Energy Equipment Research Articles

Characteristics of Particle-Wet Wall Impact Damage: Effects of Liquid Film Parameters

Particle impact damage significantly affects the safe and stable operation of energy and chemical process equipment. During particle transportation, a liquid film may form on the wall, influencing the damage mechanism caused by particle effect. This study investigates the effects of liquid film parameters, specifically viscosity and thickness, on wall damage. Hydroxypropyl Methylcellulose (HPMC) solution was applied as a liquid film, and the effect of the liquid film's viscosity and thickness was examined through impact damage experiments using a particle (45 steel) and a wall (1060 aluminum alloy) with the liquid film. Results indicate that the presence of a liquid film can inhibit wall damage. Increased liquid film viscosity and thickness reduce wall damage, but they affect the turning points of damage characteristic parameters differently concerning impact angle and velocity. Finally, the study establishes correlation between dry and wet impact by introducing the effect coefficient of liquid film. This reveals the alteration rules of the influence coefficient and proposes a wet impact damage model, providing a reference for the damage prediction in process equipment.

Power system frequency nadir prediction based on data-driven and power-frequency polynomial fitting

As the proportion of renewable energy and power electronics equipment continues to rise, the level of rotational inertia decreases considerably, resulting in severe frequency stability challenges to the power grid. It is of great significance to accurately predict the frequency nadir following a large disturbance. This paper proposes a novel data-model fusion-driven approach for the prediction of frequency nadir. As the physics-driven part, a Simplified Prediction Model (SPM) based on power-frequency polynomial fitting is developed to quickly produce the frequency nadir. As the data-driven part, Back Propagation Neural Network (BPNN) is deployed to correct the errors of the SPM to achieve more accurate results. This serial integration scheme not only obtains the final prediction result with higher accuracy, but also meets the computational efficiency requirements of online prediction. Compared with existing integration-driven methods, SPM only focuses on the active power-frequency characteristics of the system, which retains the most critical effects and greatly reduces the dependence of BPNN on sample data quality. Case studies on a modified IEEE 39-bus system verify the effectiveness of the proposed approach.

CoFe2O4 nanoparticle embedded carbon nanofibers: A promising non-noble metal catalyst for oxygen evolution reaction

There is a strong demand for noble metal-free durable catalysts to facilitate the advancement of clean, sustainable energy technologies and to replace energy equipment reliant on fossil fuels. In this study, metal-organic framework (MOF)-derived CoFe2O4 (CFO) nanoparticle-embedded electrospun carbon nanofibers (CNFs) were synthesized. Nanofibers were calcined at three different temperatures to obtain optimum electrocatalytic performance in oxygen evolution reaction (OER). Nanofibers annealed at 800 °C (CFO@CNFs 800) displayed a reduced overpotential of 300 mV while maintaining a steady current density of 10 mA cm−2. CFO@CNFs 800 demonstrated Tafel slope 42 mV dec−1 with excellent long-term stability up to 48 h. The synergistic effect of the redox activity of CFO coupled with the high surface area and conductivity of carbon nanofibers is responsible for enhanced OER performance.

Analysis of the Impact of China's Carbon Neutrality Policy on the Environment and Economy at Home and Abroad

China has made remarkable achievements in reducing greenhouse gas emissions, especially in achieving the emission reduction targets set for 2020. This progress is mainly due to China's reduced dependence on coal and other fossil fuels, which not only promotes significant improvements in air quality but also lays the foundation for the transformation of China's economic structure. Especially in the energy and industrial fields, carbon neutrality policies are driving companies to transform into greener and more sustainable operating models. Facing the economic challenges brought about by the COVID-19 epidemic, China is actively seeking a path to low-carbon economic recovery by increasing public investment in renewable energy and other fields. From an international perspective, China's carbon neutrality commitment has the potential to inspire other large carbon-emitting countries, especially countries along the Belt and Road and neighboring countries, to strengthen their emission reduction efforts. At the same time, Southeast Asian countries face shortages of technology, equipment, and raw materials, as well as insufficient investment when developing renewable energy. In this regard, Chinas experience and investment have a significant impact on Southeast Asian countries carbon neutrality policies and renewable energy development. China can use its technological and manufacturing advantages to share resources with neighboring countries in renewable energy technology and equipment manufacturing, and provide support for ASEAN countries to achieve their renewable energy goals.

