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The book contains description of a real life application of modern mathematical optimization tools in an important problem solution for power networks. The objective is the modelling and calculation of optimal daily scheduling of power generation, by thermal power plants, to satisfy all demands at minimum cost, in such a way that the generation and transmission capacities as well as the demands at the nodes of the system appear in an integrated form. The physical parameters of the network are also taken into account. The obtained large-scale mixed variable problem is relaxed in a smart, practical way, to allow for fast numerical solution of the problem.
Inhaltsverzeichnis
Control of electric power systems.- A general mathematical programming model for the scheduling of electric power generation.- Assumptions for model simplification.- The model obtained by taking into account the simplifying assumptions.- Daily scheduling.- Appendix: The transmission network of electric power systems.
Über den Autor / die Autorin
Prof. András Prékopa is a Professor at Rutgers University in the Department of Statistics.
Prof. János Mayer is a Professor at University of Zurich in the Department of Business Adminstration.
Prof. Beáta Strazicky is currently retired but was a Professor at Szent István University.
Prof. István Deák is a Professor at Corvinus University of Budapest in the Department of Computer Science.
János Hoffer works at Allianz Hungária Insurance Company in the IT Quality Assurance Section.
Ágoston Némethworks in Ex-Lh Ltd.
Béla Potecz is retired but previously worked for MAVIR Hungarian Independent Transmission Operator Company Ltd.
Zusammenfassung
The book contains description of a real life application of modern mathematical optimization tools in an important problem solution for power networks. The objective is the modelling and calculation of optimal daily scheduling of power generation, by thermal power plants, to satisfy all demands at minimum cost, in such a way that the generation and transmission capacities as well as the demands at the nodes of the system appear in an integrated form. The physical parameters of the network are also taken into account. The obtained large-scale mixed variable problem is relaxed in a smart, practical way, to allow for fast numerical solution of the problem.
