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 the use of genetic algorithms in power system analysis, complete with relevant computational tools and advice on implementation
- Addresses optimization techniques for scenarios including distributed generation, battery energy storage systems, demand response, and charging of electric vehicles
- Discusses policy applications of optimization techniques, including rural electrification as well as the integration of distributed generation in urban areas
- Accompanied with MATLAB coding for modeling and simulation implementations
1. Introduction to Optimization techniques for sizing and management of integrated power systems
2. Genetic Algorithms and Other Heuristic Techniques in power systems optimization
3. Estimation of Natural Resources for Renewable Energy Systems
4. Renewable Generation and Energy Storage Systems
5. Forecasting of Electricity Prices, Demand, and Renewable Resources
6. Optimization of Renewable Energy Systems by Genetic Algorithms
7. Creating Energy Systems Policy using genetic optimization techniques
Juan Lujano-Rojas received the B.S. from the Simón Bolķvar 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. 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. Domķnguez-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.
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