The effect of low-temperature starting on the thermal safety of lithium-ion batteries

With the widespread application of lithium-ion batteries (LIBs) in the field of energy equipment, their probability of starting or operating in low-temperature environments is also increasing. However, there is currently a lack of research on the changes in thermal safety of batteries after use in corresponding environments. In this work, the thermal safety performance, degradation mechanisms and evaluation method of LIBs at low-temperature start-up conditions are studied. The results show that starting at low temperature leads to the decrease of cell capacity. Electrochemical characterization and cell disassembly analysis indicate that the loss of active lithium ions is mainly caused by the evolution of lithium and the growth of solid electrolyte interface films. In addition, the low-temperature startup results in the decrease of thermal runaway (TR) onset temperature and the advance of TR time. In order to quantitatively evaluate the safety state of the LIBs, an evaluation model based on Informer algorithm is established. The model extracts the discharge capacity, discharge energy and dV/dQ as aging characteristic parameters, and extracts TR temperature as thermal safety characteristic parameters. The data set is processed by cubic spline interpolation. After training and verification, the accuracy of the model reaches 80 %.

Enhancing mechanical properties of 2024-Aluminium Alloy Matrix Composite strengthened by Y2O3 ceramic particles

The development of high-strength, low-weight composite materials has been a high-priority demand in the structural, aerospace, automotive, renewable energy, and sports equipment sectors. Micro-particle size Yttrium oxide (Y2O3) has been utilized to enhance the microstructure and mechanical characteristics of the 2024-Al alloy. The stir-casting technique is employed to prepare 2024-Al alloy and aluminum matrix composites (AMCs) with different Y2O3 weight fractions (1 %, 2 %, and 3 %). Scanning electron microscope ESM with EDS analysis and optical microscope imaging have been used to assess the development in the microstructure of AMCs. Furthermore, optical microscope images have been analyzed by using image processing software, ImageJ to evaluate development in microstructure grain size. Vickers' micro-hardness, tensile strength, elongation, and wear-resistant tests were conducted to assess the mechanical properties of AMCs. AMC image analysis results demonstrated a significant reduction in microstructure grain size. The highest reduction in grain size was recorded when adding 2 wt. % of Y2O3, which was approximately 22.5 % smaller than that of the plain alloy. Other mechanical test results demonstrated an increase in the hardness and tensile strength of AMCs compared with the plain alloy by approximately 58 % and 116 %, respectively, upon the addition of 2 wt. % of Y2O3, also tensile test shows increasing of AMCs elongation at failure point with increasing Y2O3 content, 132 % upon adding 3 wt. %. Finally, wear tests show a decrease in the AMC wear rate with an increasing Y2O3 weight fraction compared to the plain alloy.

Achieve the optimal capacities of renewable energy generating and storage equipment by loading factor indexes of commercial key-customers

Abstract According to the typical load characteristics of commercial key-customer, the loading factor index (LFindex) for 24 hours of the day in working day can be obtained. The solar power generation and the commercial key-customer load are estimated on the basis of the local sunshine which the optimal capacity of solar energy and power storage equipment is deduced under various load indicators in accordance with the supply-demand theory. The loading factor index is based on the average load to get the different loading factor indexes under different loads per hour. If the load index is greater than 1 that apply the distributed power generation is more applicable for solar power generation of renewable energy which can restrain the peak power consumption of commercial key-customers. Based on the selected key-customer when the loading factor index is equal to 1, the optimal solar energy setting capacity is 1.0268 PU, the optimal power storage device capacity is 0.3447 PU; the loading factor index is equal to 1.2 which get the optimal solar setting capacity is 0.618 PU and the optimal storage device capacity is 0.0261 PU; the loading factor index is equal to 1.5, both the optimal solar setting capacity is 0.0243 PU and storage device capacity is 0.0005 PU. The conclusion is that the loading factor index increases uniformly which the optimal solar installation capacity also decreases smoothly and the optimal energy storage equipment capacity decreases sharply.

