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Abstract In the current study, a new solar-driven triple cycle is proposed to allow power generation during low insolation periods. This triple cycle integrates the solar gas-turbine top cycle, the steam Rankine cycle, and the Kalina bottom cycle. During the top cycle of the proposed system, compressed air was heated to 1000 °C or higher in the solar tower receiver. The heated compressed air was then used to drive the gas turbine to generate electricity. A Rankine cycle with a back-pressure steam turbine was utilized to recover waste heat from the gas turbine, thereby generating electricity through the steam turbine. The bottom cycle is the Kalina cycle, which comprises another back-pressure turbine and utilizes ammonia–water mixture as working fluid. After driving the steam Rankine cycle, the flue gas from the gas turbine sequentially heats the ammonia–water mixture to produce power. A new operational strategy was presented to generate electricity during low insolation period without the backup of fossil fuel. In middle insolation periods, the air is heated by the solar field and then directly drives the steam Rankine cycle, bypassing the gas turbine. In low insolation periods, the heated air directly drive the Kalina cycle, bypassing the Brayton cycle and the steam Rankine cycle. The off-design performance was investigated and the irreversibility was disclosed with the aid of the energy-utilization diagram method. Thus, the proposed system can utilize low insolation to generate electricity. This study provides a possibility to improve the solar–electric efficiency.

Abstract This article introduces an effective stochastic operation framework for optimal energy management of the shipboard power systems including large, nonlinear and dynamic loads. The proposed framework divides the ship power system into several agents, which coordinate with each other based on their demands/supplies until. The alternating direction method of multipliers (ADMM) is deployed as the multi-agent framework to solve the reformulated distributed energy management problem in the ship. Two types of turbo-generators are considered in the proposed system model, including single-shaft and twin-shaft models, to increase the part-load efficiency in certain times when facing variable speed operation. The proposed distributed framework is equipped with a recursive mechanism, which helps the ship system for running optimal load scheduling when facing insufficient power generation. In order to model the uncertainty effects associated with the forecast error in the interval-ahead load demand, a stochastic framework based on unscented transform is devised which can work in the nonlinear and correlated environments of shipboard power systems. Due to the nonlinear cost function in each agent, a powerful optimization algorithm based on modified θ-firefly algorithm (Mθ-FOA) is proposed. This is a phasor algorithm, which helps for escaping from premature convergence and getting trapped in local optima. The appropriate performance of the proposed stochastic model is examined on the real dataset of a ship power system. The simulation results show the high robustness, guarantied consensus, economic operation and feasible solution when power generation shortage based on load shedding in the system.

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 China's carbon emissions have been ranking first in the world. This study filled in the gaps in research, decomposed carbon intensity from the perspective of time, space and industry. A decoupling effort model based on factor decomposition models was constructed to analyze the driving factors of carbon emissions and economic decoupling, which builded a foundation for achieving sustainable economic development. Using the Logarithmic Mean Divisia Index method (LMDI), the paper measured the carbon emission intensity of 29 provinces and cities in China from 1998 to 2019, and decomposed the decoupling effect between GDP and carbon emission on the basis of factor decomposition by tapio. The results showed that: (1) Carbon intensity declined first, then rise lightly, and finally declined steadily. For the primary industry and the tertiary industry, the carbon intensity declined steadily, while the carbon intensity increased accordingly to the overall carbon intensity. In terms of spatial evolution, the regional differences between different provinces decreased correspondingly. (2) The cumulative contribution rates of these three effects, i.e., technological progress, industrial structure and regional scale were 106.3299%, −15.1486% and 8.8188%, respectively. There were obvious differences of these cumulative contribution rates of carbon intensity among different provinces. (3) From the perspective of industrial, technological progress effect is the largest contribution for carbon intensity in the secondary industry. The Industrial structure effect mainly affects the primary and tertiary industries; and no significant difference in regional scale effect. (4) The decoupling effect gradually improved, and technological progress has played an absolute leading role in promoting the decoupling effect. Based on the research results, the key policy recommendation are put forward as follows: (1) Further improve the technological level and support clean technology enterprises. (2) Promote industrial upgrading in backward industrial provinces (3) Promote regional assistance and the introduction of high-quality foreign investment.

