## Gabriela Hug: Katalogdaten im Frühjahrssemester 2019 |

Name | Frau Prof. Dr. Gabriela Hug |

Lehrgebiet | Elektrische Energieübertragung |

Adresse | Inst. f. El. Energieübertragung ETH Zürich, ETL G 26 Physikstrasse 3 8092 Zürich SWITZERLAND |

Telefon | +41 44 633 81 91 |

hug@eeh.ee.ethz.ch | |

URL | http://www.psl.ee.ethz.ch/people/prof--gabriela-hug.html |

Departement | Informationstechnologie und Elektrotechnik |

Beziehung | Ordentliche Professorin |

Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|

227-0528-00L | Power System Dynamics, Control and Operation | 6 KP | 4G | G. Hug, A. Ulbig | |

Kurzbeschreibung | The electric power system is a system that is never in steady state due to constant changes in load and generation inputs. This course is dedicated to the dynamical properties of the electric power grid including how the system state is estimated, generation/load balance is ensured by frequency control and how the system reacts in case of faults in the system. The course includes two excursions. | ||||

Lernziel | The learning objectives of the course are to understand and be able to apply the dynamic modeling of power systems, to compute and discuss the actions of generators based on frequency control, to describe the workings of a synchronous machine and the implications on the grid, to describe and apply state estimation procedures, to discuss the IT infrastructure and protection algorithms in power systems. | ||||

Inhalt | The electric power system is a system that is never in steady state due to constant changes in load and generation inputs. Consequently, the monitoring and operation of the electric power grid is a challenging task. The course starts with the introduction of general operational procedures and the discussion of state estimation which is an important tool to observe the state of the grid. The course is then dedicated to the modeling and studying of the dynamical properties of the electric power grid. Frequency control which ensures the generation/load balance in real time is the basis for real-time control and is presented in depth. For the analysis of how the system detects and reacts dynamically in fault situations, protection and dynamic models for synchronous machines are introduced. | ||||

Skript | Lecture notes. WWW pages. | ||||

227-0530-00L | Optimization in Energy Systems | 6 KP | 4G | G. Hug, H. Abgottspon, M. Densing | |

Kurzbeschreibung | The course covers various aspects of optimization with a focus on applications to energy networks and scheduling of hydro power. Throughout the course, concepts from optimization theory are introduced followed by practical applications of the discussed approaches. | ||||

Lernziel | After this class, the students should have a good handle on how to approach a research question which involves optimization and implement and solve the resulting optimization problem by choosing appropriate tools. | ||||

Inhalt | In our everyday’s life, we always try to take the decision which results in the best outcome. But how do we know what the best outcome will be? What are the actions leading to this optimal outcome? What are the constraints? These questions also have to be answered when controlling a system such as energy systems. Optimization theory provides the opportunity to find the answers by using mathematical formulation and solution of an optimization problem. The course covers various aspects of optimization with a focus on applications to energy networks. Throughout the course, concepts from optimization theory are introduced followed by practical applications of the discussed approaches. The applications are focused on 1) the Optimal Power Flow problem which is formulated and solved to find optimal device settings in the electric power grid and 2) the scheduling problem of hydro power plants which in many countries, including Switzerland, dominate the electric power generation. On the theoretical side, the formulation and solving of unconstrained and constrained optimization problems, multi-time step optimization, stochastic optimization including probabilistic constraints and decomposed optimization (Lagrangian and Benders decomposition) are discussed. |