Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling
Editat de João P. S. Catalãoen Limba Engleză Paperback – 29 mar 2017
Methods for Coping with Uncertainty and Risk in Electric Power Generation
Outlining real-world problems, the book begins with an overview of electric power generation systems. Since the ability to cope with uncertainty and risk is crucial for power generating companies, the second part of the book examines the latest methods and models for self-scheduling, load forecasting, short-term electricity price forecasting, and wind power forecasting.
Toward Optimal Coordination between Hydro, Thermal, and Wind Power
Using case studies, the third part of the book investigates how to achieve the most favorable use of available energy sources. Chapters in this section discuss price-based scheduling for generating companies, optimal scheduling of a hydro producer, hydro-thermal coordination, unit commitment with wind generators, and optimal optimization of multigeneration systems.
Written in a pedagogical style that will appeal to graduate students, the book also expands on research results that are useful for engineers and researchers. It presents the latest techniques in increasingly important areas of power system operations and planning.
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Specificații
ISBN-13: 9781138073982
ISBN-10: 1138073989
Pagini: 462
Ilustrații: Less than 500; 62 Tables, black and white; 152 Illustrations, black and white
Dimensiuni: 156 x 234 x 28 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
ISBN-10: 1138073989
Pagini: 462
Ilustrații: Less than 500; 62 Tables, black and white; 152 Illustrations, black and white
Dimensiuni: 156 x 234 x 28 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Cuprins
Overview of Electric Power Generation Systems. Uncertainty and Risk in Generation Scheduling. Short-Term Load Forecasting. Short-Term Electricity Price Forecasting. Short-Term Wind Power Forecasting. Price-Based Scheduling for Gencos. Optimal Self-Schedule of a Hydro Producer under Uncertainty. Hydrothermal Producer Self-Scheduling. Unit Commitment and Economic Dispatch for Operations Planning of Power Systems with Significant Installed Wind Power Capacity. Operational Optimization of Multigeneration Systems. Index.
Notă biografică
João P.S. Catalão received an MSc degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2003 and a PhD from the University of Beira Interior (UBI), Covilha, Portugal, in 2007. He is currently a professor at UBI and a director of the Master Program in Electromechanical Engineering. His scientific area of interest at the Center for Innovation in Electrical and Energy Engineering (CIEEE), IST, is related to power and energy systems. He coordinates a research group in renewable energies and sustainability at UBI. He is an IEEE and an IET member, a senior member of the Portuguese Council of Engineers, and a member of the IASTED Technical Committee on Energy and Power Systems. He is the author/coauthor of more than 160 scientific papers, including 50 papers in international journals and 100 papers in conference proceedings. He is an editor of IEEE Transactions on Sustainable Energy, and an editorial board member of Electric Power Components and Systems.
For more information about Professor Catalão, see his web page at UBI.
For more information about Professor Catalão, see his web page at UBI.
Descriere
An accurate forecast of load, electricity market prices, and wind power generation is increasingly important to support decisions in power system operations and planning. Written by renowned international experts, this book presents cutting-edge research on forecasting and scheduling issues in electric power generation systems. The text covers topics such as uncertainty and risk, load forecasting, electricity prices forecasting, wind power forecasting, price-based scheduling for generating companies, optimal scheduling of a hydro producer, hydro-thermal coordination, unit commitment with wind generators, and operational optimization of multigeneration systems.