Genetic programming enabled evolution of control policies for dynamic stochastic optimal power flow

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@InProceedings{Hutterer:2013:GECCOcomp,
  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.",
}

Genetic Programming entries for Stephan Hutterer Stefan Vonolfen Michael Affenzeller

Citations