inf513 - Energy Informatics Lab (Complete module description)
Module label | Energy Informatics Lab |
Modulkürzel | inf513 |
Credit points | 6.0 KP |
Workload | 180 h |
Institute directory | Department of Computing Science |
Verwendbarkeit des Moduls |
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Zuständige Personen |
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Prerequisites |
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Skills to be acquired in this module | Successfully completing this lecture will enable the students to mathematically model simple controllable electrical generators and consumers and to simulate them together with appropriate control algorithms within smart grid scenarios. To achieve this goal, students will start with deriving computational models from physical models and evaluate them. In order to manage the integration of control algorithms, students are taught the principles of cosimulation using the "mosaik" smart grid co-simulation framework as an example. Students will be able to understand and apply distributed, agent-based control schemes to decentralized energy generators and/ or consumers. As a result, students are able to analyze the requirements for successful application to real power balancing regarding capacity utilization, robustness, and flexibility. In addition, students learn the foundations of planning and conducting simulation based experiments as well as the interpretation of the results. Special attention will be paid on establishing a balance between the results' precision and robustness and the necessary effort (design of experiments) in order to gain as much insight into interdependencies with as few experiments as possible. Professional competence The students:
The students:
The students:
The students:
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Module contents | In this practical course students:
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Literaturempfehlungen | Suggested reading: Smart Grids:
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Links | http://mosaik.offis.de |
Language of instruction | German |
Duration (semesters) | 1 Semester |
Module frequency | annual |
Module capacity | unlimited |
Reference text | Elective module in the master specialization area (energy computer science). Associated with the modules:
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Teaching/Learning method | 1P |
Previous knowledge | - Programming with Java - Programming with Python |
Examination | Prüfungszeiten | Type of examination |
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Final exam of module | At the end of the semester |
Oral exam |
Lehrveranstaltungsform | Practical training |
SWS | 4 |
Frequency | SoSe |
Workload Präsenzzeit | 56 h |