Dynamic evolution and influencing factors of internal and external synergy in China's energy industry: From the perspective of industrial chain

The distortion of factors between the upstream and downstream of the energy industry, as well as the lack of collaborative models between it and the energy equipment manufacturing industry, impede the smooth achievement of the "dual-carbon" goal, and industrial synergy can better solve this problem. From the perspective of the industrial chain, this study measures for the first time the level of internal and external synergies in the energy industry based on industrial data from 30 provinces in China from 2013-2022, and analyzes in depth the dynamic evolution of internal and external synergies in the energy industry and the factors influencing them. Using ArcMap 10.2 and the variance decomposition model, it is found that the upstream and downstream sectors of the energy industry have non-equilibrium in time and space, and their factor mismatch in resource-based regions is still more serious. Therefore, a state of synergy needs to be created between upstream and downstream sectors, between the energy industry and the energy equipment manufacturing industry. Using each of the status quo maps as well as the Kernel model, it was concluded that external synergies decrease and then increase, with a gradual decrease in the gap between regions. However, internal synergies show an upward and then downward trend. Meanwhile, the results of the GTWR model show that the effects of various influences on internal and external synergies are not only positively or negatively different, but also spatially more unbalanced. It should accelerate the promotion of policy guidance, actively cultivate the external environment, rationalize the layout of regional industries, and promote the synergistic development of the energy industry internally and externally.

Coordinated scheduling optimization of building integrated energy system with flexible load

Under the background of China's "dual carbon" goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, this paper proposes a method for the optimization of source-load coordinated scheduling in Building Integrated Energy System (BIES) with Flexible Load (FL) to promote sustainable and efficient energy development and enhance energy utilization efficiency. The method starts by analyzing the energy consumption behavior characteristics on the load side of buildings, determining the potential for FL reduction based on national standards and human comfort requirements. It then applies price-based demand response measures to shiftable and transferrable loads and incentives-based demand response measures to FL identified as reducible through potential analysis. Subsequently, a multi-objective optimization model focusing on economic efficiency and carbon emissions is established to coordinate the response curves of FL on the demand side and the operation of energy equipment on the supply side. Finally, the coordination optimization model is solved using a synergistic approach combining the logistic chaos mapping and adaptive t-distribution enhanced Non-dominated Sorting Dung Beetle Optimizer (NSDBO) algorithm, ensuring superior search effectiveness. Simulation results demonstrate that the optimization of FL responses using the Logistic-t-NSDBO algorithm effectively reduces peak-load variations while significantly lowering energy consumption costs and carbon emissions on the energy supply side.

Life-Cycle Analysis of Natural Treatment Systems for Wastewater (NTSW) Applied to Municipal Effluents

The objective of the described activity is to develop technologies or proposals that can be implemented within the cycle to enhance the relationship between climate change, water, energy, and food. The focus is on analyzing natural treatment systems for wastewater (NTSW) within the context of Macaronesia, considering factors such as life-cycle assessment (LCA), carbon footprint, impacts, and mitigation capacity. The analysis of real case data from the Canary Islands and Cape Verde will inform the development of appropriate technologies tailored to different areas and scales within Macaronesia. This work includes a comprehensive life-cycle analysis of the Santa Catarina (Cape Verde) NTSW. This analysis encompasses: (a) Inventory analysis of the construction phase: This involves the assessment of inputs and outputs associated with the construction of the NTSW, including materials, energy consumption, transportation, and waste generation. The maintenance and operation phases are then evaluated, with a focus on the ongoing maintenance and operation activities required for the NTSW, including energy consumption, water usage, chemical inputs (if any), labor, and equipment maintenance. (b) Finally, the impacts of the NTSW are evaluated. The environmental, social, and economic impacts generated by the NTSW are assessed. This includes an analysis of factors such as carbon emissions, water usage, land use, ecosystem impacts, human health effects, and economic costs. By conducting a comprehensive analysis of the Santa Catarina NTSW, the document aims to provide insights into the environmental performance and sustainability of the system. This information can then be used as a tool and experience of educational innovation for final-year undergraduate students to identify areas for improvement, develop mitigation strategies in the water sector, and inform decision-making processes regarding wastewater treatment technologies in Macaronesia. Furthermore, lessons learned from real case studies in the Canary Islands and Cape Verde can be applied to similar regions within the Macaronesia archipelago (IDIWATER project).

