The optimal scheduling of energy systems is important in ensuring the balance of energy supply and demand. Quantitative comparative studies of the development status, hot spots and trends in this field at home and abroad for more than 30 years, the research of energy systems optimal scheduling was analyzed in the CNKI and WoS databases from 1990—2022 by CiteSpace software. The results show that this field is in the conventional scientific stage but the literature has a high growth rate, the domestic literature growth rate is faster than the international and the inter-institutional exchange is close; the foreign hotspots are optimization algorithms, uncertainty and stability control for microgrids, dynamic scheduling with energy storage for integrated energy system (IES), as well as reinforcement learning and game theory for scheduling technique; the domestic hotspot trends are algorithms, dynamic optimization/bilayer optimization/time-sharing tariffs, multi-intelligent bodies, demand side management and deep learning for microgrids, as well as uncertainty, energy hubs, electricity to gas, integrated demand response, data driven, carbon trading, carbon capture and reinforcement learning for IES. The results show heuristic algorithms and deep learning techniques are expected to achieve a paradigm shift in future large-scale energy systems.

A novel hybrid design that combines solid waste plasma gasification, gas turbine, absorption heat pump, and coal-fired combined heat and power plant has been proposed. In the integrated scheme, medical waste is sent to the plasma gasifier and converted to syngas, which is conveyed into the gas turbine system after the necessary treatment. In terms of waste heat utilization of syngas and flue gas, some are used by the absorption heat pump for heating, and the rest are used to heat the feedwater of the coal-fired combined heat and power plant directly. Based on a typical coal-fired combined heat and power plant, the benefits of this system are examined in terms of both thermodynamics and economics. Once the heat supply and the net electricity from coal remain the same, the net power generated by the waste in the hybrid design is 7.47 MW, while the net waste-to-energy efficiency reaches 47.96%. In just 5.23 years, the initial investment in the proposed system is recouped, and in its 25-year lifetime, the system achieves a net present value of 50 362.94 thousand CNY.

Energy conservation and emission reduction work have attracted global attention. Accelerating lowcarbon transformation work has also reached a consensus in the shipping industry. Among them, hydrogen energy ships have good development prospects. In the face of the problem that hydrogen energy ships have no stable hydrogen source, it is urgent to find a stable hydrogen source for hydrogen energy ships. This paper introduces the development status of hydrogen production from offshore wind power and hydrogen energy ships, breaks the traditional concept of hydrogen energy, puts forward the system architecture of hydrogen production and hydrogenation on offshore platforms, and uses offshore wind power to directly prepare hydrogen, which provides a new idea for solving the hydrogen source problem of hydrogen energy ships and realizing the consumption of offshore wind power. Through the discussion and economic analysis of the integrated development of offshore wind power and marine ranching hydrogen energy ships, it is found that the integrated development of offshore wind power and marine ranching hydrogen energy ships is economically feasible, will contribute to carbon emission reduction work, and has good development prospects. This paper can serve as a reference for the comprehensive development of offshore wind power and hydrogen ships and put forward the prospect of building offshore hydrogen energy passage in coastal areas.

In the study, a computational fluid dynamics (CFD) model based on a 600 MW tangentially coal-fired boiler was established. According to orthogonal conditions (L₁₆(4⁵)), the heat flux distributions of the water-cooled wall under 100% BMCR, 75% THA, 50% THA and 35% BMCR loads were obtained. In addition, the factors also included: primary to secondary air rate, degree of air-staging, swing angles of burners and SOFA nozzles. Then, the spiral water-cooled wall temperature distributions under various conditions were calculated through coupling the heat absorption, temperature calculation and hydrodynamic characteristics of the water-cooled wall. Due to the discontinuity of orthogonal condition, the machine learning was used for predicting the spiral water-cooled wall temperature distribution within the range of parameters covered by orthogonal conditions. The results showed that a wall temperature peak up to 730 K would appear in the area among burner system. The heat transfer deterioration was easy to occur when the flame center height in furnace coincided with the phase change height of the working fluid during the boiler load adjusting process. The goodness of fit R² of the ensemble learning on the training set and the test set of the wall temperature data had reached 0.99, which could be used to predict the wall temperature of the boiler under wide load. At the same time, the machine learning established the mapping relationship between the wall temperature distribution and the operating parameters of the boiler. In the future study, the wall temperature safety of the water wall can be guaranteed by reasonably adjusting and optimizing the operating parameters through the optimization algorithm.

