Jochen Cremer supports the transition to a fair and sustainable future with his research and engagement. He is a Research Associate on applying machine learning to the optimisation and control of energy systems. He completed his PhD in 2020 from Imperial. In 2019, he was co-chair of the International Student Energy Summit 2019 that united 650 future energy leaders in July 2019 for 3 days to discuss the current and future energy system bringing together 60 top-tier speakers and over 30 partners. He chaired and started several other initiatives at Imperial College, including the Climate Entrepreneurs Club and the IEEE student branch. For two years he was the postgraduate representative of 350 postgraduate students at the Electrical Engineering department of Imperial.
In 2018, Jochen was awarded the John & Frances Jones Prize 2017-2018 of Imperial College. He was judged as the postgraduate student who has ‘made the best all-round contribution to College life, taking into account academic achievement, social and extra-curricular activities, for the academic year in question’. In 2019, Jochen was awarded the John Lever Memorial Award as he has contributed significantly to 'teaching, outreach, public engagement, enterprise and student experience' at Imperial College.
Jochen's research focuses on applying machine learning to the problem of the massive integration of renewables in very large energy systems. His track-record includes 16 papers (6 top-tier journals accepted, 4 under review, and 5 conferences). Jochen develops radical new computational methods for system operation and control that combine statistical machine learning and mathematical optimisation.
In 2019 he was an invited speaker to the IEEE Big Data & Analytics Webinar, the IEEE Sustainable Power and Energy Conference, Beijing, China, the AAPG Energy Transition Forum, Edinburgh, UK and the IEEE PES General Meeting, Atlanta, USA.
He is also an active member to CIGRE working group 2.25 on operational resilience and the IEEE taskforce on Application of Big Data Analytic on Transmission System Dynamic Security Assessment.
- PhD thesis at Imperial College London with a focus on applying machine learning to the operation of the power system
- Master thesis with a focus on control systems at the Department of Chemical Engineering, Massachusetts Institute of Technology, Boston
- Master study in Process Engineering at RWTH Aachen University, 3 months of full-time placement at BASF in Shanghai
- Bachelor study in Electrical Engineering at RWTH Aachen University, 3 months full-time placement at EON Energy Research Center, Aachen
- Bachelor thesis and 1-year studies abroad with a focus on optimisation at the Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh
- Bachelor study in Mechanical Engineering at RWTH Aachen University, 3 months full-time placement at Thyssen Krupp, Siegen
Cremer J, Konstantelos I, Strbac G, 2019, From optimization-based machine learning to interpretable security rules for operation, Ieee Transactions on Power Systems, Vol:34, ISSN:0885-8950, Pages:3826-3836
et al., 2019, Data-driven power system operation: Exploring the balance between cost and risk, Ieee Transactions on Power Systems, Vol:34, ISSN:0885-8950, Pages:791-801
Sun M, Cremer J, Strbac G, 2018, A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration, Applied Energy, Vol:228, ISSN:0306-2619, Pages:546-555
et al., 2019, Day-Ahead Scheduling of Electric Heat Pumps for Peak Shaving in Distribution Grids, Pages:27-51, ISSN:1865-0929
et al., 2018, Sample-derived disjunctive rules for secure power system operation, International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE