The large-scale utilization of renewable energy is an important way to achieve the “double carbon targets”. The technology of coupled renewable energy with hydrogen system can improve the consumption rate of renewable energy and the penetration of new energy vehicles. The coupling between the electricity-hydrogen energy system and the transportation system will be even closer in the future. Based on the access of large-scale new energy vehicles, first, the development of the electricity and hydrogen energy system was summarized, and the three working modes of electricity-hydrogen coupling system including hydrogen production, output smoothing, and coordinated operation with electricity network were introduced. Then, the research status of the electricity-transportation coupling system on planning and optimal operation, and the problems of hydrogen-transportation coupling system on hydrogen refueling station optimization and hydrogen transportation were analyzed. Finally, in combination with the existing bottlenecks, the future feasible research directions such as dynamic model construction and the influence of uncertain factors were proposed.
The condition assessment of the entire life cycle of nuclear power equipment has a significant impact on improving the safety and economy of nuclear power plants. In the past, operation and maintenance of systems, equipment, and structures of domestic nuclear power plants, mostly relied on the alarm mechanism of equipments, the simple threshold judgments of parameters, or the empirical judgments of engineers. With the implementation of online monitoring system in nuclear power plants, a large number of equipment operation data have been accumulated, and the use of data-driven technology to assess the health of equipment has become the focus of attention in the industry. In this paper, the current situation of the online monitoring system of nuclear power equipment was introduced and the common malfunction of nuclear power equipment was analyzed. The condition assessment of nuclear power equipment were categorized into three major problems (i.e., anomaly detection, life prediction, and fault diagnosis), the situation of research and application were summarized respectively, and the application potential of deep learning technology in this field was emphasized. Based on this, the challenges and possible solutions to the condition assessment of nuclear power plant equipment were further analyzed.
The massive access of 5G base stations (5G BSs) provides new possibilities for the low-carbon development of future power systems. By incentivizing 5G BSs to participate in demand response and incorporating them into the existing active distribution network (ADN) operation framework, the cost of the electricity consumption of 5G BSs can be reduced while promoting the consumption and efficient use of renewable energy sources (RES). This paper proposes a multi-objective interval optimization model for ADN operation considering low-carbon empowerment of 5G BSs. Based on the interaction mode between 5G BSs and the distribution network, a 5G BSs operating flexibility description model is constructed, and the system dynamics method is used to reveal the mechanism of 5G BSs on carbon emission reduction on the distribution side. Taking the minimization of system operating cost and carbon emissions as the goals, and considering the constraints for both the distribution network and the communication network, a multi-objective optimization model for ADN operation with 5G BSs is established. The model cooptimizes the dispatch of RES and 5G equipment, and adopts an interval method to consider the uncertainty of RES output and communication loads, which can achieve simultaneous optimization of system economy and low-carbon benefits. Combining the equivalent transformation and the non-dominated sorting genetic algorithm to solve the problem, the results of numerical studies prove the effectiveness of the proposed method.
China’s “carbon peaking and carbon neutrality” goal relies greatly on the low-carbon transition of the power system, but the existing research rarely explores the low-carbon transition of the regional power system. By using the intergovernmental panel on climate change (IPCC) greenhouse gas inventory compilation method and the network model analysis, the carbon emissions caused by the power generation and the power consumption in Guangdong-Hong Kong-Macao Greater Bay Area (the Greater Bay Area) was quantified. The logarithmic mean Divisia index (LMDI) method was used to quantify the influence of socio-economic factors on the electricity-related carbon emissions in the Greater Bay Area. The results show that Hong Kong and Macao have made slow progress in the low-carbon transition of the power system, and Guangdong’s share of the low-carbon power continues to increase. The rapidly expanding economic scale and the power demand were the most important drivers of the emissions growth in the Greater Bay Area. The low-carbon electricity imported from outside regions and the improved efficiency in the sectoral electricity consumption offset part of the emission growth.
Aimed at the problem of low-frequency oscillations caused by cross-region power transmissioin of large-scale wind farms, a single neuron adaptive proportion integration differentiation (PID) additional damping control strategy for low-frequency oscillations of the damping system is proposed in this paper. By analyzing the dynamic frequency response characteristics of doubly-fed wind turbines, a wind farm damping system oscillation controller is constructed by introducing quadratic performance indicators into the single neuron adaptive PID control algorithm. By adaptively adjusting the excitation frequency converter, the wind farm can quickly generate active power and the maximum positive damping, and suppress the low-frequency oscillation of the damping system. MATLAB is used to build a four-machine two-region power system simulation model with a wind farm. The comparison verifies that the method proposed in this paper can effectively suppress the swing of the power angle of the synchronous generator when low-frequency oscillation occurs in the system, improve the inertial response of the system, and reduce the risk of low-frequency oscillation in the power grid.
