Study of Low Carbon Tri-generation System Primed with Maisotsenko Combustion Turbine Cycle in Response to Demand Forecast


Student thesis: Doctoral Thesis

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Awarding Institution
Award date9 Apr 2019


The district-scale distributed energy systems, such as the tri-generation system, are more efficient than many conventional energy supply systems. Tri-generation system generates electricity, heating and cooling energy simultaneously, which leads to higher overall thermodynamic efficiency through the utilisation of waste heat. It also offers the opportunity to utilise local renewable energy and adds the flexibility of power supply through energy storage. The transmission losses can be reduced significantly since the power generation site is located close to the end users. To develop a more advanced district level tri-generation system, two research directions can be considered, one is the better management of the tri-generation system, and another is the technology improvement of the tri-generation system.

In terms of system management, the control strategy and related load forecasting are critical. As for technology improvement, the application of more advanced facilities is mainly considered. The design optimisation and performance test can also be conducted to guarantee the benefits of technology improvement. Though there are many previous investigations on tri-generation systems, several problems remain unsolved. Firstly, the existing data-driven load forecasting models usually lack detailed input data analysis, which inevitably reduces the prediction accuracy due to the inclusion of irrelevant and redundant input data. Secondly, the more advanced components (e.g. Maisotsenko combustion turbine cycle (MCTC) and latent heat storage (LHS)) have higher efficiency, which has potentials to serve for the tri-generation system. But there are few studies offering accurate models to provide their performance data. Besides, though there are many investigations on the tri-generation system design optimisation and control strategies, these results are not based on a dynamic model and have not tested in different climatic conditions, which leads to the inaccurate estimation of the tri-generation system performance. Therefore, this study provides a systematic investigation of an MCTC primed tri-generation system based on a hypothetical university complex. The related topics in short-term load forecasting, modelling of MCTC and LHS, design optimisation and advanced control strategies are focused on.

For better system management, the accurate load forecasting is necessary. In this study, the influential factors of the building energy consumption were first analysed using the mutual information algorithm. Then, irrelevant and redundant data were filtered. The input data were also regrouped based on their load profile characteristics. After data filtering and regrouping, a load forecasting model was developed using Artificial Neural Network algorithm to predict the next 24-hour building loads. The results of the case study echoed the rationale of the proposed load forecasting model and demonstrated that the forecasting accuracy could be enhanced by data filtering and regrouping. In addition, a two-stage model predictive control strategy (MPC) was tested based on the refined load forecasting model. The simulation results demonstrated that the two-stage MPC control strategy could make the tri-generation system operate more economically.

Another significant aspect of the investigation on the tri-generation system is technology improvement. The MCTC was selected as the prime mover in this study, considering its appropriate range of capacity for district-level application, and its high electrical generation efficiency. Due to its critical role in determining the performance of the tri-generation system, an extensive mathematical model has been proposed to estimate its performance. The dew-point effectiveness and wet-bulb effectiveness of air saturator, which are the essential parameters affecting MCTC performance, were evaluated based on a numerical heat and mass transfer model. Besides, the tri-generation system integrated with thermal storage can manage the mismatch between supply and demand sides. Due to its higher energy density and simple structure, the finned LHS has been found distinctly advantageous. By considering both the heat conduction and natural convection phenomena, a 2-D numerical model of the LHS was developed based on the effective heat capacity method, and the detailed phase change processes can be detected. The tri-generation system design optimisation was also introduced, combining genetic algorithm with a dynamic model of the proposed tri-generation system. It helps to optimally set the sizes and numbers of the components of the tri-generation system. The simulation results indicated that design optimisation could fully exploit the potential of the proposed tri-generation system in two climate regions.

Through this investigation, the better system management and technology improvement of the tri-generation system can be achieved. The main contributions of this study are,
1. Test the two-stage control strategy for district-level tri-generation system operation control with load forecasting models based on detailed input data analysis;
2. Develop a tri-generation system with more advanced prime mover MCTC;
3. Conduct design optimisation for an MCTC primed tri-generation system with dynamic model and test its performance in two different climate regions.