Biography
Dr. Volkan Kumtepeli is a Postdoctoral Researcher at the Battery Intelligence Lab within the Department of Engineering Science at the University of Oxford, having joined in February 2021. He obtained his BSc degree in Electrical Engineering from Yıldız Technical University in Istanbul, Turkey, and later completed his PhD at the Energy Research Institute, Nanyang Technological University, Singapore. His current research focuses on advanced energy system modelling, particularly exploring the intersection of optimisation and learning-based methods. He is a strong advocate for open-source software and dedicated to fostering a collaborative and inclusive research environment.
Most Recent Publications
Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization
Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization
Understanding battery aging in grid energy storage systems
Understanding battery aging in grid energy storage systems
Investigation of the distribution of relaxation times of a porous electrode using a physics-based impedance model
Investigation of the distribution of relaxation times of a porous electrode using a physics-based impedance model
Thermal performance prediction of the battery surface via dynamic mode decomposition
Thermal performance prediction of the battery surface via dynamic mode decomposition
Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models
Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models
Research Interests
- Physics-based and data-driven energy storage modelling,
- Control and optimisation
- Low carbon power
Current Projects
- Data-driven exploration of the carbon emissions impact of grid energy storage deployment and dispatch (DIGEST): A project aiming to demonstrate the carbon emissions savings possible by optimally locating and operating storage to avoid curtailment of renewables and additional fossil fuel generation.
- Energy Superhub Oxford (ESO): A demonstrator project aiming to decarbonise electricity, transport and heat mainly via a grid-scale hybrid energy storage system. (Completed in March 2023)
Research Groups
Related Academics
Most Recent Publications
Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization
Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization
Understanding battery aging in grid energy storage systems
Understanding battery aging in grid energy storage systems
Investigation of the distribution of relaxation times of a porous electrode using a physics-based impedance model
Investigation of the distribution of relaxation times of a porous electrode using a physics-based impedance model
Thermal performance prediction of the battery surface via dynamic mode decomposition
Thermal performance prediction of the battery surface via dynamic mode decomposition
Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models
Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models
Most Recent Publications
Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization
Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization
Understanding battery aging in grid energy storage systems
Understanding battery aging in grid energy storage systems
Investigation of the distribution of relaxation times of a porous electrode using a physics-based impedance model
Investigation of the distribution of relaxation times of a porous electrode using a physics-based impedance model
Thermal performance prediction of the battery surface via dynamic mode decomposition
Thermal performance prediction of the battery surface via dynamic mode decomposition
Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models
Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models