In this paper, an experimental and numerical study on the flow evolution and energy extraction performance of a flapping-airfoil power generator is conducted, and wide ranges of motion parameters are considered. PIV (Particle image velocimetry) method is used for flow visualization around the flapping airfoil, and numerical simulations predicting the flow field and power generation process are also conducted and compared with the test results. It is found that the computed flow field basically agrees well with the experimental results, and the power generation ability of the power generator is validated. At a fixed plunging amplitude H0, both the decreasing reduced frequency k at a fixed pitching amplitude θ0, and the increasing θ0 at a fixed k lead to larger sizes of flow separation. For the flapping motion studied, both plunging contribution and pitching contribution play important roles in the energy extraction, which is very different from the traditionally imposed flapping profile. Besides, at a fixed k, the increasing H0 induces a slight increase in pitching contribution, and the increasing θ0 is beneficial to power generation enhancement. Moreover, the increasing H0 induces a notable increase in output power at relatively low k, while it has little effect on efficiency enhancement.

Abstract Based on the design concept of a fourth-generation smart pipe network system, this paper innovatively proposes a new TOTS (Two-supply/One-return, triple pipe structure) arrangement method for district heating systems. Moreover, to accurately predict the heat loss due to the pipeline operation of the multi-pipe system, based on the multipole calculation method, a new heat loss theoretical analytical model for the TOTS was created; additionally, a corresponding three-dimensional numerical simulation model was established, which was analyzed and numerically solved. The results showed that in comparison with thermal loss data measured by Danfoss et al., the above analytical and numerical models have a high accuracy, and the deviation is within 2%. Additionally, through calculations, it was found that the distance between the heating pipes is an important factor that affects the total heat loss from the new multi-control heating system and the actual heat exchange between pipes.

Abstract Work exchanger network (WEN) integration is crucial way to conserve energy for gas networks. Refinery hydrogen allocation network (HAN) determines its work sources and sinks of hydrogen streams and even WEN. Hydrogen gas streams are always non-ideal and there is also energy loss in their pressurization and depressurization processes through direct work exchangers. Based on thermodynamic principles for gas property and work exchange through compressors, expanders and direct work exchangers, this paper proposes a novel methodology for cost-effective refinery hydrogen networks. A stage-wise superstructure consisting of hydrogen allocation network (HAN) and work exchanger network (WEN) is built as problem illustration and the corresponding mixed integer nonlinear programming (MINLP) models for HAN and WEN are formulated to successively perform mass and work networks integration for total annualized cost (TAC) minimization. A refinery case is studied and results show that WEN can conserve 27.4% power utility consumption and reduce 50.9% investment cost. Case study results comparison demonstrates that the consideration of thermodynamic principles is of great significance to real-world energy conservation and investment cost reduction of WEN.

Abstract This paper focused on the intake and exhaust structure optimization of a single-screw expander (SSE) by using numerical method. The effects of internal volume ratio, the shape and clearance height of the intake port and exhaust area were numerically analyzed (R123 and HFO-1336mzz(Z) were selected as working fluids) to reduce the energy losses (more than 30%) caused by pressure loss and leakage in intake process and exhaust pressure loss. The suggestions of reducing the internal volume ratio and clearance height at intake port and removing the closed helix line were given based on the simulation results. Then, the optimized SSE was developed and experimentally tested in ORC system with R123 as working fluid. Results showed that the filling factor of the prototype was reduced from 125% to nearly 100% and the highest shaft efficiency was increased from 56% to 67.7% at 3000 rpm. The enhanced performance of the new prototype proves the correctness of the numerical optimization and this is expected to provide guidance for the design of SSE.