Session | ||
TUE10: Energy storage systems 1
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Presentations | ||
Predicting Battery Cycle Life with Few-Shot Transfer Learning over Heterogeneous Datasets 1Delft University of Technology; 2Austrian Institute of Technology; 3Rimac Technology Redesign of Large-Scale Irrigation Systems for Flexible Energy Storage CITCEA-UPC, Spain Electrical Storage Design in Multi-Energy Systems: Impact of Component Model Choice 1Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems, ICE-1Energy Systems Engineering, Jülich 52425, Germany; 2RWTH Aachen University, Aachen 52056, Germany; 3The University of Melbourne, Department of Electrical and Electronic Engineering, Parkville 3010, Victoria, Australia; 4University of Manchester, Department of Electrical and Electronic Engineering, M13 9PL Manchester, UK; 5JARA-Energy, Jülich 52425, Germany Battery Storage Considerations for Mitigating the Impact of Increased Integration of Renewable Sources: Case Study for Spain 1IMDEA Energy, Spain; 2Rey Juan Carlos University, Spain Optimal Planning of Large-Scale Wind-Storage Power Plant Considering AC Power Flow Based on Stackelberg Game Theory 1The University of Hong Kong, Hong Kong S.A.R., China; 2HKU Shenzhen Institute of Research and Innovation, Shenzhen, China; 3Harbin Institute of Technology, Shenzhen, China; 4State Grid Shanghai Municipal Electric Power Company, Shanghai, China Energy Management of Large-Scale Battery Storage Systems: Field Evaluation of Battery Aging and System Efficiency 1Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, 52074 Aachen, Germany; 2Institute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University, 52074 Aachen, Germany; 3Jülich Aachen Research Alliance, JARA-Energy, 52056 Aachen, 52425 Jülich, Germany; 4Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research Helmholtz-Institute Münster: Ionics in Energy Storage (IEK-12), 52425 Jülich, Germany; 5Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, 52074 Aachen, Germany, Germany A Novel Deep Learning Method for Real-Time Estimation of Lithium-Ion Battery Capacity Exeter University, United Kingdom Optimizing Second-Life Battery Use in Renewable Energy Storage: A Deep Reinforcement Learning Approach Exeter University, United Kingdom Linear energy storage and flexibility model with ramp rate, ramping, deadline and capacity constraints 1KU Leuven & EnergyVille, Genk, Belgium; 2University College Dublin, Dublin, Ireland |