Hybrid Lithium-Ion Battery Storage Solution with Optimizing Energy Management and Online Condition Monitoring for Multi-use Applications
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Beitragende
Abstract
The paper presents current research results of the HYBAT project, in
which a hybrid lithium-ion battery storage solution is being developed for three
types of application: self-consumption optimization in industry and commerce,
capacity-firming in a renewable energy park and buffer storage for electric vehicle
charging stations. First, an overview of the principle structure and the functionalities of the HYBAT system is given. It features a hybrid storage approach consisting of a high energy and a high power lithium-ion battery and a multi-objective
optimizing energy management. The paper describes the developed energy management concepts based model predictive control and mixed integer linear programming, dynamic programming and reinforcement learning. For the application
field of self-consumption optimization in industry and commerce, a model predictive, dynamic programming based energy management is presented in detail.
Selected results of simulation-based investigations evaluate the developed energy
management concept based on technical and economic performance criteria. The
advantages of using the developed hybrid battery storage solution for multi-use
applications with optimization-based energy management concepts are elaborated.
In particular, an improved technical utilization of the storage system, increased
efficiency as well as reduced operating costs will be addressed.
which a hybrid lithium-ion battery storage solution is being developed for three
types of application: self-consumption optimization in industry and commerce,
capacity-firming in a renewable energy park and buffer storage for electric vehicle
charging stations. First, an overview of the principle structure and the functionalities of the HYBAT system is given. It features a hybrid storage approach consisting of a high energy and a high power lithium-ion battery and a multi-objective
optimizing energy management. The paper describes the developed energy management concepts based model predictive control and mixed integer linear programming, dynamic programming and reinforcement learning. For the application
field of self-consumption optimization in industry and commerce, a model predictive, dynamic programming based energy management is presented in detail.
Selected results of simulation-based investigations evaluate the developed energy
management concept based on technical and economic performance criteria. The
advantages of using the developed hybrid battery storage solution for multi-use
applications with optimization-based energy management concepts are elaborated.
In particular, an improved technical utilization of the storage system, increased
efficiency as well as reduced operating costs will be addressed.
Details
Originalsprache | Englisch |
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Titel | Proceedings of the International Renewable Energy Storage Conference (IRES 2022) |
Seiten | 76-92 |
Publikationsstatus | Veröffentlicht - 26 Mai 2023 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | Atlantis Highlights in Engineering |
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ISSN | 2589-4943 |
Externe IDs
ORCID | /0009-0001-1023-5132/work/173049956 |
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unpaywall | 10.2991/978-94-6463-156-2_7 |