- author = "Stephan Hutterer and Stefan Vonolfen and Michael Affenzeller",
- title = "Genetic programming enabled evolution of control policies for dynamic stochastic optimal power flow",
- booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
- year = "2013",
- editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks",
- isbn13 = "978-1-4503-1964-5",
- keywords = "genetic algorithms, genetic programming",
- pages = "1529--1536",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
DOI = "
doi:10.1145/2464576.2482732",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "The optimal power flow (OPF) is one of the central
Optimization problems in power grid engineering,
building an essential tool for numerous control as well
as planning issues. Methods for solving the OPF that
mainly treat steady-state situations have been studied
extensively, ignoring uncertainties of system variables
as well as their volatile behaviour. While both the
economical as well as well as technical importance of
accurate control is high, especially for power flow
control in dynamic and uncertain power systems, methods
are needed that provide (near-) optimal actions
quickly, eliminating issues on convergence speed or
robustness of the Optimization.
This paper shows an approximate policy-based control approach where optimal actions are derived from policies that are learnt offline, but that later provide quick and accurate control actions in volatile situations. These policies are evolved using genetic programming, where multiple and interdependent policies are learnt synchronously with simulation-based Optimization. Finally, an approach is available for learning fast and robust power flow control policies suitable to highly dynamic power systems such as smart electric grids.",
- notes = "Also known as \cite{2482732} Distributed at GECCO-2013.",
