Genetic Optimization Techniques for Sizing and Management of Modern Power Systems

Genetic Optimization Techniques for Sizing and Managing 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 vehicles charging, and microgrids 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 developing energy policy. Presents a range of essential techniques to the use of genetic algorithms in power system analysis, complete with relevant computational tools and advice on implementationAddresses optimization techniques for scenarios including distributed generation, battery energy storage systems, demand response, and charging of electric vehiclesDiscusses policy applications of optimization techniques including rural electrification as well as the integration of distributed generation in urban areasAccompanied with MATLAB coding for modeling and simulation implementations