Digital Energy Systems - Specialization
In the Digital Energy Systems specialization, students gain deep expertise in modern energy systems and learn to apply optimization, machine learning, and data-driven methods for optimal control, operation, and planning. The program also provides broad insight into the role of digital technologies in future energy systems with a high share of renewable energy sources.
Graduates are prepared to make informed operational, trading, and planning decisions using advanced modeling and analytics. They are highly sought after across the energy sector, including roles with system operators, utilities, technology developers, manufacturers, consultancies, and start-ups.
As energy systems become more digital and data-centric, professionals in this field are essential to delivering smart and sustainable energy solutions at both national and global levels.
Polytechnic foundation courses (10 ECTS):
The following courses are mandatory:
| 12100 | Quantitative methods to assess sustainability (Polytechnical Foundation) | 5 | point | F7 (Tues 18-22) |
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| 12106 | Quantitative methods to assess sustainability (Polytechnical Foundation) | 5 | point | Autumn E3B (Fri 13-17) |
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| 12105 | Quantitative methods to assess sustainability (Polytechnical Foundation) | 5 | point | E7 (Tues 18-22) |
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| 12101 | Quantitative methods to assess sustainability (Polytechnical Foundation) | 5 | point | Spring F3B (Fri 13-17) |
| 38400 | Innovation in Engineering (Polytechnical Foundation) | 5 | point | January |
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| 38404 | Innovation in Engineering (Polytechnical Foundation) | 5 | point | August |
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| 38402 | 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:
| 38401 | Facilitating Innovation in Multidisciplinary Teams | 5 | point | January |
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| 38405 | Facilitating Innovation in Multidisciplinary Teams | 5 | point | August |
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| 38403 | Facilitating Innovation in Multidisciplinary Teams | 5 | point | June |
The following course can be taken as an alternative to the mandatory course 12100/12106/12105/12101:
| 41636 | Design for Circular Economy | 5 | point | January |
Programme-specific courses (50 ECTS)
Structured into the following pools, each targeting a distinct area of competence:
Pools common to all specializations:
- Innovation II (5 ECTS): Develops innovation and problem-solving skills to address real-world sustainable energy challenges.
- Core competence - Mandatory (15 ECTS): Establishes a shared system-level understanding of sustainable energy systems, forming the common analytical foundation across all specialisations.
- Core competence - Technology-specific (5 ECTS): Provides technical knowledge in a specific energy technology area, adding applied domain depth to the shared system-level foundation across all specialisations.
Specialization-specific pools:
- Specialization-specific - Foundational digital skills (10 ECTS): Builds foundational digital competencies relevant to energy systems across three skill areas: machine learning & statistical learning, mathematical modelling & optimization, and scientific programming & computational tools.
- Specialization-specific - Advanced digital skills (5 ECTS): Deepens knowledge in one advanced digital skill area (machine learning & statistical learning, mathematical modelling & optimization, or scientific programming & computational tools), chosen to align with the student's thesis direction and professional goals.
- Specialization-specific - Core Methodologies (10 ECTS): Applies advanced analytical and computational methods to the planning and operational challenges of modern energy systems.
* When a course is listed in multiple pools, it may be used to satisfy the requirements of one pool only.
Innovation II (5 ECTS)
Select at least one of the following courses:
| 38102 | Technology Entrepreneurship | 5 | point | Autumn E1B (Thurs 13-17) |
| 42014 | Environmental and Resource Economics | 5 | point | Spring F4B (Fri 8-12) |
| 42015 | Energy Economics | 5 | point | Autumn E3B (Fri 13-17) |
| 42016 | Business Economics and Finance | 5 | point | Spring F3B (Fri 13-17) |
Core competence - Mandatory (15 ECTS)
The following three courses are compulsory components of the programme:
| 46205 | Feasibility studies of energy projects | 5 | point | Autumn E3A (Tues 8-12) |
| 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 - Technology-specific (5 ECTS)
Choose at least 5 ECTS from the following courses:
| 34552 | Photovoltaic systems | 5 | point | Spring F2B (Thurs 8-12) |
| 41418 | Green fuels and power-to-x | 5 | point | Spring F3A (Tues 8-12) |
| 41468 | Sustainable District Heating | 5 | point | Spring F1A (Mon 8-12) |
| 46200 | Planning and Development of Wind Farms | 5 | point | January |
| 46745 | Integration of wind power in the power system | 5 | point | Autumn E1B (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) |
| 47334 | Carbon capture, utilization, and storage | 5 | point | Spring F3A (Tues 8-12) |
Specialization-specific - Foundational digital skills (10 ECTS)
Choose at least 10 ECTS from the three thematic course groups defined below: 1) Machine learning & statistical learning, 2) Mathematical modelling & optimization, and 3) Scientific programming & computational tools. You must take one course from at least two groups.
