Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society |
| ISBN (elektronisch) | 9781665435543 |
| Publikationsstatus | Veröffentlicht - 2021 |
Publikationsreihe
| Name | IECON Proceedings (Industrial Electronics Conference) |
|---|---|
| Band | 2021-October |
| ISSN (Print) | 1553-572X |
| ISSN (elektronisch) | 2577-1647 |
Abstract
This paper presents an extension of the dual-layer model predictive control (MPC) for industrial DC microgrids. This structure allows an easier integration of stochastic methods on all levels. Moreover, in the new three-layer MPC, energy storages are proposed for each level. As a proof of concept, a control is presented for the new layer, the primary control level in microgrids. The MPC as a central control strategy, has the disadvantage that a new model has to be created for new bus participants. By a simply expandable state space model and a parameterization with data sheet values, this disadvantage can be refuted. Further, for the application on a programmable logic controller (PLC), a simpler and faster MPC with an equal control performance, is presented.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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- BibTex
- RIS
IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. 2021. (IECON Proceedings (Industrial Electronics Conference); Band 2021-October).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Three-Layer Hierarchical Model Predictive Control Concept for Industrial DC Microgrids
AU - Knöchelmann, Elias
AU - Männel, Alexander
AU - Schappler, Moritz
PY - 2021
Y1 - 2021
N2 - This paper presents an extension of the dual-layer model predictive control (MPC) for industrial DC microgrids. This structure allows an easier integration of stochastic methods on all levels. Moreover, in the new three-layer MPC, energy storages are proposed for each level. As a proof of concept, a control is presented for the new layer, the primary control level in microgrids. The MPC as a central control strategy, has the disadvantage that a new model has to be created for new bus participants. By a simply expandable state space model and a parameterization with data sheet values, this disadvantage can be refuted. Further, for the application on a programmable logic controller (PLC), a simpler and faster MPC with an equal control performance, is presented.
AB - This paper presents an extension of the dual-layer model predictive control (MPC) for industrial DC microgrids. This structure allows an easier integration of stochastic methods on all levels. Moreover, in the new three-layer MPC, energy storages are proposed for each level. As a proof of concept, a control is presented for the new layer, the primary control level in microgrids. The MPC as a central control strategy, has the disadvantage that a new model has to be created for new bus participants. By a simply expandable state space model and a parameterization with data sheet values, this disadvantage can be refuted. Further, for the application on a programmable logic controller (PLC), a simpler and faster MPC with an equal control performance, is presented.
KW - DC Microgrid
KW - MPC
KW - Three-Layer MPC Control
UR - http://www.scopus.com/inward/record.url?scp=85119489620&partnerID=8YFLogxK
U2 - 10.1109/IECON48115.2021.9589196
DO - 10.1109/IECON48115.2021.9589196
M3 - Conference contribution
SN - 978-1-6654-0256-9
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
ER -