The synthesis of energy systems is a complex task, since a plethora of conditions needs to be regarded during decision making. Thus, mathematical optimization is an excellent tool to accomplish this task and to identify an optimal system design. However, energy system synthesis is intrinsically uncertain, since the availability of components is inherently uncertain as well as the input parameters, such as energy demands. As a result, neglecting uncertainties might lead to a lack of energy supply. An insufficient energy supply possibly causes both high unexpected costs and environmental damage. Thus, uncertainties need to be regarded during optimization of energy systems. At the same time, sustainability is a further major aspect in the synthesis of energy systems. To regard sustainability performance, multiple decision criteria, such as economic, environmental, and social criteria, need to be taken into account. For this purpose, employing multi-objective optimization is perfectly suitable. However, multi-objective optimization leaves the decision maker in general with more than one solution to choose from which is often very challenging.
Therefore, in this thesis, a framework to select the best reliable and robust sustainable design of an energy system is proposed – the be-rebust framework. The framework takes into account both, uncertainty of energy supply as well as uncertainty of input parameters for optimization. Thus, the designed system is reliable and robust guaranteeing security of energy supply. Sustainability is regarded by employing multi-objective optimization. For decision support, the framework automatically selects one single design. The selected design allows for highly flexible operation regarding the considered objective criteria. The proposed be-rebust framework is applied to a real-world case study. In the case study, the design of a distributed energy supply system is optimized. For this purpose, total annualized costs and the global warming impact are minimized. The results show that reliability as well as robustness can be achieved with only low additional costs. Employing the framework enables to select a sustainable design with higher operational flexibility than provided by designs identified by sole application of multi-objective optimization. The results verify the excellent performance of the proposed framework to select the best sustainable energy system design which is reliable and robust.