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 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.

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.

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.

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.

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.

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.

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.

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.

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.