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

Authors:

Stephan Hutterer,

Stefan Vonolfen,

Michael AffenzellerAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 1529 - 1536

https://doi.org/10.1145/2464576.2482732

Published: 06 July 2013 Publication History

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 behavior. 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 learned 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 learned 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.

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Cited By

Affenzeller MBeham AVonolfen SPitzer EWinkler SHutterer SKommenda MKofler MKronberger GWagner S(2015)Simulation-Based Optimization with HeuristicLab: Practical Guidelines and Real-World ApplicationsApplied Simulation and Optimization10.1007/978-3-319-15033-8_1(3-38)Online publication date: 7-Apr-2015
https://doi.org/10.1007/978-3-319-15033-8_1

Index Terms

Genetic programming enabled evolution of control policies for dynamic stochastic optimal power flow
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Published In

cover image ACM Conferences

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2013

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GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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Cited By

Affenzeller MBeham AVonolfen SPitzer EWinkler SHutterer SKommenda MKofler MKronberger GWagner S(2015)Simulation-Based Optimization with HeuristicLab: Practical Guidelines and Real-World ApplicationsApplied Simulation and Optimization10.1007/978-3-319-15033-8_1(3-38)Online publication date: 7-Apr-2015
https://doi.org/10.1007/978-3-319-15033-8_1

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