Stud.IP Uni Oldenburg
University of Oldenburg
15.10.2019 19:23:21
inf513 - Energy Informatics Practical (Complete module description)
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Module label Energy Informatics Practical
Module code inf513
Credit points 6.0 KP
Workload 180 h
Faculty/Institute Department of Computing Science
Used in course of study
  • Master's Programme Business Informatics (Master) >
  • Master's Programme Computing Science (Master) >
  • Master's Programme Embedded Systems and Microrobotics (Master) >
Contact person
Module responsibility
Authorized examiners
Entry requirements
Programming with JAVA
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:
  • derive and evaluate computational models from physical models
  • use the "mosaik" smart grid co-simulation framework
  • analyze the requirements for successful applications to real power balancing regarding capacity utilization, robustness, and flexibility
  • name the foundations of planning and conducting simulation based experiments as well as the interpretation of the results
  • are aware of the 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.


Methodological competence
The students:
  • model simple controllable electrical generators and consumers
  • simulate simple controllable electrical generators and consumers with appropriate control algorithms within smart grid scenarios
  • apply distributed agent-based control schemes to decentralized energy generators and/ or consumers
  • evaluate simulation results
  • search information and look into methods to implement models
  • propose hyphothesis and check their validity with design of experiments methods


Social competence
The students:
  • apply the pair progamming development technique
  • discuss design decisions
  • identify work packages and are responsible for it


Self-competence
The students:
  • reflect on their own use of power as a limited resource
  • accept and use criticism to develop their own behaviour
Module contents
In this practical course students:
  • model controllable, modulating electrical energy generators and consumers,
  • put their hands on mosaik (installation, description and configuration of scenarios, conduction of simulations),
  • learn the principles of agent-based heuristics for optimization problems in future smart grid scenarios,
  • learn about the challenges of implementing agent-based mechanisms (multi-criticality, convergency, quality) on the training,
  • learn the foundations for choice and design of simulation based experiments.
Reader's advisory
Suggested reading:

Smart Grids:
  • Konstantin, P.: "Praxisbuch Energiewirtschaft", Springer, 2006
  • Schwab, A.: "Elektroenergiesysteme", Springer, 2009


Multiagentensysteme:
  • Sutton, R. S.; Barto, A. G.: "Reinforcement Learning", MIT Press, 1998
  • Weiss, G.: "Multiagent Systems", MIT Press, 2013
  • Ferber J.; Kirn, S.: "Multiagentensysteme: eine Einführung in die Verteilte Künstliche lntelligenz", Addison-Wesley, 2001


Co-Simulation
  • Ptolemaeus, C.: "System Design, Modeling, and Simulation", UC Berkeley, 2013
  • Law, A.: "Simulation Modeling and Analysis", McGraw-Hill, 2015


Versuchsplanung:
  • Kleppmann, W.: "Versuchsplanung", Hanser, 2013
  • Klein, B.: "Versuchsplanung - DoE", Oldenbourg, 2011
  • Goos, P.; Jones, B.: "Optimal Design of Experiments", Wiley, 2014
  • Box, G. E. P.; Hunter, J. S.; Hunter, W. G.: "Statistics for Experimenters", Wiley, 2005
  • Forrester, A.; Sobester, A.; Keane, A.: "Engineering Design via Surrogate Modelling", Wiley, 2008
Links
Language of instruction German
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity unlimited
Reference text
Elective module in the master specialization area (energy computer science).

Associated with the modules:
  • Energieinformationssysteme
  • Smart Grid Management
Modullevel AS (Akzentsetzung / Accentuation)
Modulart je nach Studiengang Pflicht oder Wahlpflicht
Lern-/Lehrform / Type of program
Vorkenntnisse / Previous knowledge - Programmierung mit Java
- Programmierung mit Python
Examination Time of examination Type of examination
Final exam of module
At the end of the semester
Oral exam
Course type Practical
SWS 4.00
Frequency SuSe
Workload attendance 56 h