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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
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
1. Introduction to Optimization techniques for sizing and management of integrated power systems
1.1. A General Overview of This Book
1.2. This Book and Its Target Audience
2. Genetic Algorithms and Other Heuristic Techniques in power systems optimization
2.1. General Perspective and State-of-the-Art of Genetic Algorithms
2.1.1 Mono-Objective Optimization
2.1.2. Multi-Objective Optimization
2.2. General Perspective and State-of-the-Art of Parallel Genetic Algorithms
2.3. Other Heuristic Techniques and Their Combination with Genetic Algorithms
3. Estimation of Natural Resources for Renewable Energy Systems
3.1. State-of-the-Art of Renewable Resource Databases
3.2. Estimating the Typical Meteorological Year of Wind Speed
3.3. Estimating the Typical Meteorological Year of Solar Irradiation
3.4. Estimating the Typical Meteorological Year of Ambient Temperature
4. Renewable Generation and Energy Storage Systems
4.1. State-of-the-Art of Renewable Generation Model and Battery Energy Storage Devices
4.2. Simulation Models of Wind Generation
4.3. Simulation Models of Solar Photovoltaic Generation
4.4. Battery Energy Storage System
4.4.1. Simulation Models of Lead-Acid Batteries
4.4.2. Simulation Models of Vanadium Redox Flow Batteries
4.4.3. Simulation Models of Lithium-Ion Batteries
4.5. Distribution System Model
4.5.1. Deterministic Power Flow Calculation
4.5.2. Probabilistic Power Flow Calculation
5. Forecasting of Electricity Prices, Demand, and Renewable Resources
5.1. State-of-the-Art of Time Series based Forecasting for Renewable Energy Systems
5.2. Electricity Price Forecasting
5.3. Electricity Demand Forecasting
5.4. Forecasting of Environmental Variables
6. Optimization of Renewable Energy Systems by Genetic Algorithms
6.1. Maximum Power Point Tracking of a Photovoltaic Generator
6.2. Rural Electrification and Vulnerability Assessment and Its Mitigation
6.3. Vulnerability Assessment of Energy Systems and Its Mitigation
6.3.1 Mono-Objective Optimization (Net Present Cost)
6.3.2. Multi-Objective Optimization (Greenhouse Gas Emissions)
6.4. Day-Ahead Battery Energy Storage System Operation
6.5. Integration of Distributed Photovoltaic Generation
6.6. Local Electricity Markets
6.7. Residential Demand Response under Real-Time Pricing
6.8. Electric Vehicle Charging Station
7. Creating Energy Systems Policy using genetic optimization techniques
7.1. State-of-the-Art of Computational Tools for Energy System Analysis
7.2. Large-Scale Electrification in Developing Countries
7.3. Massive Integration of Distributed generation in Developed Countries
- No. of pages:
- © Elsevier 2022
- 1st January 2022
- Paperback ISBN:
Juan Miguel Lujano Rojas received the B.S., M.S., and Ph.D. degrees from the Simón Bolívar University, Venezuela, and the University of Zaragoza, Spain, in 2007, 2010, and 2012, respectively. From 2013 to 2015, he worked in the FP7 project entitled: Smart and Sustainable Insular Electricity Grids Under Large-scale Renewable Integration (SINGULAR). Later, Lujano worked in the Institute for Systems and Computer Engineering, Research and Development in Lisbon (INESC-ID) between 2015 and 2018. From 2018 to 2019, he re-joint the University of Zaragoza to work in the vulnerability assessment of energy systems. He is currently working as a researcher for Ton Duc Thang University, Vietnam.
Lecturer in Electrical Engineering, INESC, Lisbon Portugal, currently on secondment to Ho Chi Minh University, Ho Chi Minh, Vietnam
Rodolfo Dufo López received the B.S., M.S., and Ph.D. 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 a Professor with the Department of Electrical Engineering. He has published over 50 journal papers and conference proceedings. His research interests include renewable energy, distribution systems, smart grid, power quality, generation, and demand-side management, and power systems analysis.
Professor of Electrical Engineering, University of Zaragoza, Zaragoza, Spain
José Antonio Domínguez Navarro received the Ph.D. degree in electrical engineering from the University of Zaragoza, Zaragoza, Spain, in 2000. In 1992, he joined the University of Zaragoza, where he is currently a Lecturer in the Electrical Engineering Department. He works in research projects related to the optimization of power distribution networks. His current areas of interest are applications of soft computing in electric power systems, power distribution networks planning, electric markets, and metaheuristic optimization.
Lecturer in Electrical Engineering, University of Zaragoza, Zaragoza, Spain
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