Coupled with the energy storage system can improve the peak shaving capacity of the thermal power unit. To improve the thermoelectric decoupling ability of the combined heat and power unit, a coupled thermal power plant combined heat and power unit with liquid carbon dioxide energy storage system is proposed. The system utilizes the condensate to recover the compression heat of the carbon dioxide during the charge process, and supplies heat to the users together with the heating extraction steam. Besides, the heating extraction steam is employed to preheat the carbon dioxide of the expander inlet during the discharge process. Based on the established thermodynamic models, the thermal performance analysis of the coupled system was carried out with the thermal efficiency, exergy efficiency, and electricity storage efficiency as assessment criteria. The sensitivity analysis results indicate that increasing both the expander inlet temperature and the discharge pressure can obtain a higher system exergy efficiency and electricity storage efficiency; increasing the charge pressure results in a higher system thermal efficiency, while the exergy efficiency first increases and then decreases. The parameter optimization of the corresponding CO₂ energy storage system was carried out under the design parameters. Results show that when the charge pressure is 10.5 MPa and the discharge pressure is 18.0 MPa, the coupled system achieves the optimal efficiency of 64.92%.

The flow channel structure at the cathode side is one of the main factors affecting the performance of proton exchange membrane fuel cells. The flow channel at the cathode side needs to discharge liquid water out of the fuel cell in time and make oxygen flow to the cathode catalytic layer as much as possible. Thus, the phenomenon of cathode flooding and concentration polarization is avoided. An innovative 3D cathode side channel, sugar gourd type channel, is designed. The sugar gourd type channel is formed by adding arc-shaped side trapezoidal block based on the traditional straight flow channel. The simulation results show that, compared with the traditional straight flow channel, the current density in the high current density region is increased by about 8%. And because of the special structure of the sugar gourd type channel, the air flow advances to the outlet in the form of pulse decline, and the heat and mass transfer are significantly enhanced. In addition, the influence of arc-shaped side trapezoidal height on overall performance is further explored.

In order to study the thermodynamic characteristics of oil-free scroll expander in detail, leakage and thermodynamic model are established based on the first law of thermodynamics and the equation of energy and mass balance. Through the established leakage model, different working condition parameters are further changed to study the changing trend of the leakage in the scroll expander. Under the comprehensive consideration of the influence of heat transfer and leakage on the thermodynamic model, the Euler method is used to solve the established thermodynamic model, pressure, temperature and mass variation of the working medium with the orbiting angle of the main shaft during one operation cycle of the scroll expander are obtained and analyzed. Finally, an experiment platform is set up to test and verify the established thermodynamic model of the scroll expander, which can provide a certain reference for the performance analysis of the scroll expander.

The process of conventional hydrometallurgical recovery of lithium batteries not only consumes corrosive acid and long-time reaction, but also produces secondary wastes. In this paper, microwave-assisted deep eutectic solvent (DES) is used to leach and recover valuable metals from cathode material LiCoO₂ (LCO). The leaching and recovery process is not only green and low-pollution, but also owns a fast reaction rate, good solubility stability of the valuable metal and high purity of the recovered product. Meanwhile, FT-IR, XRD, ICP-MS, SEM and electrochemical analysis methods are used to explore the mechanism of microwave-assisted DES leaching of valuable metals from LCO. The effects of experimental factors on the extraction efficiency of valuable metals are obtained by orthogonal test method. The degree of influence is DES>temperature>liquid-solid ratio>time. Afterwards, according to the results of orthogonal experiments, the single-factor experiments are successively adopted to explore the optimal experimental conditions for microwave-assisted leaching of valuable metals, 99.86%of Li and 99.05% of Co can be extracted under the condition of choline chlorine-oxalic acid (ChCl-OA), 180 ℃, 10 min and liquid/solid ratio (L/S) of 60 mL/g. At this time, Co exists in the leaching solution as formic acid cobalt. Finally, a green and efficient strategy for extraction of valuable metals from spent LiBs (LCO) through microwave-assisted DES is proposed, which provides an important reference method for recovery of valuable metals from spent lithium-ion batteries.

