The global energy scenario is changing, and clean generation is leading the way. As new ways of producing energy becomes more popular, microgeneration takes a greater share of the market, supplying both isolated and grid connected systems. In such context, hybrid systems that combine two or more different types of sources are a promising approach, although their valuable application depends on many intrinsic and ambient factors and should be thoroughly evaluated. To assess system performance in advance, not only to better predict its operation but also to well design it for specific applications without the need to install it on site, accurate computational models that reflect its real behavior should be developed. Aiming to contribute on this matter, this work presents a dynamic model for a solar-wind microgeneration system, which allows the evaluation of the system’s behavior under different demand scenarios, as well as to implement different energy management strategies. In addition to the base model comprised a photovoltaic panel, a wind turbine, an inverter, and an energy storage element, this work presents the implementation and performance comparison of two different maximum power point tracking algorithms based on soft computing techniques and on real data collected at a meteorological station. Results validate the hybrid microgeneration model and provides insight on the specification of maximum power point tracking algorithms for specific applications.