Renewable Energy Systems (M. Sc.)
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This thesis presents an insight into the possibility of implementing the intelligent sector coupling between the heat and electricity supply grids by using the combination of a solar PV system, decentralized heat pumps and decentralized thermal energy storage in an existing district heating system with variable temperatures in Germany. The district heating system was optimized for the summer period utilizing the existing system’s Modelica language model in Dymola software. In order to implement the intelligent sector coupling between heat and generated renewable energy-based electricity a separate control theory was developed so that the generated electricity can be utilized in an efficient way to operate the decentralized heat pumps. Moreover, the role of decentralized thermal energy storage was also considered for the appropriate sizing of solar PV system so as to utilize the maximum amount of renewable energy-based electricity to produce heat during the summer period. The assessment of the energy production and utilization from different systems was carried out by performing the electrical and heat energy analysis of the simulation results.
The findings of the thesis exhibited an appropriately sized solar PV system which fulfilled up to the 80 % of total electricity consumption for heat production during the summer period by implementing an intelligent sector coupling between the heat and electricity supply grids.
With the increasing rate of urbanization in Africa, the need for sustainable building solutions have become more paramount. More so, the tropical weather condition in Africa, means that building requirement for cooling activities would equally increase. Thus, resulting in growing energy consumption in Buildings. Therefore, it is based on these concerns, that the proposed herbarium building in the Okavango Research Institutes (ORI), in Maun Botswana, seeks to examine environmentally sustainable solution for the proposed herbarium building. This research study, therefore, focuses on evaluating the environmental impact and energy saving potential from the use of commonly available construction materials used for façade units of a building using life cycle assessment methodologies. Thus, clay bricks and hollow concrete blocks were considered for analysis.
The life cycle assessment professional software tool, SimaPro 8.4.0 was used in performing a life cycle assessment of the considered building components (clay bricks, hollow concrete blocks, reinforce concrete, window units and PV system). Hence, using this assessment tool, environmental impact assessment on the considered building components was carried out, and the evaluation of the impact category using two environmental indicators, namely: global warming potential and cumulative energy demand, was performed.
Furthermore, a comparative study between the considered façade materials (clay brick and hollow concrete block) was performed in order to evaluate the contribution of the different façade unit to the ecological sink of the building system. Also, a comparative study between the façade unit and the other building components was performed to understand the significance of the façade unit in the overall ecological sink of the building. In addition, a CO2 mitigation potential from the use of a PV system for the supply of energy for cooling activities in the proposed herbarium was evaluated against the use of electricity for supply from the Botswana national grid.
Consequently, the results obtained showed that hollow concrete block offers a more environmentally friendly solution than clay bricks when used as façade unit in a building system. This because, it contributes twice as less GHG emission to the building ecological sink when compared with clay bricks. Although, clay bricks has a slightly better energy savings potential during the use-phase than hollow concrete block, the environmental impact associated with the production of clay bricks in form of embodied energy, outweighs the energy saving gained during the use-phase for a reference lifetime of 50 years when compared with hollow concrete block.
Also, the PV system’s CO2 mitigation potential when used for cooling activities during the reference lifetime of the building (50 years), resulted in a CO2 saving potential of 2.03E+06 CO2 Kg-eq. This finding showed that the PV system has a more significant impact on the reduction in the environmental impact of the building system than the environmental impact associated to the façade unit of the proposed herbarium building in Botswana.
Smart meter technology implementation in the last decade had initiated many data collection processes, which have provided a strong foundation for the development of Artificial Intelligence (AI) based load monitoring systems. It is easier to identify the energy-saving potential with the help of advanced load monitoring systems. Since 2015, deep-learning-based Nonintrusive load monitoring (NILM) is being focused in the research community. It requires minimal hardware, which can justify its development and maintenance cost. Several AI-based models and tools are available for load monitoring, but it is challenging to identify a suitable model for the specific application. There is still a domain-specific transformation, and considerations are usually required. The residential sector has been the focus area due to the market size, but the industrial sector still has massive potential for research and development.
Thus, in the presented thesis, dairy farms in Germany are targeted for developing a power disaggregation algorithm based on deep learning, which can identify the on/off state of individual appliances in the farm from the aggregated load profile data. Mainly four appliances named milk cooling (MK), milk pump (MP), vacuum pump (VP), and cleaning automatic machine (SA) are targeted for disaggregation. NILM is a promising approach to identify individual operating times of appliances. Thus, deep neural networkbased algorithms are developed, focusing mainly on one-dimensional convolution neural network (1D-CNN) and recurrent neural network (RNN).
Literature research was carried out to determine the state-of-the-art of deep-learningbased NILM and understand AI technology. Data acquisition for model development and testing was made from four dairy farms based out of Bavaria, Germany. The presented work provides a detailed discussion about data pre-processing and development of models. The result shows that deep-learning-based disaggregation algorithms outperform for this application area, and the proposed model successfully identifies the states of individual appliances. The presented work provides a foundation for modifying the proposed algorithm or developing a new algorithm for real-time power disaggregation.