Generator Start-up Sequences Optimization for Network Restoration Using Genetic Algorithm and Simulated Annealing

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{ka-sh-15a,
  author =       "Paul Kaufmann and Cong Shen",
  title =        "Generator Start-up Sequences Optimization for Network
                 Restoration Using Genetic Algorithm and Simulated
                 Annealing",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and 
                 Terrence Soule and Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and 
                 Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and 
                 Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Keswsentini and Gabriela Ochoa and 
                 Francisco Chicano and Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "409--416",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Combinatorial Optimization and Metaheuristics",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754647",
  DOI =          "doi:10.1145/2739480.2754647",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In the domain of power grid systems, scheduling tasks
                 are widespread. Typically, linear programming (LP)
                 techniques are used to solve these tasks. For cases
                 with high complexity, linear system modelling is often
                 cumbersome. There, other modelling approaches allow for
                 a more compact representation being typically also more
                 accurate as non-linear dependencies can be captured
                 natively.

                 In this work, we focus on the optimization of a power
                 plant start-up sequence, which is part of the network
                 restoration process of a power system after a blackout.
                 Most large power plants cannot start on their own
                 without cranking energy from the outside grid. These
                 are the non-black start (NBS) units. As after a
                 blackout we assume all power plants being shut down,
                 self-contained power plants (black start (BS) units),
                 such as the hydroelectric power plants, start first and
                 boot the NBS units one after each other. Once a NBS
                 unit is restored, it supports the restoration process
                 and because an average NBS unit is much larger than a
                 BS unit, NBS unit's impact on the restoration process
                 is typically dominant. The overall restoration process
                 can take, depending on the size of the blackout region
                 and the damaged components, some hours to weeks. And as
                 the blackout time corresponds directly to economic and
                 life losses, its reduction, even by some minutes, is
                 worthwhile.

                 In this work we compare two popular metaheuristics, the
                 genetic (GA) and simulated annealing (SA) algorithms on
                 start-up sequence optimization and conclude that an
                 efficient restoration plan can be evolved reliably and,
                 depending on the implementation, in a very short period
                 of time allowing for an integration into a real-time
                 transmission system operation tool.",
  notes =        "Also known as \cite{Kaufmann:2015:GECCO},
                 \cite{2754647} GECCO-2015 A joint meeting of the twenty
                 fourth international conference on genetic algorithms
                 (ICGA-2015) and the twentith annual genetic programming
                 conference (GP-2015)",
}

Genetic Programming entries for Paul Kaufmann Cong Shen

Citations