A preference adjustable capacity configuration optimization method for hydrogen-containing integrated energy system considering dynamic energy efficiency improvement and load fast tracking

The new hydrogen-containing integrated energy system proposed in this paper has complementary coupling of multiple energy forms such as electricity, gas, cold, heat, and hydrogen. For the same type of load demand, equipment with different energy input forms can be dispatched for joint supply. However, different equipment differ in dynamic response speed and energy utilization efficiency, and the reasonable configuration of equipment capacity will directly affect the overall dynamic characteristics and energy efficiency level of the system. Therefore, this paper first proposes an evaluation index that can comprehensively characterize the dynamic energy efficiency and load tracking ability of energy equipment. In addition, a preference adjustable capacity configuration optimization method based on utopian point tracking is proposed with the two optimization objectives of this indicator and the system's equivalent annualized investment cost, and the solution complexity is reduced through segmented linearization of the objective. Simulations presented here show that the capacity configuration optimization method proposed in this paper has a positive effect on improving the dynamic energy efficiency and load tracking ability of the new hydrogen-containing integrated energy system, and can also meet the preference setting needs of different investment entities for capacity configuration optimization goals.

Energy Hub Model for the Massive Adoption of Hydrogen in Power Systems

A promising energy carrier and storage solution for integrating renewable energies into the power grid currently being investigated is hydrogen produced via electrolysis. It already serves various purposes, but it might also enable the development of hydrogen-based electricity storage systems made up of electrolyzers, hydrogen storage systems, and generators (fuel cells or engines). The adoption of hydrogen-based technologies is strictly linked to the electrification of end uses and to multicarrier energy grids. This study introduces a generic method to integrate and optimize the sizing and operation phases of hydrogen-based power systems using an energy hub optimization model, which can manage and coordinate multiple energy carriers and equipment. Furthermore, the uncertainty related to renewables and final demands was carefully assessed. A case study on an urban microgrid with high hydrogen demand for mobility demonstrates the method’s applicability, showing how the multi-objective optimization of hydrogen-based power systems can reduce total costs, primary energy demand, and carbon equivalent emissions for both power grids and mobility down to −145%. Furthermore, the adoption of the uncertainty assessment can give additional benefits, allowing a downsizing of the equipment.

A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electricity Price

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

Shifting Toward Clean Energy Technology: Assessing Vietnam Critical Minerals Demand and Domestic Resources

Abstract Vietnam is progressing toward an energy transition to achieve the 2050 net-zero emissions target by switching from fossil fuel-based to clean energy technologies. According to Politburo’s Resolution No. 55, the renewable energy share in the total primary energy supply will account for 30% by 2045. The newly approved National Power Development Plan set the target to increase the renewables share to 63% of total installed capacity by 2050. The demand for clean energy equipment, such as solar photovoltaic modules, wind turbines, and batteries, is expected to grow rapidly as a result of the decarbonization pathways for the power and transportation sectors in the coming decades. The manufacturing of clean energy equipment will require substantial amounts of critical minerals. Therefore, this study evaluates the demand for major critical minerals in producing clean energy equipment, including cobalt, copper, lithium, nickel, silicon, and rare earth elements. The critical minerals demand is estimated based on critical mineral intensities and additional capacities of clean energy equipment to be installed in 2021-2030 and 2031-2050 stages under Vietnam’s current policies. The results show that the total critical minerals demand in 2021-2030 and 2031-2050 is estimated to increase approximately three-fold and 15-fold, respectively, compared to the 2021 level. Furthermore, the article reviews Vietnam’s critical mineral resources and the potential contribution to domestic and global supply in the coming years.

