Genetic Optimization Techniques for Sizing and Management of Modern Power Systems

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 INDICE: 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

• ISBN: 978-0-12-823889-9
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 256
• Fecha Publicación: 01/11/2022
• Nº Volúmenes: 1
• Idioma: Inglés