• Energy Research
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Abstract Multiple energy generating cycles such as tri-generation cycles, which produce heat and cold in addition to power through burning of a primary fuel, have increasingly been used in recent decades. On the other hand, advanced exergy analysis of thermodynamic systems by splitting exergy destruction into endogenous and exogenous parts identifies internal irreversibilities of each of the components and the effect of these irreversibilities on the performance of other components. Therefore, main sources of exergy destruction in cycles can be highlighted and useful recommendations in order to improve the performance of thermodynamic cycles can be presented. In the present work, a tri-generation cycle with 100 MW power production, 70 MW heat and 9 MW cooling capacity is considered. For this tri-generation cycle, effects of various thermodynamic parameters on the amount of endogenous and exogenous exergy destructions, exergy loss and the amount of fuel consumption, are investigated. The results indicate that, increasing compressor pressure ratio, pre-heater outlet temperature and excess air leads to better combustion and lower exergy loss and fuel consumption. Increasing the mass flow rate of steam generator, while keeping the cycle outlet temperature constant and considering cooling capacity variable, lead to increase the first- and second-law efficiencies of the cycle.

Abstract Multiple energy generating cycles such as tri-generation cycles, which produce heat and cold in addition to power through burning of a primary fuel, have increasingly been used in recent decades. On the other hand, advanced exergy analysis of thermodynamic systems by splitting exergy destruction into endogenous and exogenous parts identifies internal irreversibilities of each of the components and the effect of these irreversibilities on the performance of other components. Therefore, main sources of exergy destruction in cycles can be highlighted and useful recommendations in order to improve the performance of thermodynamic cycles can be presented. In the present work, a tri-generation cycle with 100 MW power production, 70 MW heat and 9 MW cooling capacity is considered. For this tri-generation cycle, effects of various thermodynamic parameters on the amount of endogenous and exogenous exergy destructions, exergy loss and the amount of fuel consumption, are investigated. The results indicate that, increasing compressor pressure ratio, pre-heater outlet temperature and excess air leads to better combustion and lower exergy loss and fuel consumption. Increasing the mass flow rate of steam generator, while keeping the cycle outlet temperature constant and considering cooling capacity variable, lead to increase the first- and second-law efficiencies of the cycle.

Abstract Accurate power forecasting is of great importance to the turbine control and predictive maintenance. However, traditional physics models and statistical models can no longer meet the needs of precision and flexibility when thermal power plants frequently undertake more and more peak and frequency modulation tasks. In this study, the recurrent neural network (RNN) and convolutional neural network (CNN) for power prediction are proposed, and are applied to predict real-time power of turbine based on DCS data (recorded for 719 days) from a power plant. In addition, the performances of two deep learning models and five typical machine learning models are compared, including prediction deviation, variance and time cost. It is found that deep learning models outperform other shallow models and RNN model performs best in balancing the accuracy-efficient trade-off for power prediction (the relative prediction error of 99.76% samples is less than 1% in all load range for test 216 days). Moreover, the influence of training size and input time-steps on the performance of RNN model is also explored. The model can achieve remarkable performance by learning only 30% samples (about 216 days) with 3 input time-steps (about 60 s). Those results of the proposed models based on deep-learning methods indicated that deep learning is of great help to improve the accuracy of turbine power prediction. It is therefore convinced that those models have a high potential for turbine control and predictable maintenance in actual industrial scenarios.

Abstract Accurate power forecasting is of great importance to the turbine control and predictive maintenance. However, traditional physics models and statistical models can no longer meet the needs of precision and flexibility when thermal power plants frequently undertake more and more peak and frequency modulation tasks. In this study, the recurrent neural network (RNN) and convolutional neural network (CNN) for power prediction are proposed, and are applied to predict real-time power of turbine based on DCS data (recorded for 719 days) from a power plant. In addition, the performances of two deep learning models and five typical machine learning models are compared, including prediction deviation, variance and time cost. It is found that deep learning models outperform other shallow models and RNN model performs best in balancing the accuracy-efficient trade-off for power prediction (the relative prediction error of 99.76% samples is less than 1% in all load range for test 216 days). Moreover, the influence of training size and input time-steps on the performance of RNN model is also explored. The model can achieve remarkable performance by learning only 30% samples (about 216 days) with 3 input time-steps (about 60 s). Those results of the proposed models based on deep-learning methods indicated that deep learning is of great help to improve the accuracy of turbine power prediction. It is therefore convinced that those models have a high potential for turbine control and predictable maintenance in actual industrial scenarios.