Oxidation reduction potential (ORP) analysis method has been gradually used in the field of limestone-gypsum wet desulfurization slurry oxidation control, but there is a lack of corresponding theoretical research. In this paper, firstly, the standard electrode potentials of each pair in the slurry oxidation process, E^θ(O₂(αq))/H₂O and E^θ(S(Ⅵ)/S(Ⅳ)), were obtained based on density functional theory and acid dissociation equilibrium calculation; and the standard electromotive force of each reaction was calculated. Then, based on the main reaction of slurry oxidation, 2HSO₃^–+O₂→2H⁺+2SO₄^2–, the theoretical calculation model of the electromotive force of the reaction system was established by the Nernst equation. It was found that the measured ORP of the electromotive force of the reaction system was quite different from that by theoretical calculation, which meant the Nernst equation was not suitable for the slurry oxidation system. Finally, a good multivariate linear fitting relationship between the measured ORP, pH, ln(c(Ca²⁺)), ln(c(HSO₃^–)) and ln(c(O₂)) was established by the stepwise regression. The results indicated that the process of slurry oxidation was not only related to the single indicator of ORP, but also controlled by pH, calcium ion and dissolved oxygen concentration. When ORP is used as the oxidation control indicator of wet desulfurization slurry, the influence of pH, calcium ion and dissolved oxygen concentration should be taken into account at the same time.

There is a common mismatch between heating supply and demand parameters for industrial heating retrofits of pure condensing thermal power units, and the benefits of thermal power plants can be improved by adopting a reasonable matching scheme of supply and demand parameter. Aiming at the phenomenon of energy mismatch caused by the excessively high extraction parameters of the unit in the industrial heating scene, considering that it is suitable for high-parameter industrial heating, this paper proposes a scheme of using the centripetal turbines for cascade utilization of extraction steam. Taking the industrial heating transformation of a domestic 330 MW cogeneration unit as an example, the thermal system model was constructed to comprehensively evaluate the performance indicators changes of the system from three aspects: thermal performance, exergy environment and economic performance. We also comprehensively compared the effect of upgrading the direct heat reduction and pressure reduction method to the radial turbine power generation steam energy cascade utilization scheme. The calculation results show that under the same heating parameters, compared with the traditional method of temperature reduction and pressure reduction, the comprehensive benefits of the industrial heating steam energy cascade utilization scheme based on centripetal turbine power generation is significant. The specific performance is that under rated conditions, the gross coal consumption rate for power generation can be reduced by 0.89 g/(kW·h), the extraction exergy efficiency can reach 97.66%, and the direct economic benefit is relatively increased by 1.04%, and the carbon transaction cost relative reduction of 0.34%. In addition, the relative advantages of all aspects performance of the system will expand with the increase of the amount of steam extracted by the system for industrial heating.

