Abstract
Hybrid renewable energy systems (HRESs) can alleviate the grid dependence for power in rural and distant locations. The intermittent nature of renewable energy sources acting alone does not make the system reliable; however, combining one or more sources (like solar, wind, diesel, biomass, micro-hydel, etc.) with adequate storage options or intelligent control of hybrid systems ensures power availability to the end user. As a result, it is imperative that the technical aspects of such a hybrid system can be analyzed with respect to optimal sizing of sources, proper control design and mechanism for energy management, and adequate backup via the storage option that ascertain reliable power supply to the consumer/end user or at the distributed generation end. This paper presents an overview of the applications of Genetic Algorithms, Fuzzy logic, Particle Swarm optimization, and similar other evolutionary and nature inspired algorithms that have been employed for the optimization, control, and power management strategies for renewable energy studies involving hybrid power generation schemes. Analysis of the algorithms and the potential applications of new improved algorithms for optimization, control, and power management of HRES is discussed and reported.
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