- 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",
- month = "11-15 " # jul,
- organisation = "SIGEVO",
- address = "Madrid, Spain",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Evolutionary Combinatorial Optimization and Metaheuristics",
-
URL = "
https://ci.bwl.uni-mainz.de/files/2018/02/ka-sh-15a-1.pdf",
-
URL = "
http://doi.acm.org/10.1145/2739480.2754647",
-
DOI = "
doi:10.1145/2739480.2754647",
- size = "8 pages",
-
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)",