In order to well evaluate the availability of electrochemical method using to detect the creep damage of martensitic heat resistant steel, a set of T92 internal pressure creep test samples with different creep damage degrees were selected. The microstructure evolution in the process of creep, especially the Laves phase precipitation behavior, was systematically characterized and analyzed; meanwhile, electrochemical response of Laves phase in alkaline were also investigated in detail. According to the scanning electron microscopy(SEM), transmission electron microscopy(TEM) and electron probe X-ray micro-analyzer(EPMA) results, the Laves phase in T92 precipitated and grew rapidly during the internal pressure creep process. Its particle size and area percentage gradually increased, clustered and with element segregated and redistributed. According to potentiodynamic polarization curve in NaOH solution, Laves phase can selectively dissolve in strong alkali solution. When the concentration of NaOH reaches 8 mol/L, the current peak and corresponding electric value of selective dissolution of Laves phase are well correlated with the internal pressure creep time. In conclusion, the potentiodynamic polarization curve of T92 in strongly alkaline solution can effectively reflect the content of Laves phase, varying in consistent with its electric quantity; and can further associate with creep life damage. It is promising to be used as a nondestructive testing technology for the creep life assessment of pipelines in the field.

With the gradual increase in the proportion of new energy in the energy system, increasing the difficulty of AGC frequency regulation of thermal power units, energy storage systems combined with thermal power units AGC frequency regulation technology in China's power industry is developing rapidly. In this paper, the joint frequency modulation system of thermal power unit and energy storage system is studied for the problem of not considering the characteristics of unit variable load process and the lack of relevant evaluation system. Firstly, the thermal unit coal consumption, lifetime, environmental friendliness and energy storage lifetime models are established and uniformly transformed into cost terms during the thermal unit variable load process, thus constituting the corresponding evaluation system. Secondly, the system day-ahead operation optimization model is established, and the system load allocation strategy is further refined by considering the optimization results. Finally, the method proposed in this paper is verified by a case study. The results show that the optimal allocation strategy of joint frequency modulation load of thermal power unit and energy storage proposed in this paper can achieve the purpose of energy storage state management; and through the optimal selection of unit variable load rate and the coordinated response with the energy storage system, the process of unit load change is alleviated the problems of excessive environmental protection and rapid loss of lifespan.

It is known that the interdiffusion at the aluminide coating/matrix interface during the long time exposure at high temperature would change the microstructure of the matrix and deteriorate the mechanical properties of the matrix. To analyze the high temperature strength of aluminide coating on T92 steel for ultra-supercritical unit, aluminide coating is prepared on the inner wall of T92 steel boiler tube by low temperature powder embedding method, and the tensile test was carried out at room temperature to 625 ℃ and the durability test was carried out at 625 ℃ environment. The effect of aluminide coating on the tensile properties and durability life of T92 matrix are studied by combining scanning electron microscope (SEM), optical microscope (OM) and X-ray diffraction analysis(XRD). The results show that the aluminide coating prepared on the inner wall of T92 boiler tube by low temperature powder embedding aluminizing, which is metallurgically combined with the matrix, has a double-layer structure, and each layer is continuous and uniform. The total thickness of the prepared aluminide coating is about 30.4 μm. The coating has columnar crystal structure and the surface is cracked in the room temperature is increased to 625 ℃. During the creep rupture process at 625 ℃ environment, FeAl coating have many cracks, but the crack depth is shallow and very few cracks extend to the matrix. The coating peels off locally large strains under the high stress state. It can be concluded that even deforms of the aluminide coating occurred during the long-time creep process, it can still has good metallurgical bonding with T92 matrix.

In view of the current lack of dynamic modeling and analysis of steam modeling in the integrated energy system of industrial parks, the numerical method needs to balance between accuracy and computational efficiency. Based on the basic equation of steam flow, this paper establishes a mathematical model of the hydrothermal process of steam flow. Hydrothermal model is carried out by using a mixture of analytical and numerical methods, and the thermal model is solved by finite difference method for simulation. Compared with the simulation data of commercial simulation software, the solution error of this method is about 0.43%, and the influence of different solution conditions on the thermal process is explored. It was found that an increase in spatial step size would increase the volatility of the temperature curve. As the inner diameter of the pipeline increases, the temperature transfer slows down and the response speed of the model decreases; The initial pressure does not have a significant impact on the solving process and accuracy of the model.