With the rapid development of distributed power generation research and application, the distributed trading market, as a new type of power trading mode, can effectively increase the consumption rate of renewable energy and is an important means to promote the realization of the goal of “carbon peaking and carbon neutrality”. Introducing the market evaluation mechanism into distributed transactions will prompt users to consider the impact of the market evaluation mechanism on their trading strategies and promote the sound development of the distributed transaction market. The distributed power trading market among micro-grid users is studied in this paper. First, taking the market participants and transaction supporting software and hardware as the research object, a multi-dimensional performance evaluation index system is established from the aspects of power supply capacity, user satisfaction, and platform security. Next, the research status of distributed power trading market evaluation methods is summarized. The key technologies of distributed power trading performance evaluation are analyzed from the establishment of index system, the index calculation method, and the comprehensive evaluation method. Finally, in combination with the current development status, the research direction of the distributed power trading performance evaluation in the future is prospected.
The failure of modular multilevel converter (MMC) submodules in the flexible direct current (DC) system affects the normal operation of the system, and the mutual restriction of reliability and economy is one of the key issues of MMC redundancy configuration optimization. A multi-objective optimization function of MMC reliability and economy mathematical model with redundant submodules was established. Based on the weight coefficient and NSGAII multi-objective optimization methods, a dual collaborative optimization for redundancy quantity of flexible controller was proposed. Combining the advantages of the two methods, the intersection of the two optimization results was calculated under the same redundancy quantity selection preference. A model based on a DC project in a flexible station area of Nanjing was built in MATLAB. The simulation results prove that the proposed method can not only meet the reliability of the flexible DC system, but also significantly improve the economy. It provides ideas for redundancy quantity of MMC submodules in the actual flexible DC project.
In order to suppress subsequent commutation failures of high voltage direct current (HVDC), the dynamic process of electrical and control quantities during system recovery is studied, and the main reason for subsequent commutation failures is proposed in the paper. During the recovery process, the voltage of the converter bus after the fault is in a state of drop, the actual firing angle of the inverter is in overshoot, and direct current continues to rise. These factors result in an insufficient system commutation capability to complete the transfer of valve arm inductance energy during the commutation process. A suppression strategy for subsequent commutation failure considering the commutation capability of the system recovery process is proposed. By limiting the direct current (DC) when the firing angle is in overshoot, the system commutation capability is increased, and subsequent commutation failures are suppressed. In addition, the DC system is effectively recovered. The proposed theory is tested and verified based on the HVDC CIGRE Benchmark in PSCAD/EMTDC.
Aimed at the uncertainty of charging starting and ending point caused by incomplete charging and discharging in practical applications of lithium-ion battery, an estimation method of battery health based on dual charging state factors is proposed. A battery aging experiment bench is built, and eight nickel-cobalt-manganese lithium-ion batteries are subjected to aging test. Different from the traditional single state factor estimation, the average value of equal time difference current at the front end of constant voltage charging curve and the equal amplitude voltage charging time at the end of constant current charging curve are selected under different aging conditions to construct health factors. The corresponding relationship between state of charge (SOC) and open circuit voltage (OCV) of the experimental battery in different aging states is analyzed and the correctness of health factor is proved by theoretical deduction and experimental results. An improved support vector regression model with a strong generalization ability is established, and the hyperparameters of the model are optimized through the particle swarm optimization algorithm. The results show that the proposed dual-charging health factor is closely related to battery capacity aging and attenuation. The improved support vector regression model can estimate the health status in different aging states in real time, and has the ability to characterize local capacity rebound change, which can be used as an effective method for estimating the state of health of an embedded battery management system.
