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Genetic Optimization Techniques for Sizing and Management of Modern Power Systems

AvJuan Miguel Lujano Rojas,Rodolfo Dufo Lopez

Häftad, Engelska, 2022

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Beskrivning

Genetic Optimization Techniques for Sizing and Management of Modern Power Systems explores the design and management of energy systems using a genetic algorithm as the primary optimization technique. Coverage ranges across topics related to resource estimation and energy systems simulation. Chapters address the integration of distributed generation, the management of electric vehicle charging, and microgrid dimensioning for resilience enhancement with detailed discussion and solutions using parallel genetic algorithms. The work is suitable for researchers and practitioners working in power systems optimization requiring information for systems planning purposes, seeking knowledge on mathematical models available for simulation and assessment, and relevant applications in energy policy.

Presents a range of essential techniques for using genetic algorithms in power system analysis, including
economic dispatch, forecasting, and optimal power fl ow, among other topics.
Addresses relevant optimization problems, such as neural network training and clustering analysis, using
genetic algorithms.
Discusses clearly and straightforwardly the implementation of genetic algorithms and its combination with
other heuristic techniques.
Describes the iHOGA� and MHOGA� commercial tools, which utilize genetic algorithms for designing
and managing energy systems based on renewable energies.

Produktinformation

Utgivningsdatum:2022-09-29
Mått:152 x 229 x 21 mm
Vikt:450 g
Format:Häftad
Språk:Engelska
Antal sidor:350
Förlag:Elsevier Science
ISBN:9780128238899

Utforska kategorier

Energiteknik inom Naturvetenskap och teknik

Mer om författaren

Juan Lujano-Rojas received the B.S. from the Simón Bolívar University, Venezuela, and the M.S. and Ph.D. degrees from the University of Zaragoza, Spain, in 2007, 2010, and 2012, respectively. From 2013 to 2015, he worked on the FP7 project entitled: Smart and Sustainable Insular Electricity Grids under Large-Scale Renewable Integration (SINGULAR). Between 2015 and 2018, Lujano worked in the Institute for Systems and Computer Engineering, Research and Development in Lisbon (INESC-ID). In 2018 he rejoined the Universityof Zaragoza, where he is currently working as a Professor. Rodolfo Dufo-López received the BS, MS, and PhD degrees from the University of Zaragoza, Spain, in 1994, 2001, and 2007, respectively. In 2004, he joined the University of Zaragoza, where he is currently an Associate Professor in the Department of Electrical Engineering. His research interests include renewable energy (photovoltaic, wind, hydro), electricity storage (batteries, pumped hydro storage, hydrogen), and simulation and optimization of renewable-based energy systems. José A. Domínguez-Navarro received the BS and PhD degrees in industrial engineering from the University of Zaragoza, Spain, in 1992 and 2000, respectively. In 1992, he joined the University of Zaragoza, where he is currently an Associate Professor in the Electrical Engineering Department. He carried out several research stays at the INESCN research center in Oporto (Portugal) in 1993, at the University of Strathclyde in Glasgow (United Kingdom) in 2013, and at the Norwegian University of Science and Technology in Trondheim (Norway) in 2015. He works in research projects related to the optimization of power distribution networks. His current areas of interest are electrical network planning, renewable energy integration, and application of computing techniques (neural networks, fuzzy systems, and heuristic optimization algorithms) in power systems.

Innehållsförteckning

1. Introduction to Optimization techniques for sizing and management of integrated power systems2. Genetic Algorithms and Other Heuristic Techniques in power systems optimization3. Estimation of Natural Resources for Renewable Energy Systems4. Renewable Generation and Energy Storage Systems5. Forecasting of Electricity Prices, Demand, and Renewable Resources6. Optimization of Renewable Energy Systems by Genetic Algorithms7. Creating Energy Systems Policy using genetic optimization techniques