Abstract

The ever-increasing demand for energy in different sectors, such as building sector as one of the main consumers of the energy, is a result of a considerable surge in the world population, starting since the beginning of the industrial revolution in the late 18th century until the present. One of the direct consequence of this rapid growth was the overuse of fossil fuels as the world's main energy source resulting in a rapid depletion of them and thereby increasing the level of CO2 equivalent emissions at an atmospheric level known as greenhouse gasses. Increasing the concentration of these gasses at atmospheric level, exceeding the 400 PPM level for the first time in history, puts the earth at the point of no return. In order to sustain the economic growth while reducing the greenhouse gas concentration at an atmospheric level at the current stage, providing a clean sustainable solution which allows for a steady flow of energy is one of the most vital challenges facing the politician and energy planners. One of the solutions proposed by the energy planners which touches the higher level of energy management is to promote the usage of District Heating Systems (DHS).
While designing an efficient DHS is highly dependant on accurate modeling of the thermal performance of the buildings, district users; yet, limited simulation tools capable of modeling the district energy systems, at a larger scale with a numerous user’s types and with an appropriated level of precision which can potentially be a very laborious and time-consuming process, have been developed. Besides many associated limitations, providing a realistic demand profile of the district energy systems is not a straightforward task due to a high number of parameters involved in predicting a detailed demand profile. To this end, this dissertation focuses on the development of the procedure for energy modeling and optimization of the Hybrid Community District Heating System (H-CDHS) with integrated centralized thermal storage, the 4th generation of district heating systems.
To do so, this study describes the procedure used to develop two types of simplified models to predict the thermal load of a variety of buildings (residential, office, attached, detached, etc.). The predictions were also compared with those made by the detailed simulation models. The simplified model was then utilized to predict the energy demand of a variety of district types (residential, commercial or mix), and its prediction accuracy was compared with those made by detailed model: A good agreement was observed between the results. In next step, the proposed procedure was utilized to predict the heating demand profile of an existing community, WWH community in Glasgow. High prediction accuracy and low computational time of the proposed method illustrates the potential of the proposed method in predicting the heating demand profile of larger scale communities.
In the last step, the proposed load prediction method was coupled with energy simulation tool (TRNSYS) and optimization tool (MATLAB/Simulink) in order to develop a simplified methodology for dynamic optimization of a hybrid community-district heating system (H-CDHS) integrated with a thermal energy storage system. Two existing and newly built community have been defined and the results of the optimization on the equipment size of both communities have been studied. The results for the newly build community is then compared with the one obtained from the conventional equipment sizing methods as well as static optimization methods to obtain potential reduction in equipment size using the proposed method.