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Composite Control Scheme Based on Practical Droop and Tube Model Predictive Control for Electric Vehicles in Grid Frequency Regulation
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-2793-9048
2024 (English)In: ICIT 2024 - 2024 25th International Conference on Industrial Technology, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper, Published paper (Refereed)
Abstract [en]

Power imbalances between generation and consumption will cause frequency deviations in the grid. In modern power systems, intermittent renewable energy sources (RES) have resulted in more frequent frequency violations, as traditional power plants cannot compensate for power gaps timely. Electric vehicles (EVs) can participate in load frequency control (LFC) through aggregators and are capable of reacting faster to control commands than conventional frequency control reserves (FCR) in generators. Thus EVs hold great promise in assisting with LFC. This paper proposes a composite control scheme that fully utilizes EVs for LFC in both normal scenarios and contingencies. The designed droop control can greatly reduce instantaneous frequency deviations (IFD) in emergencies, while the tube model predictive control (Tube MPC) can ensure smooth frequency trajectories during normal operations. Based on realistic models, simulation results illustrate the effectiveness of the proposed method.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024.
Series
Proceedings of the IEEE International Conference on Industrial Technology, ISSN 2641-0184
Keywords [en]
Electric Vehicle, Load Frequency Control, Tube MPC
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-348276DOI: 10.1109/ICIT58233.2024.10541023Scopus ID: 2-s2.0-85195805620OAI: oai:DiVA.org:kth-348276DiVA, id: diva2:1874643
Conference
25th IEEE International Conference on Industrial Technology, ICIT 2024, Bristol, United Kingdom of Great Britain and Northern Ireland, Mar 25 2024 - Mar 27 2024
Note

QC 20240624

Part of ISBN 979-835034026-6

Available from: 2024-06-20 Created: 2024-06-20 Last updated: 2024-06-24Bibliographically approved

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Lu, YizhouXu, Qianwen

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other style
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  • de-DE
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Output format
  • html
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  • asciidoc
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