Genetic Optimization Techniques for Sizing and Management of Modern Power Systems
- 1st Edition - September 28, 2022
- Imprint: Elsevier
- Authors: Juan Miguel Lujano Rojas, Rodolfo Dufo Lopez, Jose Antonio Dominguez Navarro
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 8 8 9 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 4 2 0 6 - 3
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 primar… Read more
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Request a sales quoteGenetic 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.
Graduate students and early career researchers interested in energy system analysis or optimization techniques; Other readers are decision-makers and planning engineers interested in developing energy policy
- Cover image
- Title page
- Table of Contents
- Copyright
- Acknowledgment
- 1: Introduction to optimization techniques for sizing and management of integrated power systems
- Abstract
- 1.1: Heuristic optimization techniques
- 1.2: Basic implementation of particle swarm optimization
- 1.3: Case studies
- 1.4: Conclusions
- References
- 2: Genetic algorithms and other heuristic techniques in power systems optimization
- Abstract
- 2.1: Evolution and development of genetic algorithms
- 2.2: General formulation of an optimization problem
- 2.3: General structure of a genetic algorithm
- 2.4: Basic implementation of a genetic algorithm
- 2.5: Case studies
- 2.6: Other heuristic techniques
- 2.7: Conclusions
- References
- 3: Estimation of natural resources for renewable energy systems
- Abstract
- 3.1: Development of renewable energies
- 3.2: Natural resources and renewable energy systems
- 3.3: Training a neural network using a genetic algorithm
- 3.4: Case study
- 3.5: Conclusions
- References
- 4: Renewable generation and energy storage systems
- Abstract
- 4.1: Wind generation
- 4.2: Solar photovoltaic generation
- 4.3: Inverter
- 4.4: Conventional generation
- 4.5: Battery energy storage systems
- 4.6: Economic dispatch by genetic algorithm
- 4.7: Unit commitment combining priority list and genetic algorithm
- 4.8: Day-ahead battery management by genetic algorithm
- 4.9: Design of hybrid systems by genetic algorithm
- 4.10: Case studies
- 4.11: Conclusions
- References
- 5: Forecasting of electricity prices, demand, and renewable resources
- Abstract
- 5.1: Benefits of an accurate forecasting system
- 5.2: Forecasting techniques
- 5.3: Uncertainty management
- 5.4: Using a neural network for forecasting purposes
- 5.5: Scenario reduction using a genetic algorithm
- 5.6: Case studies
- 5.7: Conclusions
- References
- 6: Optimization of renewable energy systems by genetic algorithms
- Abstract
- 6.1: Introduction
- 6.2: Main options
- 6.3: Input data
- 6.4: Presizing
- 6.5: Optimization data
- 6.6: Economic calculations
- 6.7: Optimization by GA or MOEA
- 6.8: Examples of application
- 6.9: Conclusions
- References
- 7: Operation and management of modern electrical systems
- Abstract
- 7.1: Energy policy and environmental problems
- 7.2: Energy sources and commodities
- 7.3: Energy system models
- 7.4: Optimal power flow
- 7.5: Solving the optimal power flow by genetic algorithms
- 7.6: Case studies
- 7.7: Conclusions
- References
- Index
- Edition: 1
- Published: September 28, 2022
- Imprint: Elsevier
- No. of pages: 350
- Language: English
- Paperback ISBN: 9780128238899
- eBook ISBN: 9780128242063
JR
Juan Miguel Lujano Rojas
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 University
of Zaragoza, where he is currently working as a Professor.
Affiliations and expertise
Professor at the University of Zaragoza, Spain.RL
Rodolfo Dufo Lopez
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.
Affiliations and expertise
Associate Professor at the Department of Electrical Engineering, University of Zaragoza, Zaragoza, SpainJN
Jose Antonio Dominguez Navarro
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.
Affiliations and expertise
Associate Professor in the Electrical Engineering Department, University of Zaragoza, Spain.Read Genetic Optimization Techniques for Sizing and Management of Modern Power Systems on ScienceDirect