Jakob Moritz Fabian Rager
Energy planning recently received more attention in Switzerland through the new strategy phasing out nuclear energy by 2034. Often however the energy planning is only done from the electrical side. This work takes a different angel and helps communities and energy utilities planning tomorrows energy system from a heat based perspective. After the data collection and structuring, the methodology presented here designs an energy system. Based on the quality of the collected data, the approach to define the energy demand should be chosen. In order to reduce calculation time, a data reduction approach is developed to reduce the input data without loosing significant information and precision. In particular, the methodology focuses on the integration of a stochastic resource, in this case solar thermal heat production, in combination with thermal energy storage. The thermal energy storage can be used as a short or long term thermal energy storage. The framework compares design solutions for the two storage types considering either a total cost approach or a life cycle assessment approach using the cumulative exergy demand (CExD). The proposed mathematical programming framework is based on a mixed integer linear programming (MILP) approach, that can work on different levels of detail between building to community or city level. The optimization problem can also be further simplified to a linear problem, increasing the size of problem that can be solved while reducing or keeping a constant computation time. The discussed cases show an interest in further investigating storage solution using both, the short and long term storage at once, because they allow to reduce the system's overall costs or CExD significantly. The framework is then extended to consider buildings as an energy storage. The building's internal temperature can be raised from 20 \degree C up to 23 \degree C, giving a comfort temperature range that can be used for storing heat. The integrating of both storage types, the thermal energy storage and the building as an energy storage, show no significant impact on the energy system design. However, costs or CExD can be reduced. In addition, the heat demand can be modified through the decision of optimal energy retrofitting strategy for a group of buildings. The framework decides which of the building to refurbish based the overall CExD including CExD used for retrofitting the building. Finally, a method is proposed to integrate uncertainty of the model's input parameters into the system design. A global sensitivity analysis evaluates the impact of each uncertain parameter onto the system, allowing to focusing on the outputs of interest. Robust optimization is applied with a simulation-based approach, the additional costs for a robust design are calculated, as well as the different unit sizes. The low complexity of the developed models allows for an easy integration of new data collected during the development of a project, which is often the case in urban energy planning applications.
EPFL2015
