In the context of the great research pulse on clean energy transition, distributed energy systems have a key role, especially in the case of integration of both renewable and traditional energy sources. The stable interest in small-scale gas turbines can further increase owing to their flexibility in both operation and fuel supply. Since their not-excellent electrical efficiency, research activities on micro gas turbine (MGT) are focused on the performance improvements that are achievable in several ways, like modifying the Brayton cycle, integrating two or more plants, using cleaner fuels. Hence, during the last decades, the growing interest in MGT-based energy systems encouraged the development of many numerical approaches aimed to provide a reliable and effective prediction of the energy systems’ behavior. Indeed, numerical modeling can help to individuate potentialities and issues of each enhanced layout or hybrid energy system, and this review aims to discuss the various layout solutions proposed by researchers, with particular attention to recent publications, highlighting the adopted modeling approaches and methods.

In this work, a multi-variable multi-objective methodology aimed to perform the validation of the thermodynamic model has been applied to the Capstone C30 micro gas turbine. The methodology is based on a genetic optimization algorithm, where decision variables and objectives are set depending on available experimental data. The results of the studied case highlight the capability of the method to point out some experimental data inconsistencies and that it can lead to a consistency thermodynamic reconstruction of the micro turbine behaviour

Combined heat and power plants are recognized as very effective solutions to achieve the increasingly stringent requirements in primary energy saving. The paper addresses the use of a specifically developed methodology to conduct several analyses on the basis of the loads of a specific hospital facility and through the study of the cogeneration system-user interaction. Predictive analyses are carried out using a multi-objective approach to find optimized plant configurations that approaches the best energetic results while ensuring a reasonable profit.Optimized plant configurations and management strategies indicate primary energy savings exceeding 17%. Finally, a sensitivity analysis is carried out to evaluate the robustness of the result