Validating intelligent power and energy systems – A discussion of educational needs

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Validating intelligent power and energy systems – A discussion of educational needs

Conference Paper (2017)

Author(s)

Panos Kotsampopoulos (National Technical University of Athens)

Nikos Hatziargyriou (National Technical University of Athens)

T. I. Strasser (AIT Austrian Institute of Technology)

Cyndi Moyo (AIT Austrian Institute of Technology)

Sebastian Rohjans (Hamburg University of Applied Sciences)

C Steinbrink (OFFIS e.V)

Sebastian Lehnhoff (OFFIS e.V)

P. Palensky (TU Delft - Electrical Engineering, Mathematics and Computer Science)

A. A. van der Meer (TU Delft - Electrical Engineering, Mathematics and Computer Science)

D. E. Morales Bondy (Technical University of Denmark (DTU))

K. Heussen (Technical University of Denmark (DTU))

Mihai Calin (DERlab)

A. Khavari (DERlab)

M. Sosnina (DERlab)

J. E. Rodriguez (Technalia)

G. M. Burt (University of Strathclyde)

Research Group

Intelligent Electrical Power Grids

Education Learning Validation Training Smart grids Cyber-physical energy systems

DOI related publication

https://doi.org/10.1007/978-3-319-64635-0_15 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:34f9cb66-46b5-4781-aac6-fd9758a57762

More Info

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Publication Year

2017

Language

English

Research Group

Intelligent Electrical Power Grids

Pages (from-to)

200-212

Publisher

Springer

ISBN (print)

978-3-319-64634-3

ISBN (electronic)

978-3-319-64635-0

Event

HoloMAS 2017 (2017-08-28 - 2017-08-30), Lyon, France

Downloads counter

312

Abstract

Traditional power systems education and training is flanked by the demand for coping with the rising complexity of energy systems, like the integration of renewable and distributed generation, communication, control and information technology. A broad understanding of these topics by the current/future researchers and engineers is becoming more and more necessary. This paper identifies educational and training needs addressing the higher complexity of intelligent energy systems. Education needs and requirements are discussed, such as the development of systems-oriented skills and cross-disciplinary learning. Education and training possibilities and necessary tools are described focusing on classroom but also on laboratory-based learning methods. In this context, experiences of using notebooks, co-simulation approaches, hardware-in-the-loop methods and remote labs experiments are discussed.