Digital Energy Systems - Study line
Digital Energy Systems
In the study track Digital Energy Systems, the students achieve a good understanding of the energy system and learn how optimization, machine leanring, and data-driven methods can be exploited for the optimal operation and planning of energy systems. Further, the students will gain a broad understanding of the role of digital solutions in future modern energy systems with high penetration of renewable energy sources.
The graduates will be able to make optimal operational, trading, and planning decisions for plants or energy systems using advanced modeling, optimization, machine learning and data-driven methods. Graduates in the study track Digital Energy Systems are highly qualified candidates for jobs in the energy sector including system operators, utilities, developers, manufacturers, consultancy companies, and start-ups. Digital solutions driven by big data availibility in modern energy systems are playing an increasingly important role within sustainable energy and it is expected to contribute significantly to the energy supply both nationally and worldwide.
Polytechnic foundation courses (5 ECTS):
The following course is mandatory:
| 42500 | Innovation in Engineering (Polytechnical Foundation) | 5 | point | January |
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| 42504 | Innovation in Engineering (Polytechnical Foundation) | 5 | point | August |
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| 42501 | Innovation in Engineering (Polytechnical Foundation) | 5 | point | June |
Students with advanced innovation competences may take one of the following courses as an alternative to 42500/42501/42504:
| 42502 | Facilitating Innovation in Multidisciplinary Teams | 5 | point | January |
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| 42505 | Facilitating Innovation in Multidisciplinary Teams | 5 | point | August |
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| 42503 | Facilitating Innovation in Multidisciplinary Teams | 5 | point | June |
Programme specific courses (55 ECTS)
Innovation course II - mandatory (5 ECTS):
| 41636 | Design for Circular Economy | 5 | point | January |
Core competence courses - mandatory for the study line (10 ECTS)
| 46740 | Distributed energy technologies, modelling and control | 5 | point | Spring F1B (Thurs 13-17) |
| 46770 | Integrated energy grids | 5 | point | Spring F5A (Wed 8-12) |
Core competence courses - mandatory for the study track (10 ECTS)
| 46750 | Optimization in modern power systems | 5 | point | Autumn E3A (Tues 8-12) |
| 46765 | Machine learning for energy systems | 5 | point | Autumn E5B (Wed 13-17) |
Core competence courses - choose 10 ECTS among the following courses:
| 02180 | Introduction to Artificial Intelligence | 5 | point | Spring F3A (Tues 8-12) |
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| 02450 | Introduction to Machine Learning and Data Mining | 5 | point | Spring F4A (Tues 13-17), Autumn E4A (Tues 13-17) |
| 02418 | Statistical modelling: Theory and practice | 5 | point | Autumn E4A (Tues 13-17) |
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| 02807 | Computational Tools for Data Science | 5 | point | E7 (Tues 18-22) |
| 42015 | Energy Economics | 5 | point | Autumn E3B (Fri 13-17) |
| 42112 | Mathematical Programming Modelling | 5 | point | January |
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| 42101 | Introduction to Operations Research | 5 | point | Autumn E2A (Mon 13-17), Spring F2A (Mon 13-17) |
Core competence courses - choose 10 ECTS among the following courses:
| 34552 | Photovoltaic systems | 5 | point | Spring F2B (Thurs 8-12) |
| 41468 | Sustainable District Heating | 5 | point | Spring F1A (Mon 8-12) |
| 46300 | Wind Turbine Technology and Aerodynamics | 10 | point | Autumn E1 (Mon 8-12, Thurs 13-17) |
| 47301 | Hydrogen energy and fuel cells | 5 | point | Spring F1B (Thurs 13-17) |
| 47330 | Energy storage and conversion | 5 | point | Autumn E1A (Mon 8-12) |
Core competence courses - choose 10 ECTS among the following courses:
| 02417 | Time Series Analysis | 5 | point | Spring F4B (Fri 8-12) |
| 02435 | Decision-Making Under Uncertainty | 5 | point | Spring F4A (Tues 13-17) |
| 02465 | Introduction to reinforcement learning and control | 5 | point | Spring F4B (Fri 8-12) |
| 38105 | Digital Trends for Entrepreneurs | 5 | point | Autumn E3A (Tues 8-12) |
| 46705 | Power grid analysis | 5 | point | Spring F3A (Tues 8-12) |
| 46755 | Renewables in electricity markets | 5 | point | Spring F2A (Mon 13-17) |
Elective courses (30 ECTS)
Suggestions for elective courses (30 ECTS)
| 02456 | Deep learning | 5 | point | Autumn E2A (Mon 13-17) |
| 02582 | Computational Data Analysis | 5 | point | Spring F2B (Thurs 8-12) |
| 41417 | Digitalization of Thermal Energy Technologies – Modelling and Simulation Methods | 5 | point | Autumn E1B (Thurs 13-17) |
| 42014 | Environmental and Resource Economics | 5 | point | F7 (Tues 18-22) |
| 42114 | Integer Programming | 5 | point | Autumn E4A (Tues 13-17) |
| 42136 | Large Scale Optimization using Decomposition | 5 | point | Spring F2B (Thurs 8-12) |
| 42186 | Model-based machine learning | 5 | point | Spring F5B (Wed 13-17) |
| 42577 | Introduction to Business Analytics | 5 | point | Autumn E1A (Mon 8-12) |
| 46200 | Planning and Development of Wind Farms | 5 | point | January |
| 46205 | Feasibility studies of energy projects | 5 | point | Autumn E3A (Tues 8-12) |
| 46500 | Probabilistic Methods in Wind Energy | 5 | point | Autumn E2A (Mon 13-17) |
| 46700 | Introduction to Electric Power Systems | 10 | point | Autumn E4 (Tues 13-17, Fri 8-12) |
| 46745 | Integration of wind power in the power system | 5 | point | Autumn E3B (Fri 13-17) |
Master's thesis (30 ECTS):
MSc. thesis within the area of the specialization shall be conducted. The project can be completed in collaboration with a relevant company.
Study track responsible
Lesia Mitridati Assistant Professor lemitri@dtu.dk