Rodolfo Dufo Lopez – författare

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, includingeconomic dispatch, forecasting, and optimal power fl ow, among other topics. Addresses relevant optimization problems, such as neural network training and clustering analysis, usinggenetic algorithms. Discusses clearly and straightforwardly the implementation of genetic algorithms and its combination withother heuristic techniques. Describes the iHOGA� and MHOGA� commercial tools, which utilize genetic algorithms for designingand managing energy systems based on renewable energies.

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, includingeconomic dispatch, forecasting, and optimal power fl ow, among other topics.- Addresses relevant optimization problems, such as neural network training and clustering analysis, usinggenetic algorithms.- Discusses clearly and straightforwardly the implementation of genetic algorithms and its combination withother heuristic techniques.- Describes the iHOGA® and MHOGA® commercial tools, which utilize genetic algorithms for designingand managing energy systems based on renewable energies.

Power and Energy Engineering are important and pressing topics globally, covering issues such as shifting paradigms of energy generation and consumption, intelligent grids, green energy and environmental protection. The 11th Asia-Pacific Power and Energy Engineering Conference (APPEEC 2019) was held in Xiamen, China from April 19 to 21, 2019. APPEEC has been an annual conference since 2009 and has been successfully held in Wuhan (2009 & 2011), Chengdu (2010 & 2017), Shanghai (2012 & 2014), Beijing (2013 & 2015), Suzhou (2016) and Guilin (2018), China. The objective of APPEEC 2019 was to provide scientific and professional interactions for the advancement of the fields of power and energy engineering. APPEEC 2019 facilitated the exchange of insights and innovations between industry and academia. A group of excellent speakers have delivered keynote speeches on emerging technologies in the field of power and energy engineering. Attendees were given the opportunity to give oral and poster presentations and to interface with invited experts.