Software-supported Investment Optimization for District Heating Supply Systems
Abstract
Investment decisions for the supply of district heating systems with solar fraction have a very high complexity. The optimal solution means: optimal kind, amount and size of heat generation units and storages. Often “optimal” means lowest costs, but often also aspects of greenhouse gas emissions and primary energy consumption has to be considered. If solar thermal or photovoltaic plants are taken into account, optimal configuration for the given system set-up and heat-demand has to be found out. Optimal adjustments and types of plants should be chosen as well as optimal size of the plants or alternatively optimal usage of a given maximum area. Mathematic optimization can help in the process of finding this optimum. It gives the mathematically exact solution and finds even intuitively unexpected optimal investment-decisions and operation schedule out of the pool of defined possibilities. A tool, based on a developed python framework, is demonstrated with focus on solar energy solutions. Aspects of performance in terms of computing time are discussed.