Master's Thesis Laura Esling

 

Energetic and Economic Analysis of City Districts under uncertain parameters

Methodology overwiew: model uncertainties evaluation using Monte Carlo method Copyright: EBC Methodology overwiew: model uncertainties evaluation using Monte Carlo method

The issue of global warming and climate change calls for substantial decrease in the use of fossil fuels and greenhouse gas emissions. Large cities offer a great potential for energetic optimization and emission diminution, for instance via usage of efficient energy systems. For this reason, the development of a city district model is useful by indicating directions for optimized design solutions. However, models are based on uncertain input data, which strongly influences the reliability of model results. There are many uncertainty sources in city district modelling, such as the evolution of energy prices, the weather or the occupancy profile of the different city buildings. A huge challenge in simulation is how to deal with the complexity and the uncertainties of the input parameters.

The aim of the study is to quantify the impact of parameter uncertainties on energetic and economic efficiency predictions of different energy system solutions. For this reason, an uncertainty analysis will be performed using the Monte Carlo method. This master-thesis will study an optimization model, which takes as an input, the city district with pre-defined energy systems configurations. This model determines the energy balance of the district such as the economic calculation defined by directive VDI 2067. The first step is the choice of uncertain parameters to be taken into account. The most influential parameters will be screened by means of a sensitivity analysis. In a second phase, a probability density function will be assigned to each relevant program input to characterize their uncertainty. Then the Monte Carlo calculations will be conducted, that means multiple simulations of the outputs of the model by randomly sampling the parameter space according to the defined probability distributions. The purpose is to obtain the standard uncertainty of the energy balance and the economic values. In this way, it is possible to analyse the solutions stability and reliability.