Abstract To improve the conversion efficiency of thermophotovoltaic devices, we designed a thermophotovoltaic system based on an InAs/InGaAsSb/GaSb three-junction tandem cell. The tandem cell can recover photons in the wavelength range of 200–3650 nm and therefore enhance the output power of the system. To further improve system performance, we designed a multilayer circular truncated cone metamaterial emitter matching the tandem cell. Existing TPV systems based on multi-junction tandem PV cells can achieve conversion efficiencies of 33.3%–41%, while the thermophotovoltaic system coupled with the multilayer circular truncated cone metamaterial can recover more photons of 1.44 mol/(m2·s) and achieve a higher conversion efficiency of 52.8% at 1773 K. The thermophotovoltaic system designed here demonstrates an extremely high energy conversion efficiency and has good application prospects.

Abstract To improve the conversion efficiency of thermophotovoltaic devices, we designed a thermophotovoltaic system based on an InAs/InGaAsSb/GaSb three-junction tandem cell. The tandem cell can recover photons in the wavelength range of 200–3650 nm and therefore enhance the output power of the system. To further improve system performance, we designed a multilayer circular truncated cone metamaterial emitter matching the tandem cell. Existing TPV systems based on multi-junction tandem PV cells can achieve conversion efficiencies of 33.3%–41%, while the thermophotovoltaic system coupled with the multilayer circular truncated cone metamaterial can recover more photons of 1.44 mol/(m2·s) and achieve a higher conversion efficiency of 52.8% at 1773 K. The thermophotovoltaic system designed here demonstrates an extremely high energy conversion efficiency and has good application prospects.

Abstract In this paper, energy and exergy analyses of the geothermal-based hydrogen production via thermochemical water decomposition using a new, four-step copper–chlorine (Cu–Cl) cycle are conducted, and the respective cycle energy and exergy efficiencies are examined. Also, a parametric study is performed to investigate how each step of the cycle and its overall cycle performance are affected by reference environment temperatures, reaction temperatures, as well as energy efficiency of the geothermal power plant itself. As a result, overall energy and exergy efficiencies of the cycle are found to be 21.67% and 19.35%, respectively, for a reference case.

Abstract In this paper, energy and exergy analyses of the geothermal-based hydrogen production via thermochemical water decomposition using a new, four-step copper–chlorine (Cu–Cl) cycle are conducted, and the respective cycle energy and exergy efficiencies are examined. Also, a parametric study is performed to investigate how each step of the cycle and its overall cycle performance are affected by reference environment temperatures, reaction temperatures, as well as energy efficiency of the geothermal power plant itself. As a result, overall energy and exergy efficiencies of the cycle are found to be 21.67% and 19.35%, respectively, for a reference case.

Abstract The consumption of buildings for the production of heat is expected to decrease in Switzerland in the coming years, in particular following policies encouraging the refurbishment of buildings. This will notably have an impact on the natural gas network, in parallel with the penetration of electric-driven heat pumps. Through a detailed optimization scheme, the evolution of the natural gas (NG) distribution network is studied over a future period of forty years, i.e. up to 2050, on the territory of a large Swiss canton. By way of installing large shares of co-generation units, it is shown that the NG network does not lose its meshed structure, while continuing to play a central role in the production of heat and the generation of part of the additional electricity demand associated with the concomitant penetration of heat pumps. As a novel result, the developed optimization framework allows a detailed, geographically precise view of both the evolution of the NG network, as well as of the optimal location of selected technologies. The adoption of energy networks convergence in urban zones therefore can lead to relevant synergies, avoiding over-investments, increasing system resilience and fostering the use of efficient technologies.