inf5120 - Digitalised Energy System Co-Simulation (Complete module description)

Programming mit Python, Simulation-based Smart Grid Engineering and Assessment

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, student will start with deriving computational models  from physical models and by evaluating them. In order to manage the integration of control algorithms.  Students are taught the principles of cosimulation using the example of the "mosaik" smart grid  cosimulation framework.
Students are put into the position to understand and apply distributed, agent- based control schemes to decentralised 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 practically apply the foundations for planning  and conducting simulation based experiments as well as the interpretation of the results. Attention is especially paid to a tradeoff between precision and robustness of the results and the necessary efforts (design of experiments) in order to gain as much insight into interdependencies with as few experiments.
Profesional competence
The student:

• derive and evaluate computational models from physical models
• use the "mosaik" smart grid cosimulation framework
• analyze the requirements for successful application to real power balancing regarding capacity  utilization, robustness, and flexibility
• name the foundations for planning and conducting simulation based experiments as well as the  interpretation of the results
• are aware to the tradeoff between precision and robustness of the results and the necessary efforts (design of experiments) in order to gain as much insight into interdependencies with as few experiments.

Methological competence
The student:

• 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 decentralised energy generators and/ or consumers
• evaluate simulation results
• search information and look into methods to implement models
• propose hyphothesis and ckeck their validity with simulation experiments

Social competence
The student:

• apply the development technique pair programming
• discuss design decisions
• identify work packages and take responsibility for it

Self-competence
The student:

• reflect on their own use of the limited resource power
• accept and use criticism to develop their own behaviour

In this practical course students:

• mathematically model controllable, modulating electrical energy generators and consumers and translate them to executyble simulation models,
•  put hands on mosaik (installation, description and configuration of scenarios, conduction of simulations),
• learn the principles of co-simulation of energy systems,
• learn about the challenges of implementing coordination mechanisms (multi-criticality, convergency, quality) on the training,
• apply foundations of design of experiments to practical simulation based experiments.

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

At the end of the lecture time

Practikal Work
A practical assignment includes the theoretical preparation, set-up and execution of a design task on the basis of a case study or the experiment as well as the written presentation of the work steps, the steps, the process and the results of the experiment and their critical evaluation.