Automated Optimization-Based Synthesis of Distributed Energy Supply Systems

In this thesis, two novel synthesis methodologies are proposed to facilitate the use of optimization for efficient and reliable DESS synthesis, thus making optimization accessible for practitioners: The automated superstructure-based and the superstructurefree synthesis methodology. The proposed methodologies avoid both the a priori definition of a superstructure and the manual definition of many technology-specific replacement rules while accounting for the major characteristics inherent to DESS synthesis problems. The superstructure-based framework (chapter 4) relies on an algorithm for automated superstructure-generation. The method employs a successive superstructure expansion and optimization strategy that continuously increases the number of units included in the superstructure until the optimal solution is identified. The superstructure-free approach (chapter 5) combines evolutionary optimization and deterministic optimization for simultaneous alternatives generation and optimization. A knowledge-integrated mutation operator is proposed that relies on a hierarchically-structured graph, the so-called energy conversion hierarchy (ECH). The ECH efficiently defines all reasonable replacement rules, thus avoiding their manual definition. The mutation operator performs structural replacements for the evolutionary generation of solution alternatives. Both synthesis methodologies use a generic component-based modeling framework, thus making the methodologies independent of the employed mathematical programming formulation. In this thesis, a robust MILP formulation is used that allows to simultaneously optimize the structure, sizing, and operation of distributed energy supply systems accounting for time-varying load profiles, continuous equipment sizing, economy of scale of equipment investment, and part-load equipment performance.