To address issues such as data information security and difficulty in centralized management and control of data assets, a prototype system for data lifecycle security protection in power plant scenarios is designed. Firstly, the particularity and existing problems of the current field of power plant data protection scenarios are analyzed in detailed. Secondly, in response to the pain point of the lack of industry standards for data classification and grading in power plants, an automated classification and grading method is proposed to standardize the grading and classification of power plant data. Finally, in terms of system development, based on the analysis of the scope of power plant data protection and functional requirements, the functional architecture design and technical architecture design of the prototype system are completed. This system provides specific work steps from data asset sorting, automated classification and grading, full lifecycle management, security assessment, and other aspects, providing a complete solution for data security protection in power plants, and providing a basis for effectively achieving full lifecycle security of power plant data in the future.

The stable operation of carbon market depends on the accuracy of carbon emission data, and the data quality of continuous emission monitoring system (CEMS), as an adjunct to the accounting method, still needs to be improved. Uncertainty assessment is an important part of the construction of CEMS and data quality control. To assess and improve the data quality of CEMS, the uncertainty assessment of the carbon emission online monitoring data of an F-class gas unit was carried out. The results show that, the extended uncertainty of carbon emissions is 4.838%~5.012% (k=2), and the main source of uncertainty is the flow rate measurement, in which the velocity field coefficient detection is the main reason for the high uncertainty in the flow rate measurement, and the operating load does not have a more obvious effect on the results. Therefore, improving the accuracy of flow rate and concentration measurement instruments or conducting regular calibration tests can effectively reduce the uncertainty and improve data quality.

The online monitoring methods for carbon emissions of domestic coal-fired power units in the short and medium term are still mainly based on accounting methods, and the online monitoring methods for carbon emissions are not yet fully developed. An online carbon emission accounting method for large coal-fired power plants is explored based on routine monitoring data from the units’ fuel management system, leading to the formation of a rapid carbon emission accounting method. Additionally, five direct online monitoring approaches for carbon emissions are designed to measure volume fraction, flow velocity and humidity of CO₂. Building upon the rapid accounting method, the calculation deviations of different carbon emission calculation methods within different calculation periods are compared. The results show that, as the time span of the calculation period increases, the calculation deviations for carbon emissions tend to stabilize. Results from a 60-day monitoring period indicate that the computational deviations for the system method, monitoring method, modified monitoring method, oxygen balance method, modified oxygen balance method, and calorific value method are –11.5%, 7.7%, 4.6%, 9.7%, 7.1%, and 17.0%, respectively. After long-term comparative corrections, all these methods are viable for online monitoring of carbon emissions from coal-fired power plants, providing support for managing and controlling carbon emissions and carbon asset management within these facilities.

To improve timeliness and accuracy of the location of condenser leakage cooling tube and solve the problem that the leakage cooling tube cannot be located online, the optimal route of leakage cooling tube location technology is demonstrated by combining theoretical analysis with experimental research. The results show that the tracer gas leakage detection technology has the characteristics of high positioning efficiency and high positioning accuracy, and is suitable for developing on-line locating technology of condenser leakage cooling tube. Through establishing the relationship model between and among the helium concentration change value, time, and water tank liquid level during the isolation of one half of the condenser for draining water, the height of the condenser leakage cooling tube can be located, so as to determine the condenser leakage tube row.

With the rapid development of renewable energy, the demand for grid-scale energy storage solutions is increasing to address the challenges posed by intermittent and variable power generation. As an integration of various mature electrothermal conversion and storage technologies, Carnot battery is gaining increasing attentions due to its scalability and independence from geographical constraints. The fundamental principles, key technologies, application prospects and current research status of Carnot battery are reviewed. The definition of high-temperature Carnot battery technology and the operational characteristics and technical challenges of related key equipment such as compressors and expanders are discussed. Additionally, practical application cases and technological prospects of Carnot battery systems based on electric heating and bidirectional cycles (such as Brayton and Rankine cycles) are analyzed, providing a reference for future research and technological development.

The proton exchange membrane (PEM) electrolyzer can convert green electricity into hydrogen energy, but the conversion efficiency of PEM electrolyzer is low, the thermal energy in the electrolyzer outlet water is not fully utilized. To fully use the waste heat in the PEM electrolyzer hydrogen production system, integrated systems incorporating a 660 MW coal-fired unit and PEM electrolyzer are proposed in both power generation (PG) and combined heat and power (CHP) scenarios. EBSILON and MATLAB Simulink softwares are applied for modelling, and thermodynamic and economic analysis is conducted. In PG scenario, the electrolyzer outlet water is used to heat the feedwater of coal-fired unit. While for CHP scenario, the electrolyzer outlet water is used to heat the return water from heating supply network along with the extraction steam. The produced oxygen is sent into the boiler for combustion. The results show that, compared with the reference coal-fired unit, in PG scenario, the power output is enhanced by 2.55 MW with an power supply efficiency rise of 0.17%, and the boiler efficiency increases by 0.04%. While in CHP scenario, the power output can be enhanced by 5.83 MW with an efficiency rise of 0.40%. After attributing the net power output increment to the PEM hydrogen production system, the exergy efficiency of the PEM hydrogen production system is 69.74% in PG mode with an increase of 3.44%, and 75.41% in CHP mode with an increase of 9.11%. For these two scenarios, the system exergy efficiencies reach up to 40.20% and 40.18%. Economic analysis shows that, the annual income growth by selling electricity for PG and CHP scenarios are 5 460 000 yuan and 12 460 000 yuan, with the increments of net present value of 61 320 000 yuan and 140 140 000 yuan, respectively. The levelized cost of hydrogen production in the reference system is 42.72 yuan/kg. The levelized cost of hydrogen production in PG and CHP modes in the integrated system is 42.55 yuan/kg and 40.79 yuan/kg, respectively.

