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DOE OSTI · 1757941

An Improved Genetic Algorithm approach to the Unit Commitment/Economic Dispatch problem

Ponciroli, R.·Stauff, N. E.·Ramsey, J.·Ganda, F.·Vilim, R. B.
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Abstract

The deployment of new technologies, the importance of accurately modeling the dynamics of the generating units and the introduction of new policies are making the solution of the Unit Commitment/Economic Dispatch problem more and more complicated.In the present scenario, traditionally followed scheduling criteria might not lead to the optimal fleet configuration any more. In addition, most of the widely used techniques have limited capabilities at modeling the nonlinear dynamics of committed power plants. When realistic power systems comprising of several tens of generating units are modeled, the resulting optimization problem turns to be computationally intensive for the current computing capabilities. In this paper, an improved version of a GA-based optimization algorithm is presented. A detailed methodology aimed at obtaining a more efficient version of the GA, and a more detailed and accurate description of the flexible operation flexibility of the power plants is described.

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genetic algorithmMonte Carlo samplingunit commitmentunit dynamics

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Ponciroli, R., Stauff, N. E., Ramsey, J., Ganda, F., Vilim, R. B.. 2020-09-01. An Improved Genetic Algorithm approach to the Unit Commitment/Economic Dispatch problem. https://doi.org/10.1109/tpwrs.2020.2986710

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