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Renewable energy has become more popular since it is cost-effective and more efficient than conventional energy sources. Biomass-based renewable energy is primarily used in emerging economies to ensure environmental sustainability. This study examines the asymmetric correlation between biomass energy consumption and CO2 emissions in the top-10 biomass energy consumer countries (Brazil, Canada, Thailand, China, Italy, India, Germany, USA, UK, and Japan). A new approach "Quantile-onQuantile (QQ)" is employed by utilizing the data from 1991 to 2018. Biomass energy consumption, with the exception of Thailand, significantly mitigates CO2 emissions at various quantiles in selected countries. As a robustness check, we used the quantile regression test, whose findings are consistent with the outcomes from the quantile-on-quantile method. However, the degree of asymmetry in the biomass energy-CO2 nexus varies by country, necessitating extra attention and government vigilance when developing biomass energy and environmental policies.

Abstract Metallic solid-liquid phase change materials (SLPCMs) are crucial for the thermal energy storage technology of various industrial systems. However, the encapsulation of metallic SLPCMs is still technically difficult. In this pursuit, the present research envisaged the development of a novel technology to successfully prepare the core(=Al-Si/Bi)/void/shell(=Al2O3) composite SLPCMs by using Al/Bi immiscible alloy powders as starting material and tetraethoxysilane as SiO2 source. The Al-Si alloy and Al2O3 shell were in-situ synthesized by the displacement reaction between SiO2 and molten Al. Interestingly, most of the Bi distributed in the shell of Al/Bi immiscible alloy powders could not only improve the activity of alloy powders and promote the formation of precursor shell, but also be recycled by evaporation to form the void layer during the calcination process of composite SLPCMs. The produced void layer provided a space buffer to alleviate the volume expansion of the core SLPCM, and thereby improving the thermal cycling stability of the prepared composite SLPCMs. The thermal cycling test results showed that after 300 thermal cycles, the melting latent heat reduction of the core(=Al-Si/Bi)/void/shell(=Al2O3) composite SLPCMs (24.3–31.7 J/g) was much less than that of the core(=Al-Si)/shell(=Al2O3) composite SLPCM (58.1 J/g). Moreover, the prepared Al-Si/Bi/Al2O3 exhibited an adjustable melting temperature (571.9 °C to 631.9 °C) and average particle diameter (39.3 μm to 112.6 μm), relatively high thermal conductivity [2.068 W(mK)−1 to 2.966 W(mK)−1], and excellent thermal energy storage capacity (209.5 J/g to 278.2 J/g). Thus, the prepared Al-Si/Bi/Al2O3 composite SLPCMs are potential thermal energy storage materials, which can be used to improve the energy efficiency of various industrial systems.

This paper proposes an improved L index which is adapted to analyze the voltage stability of power system with the ZIP loads. First it introduces derivation of the traditional L index. Then, it analyzes the differences between ZIP loads and PQ loads and then proposes an improved version of L index based on the derivation. Furthermore, the proposed improved index is used to analyze the voltage stability under different operation conditions of the power system with ZIP loads. The simulations show the effectiveness of the proposed improved L index.

In Belgium, and many other countries, rooftop solar panels are becoming a ubiquitous form of decentralised energy production. The increasing share of these distributed installations however imposes many challenges on the operators of the low-voltage distribution grid. They must keep the voltage levels and voltage balance on their grids in check and are often regulatory required to provide sufficient reception capacity for new power producing installations. By placing solar panels in different inclinations and azimuth angles, power production profiles can possibly be shifted to align more with residential power consumption profiles. In this article, it is investigated if the orientation of solar panels can have a mitigating impact on the integration problems on residential low voltage distribution grids. An improved simulation model of a solar panel installation is constructed, which is used to simulate the impact on a residential distribution grid. To stay as close to real-life conditions as possible, real irradiation data and a model of an existing grid are used. Both the developed model as the results on grid impact are evaluated.

AbstractAlloy anodes composed of microsized particles receive increasing attention recently, which outperform the nanostructured counterparts in both the manufacturing cost and volumetric energy density. However, the pulverization of particles and fracture of solid electrolyte interphase (SEI) during cycling brings about fast capacity degradation. Herein, it is shown how normally considered fragile SEI can become highly elastic through electrolyte chemistry regulation. Compared to the SEI constructed in classic carbonate electrolyte, the atomic force microscopy tests reveal that the one built in ether‐based electrolyte doubles the maximum elastic strain to accommodate the repeated swelling‐contracting. Such an SEI effectively encapsulates the microsized Sb anodes to prevent the capacity loss from particle isolation. Coupled with an intercalation‐assisted alloying reaction mechanism, a sustained capacity of ≈573 mAh g−1 after 180 cycles at 0.1 A g−1 with outstanding initial Coulombic efficiency is obtained, which is among the highest values achieved in K‐ion batteries. This study emphasizes the significance of building robust SEI, which offers the opportunity to enable stable microsized alloy anodes.

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.

This paper proposes a sustainability-oriented multiobjective optimization model for siting and sizing DG plants in distribution systems. Life cycle exergy (LCE) is used as a unified indicator of the entire system’s environmental sustainability, and it is optimized as an objective function in the model. Other two objective functions include economic cost and expected power loss. Chance constraints are used to control the operation risks caused by the uncertain power loads and renewable energies. A semilinearized simulation method is proposed and combined with the Latin hypercube sampling (LHS) method to improve the efficiency of probabilistic load flow (PLF) analysis which is repeatedly performed to verify the chance constraints. A numerical study based on the modified IEEE 33-node system is performed to verify the proposed method. Numerical results show that the proposed semilinearized simulation method reduces about 93.3% of the calculation time of PLF analysis and guarantees satisfying accuracy. The results also indicate that benefits for environmental sustainability of using DG plants can be effectively reflected by the proposed model which helps the planner to make rational decision towards sustainable development of the distribution system.