Discovering near-optimal pricing strategies for the deregulated electric power marketplace using genetic algorithms

Abstract A new approach based on genetic algorithms has been used to a general class of problems arising in network industries, in particular, to the first-best pricing problems for the electric power sector. The fundamental challenge in these applications is the treatment of various network constraints. Building on the work of Michalewicz, this study extends the state of the art of constraint-handling techniques to a realistic network problem. The computational approach studied here can be extended to other constraint-rich problems.

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