| 02452 | Machine Learning | 5 | point | Autumn E4A (Tues 13-17) |
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| 02582 | Computational Data Analysis | 5 | point | Spring F2B (Thurs 8-12) |
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| 02417 | Time Series Analysis | 5 | point | Spring F4B (Fri 8-12) |
| 42112 | Mathematical Programming Modelling | 5 | point | January |
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| 42114 | Integer Programming | 5 | point | Autumn E4A (Tues 13-17) |
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| 02612 | Constrained Optimization | 5 | point | Spring F4A (Tues 13-17) |
| 46120 | Scientific Programming for Wind Energy | 5 | point | Spring F2B (Thurs 8-12) |
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| 02807 | Computational Tools for Data Science | 5 | point | E7 (Tues 18-22) |
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| 02476 | Machine Learning Operations | 5 | point | January |
Specialization-specific - Advanced digital skills (5 ECTS)
Choose at least 5 ECTS from the following courses. Courses from the Specialisation-specific competence - Foundational digital skills pool are also eligible, provided they have not already been counted towards that pool requirement.
| 02421 | Stochastic Adaptive Control | 5 | point | Spring F3A (Tues 8-12) |
| 02435 | Decision-Making Under Uncertainty | 5 | point | Spring F4A (Tues 13-17) |
| 02456 | Deep learning | 5 | point | Autumn E2A (Mon 13-17) |
| 02477 | Bayesian machine learning | 5 | point | Spring F2A (Mon 13-17) |
| 02517 | Responsible AI: Algorithmic fairness and explainability | 5 | point | Autumn E2B (Thurs 8-12) |
| 02611 | Optimization for Data Science | 5 | point | Spring F5B (Wed 13-17) |
| 02613 | Python and High-Performance Computing | 5 | point | Spring F5A (Wed 8-12) |
| 42136 | Advanced Operations Research Methods | 5 | point | Spring F2B (Thurs 8-12) |
Specialization-specific - Core methodologies (10 ECTS)
The following two courses are mandatory for the specialization:
| 46750 | Optimization in modern power systems | 5 | point | Autumn E5A (Wed 8-12) |
| 46765 | Machine learning for energy systems | 5 | point | Autumn E2A (Mon 13-17) |
Elective courses (30 ECTS):
Any course classified as MSc-level in DTU’s course catalogue may be taken as an elective. This also includes programme-specific courses beyond the minimum requirements listed above. Master’s students may count up to 10 ECTS from DTU bachelor-level courses or equivalent courses from other higher education institutions. Additionally, MSc-level courses from other Danish or international universities can be included as electives.
In addition to the programme-specific courses mentioned above, the following elective courses are suggested for this specialization:
| 02195 | Quantum Algorithms and Machine Learning | 5 | point | Spring F2A (Mon 13-17) |
| 02270 | Cybersecurity Fundamentals | 5 | point | Autumn E5B (Wed 13-17) |
| 02280 | Artificial Intelligence and Multi-Agent Systems | 10 | point | Spring F4A (Tues 13-17) |
| 02411 | Statistical Design and Analysis of Experiments | 5 | point | Autumn E2A (Mon 13-17), Spring F1A (Mon 8-12) |
| 02427 | Advanced Time Series Analysis | 10 | point | Autumn E5 (Wed 8-17) |
| 02428 | Dynamic Optimization | 5 | point | Autumn E2A (Mon 13-17) |
| 02458 | Cognitive Modelling | 5 | point | Autumn E2B (Thurs 8-12) |
| 02460 | Advanced Machine Learning | 5 | point | Spring F1B (Thurs 13-17) |
| 02616 | Large-scale Modelling | 5 | point | Autumn E3A (Tues 8-12) |
| 02619 | Model Predictive Control | 5 | point | Autumn E2B (Thurs 8-12) |
| 34366 | Intelligent systems | 5 | point | E7 (Tues 18-22) |
| 38113 | Applied AI for Entrepreneurs | 5 | point | Autumn E2B (Thurs 8-12) |
| 41417 | Digitalization of Thermal Energy Technologies – Modelling and Simulation Methods | 5 | point | Autumn E1B (Thurs 13-17) |
| 42893 | Psychology for engineers | 5 | point | Autumn E5B (Wed 13-17) |
| 46500 | Probabilistic Methods in Wind Energy | 5 | point | Autumn E2A (Mon 13-17) |
| 46705 | Power grid analysis | 5 | point | Spring F3A (Tues 8-12) |
| 46755 | Renewables in electricity markets | 5 | point | Spring F2A (Mon 13-17) |
Master's thesis (30 ECTS):
The MSc thesis must be conducted within the area of the specialization. The project may be completed in collaboration with a relevant company.
Specializations are recommended pathways for selecting courses within the curriculum. Applicants are admitted to the programme and not to a specific specialization and may choose any courses within the curriculum according to the guidelines provided. However, if the requirements for a specialization are completed, the specialization title may be added to the diploma.
Study track responsible
Lesia Mitridati Associate Professor Mobile: +45 31964979 lemitri@dtu.dk