Energy and media supply systems and related infrastructure at industrial sites have grown historically and is largely dependent on the use of fossil fuels. High fuel prices and the emission reduction targets of companies challenge existing supply concepts. Supply concepts usually remain in place for decades due to the long-lived nature of generation technologies and distribution systems. Today's investment decisions are therefore confronted with a changing environment in which the share of volatile renewables from solar and wind is continuously increasing. The long planning horizons make design decisions very complex. Optimization-based design approaches automatically derive cost- or carbon-optimal selections of generation technologies and procurement tariffs. Thus, they enable faster and more accurate planning decisions in techno-economic feasibility studies.

In this work, a novel optimization model for techno-economic feasibility studies in industrial sites is developed. The optimization model uses a generic technology formulation with base classes, which takes into account the large variety of technologies and procurement tariffs at industrial sites. The optimization model also includes two reserve concepts: an operating reserve concept for short-term disruptions and a redundancy concept for long-term plant failures. The two concepts ensure security of supply for production-related energy requirements and thereby contributes to avoidance of costly production outages.

The optimization model is integrated into an optimization framework to effectively calculate decarbonization strategies. The framework uses time series aggregation and heuristic decomposition techniques. Time series aggregation is performed by an integer program and results in a robust selection of representative days. The selection of representative days is used in a multi-year planning model to derive transformation roadmaps. Transformation roadmaps analyze the evolution of energy supply systems to long-term trends and consider adaptive investment decisions. A transformation strategy with myopic foresight (MYOP) solves the multi-year planning problem sequentially and is solved up to 98 % faster than a transformation approach with perfect foresight (PERF). The high uncertainties in early planning phases and the resulting need for detailed sensitivity analysis make this approach the preferred choice for many feasibility studies.

The newly developed optimization framework is used in numerous research and consulting projects for urban districts, microgrids and factories. In this work, the capabilities of the framework are demonstrated for three use cases (automotive, pharmaceutical, dairy) of factory sites in southern Germany. In the use cases, decarbonization strategies for electricity, steam, heating and cooling supply are analyzed. Simulation evaluations identify changing operating patterns of combined heat and power (CHP) plants along the 15-year planning horizon. In addition, electrification of heating demand leads to a significant increase of total electricity demands. The results derived with the framework provide decision makers in industrial companies a clear view of the long-term impact of their investment decisions on decarbonization strategies.