In view of the research situation of backpressure extraction steam turbine technology for ultrasupercritical power generation units, the off-design condition calculation model and exergy analysis model of the unit was established, and the influences of steam mass flow, temperature, pressure, steam turbine back pressure and the BEST heat extraction stage on the thermal performance of the unit were analyzed. The exergy efficiency of unit increased first and then decreased with main steam flow rate, reaching the maximum value (52.42%) when main steam flow rate was 750 kg/s. Exergy efficiency of unit increased with the increase of main steam temperature and pressure but decreased with the increase of turbine back pressure. The thermal performance of unit was more affected by back pressure at low load. In addition, the BEST series has a significant effect on the thermal performance of the unit, and the BEST match degree with the system design scheme is achieved when the series is 4, and the thermal performance is optimal. The calculation method and BEST series selection scheme can provide reference for the scheme design and operation optimization of ultra-supercritical units.

Aiming at the problem of inaccurate prediction of NO_x emission concentration when the current coal-gas boiler gas mixture is uncertain and changing, a combined online prediction method based on attention mechanism is proposed. First, the characteristic variables of the model are determined by combining the maximum information coefficient method with the Pearson correlation coefficient method; Secondly, vector autoregressive(VAR) model is constructed online with sliding time window for linearly correlated characteristic variables to realize the prediction of NO_x emission concentration under the input of multi-dimensional time series linear correlation variables For non-linear-related feature variables, the relationship between NO_x emission concentration is predicted by constructing an online Recurrent extreme learning machine(OR-ELM) model online learning. Finally, Attention Mechanism(AM) is used to dynamically weight the two forecasting models to achieve trend forecasting. Through field data verification, it shows that the VAR-OR-ELM combined online prediction model constructed in this paper can accurately predict the variation trend of NO_x emission concentration after 10 minutes. Combining prediction accuracy and prediction time, the combined prediction model is better than other single prediction models.

The denitration efficiency is closely related to the uniformity of flue gas and reductant agent within the selective catalytic reduction (SCR) reactor for the coal-fired unit. Based on the established mathematical model for SCR denitration reaction, a user-defined subprogram is used to couple the multi-component flue gas flow with reaction process. The reliability and effectiveness of the CFD model are verified by comparing the measured and simulated data of the SCR performance of 330 MW level coal-fired units at different loads. According to the hydrodynamics of flue gas and reductant agent together with the chemical reaction process in SCR reactor, a new intensification scheme is proposed by optimizing the structure of deflectors in front of the ammonia injection grids. Furthermore, the effects of operational conditions on emission mass concentration of NO and NH₃ are investigated. The results indicates that, the maldistribution of the incoming flue gas to the ammonia injection grids leads to the poor mixing behavior of flue gas and reducing reagent. However, the denitration efficiency of the SCR reactor can be improved by about 3.37% through adjusting the upstream guiding plate structure and installing the baffle around the flue duct wall. Taking the SCR denitration device in this work as an example, the appropriate molar ratio of NH₃ to NO is 0.94 when the initial NO mass concentration is 650 mg/m³, which could meet with the emission limit for air pollutions of 50 mg/m³ for NO_x and 2.5 mg/m³ for NH₃, respectively.

Through the analysis of the principle of the existing methods for determination the water content in oil and the components of gear oil, as well as monitoring needs of water content in gear oil, it is found that the existing methods can not accurately and quickly detect the water content in gear oil. the new gear oil and running gear oil for wind power was selected as experiment object, through study of selection of test conditions and the accuracy of the results, it is found a method that can accurately and quickly detect the water content in the gear oil. This method is a combination of the Karl Fischer coulometry water analyzer and the heating furnace. With compressed nitrogen or air as the carrier gas, after the carrier gas is dried through the molecular sieve, it cross the sealed test bottle containing 1g oil sample and 1mL n-heptane by the sleeve air needle, Heat the bottle at 150 ℃, and transfer the water in the oil sample to enter the Karl Fischer water analyzer for detection.

