proposed architecture combines upper-level receding horizon optimization with lower-level priority-based dispatch, enabling cost-efficient energy allocation and fair distribution among EVs. The system was deployed at the Risø campus of the Technical University of Denmark (DTU) and tested over two days under realistic operational conditions, including heterogeneous EV behavior and limited grid capacity. The control system demonstrated autonomous operation, responsiveness to price signals, and effective coordination between control layers. High energy delivery rates were achieved, nearly 100% on the first test day and close to 90% on the second, despite operating under a constrained energy budget. The study also documents practical challenges encountered during deployment, such as charger communication faults and EV-side issues, and proposes adaptation strategies. These results confirm the feasibility of distributed smart charging in real-world conditions and provide actionable insights for future implementations.

Keywords: electric vehicles; experimental validation; receding horizon optimization; smart charging; distributed hierarchical control; two-level control; charging flexibility (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/21/5581/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/21/5581/ (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:18:y:2025:i:21:p:5581-:d:1778268

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

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