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Abstract Catalyst layer structural changes in polymer electrolyte membrane fuel cells have significant impact on the cell performance and durability. In this study, ex-situ experiments are designed to investigate the effect of humidity and/or thermal cycles on the structural changes of catalyst layers. The relative humidity and temperature are controlled by an environmental chamber and the catalyst layer structure is characterized by scanning electron microscopy and optical microscopy. The experimental results indicate that crack growth and development, catalyst agglomerate detachment, and surface bulges are the main structural changes of the catalyst layers. Applying relative humidity and thermal cycling simultaneously causes the most significant crack growth, while applying thermal cycling alone causes no appreciable changes. This indicates that the absolute humidity is the key parameter for the crack growth. Through cyclic voltammetry analysis, it is shown that the electrochemical active surface area decreases from 64.1 m2 g−1 to 49.1 m2 g−1 after 500 combined relative humidity and thermal cycles. Analyses of electrochemical impedance spectroscopy show that the charge transfer resistance and ohmic resistance increase significantly after 500 combined relative humidity and thermal cycles, causing the cell performance degradation.

The purpose of research on calculation of low voltage distribution network theoretical line loss is define a calculating method and through the method to calculate the low voltage distribution network theoretical line losses. The calculation of low voltage distribution network theoretical line losses is the foundation of the power system economic operation, the reactive power optimization and the grid technique modification. Through the theoretical loss calculation, the operation management department can undertake line loss analysis, thus they can adopt feasible loss reduction measures, and achieve maximum economic benefits. this paper puts forward through the power flow calculation system, to calculate the low-voltage distribution network users theoretical line loss. Matpower is a softwate package of matlab, which is for power flow and optimal power flow. Based on the relationship between power loss and power flow calculation, calculated the power flow using matpower package, and explained the role in the use of computing to slove the line losses. Through the IEEE (Institute of Electrical and Electronic Engineers) example of 14 nodes and some specific instances of low voltage distribution network calculation, proves that the method is feasible. The results also shows that matpower is suitable for calculating low voltage distribution network theoretical line loss.

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.

AbstractRecently, modern power systems depend heavily on MicroGrids (MGs), which can accommodate Distributed Energy Resources (DERs) economically and with high flexibility. MGs integrated with DERs can assist in enhancing energy security, significant cost savings, and reduction in emission of greenhouse gases. In this paper, the assessment of operating performance of proposed MG system with DERs is employed to investigate the multi‐objective problems of cost optimization and economic scheduling. A grid‐connected Micro‐grid (MG) combined with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is implemented to model the problem. This proposed model is considered as a test system for cost optimization and battery charging/discharging optimization. The developed framework is presented as multi‐objective function with constraints that can be tackled using an effective optimization technique. The above stochastic multi‐objective problem is optimized using various commonly used Physics based Meta‐heuristic techniques such as Simulated Annealing (SA), Harmony Search (HS), Slime Mold Algorithm (SMA), Gravitational Search Algorithm (GSA), Black Hole Optimization (BHO), Sine Cosine Algorithm (SCA), Multiverse optimization (MVO) and Lightning Search Algorithm (LSA). The assessment of the aforementioned physics‐based optimization techniques used on the proposed MG test system is compared using the results. According to the analysis, Black Hole Optimization (BHO) and Lightning Search Algorithm (LSA) both provide greater cost savings overall and for battery charging, respectively. The suggested optimization methods will take the BESS charging/discharging pattern and total cost savings into account.

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.