In this study a probabilistic approach for optimal sizing of cogeneration systems under long-term uncertainty in energy demand is proposed. A dynamic simulation framework for detailed modeling of the energy system is defined, consisting in both traditional and optimal operational strategies evaluation. A two-stage stochastic optimization algorithm is developed, adopting Monte Carlo method for the definition of a multi-objective optimization problem. An Italian hospital facility has been used as a case study and a gas internal combustion engine is considered for the cogeneration unit. The results reveal that the influence of uncertainties on both optimal size and annual total cost is significant. Optimal size obtained with the traditional deterministic approach are found to be sub-optimal (up to 30% larger) and the predicted annual cost saving is always lower when accounting for uncertainties. Pareto frontiers of different CHP configurations are presented and show the effectiveness of the proposed method as a useful tool for risk management and focused decision-making, as tradeoffs between system efficiency and system robustness

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Micro-district heating networks based on cogeneration plants and renewable energy technologies are considered efficient, viable and environmentally-friendly solutions to realizing smart multi-energy microgrids. Nonetheless, the energy production from renewable sources is intermittent and stochastic, and cogeneration units are characterized by fixed power-to-heat ratios, which are incompatible with fluctuating thermal and electric demands. These drawbacks can be partially overcome by smart operational controls that are capable of maximizing the energy system performance. Moreover, electrically driven heat pumps may add flexibility to the system, by shifting thermal loads into electric loads. In this paper, a novel configuration for smart multi-energy microgrids, which combines centralized and distributed energy units is proposed. A centralized cogeneration system, consisting of an internal combustion engine is connected to a micro-district heating network. Distributed electric heat pumps assist the thermal production at the building level, giving operational flexibility to the system and supporting the integration of renewable energy technologies, i.e., wind turbines, photovoltaic panels, and solar thermal collectors. The proposed configuration was tested in a hypothetical case study, namely, a University Campus located in Trieste, Italy. The system operation is based on a cost-optimal control strategy and the effect of the size of the cogeneration unit and heat pumps was investigated. A comparison with a conventional configuration, without distributed heat pumps, was also performed. The results show that the proposed configuration outperformed the conventional one, leading to a total-cost saving of around 8%, a carbon emission reduction of 11%, and a primary energy saving of 8%.