The impact of land-use policy on household energy transition: Evidence from China's conversion of cropland to forestland program

The implementation of the Conversion of Cropland to Forestland Program (CCFP) has affected land-use patterns and biomass availability in China, consequently reshaping household energy consumption patterns. Utilizing data from the China Health and Retirement Longitudinal Study, this study employs a multinomial logit model to examine the impact of CCFP subsidies on household energy transitions, analyzed through the lenses of the energy ladder and energy types. Additionally, this study differentiates the effects of CCFP subsidies across households with varying characteristics. The empirical results reveal that, from the energy ladder perspective, CCFP subsidies hinder household energy transitions from biomass to both traditional and advanced commercial energy sources. From the energy type perspective, the subsidies impede transitions from biomass energy to coal, electricity, and LPG/LNG. These adverse effects may be attributed to the CCFP subsidy's role in increasing biomass supply and encouraging the use of biomass energy equipment. Moreover, the impacts of CCFP subsidies differ among households based on education level, location, and degree of engagement in farming. Overall, this study highlights the importance of considering land use policies and patterns in the formulation of energy policies aimed at promoting energy transition and alleviating energy poverty.

Bioinspired design of rhodium single-atom nanozymes enable a non-poisoning pathway for direct formic acid oxidation in enzymatic biofuel cells

Solving the dilemma of CO poisoning in electrocatalysts is the crucial step in the commercialization of direct formic acid fuel cells, however, it remains a challenge to design catalysts with high reaction tendency for the direct formic acid oxidation reaction (FAOR) as elaborately as natural enzymes. Herein, inspired by formate oxidase (FOD) with a delicate catalytic pocket, we report that rhodium single-atom nanozymes (Rh SAzymes) can catalyze direct FAOR without worrying about CO poisoning. Impressively, Rh SAzymes exhibit non-CO pathway for direct FAOR in both enzyme-like biocatalytic and electrocatalytic conditions. By combining docking studies and density functional theory calculations, we reveal that this striking reaction tendency mainly stems from thermodynamically unfavorable pathway in the generation and adsorption of CO, much more like that of natural FOD. Moreover, we successfully construct the membrane-less FA/O2 enzymatic biofuel with an open-circuit potential (OCP) of 0.68±0.01 V and a maximum power density (Pmax) of 0.41±0.01 mW cm−2. With 100 % selectivity toward direct anti-poisoning pathway and flexible configuration, the device realizes long-term stable operation and miniaturized goals. Our work not only provides a methodological template for exploring the CO-resilient reaction mechanism of enzyme-like biocatalysts as well as their homologous electrocatalysts in canonical reactions of fuel cells but also paves avenues for the integration of nanozymes in energy equipment.

Two-layer optimal scheduling method for regional integrated energy system considering flexibility characteristics of CHP system

A regional integrated energy system (RIES) guarantees the fulfillment of a diversified load demand of users by coordinating all types of energy equipment and is a two-layer optimal scheduling method that takes into consideration the flexibility of the characteristics of the combined heat and power (CHP) system is proposed in this paper. First, the thermoelectric output characteristics of hydrogen fuel cell (HFC) and CHP units are analyzed to establish an energy equipment model that takes into consideration the variability of the operating conditions. Subsequently, a two-layer optimal scheduling model of the RIES is established, where the upper layer takes into consideration the operating costs, carbon emission penalties, and renewable energy consumption capacity to determine the strategy for allocating the energy flow, and the lower layer considers the energy efficiency as the optimization objective to determine the output modes of the HFC and CHP units. Finally, an adaptive closed-loop control strategy is used to avoid the continuous startup and shutdown of the CHP unit. The simulation results show that the proposed method can effectively reduce operating costs and carbon emissions, improve energy efficiency and renewable energy consumption, and promote the efficient utilization of integrated energy resources.

