Hybrid optimization and artificial intelligence applied to energy systems: a review

The modern world demands more and better sources of energy to satisfy its growing consumption needs. The global warming of our planet and the problems derived from it are the natural technological restrictions that force the development of new supply alternatives that replace the traditional ways of burning fossil fuels. Today, most advanced countries are betting on obtaining clean energy through research and development (R&D) of devices that work without the primitive principle used until a few years ago. There are several lines of research in this regard in relation to the different ways to generate energy. Solar energy, wind energy, hydroelectric energy, biomass energy, biogas, tidal energy, geothermal energy, and hydrogen energy, among others, stand out for their importance. Research efforts are not only directed in terms of production but also in the management, distribution, and storage of the product. The development of this new technology requires considerable efforts in engineering, physics, mathematics, and computer science. In this document we explore and group into representative clusters, the main application areas related to the subject of energy systems as well as the quantitative approaches used for their modeling and optimization in the design, creation, and scaling of emerging prototypes. We present some of the main authors, methods, and results obtained in the art of modeling and optimization of energy systems with special emphasis on hybrid optimization and the use of artificial intelligence (AI). We discuss the differences between deterministic and stochastic AI and present a generic overview of the benefits of these emerging science in the development of nonpolluting energy systems.