Two-stage stochastic optimization of district energy systems: a case study from the United States

To achieve climate neutrality, diverse policy measurements and incentives have to be realized. Various sectors have to adapt towards a sustainable and carbon-neutral future. One of these sectors which has to reduce emissions is space heating and cooling. Especially in rural areas, people are dependent on regional and individual solutions. But also bigger cities often lack of a common district heating and cooling infrastructure. In this thesis, a two-stage stochastic optimization model is developed to get robust and cost-optimal investment decisions. Different heating, cooling and storage options are modeled, making a broad portfolio available. As future developments are hard to predict, different scenarios are introduced to cope with energy price changes, demand fluctuations and policy adaptions. The results are based on a US-American case study in Washington D.C. for a small district of 35 buildings. This case study will help to gain insights on how beneficial a common thermal energy supply will be compared to individual solutions. The optimal solution of the model will give a trend on promising technologies as well as technology correlations.