Aiming at the characteristics of complex and variable load and strong coupling of integrated energy system, a combined forecasting model based on variational mode decomposition (VMD), Prophet model, long- and short-term memory network (LSTM) and autoregressive integrated moving average (ARIMA) model is proposed for short-term electrical load prediction. Firstly, the electric load eigen mode functions with different center frequencies and relatively stable ones are obtained by VMD. Then, after calculating the value of zero cross rate, the modal components of each group are superimposed respectively to form the high-frequency and low-frequency timing components, and the Prophet model is used to extract the high-frequency components for timing features. Finally, the ARIMA prediction model is used to predict the low frequency component, and the LSTM neural network model is applied to predict the high frequency component. The final predicted electric load is obtained by superimposing the respective prediction results. The proposed method is applied to the actual integrated energy system, and the example analysis shows that the combined forecasting method presented above has good forecasting performance for the integrated energy system

With the increase of wind power penetration in power systems, the inertia of the power system decreases and the frequency regulation resources are insufficient. The frequency response margin approaches the critical value, which results in great challenges to frequency security. To solve the problem, from the perspective of regulation mechanism, a wind-thermal cooperative frequency control model based on virtual inertia control of wind power is constructed. The kinetic energy of rotor is used for fast frequency regulation. Then, a load frequency controller of wind-thermal cooperative power system based on a novel active disturbance rejection control (ADRC) method is proposed and designed. This improves the anti-disturbance capability to uncertain wind power and load disturbances. The designed cascade extended state observer also solves the contradiction between high frequency noise suppression and fast response performance of conventional ADRC. The excessive regulation of wind power and thermal power units caused by frequency measurement noise can be avoided and the quality of wind-thermal coordination regulation can be improved. Finally, genetic algorithm based particle swarm optimization is used to optimize the parameters of the proposed load frequency controller. The simulation results show that, compared with the conventional ADRC and other conventional control methods, the proposed strategy can effectively improve the frequency response characteristics, and suppress the effect of measurement noise on the amplitude of system frequency response and control signal.

Enhancing peak shaving capability of supercritical carbon dioxide (S-CO₂) boiler is the key to realize flexible operation of S-CO₂ coal-fired power plants. Research on dynamic characteristics of S-CO₂ boiler is beneficial to optimize the boiler operation control strategies. A dynamic simulation model of S-CO₂ boiler is established by the principles of thermodynamics and heat transfer based on the boiler of a 5 MW S-CO₂ cycle power unit designed and built by Xi’an Thermal Power Research Institute, and the reliability of the model is validated with operational data from unit. Based on the simulation model, dynamic characteristics of the above S-CO₂ boiler under step disturbance of different boundary conditions, such as fuel flow rate, working fluid flow rate, and working fluid temperature, are analyzed. The results show that, the S-CO₂ boiler has large thermal inertia, stability times of working fluid temperature at the boiler outlet are different under disturbance of different boundary conditions. With the increase of disturbance range of boundary conditions, stability times become longer. With the increase of boiler heat load, the working fluid pressure at the S-CO₂ boiler outlet decreases, and the working fluid flow rate at the S-CO₂ boiler outlet increases instantaneously in the initial stages of a dynamic process.

Against the shortcomings of intermittency and instability of photovoltaic power generation in microgrids, a hybrid energy storage system composed of vanadium redox batteries (VRB) and super capacitors (SC) is utilized to smooth out the power fluctuations in standalone microgrids, thus to improve the power supply reliability of standalone microgrids. Considering the capacity allocation problem of the hybrid energy storage system, a multi-objective hybrid energy storage system capacity optimization model that minimizes the average annual cost of the hybrid energy storage system and the load shortage rate is developed. Aiming at the poor local search ability of the conventional elite non-dominated solution sorting genetic algorithm (NSGA-II) algorithm for solving the multi-objective optimization problem, an NSGA-II algorithm based on the improved elite retention strategy is proposed. By introducing a new fitness function, the algorithm is sorted and reasonably retains the elite individuals, so it improves the optimization effect, thus to enhance the local search ability, continuously approach the Pareto true frontier, and obtain better capacity configuration solutions. Finally, the rationality of the proposed method is verified by arithmetic examples.