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A micro gas turbine (MGT) is a potential option for distributed energy systems driven by fuel and solar energy. The dynamic characteristics of a solar-hybrid microturbine system are essential to its control development and performance assessment. In this paper, a dynamic model is proposed, including an MGT (T100), a tubular air receiver and a sensible thermal energy storage system, which are experimentally validated well. Based on the developed model, the dynamic responses of a solar-hybrid microturbine system in stand-alone mode are studied considering load and Direct Normal Irradiance (DNI) changes. Results show that the fuel consumption is greatly reduced by integrating solar energy, which comes at the cost of system instability; if the investigated system experiences a significant load decrease, heliostats must be oriented away from the air receiver to ensure a stable operation, whereas the load must be increased incrementally to avoid a compressor surge in the case of a significant load increase. Compared to load change, DNI change will cause smaller overshoots of the MGT's rotational speed and surge margin. The system is simulated with measured DNI and shows that the thermal energy storage can reduce the fluctuation of combustor inlet temperature from 130.8 °C to 12.8 °C. • A dynamic model including micro gas turbine, solar receiver and thermal energy storage sub-models is experimentally validated. • Dynamic performance of a solar-hybrid microturbine system is investigated with changing load or solar irradiation. • Control strates are proposed for the investigated system. • The maximum share of solar energy reaches 64.7% for the system with an electrical efficiency of 24.1%. [ABSTRACT FROM AUTHOR]

Intelligent design and operation optimization allow energy systems to take advantage of the flexibility that multi-generation provides. This study proposes a basic solar-driven system integrated with thermal energy storage for round-the-clock energy harvesting. A modified configuration is then designed incorporating innovative multi-heat recovery approaches to increase the capacity and product diversity of the basic system. The modified system is able to cover vital urban utilities such as electricity, fresh water, cooling, and hydrogen throughout the day. To overcome the time-consuming procedure of dynamic techno-economic simulation as well as the limitation of commercial engineering equation solvers for tri-objective optimization, a deep learning approach is developed to reduce the computational complexity and improve the analysis accuracy. In this regard, the trained neural networks play an intermediary role in coupling the developed code with the MATLAB optimization toolbox. A comparison between the modified and conventional configurations indicates that implementing the multi-heat recovery approach results in a 29% increase in power generation while only increasing the overall system cost by 1.97%. From an economic perspective, the Sankey diagram depicts that the storage unit with a cost rate of 12.25 $/h accounts for 6.77% of the plant's cost rate, which enables the system to operate continuously. According to the sensitivity analysis and contour plots, the number of collectors significantly affects the total cost rate and fresh water production capacity while it has no tangible effect on the exergy efficiency. • A novel multigeneration system with thermal energy storage is designed. • A modified configuration with heat recovery is introduced to boost the capacity. • The comparative study indicates a 17.6% improvement in the power generation. • A dynamic simulation code is developed based on meteorological data. • A deep learning techno-economic optimization is applied to the system. [ABSTRACT FROM AUTHOR]

Thermal energy storage system in concentrating solar power plants can guarantee sustainable and stable electricity output in case of highly unstable solar irradiation conditions. In this paper, the lumped parameter method is used to develop the models of different thermal energy storage systems. In order to improve the reliability as well as the prediction accuracy of developed models, the charging/discharging process is firstly simulated, and then the dynamic characteristics of thermal energy storage systems are fully tested by imposing 15% step disturbance of mass flow. The results show that the charging/discharging characteristics of the three different thermal energy storage systems are almost the same, which have little to do with the storage mediums used in the systems. Besides, for the representative 1MWe solar parabolic trough power plant, a 15% step up disturbance of oil mass flow will result in a small increase (1.8% and 1.3% respectively) on the outlet temperature of oil and molten salt. On the other side, a 15% step down disturbance of oil mass flow will lead a 2.7% and 2.2% decrease on the outlet temperature of oil and molten salt respectively. In order to verify the validity of the proposed models, the simulation results are compared with both the design points and representative experimental data from the 1MWe solar parabolic trough power plant. The results show that the maximum relative error is not more than 1% when comparing with the design points and the maximum relative error is not more than 12% when comparing with the representative experimental data. Conclusions of this paper are good references for system design, control and commissioning of concentrating solar power plants. • Thermal energy storage can provide sustainable and stable electricity output. • Lumped parameter method is used to build the model of thermal energy storage. • The dynamic characteristics are tested by a 15% step disturbance of mass flow. • A 15% step-up will result in a 1.3% increase in molten salt outlet temperature. • A 15% step down will result in a 2.2% decrease in molten salt outlet temperature. [ABSTRACT FROM AUTHOR]