title: Geothermal Systems in District Heating and Cooling: Multi-objective and Artificial Neural Network Methods for Exergo- and Enviro-economic Optimization
Creators: name: Hemmatabady, Hoofar
Publisher: name: UNSPECIFIED
Year: 2022

description: The significant share of heating, the increasing demand of cooling and the increasing trend towards smart energy systems has made sustainable district heating and cooling (DHC) a viable option for future energy supply in European households. The temporal mismatch between supply and demand is a major obstacle towards the increased utilization of solar energy and waste heat, which can be overcome by seasonal energy storage technologies. Borehole thermal energy storage (BTES) is such a technology. Due to complexity of smart DHC networks, BTES systems need to be implemented considering their interaction with other system components. This has been a main issue that has led to reduced efficiency of some existing projects. Consequently, to exploit BTES systems in sustainable thermal load supply, design guidelines are required for their efficient integration in DHC networks. In this study, Considering the experience from demonstration and pilot projects, different configurations of BTES systems are proposed. The scenarios are categorized into solar-coupled or standalone for heating or combined heating and cooling applications, which are modelled and parametrized in TRNSYS. The proposed scenarios need to be evaluated from technical, economic and environmental points of view in order to ensure efficient operation and to promote market growth. To do so, a dynamic exergo-economic assessment approach is adapted to geothermal systems and is utilized to optimize the scenarios from technical and economic aspects. Moreover, an enviro-economic method is utilized to simultaneously minimize cost and emissions. Finally, the results from exergo- and enviro-economic methods are compared and discussed. For conducting multi-objective optimizations using the proposed evaluation methods, different computational models are proposed and improved at each stage of this study. Initially, a direct optimization approach is developed by coupling TRNSYS and MATLAB. Thereafter, to cope with the high computational cost of the required long-term assessments of geothermal systems, an indirect optimization method is proposed. The indirect method utilizes an artificial neural network (ANN) as a proxy model in an intermediate step of the multi-objective optimization procedure. Furthermore, parallel computation of the objective functions is implemented in the computational model to enhance the speed of the direct and indirect optimizations. Finally, a step-wise optimization method is developed for the operational optimization and control of geothermal systems. Utilizing the developed computational models, multi-objective optimization results of solar-coupled and standalone geothermal layouts reveal that the lowest emissions are realized by central solar-coupled systems, which are discharged actively by heat pumps. Lowering grid temperature level of solar-coupled systems using decentral heat pumps leads to efficient system designs with lower costs, though the most efficient system layouts consist of central heat pumps. Moreover, standalone geothermal systems with passive cooling are suggested as systems with the lowest costs as well as reasonably low emissions and thermodynamic inefficiencies for combined heating and cooling applications. Finally, a hybrid design of solar-coupled and standalone geothermal layouts for combined heating and cooling applications improves the system’s performance compared to each layout separately. The comparison between the results of exergo- and enviro-economic optimization methods confirms that an increase in exergetic efficiency leads to a decrease in environmental impacts and both methods show the same ranking for the evaluated scenarios. Enviro-economic approach is suggested for defining dimensions of geothermal systems, which needs to be supplemented by the developed dynamic exergy analysis to analyze and optimize the operation of different components of a geothermal layout. Finally, the combination of an ANN and multi-objective optimization methods has proven to be an accurate and robust approach for long-term evaluation and comparison of geothermal heating and cooling systems.