Elsevier 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 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.