Geothermal Systems in District Heating and Cooling: Multi-objective and Artificial Neural Network Methods for Exergo- and Enviro-economic Optimization

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

A Modelica Toolbox for the Simulation of Borehole Thermal Energy Storage Systems

Borehole thermal energy storage (BTES) systems facilitate the subsurface seasonal storage of thermal energy on district heating scales. These systems’ performances are strongly dependent on operational conditions like temperature levels or hydraulic circuitry. Preliminary numerical system simulations improve comprehension of the storage performance and its interdependencies with other system components, but require both accurate and computationally efficient models. This study presents a toolbox for the simulation of borehole thermal energy storage systems in Modelica. The storage model is divided into a borehole heat exchanger (BHE), a local, and a global sub-model. For each sub-model, different modeling approaches can be deployed. To assess the overall performance of the model, two studies are carried out: One compares the model results to those of 3D finite element method (FEM) models to investigate the model’s validity over a large range of parameters. In a second study, the accuracies of the implemented model variants are assessed by comparing their results to monitoring data from an existing BTES system. Both studies prove the validity of the modeling approaches under investigation. Although the differences in accuracy for the compared variants are small, the proper model choice can significantly reduce the computational effort

Optimized Layouts of Borehole Thermal Energy Storage Systems in 4th Generation Grids

Borehole thermal energy storage (BTES) systems are a viable option to meet the increasing cooling demand and to increase the sustainability of low-temperature district heating and cooling (DHC) grids. They are able to store the rejected heat of cooling cycles on a seasonal basis and deliver this heat during the heating season. However, their efficient practical implementation requires a thorough analysis from technical, economic and environmental points of view. In this comparative study, a dynamic exergoeconomic assessment is adopted to evaluate various options for integrating such a storage system into 4th generation DHC grids in heating dominated regions. For this purpose, different layouts are modeled and parameterized. Multi-objective optimization is conducted, varying the most important design variables in order to maximize exergetic efficiency and to minimize levelized cost of energy (LCOE). A comparison of the optimal designs of the different layouts reveals that passive cooling together with maximizing the heating temperature shift, accomplished by a heat pump, lead to optimal designs. Component-wise exergy and cost analysis of the most efficient designs highlights that heat pumps are responsible for the highest share in inefficiency while the installation of BTES has a high impact in the LCOE. BTES and buffer storage tanks have the lowest exergy destruction for all layouts and increasing the BTES volume results in more efficient DHC grids