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• Robust optimal integration of heat pumps in distributed energy systems is analyzed. • Climate, load and economic uncertainties are included by the Monte Carlo method. • The proposed multi-objective stochastic method optimizes both sizing and operation. • The method maximizes the expected performances, even in worst-case scenarios. • Heat pumps improve the system performances and reduce the effects of uncertainties. Heat pumps represent a link between different energy vectors and their application in thermal and electrical grids can improve the overall operational flexibility of the system. In this work, the optimal integration of electrically driven heat pumps within a hybrid distributed energy system is investigated. A multi-objective stochastic optimization methodology is proposed to evaluate the integrated optimal sizing and operation of the energy systems under uncertainties in climate, space occupancy, energy loads, and fuel costs. A case study is considered, namely a University campus, and two different configurations, with and without heat pumps, are compared. Both configurations include a cogeneration system, photovoltaic and solar thermal panels, and a wind turbine. The results show how the integration of heat pumps can reduce the operational cost of the system, increase the renewables share, provide a more robust design of the system, and moderate the risk of the investment. Indeed, the configuration with heat pumps entails a 50% higher expected value of the energy savings, a 28% increase of the renewable energy production, and higher energy savings in the worst-case scenario (13% vs. 5%). [ABSTRACT FROM AUTHOR]