The penetration rate of distributed photovoltaic power stations in the power system is increasing year by year, to ensure the safe and stable operation of the power grid, a distributed photovoltaic ultra-short-term power prediction method based on combined neural networks is proposed. Firstly, a 1DCNN&1DCNN-LSTM combined neural network model is constructed by using 1D convolutional neural network (1DCNN) and long short-term memory (LSTM) neural networks, to obtain multi location numerical weather prediction (NWP) information and historical power information, using combined neural network model for spatially correlated photovoltaic power prediction and time series prediction; and a fully connected neural network (FCNN) is added to the combined neural network model, which is used to learn and assign weights to the two prediction results, achieving ultra-short-term prediction of distributed photovoltaic power generation. The validation was conducted using measured data from a photovoltaic power station in Hebei, and the results showed that this method can effectively improve the accuracy of distributed photovoltaic prediction and has certain practical value.

With the promotion of China's "carbon peaking and carbon neutral" strategy, thermal power units are more involved in deep peak regulation. Under the conditions of deep peak regulation, the thermal power unit is insufficient in heat storage, and the primary frequency regulation capability decreases, resulting in a large deviation between the unit's primary frequency regulation capability calibrated under the rated operating condition and the actual frequency regulation capability, threatening the frequency security of the power grid. Aiming at this problem, an online estimation method of primary frequency regulation capability of deep peak regulation thermal power units based on LSTM neural network is proposed. The static model of steady-state unit design was improved to a dynamic model, considering the dynamic operation process of the unit by using the time sequence memory ability and nonlinear feature extraction ability of LSTM neural network, and the errors caused by the disturbance factors such as the load changing process and the historical action of primary frequency regulation were corrected. Based on the hierarchical modeling method, the sub-models with different neural network structures were designed for the different characteristics of the factors affecting the frequency regulation capacity, such as heat storage of the unit and steam turbine work performance, and the effects of furnace side were taken into account to improve the accuracy of frequency regulation estimation results. Compared with the traditional method used in the power system, the estimation result of this method has higher accuracy, and has better performance under different working conditions such as steady state and variable load.

A large amount of data is generated during steam turbine operation. In order to meet the requirements of high quality data driven by big data and simulation modeling, efficient data cleaning is very necessary. The semi-supervised data cleaning model of steam turbine is built by using the excellent nonlinear fitting ability of long and short memory layer for time series data. The model selects three boundary conditions of the unit as input to predict the cleaning data. Outliers are eliminated according to the residual difference between the predicted value and the actual value. Then, the predicted value of the model is used to fill the data to ensure the integrity of the data. The model is used to clean the data of a 650 MW unit in a power plant. To overcome the problems caused by sample imbalance in the selection of cleaning model indicators, the accuracy rate is improved and taken as the measurement index of cleaning effect. The results show that, the improved accuracy of the data cleaning model of the deep long and short memory network is higher than that of the other three common cleaning methods, which can effectively identify whether the data is abnormal, and can use the predicted value to fill the data to ensure the consistency of data before and after cleaning.

Distributed energy power stations are developing rapidly because of their cleanliness, environmental protection, economy and high efficiency. However, there are few data used for fault diagnosis of plant equipment, so a method to predict the health state and aging degree of equipment is urgently needed. Based on this, a prediction model which can analyze the running state of equipment and obtain the deterioration trend of equipment is proposed. Firstly, multi-dimensional data of the equipment is preprocessed, and an improved Mahalanobis distance based equipment health model of distributed energy power station is constructed quantitatively by combining the analytic hierarchy process (AHP) with Gaussian mixture distribution. Then, the combined prediction model based on the improved sparrow algorithm and short and long time memory neural network is established to predict the trend and correlation analysis of the deterioration of distributed energy power plant equipment. The experimental results show that the proposed fusion health model can predict equipment anomalies in the case of insufficient actual fault data of distributed energy power stations.