Stochastic (Optimal) Power Flow – Comparison of Numerical Approaches

The solution of the power flow equations is of paramount importance for the design, operation, and control of (linear) electrical networks. The objective of the so-called power flow problem is to find operating conditions for a set of connected power generating units given load specifications. It is well-known that power flow equations are subject to uncertainty. Typical sources of uncertainty in power flow problems are generation or load profiles, line parameters, or network conditions. With increasing penetration of renewable energy sources a structured approach to solve uncertain power flow problems is desirable, especially for later use in context of power dispatch. When uncertainties in the power network are treated as random variables, the problem is referred to as stochastic power flow or probabilistic power flow.

The objective of the research project is to compare different (numerical) solution approaches to solve the stochastic power flow problem with respect to accuracy and simulation times. Viable approaches include (Markov chain) Monte-Carlo, two-point estimate methods, and/or unscented transforms.

Weitere Informationen

Unternehmen

Helmholtz Gemeinschaft

Bereich/Abteilung

Institute for Applied Computer Science (IAI)

Abschlussart

Bachelorarbeit / Masterarbeit

Ansprechpartner/in

Fachliche Auskünfte erteilt Ihnen gerne Tillmann Mühlpfordt, M.Sc., IAI, 0721/608-26459 (tillmann.muehlpfordt@kit.edu)

Branche

Forschung und Entwicklung

Anforderungen

electrical engineering, mechanical engineering, computer science
Numerical/scientific computing, Matlab, Python, and/or Julia

Zusatzinformationen

Vertragsdauer: befristet according to the study regulations
Eintrittstermin: as soon as possible
Bewerbung bis: 04.11.2016

Bewerbung: Bitte senden Sie Ihre Bewerbung online unter Angabe der Stellenausschreibungsnummer IAI 04-16 an Frau Berger, Berufliche Ausbildung, Telefon 0721 608-25184.

Bei entsprechender Eignung werden schwerbehinderte Bewerber/innen bevorzugt berücksichtigt.

Karlsruher Institut für Technologie 04.05.2016

Jetzt online bewerben:

https://stellen.jobs.kit.edu/cgi-bin/appl/cv.pl?action=show&tmpl=cvpage_job_1&job_nr=DD88318C-22A9-4A18-AF81-ED9AA7CD4086&lang=uk