Sustainable Supplier Selection Using Fuzzy AHP (AHP-F) and Fuzzy ARAS (ARAS-F) Techniques for Fertilizer Supply in the Agricultural Supply Chain

Implementing the right strategies in the agricultural supply chain in the supply of seeds, pesticides, fertilizer, energy, fuel and agricultural mechanization tools and equipment has a great role in increasing agricultural productivity. The main purpose of the study is to rank and evaluate alternatives in choosing a sustainable fertilizer supplier in the agricultural supply chain by using AHP-F and ARAS-F techniques. In an environment of uncertainty and complex supply chain structure, multi-criteria decision making (MCDM) methods are widely used to solve supplier selection problems. In this study, the importance levels and weights of the criteria in the selection of sustainable fertilizer suppliers were measured by the AHP-F method. The criteria that are important for fertilizer supplier selection were evaluated by taking expert opinions, the uncertain and uncertain opinions of the decision makers were modeled with the AHP-F approach and the weights of the criteria were determined. Among the criteria, resource consumption (FSC05) has the highest weight. Then, alternative rankings were obtained with the ARAS-F method. Fertilizer supplier alternatives in the agricultural supply chain were ranked with the ARAS-F method, using the criterion weights found with AHP-F. In the ranking of alternatives, alternative fertilizer supplier FS03 ranked first with the highest value. This study provides a resource for businesses and other stakeholders to make decisions regarding sustainable fertilizer supplier selection.

A two-stage optimal operation strategy for community integrated energy system considering dynamic and static energy characteristics

The community integrated energy system (CIES) can provide users with reliable and economical energy supply through centralized utilization of various energy equipment. However, due to the significant difference in inertia between different energy sources, the response speed of equipment using different energy sources varies greatly, making it difficult to cooperate well and limiting their ability to cope with source-load uncertainty. Based on this consideration, a two-stage optimal operation strategy for CIES is proposed to utilize dynamic and static energy separately. First, the dynamic and static energy are defined and divided, and their different functions are analyzed. Based on this, a two-stage optimal operation architecture is proposed. Then, a day-ahead interval optimal operation model considering dynamic and static energy matching is established. In which, the uncertainty of source and load is characterized by nonlinear interval numbers, and the interval possibility degree is used for deterministic transformation of nonlinear interval numbers. Thirdly, a dynamic and static energy replacement mechanism based on model predictive control is given, and an intra-day rolling optimal operation model considering dynamic and static energy replacement is established. Finally, a CIES is used as an example to demonstrate the rationality and effectiveness of the proposed strategy. Simulation results show that the proposed strategy can effectively utilize different types of energy equipment and significantly reduce the adverse effects of source-load uncertainty.

Physics-informed probabilistic slow feature analysis

This paper presents a novel approach called physics-informed probabilistic slow feature analysis. The probabilistic slow feature analysis method has been employed to extract slowly varying latent patterns from high-dimensional measured data. The extracted slow features have proven effective in industrial applications such as soft sensing and process monitoring. However, industrial processes come with various physical constraints that must be taken into account, such as energy requirements, equipment limitations, and safety considerations. The conventional black-box nature of the slow feature model often leads to physically inconsistent or unacceptable results. To address this issue, we propose integrating physics principles into the probabilistic slow feature model, ensuring that the extracted features adhere to physics laws. Our formulation incorporates two types of physical constraints: linear algebraic equality and inequality constraints. Through an industrial case study, we demonstrate the effectiveness of our methodology, showcasing the advantages of incorporating physics in feature extraction. These advantages include improved interpretability, reduced data dimensionality, and enhanced generalization performance.