Survey of Projects Involving Evolutionary Algorithms Sponsored by the Electric Power Research Institute

  • A. Martin Wildberger
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 111)


Over the last five years, the Electric Power Research Institute (EPRI) has been engaged in a number of research and development projects using evolutionary algorithms for applications in the electric power industry. (EPRI 1994b) Most of these projects also involve theory, since advances in theoretical understanding is often necessary in order to address critical barriers faced in practical applications. Projects reviewed briefly in this paper include:

This chapter briefly describes:
  • successful use of global optimization by genetic algorithms combined with parallel local solutions by coupled gradient neural networks to solve the unit commitment problem (a highly constrained, mixed-integer programming problem usually attacked by Lagrangian relaxation methods).

  • projects co-sponsored with the National Science Foundation that are using evolutionary methods in combination with other techniques in the context of “intelligent control systems.”

  • successful use of a genetic algorithm to test an expert system for improving the performance of fossil-fueled power plants.

  • successful use of genetic algorithms to optimize a neural network for heat-rate improvement of nuclear plant operation; now being extended to construct a generic tool for the automated design and optimization of neural networks in any context.

  • exploration of ways to use autonomous evolving agents as a modeling technique for the electric power market and ultimately for the industry itself as it moves toward increased deregulation and competition.

  • plans for the use of multiple adaptive agents as a modeling method to allow real-time, distributed control of the electric power grid.


Genetic Algorithm Expert System Electric Power Research Institute Electric Power Industry Adaptive Control System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • A. Martin Wildberger
    • 1
  1. 1.Mgr., Mathematics & Information Science Strategic Research & DevelopmentElectric Power Research InstitutePalo AltoUSA

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