Synthesis of energy systems is a complex design task with a plethora of decision options. To evaluate these decision options, mathematical optimization is often used to identify the optimal solution. However, for decision support, more information than just the optimal solution is required. The decision maker needs to know design alternatives and their trade-offs to make a well-informed decision. Hence, mathematical optimization should be used as tool to generate multiple design alternatives. One way to generate design alternatives is the exploration of the near-optimal solution space.In this thesis, a decision support system is proposed for decision support by analysis of the near-optimal solution space. The near-optimal solution space consists of the near-optimal design space and the near-optimal objective space. For exploration of the objective space, a method is proposed to efficiently identify solutions which reveal trade-offs in the objective functions. For the design space, a method is proposed to span all near-optimal design alternatives by minimizing and maximizing design variables. The decision support system provides a holistic analysis of the near-optimal solution space by combining solutions from the near-optimal objective space and the design space. All generated solutions are analyzed to reveal feasible ranges of variables and objective functions. Additionally, the analysis determines trade-offs between decisions in both the design space and the objective space. Based on the results of the analysis, the decision maker can derive preferences. In an interactive feedback loop, these preferences are added to the synthesis problem to support the final synthesis decision.The proposed decision support system is applied to two real-world case studies. The first case study originates from pharmaceutical industry and focuses on the supply side of an energy system; the second case study is a retrofit of an urban energy system and also takes into account demand-side measures such as investments in insulation. For these two entirely different case studies, the decision support system provides decision support by identifying feasible designs, their costs and emissions, and the most important design trade-offs. Thereby, the decision maker is enabled to take well-informed decisions in the synthesis of energy systems.

Synthesis of energy systems is a complex design task with a plethora of decision options. To evaluate these decision options, mathematical optimization is often used to identify the optimal solution. However, for decision support, more information than just the optimal solution is required. The decision maker needs to know design alternatives and their trade-offs to make a well-informed decision. Hence, mathematical optimization should be used as tool to generate multiple design alternatives. One way to generate design alternatives is the exploration of the near-optimal solution space.In this thesis, a decision support system is proposed for decision support by analysis of the near-optimal solution space. The near-optimal solution space consists of the near-optimal design space and the near-optimal objective space. For exploration of the objective space, a method is proposed to efficiently identify solutions which reveal trade-offs in the objective functions. For the design space, a method is proposed to span all near-optimal design alternatives by minimizing and maximizing design variables. The decision support system provides a holistic analysis of the near-optimal solution space by combining solutions from the near-optimal objective space and the design space. All generated solutions are analyzed to reveal feasible ranges of variables and objective functions. Additionally, the analysis determines trade-offs between decisions in both the design space and the objective space. Based on the results of the analysis, the decision maker can derive preferences. In an interactive feedback loop, these preferences are added to the synthesis problem to support the final synthesis decision.The proposed decision support system is applied to two real-world case studies. The first case study originates from pharmaceutical industry and focuses on the supply side of an energy system; the second case study is a retrofit of an urban energy system and also takes into account demand-side measures such as investments in insulation. For these two entirely different case studies, the decision support system provides decision support by identifying feasible designs, their costs and emissions, and the most important design trade-offs. Thereby, the decision maker is enabled to take well-informed decisions in the synthesis of energy systems.

Synthesis of energy systems is a complex design task with a plethora of decision options. To evaluate these decision options, mathematical optimization is often used to identify the optimal solution. However, for decision support, more information than just the optimal solution is required. The decision maker needs to know design alternatives and their trade-offs to make a well-informed decision. Hence, mathematical optimization should be used as tool to generate multiple design alternatives. One way to generate design alternatives is the exploration of the near-optimal solution space.In this thesis, a decision support system is proposed for decision support by analysis of the near-optimal solution space. The near-optimal solution space consists of the near-optimal design space and the near-optimal objective space. For exploration of the objective space, a method is proposed to efficiently identify solutions which reveal trade-offs in the objective functions. For the design space, a method is proposed to span all near-optimal design alternatives by minimizing and maximizing design variables. The decision support system provides a holistic analysis of the near-optimal solution space by combining solutions from the near-optimal objective space and the design space. All generated solutions are analyzed to reveal feasible ranges of variables and objective functions. Additionally, the analysis determines trade-offs between decisions in both the design space and the objective space. Based on the results of the analysis, the decision maker can derive preferences. In an interactive feedback loop, these preferences are added to the synthesis problem to support the final synthesis decision.The proposed decision support system is applied to two real-world case studies. The first case study originates from pharmaceutical industry and focuses on the supply side of an energy system; the second case study is a retrofit of an urban energy system and also takes into account demand-side measures such as investments in insulation. For these two entirely different case studies, the decision support system provides decision support by identifying feasible designs, their costs and emissions, and the most important design trade-offs. Thereby, the decision maker is enabled to take well-informed decisions in the synthesis of energy systems.