Load Profile Cycle Recognition for Industrial DC Microgrids with Energy Storage Systems

verfasst von: Alexander Männel, Kevin Müller, Elias Knöchelmann, Tobias Ortmaier
Abstract: Many consumers in production plants like industrial robots or tool machines perform repetitive movements, which lead to a cyclic load demand. However, these load profiles can usually only be roughly estimated at the planning stage. Hence, a subsequent online adaptation of the energy distribution is useful for cases, such as balancing between the charging and discharging amount of energy storage systems to improve those lifetime and usage. This paper presents a novel method of online adaptation for the load distribution of production processes within industrial direct current (DC) microgrids. The online load profile cycle recognition was used to adapt the energy distribution among the sources and loads in the DC microgrid. These sources can be inverters, rectifiers, energy storage systems or decentralized power supply units, such as photo voltaic systems. The approach consists of three major points, the load profile cycle recognition, the load profile analysis and the online adaptation of the energy distribution. This solution was tested in simulation and in experiment with a test rig, that contains an inverter and an energy storage system. The results show, that the load profile will be recognized latest from the third cycle and that the imbalance between charging and discharging amounts of the energy storage is less than 0.6% for each cycle after adaptation.
Organisationseinheit(en): Institut für Mechatronische Systeme
Externe Organisation(en): Bosch Rexroth AG
Typ: Aufsatz in Konferenzband
Seiten: 904-911
Anzahl der Seiten: 8
Publikationsdatum: 2020
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Elektrotechnik und Elektronik, Steuerungs- und Systemtechnik
Elektronische Version(en): https://doi.org/10.15488/10381 (Zugang: Offen)
https://doi.org/10.1109/ISIE45063.2020.9152432 (Zugang: Geschlossen)