In the conventional lightning location system (LLS) based on time difference of arrival (TDOA), the nonlinear equations in lightning location calculation easily get to be divergent when the time information acquired from electromagnetic sensors is redundant. The LLS setup in lightning-sensitive regions in China usually experiences a development from detecting the thunderclap signal to the electromagnetic signal, such as the LLSs in oil tank farms. Therefore, an improved TDOA lightning location approach was proposed considering the acoustic and electromagnetic information emitted from lightning discharges. The targeted lightning monitoring region was divided into 16 sub-regions according to the location of the existing detection stations. The lightning location was calculated based on the Levenberg-Marquardt (L-M) iterative algorithm, which improves the lightning location accuracy and the resistance ability to measuring errors. The results show that the average error of the traditional lightning location method is 203.2 m. In contrast, the proposed approach can reduce the lightning location error to 108.4 m by considering the acoustic information and L-M iteration algorithm. The location accuracy at the edge of the targeted area is improved by 51.2%. This research can be potentially counseled in the improvement of existing LLSs and making an effective use of acoustic information.
This paper introduces the current situation of domestic and foreign offshore wind power grid-connected via voltage source converter based high voltage direct current(VSC-HVDC) transmission standards, and selects representative standards of offshore wind power grid-connected via VSC-HVDC. It also compares the domestic and foreign offshore wind power grid in terms of power control, fault ride-through, power quality, stability, etc., and analyzes the development trend of offshore wind power grid-connected via VSC-HVDC standards. In order to promote the development of offshore wind power industry, it provides reasonable suggestions for the formulation and revision of Chinese offshore wind power grid-connected via VSC-HVDC standards.
The state of health (SOH) estimation accuracy of lithium-ion battery affects the safety and service life of batteries. Aimed at the problem in SOH estimation of lithium-ion battery, a cuckoo search support vector regression (CS-SVR) model based on the evidence reasoning (ER) rule was proposed for SOH estimation. The lithium-ion battery data from NASA Ames Center was used to perform the SOH estimation test. In this method, the average voltage and average temperature of battery discharge cycles were taken as model input, and a fusion belief degree matrix of input data was obtained by the ER rule. The SOH estimation result of the battery was obtained by inputting a fusion belief degree matrix into the SVR model optimized by the CS algorithm. The results show that the CS-SVR algorithm based on the ER rule has a better estimation performance than the five existing models.
Intentional islanding restoration of distribution systems with multi-generations is of great importance to ensure the power supply of critical loads under extreme conditions, which is beneficial to improve the reliability of distribution systems. There are transient voltage and frequency fluctuations in the process of intentional islanding restoration, when the loads and distributed generations are gradually connected to the grid. The safety and stability of the intentional islanding are affected by the fluctuations, and networking process may fail in serious cases. Hence, the rapid power response of the energy storage system is utilized to suppress voltage and frequency fluctuations. A fluctuation suppression model based on energy storage system control is established, where a voltage and current double-loop feed-forward disturbance compensation control system is designed. A vector control method for energy storage system with improved dual-loop control is proposed, which solves the problems of traditional V/f control voltage offset and excessive voltage fluctuation. MATLAB/Simulink is used to build simulation models in different control modes in accordance with the black-start principle. The simulation results show that the improved double-loop control based on the vector method has a stronger anti-interference ability and significantly improved the islanding black-start self-organizing networking process. Voltage and frequency fluctuations are reduced, and the dynamic response performance of the system is improved.
Wind-solar-electric vehicles coordinated optimization scheduling can effectively reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system. Most of the existing optimization scheduling models take the minimum equivalent load fluctuation as the optimization objective, which, only considering the overall fluctuation of equivalent load, cannot measure the matching degree of output-load, and do not consider the difference of output in different output scenarios. Therefore, a wind-solar-electric vehicles coordination scheduling method for high proportion new energy grid-connected scenarios is proposed. First, the disordered charging model of electric vehicles by Monte Carlo simulation is constructed. Then, a wind-solar output typical day classification model using Gap statistical and K-means++ is constructed based on the forecasting data of wind and solar power. Finally, taking the minimum equivalent load variance and load tracking coefficient as the double optimization objectives, a wind-solar-electric vehicles coordination optimization scheduling model is established, and the NSGA-II algorithm is used to solve it. The results demonstrate that the proposed model can effectively improve the matching degree of wind-solar output and load, and reduce the fluctuation of equivalent load, so as to reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system.
DC-link for the capacitor is one of the most vulnerable components of the grid connected converter, whose capacitance identification will help to improve the system reliability by finding and replacing the aging capacitor in time. An identification method for the DC-link capacitor capacitance of the grid connected inverter based on pre-charging circuit is proposed. By analyzing the relationship between the capacitance and the charging current, charging voltage during pre-charging process, and combining the historical operating data, the set of capacitance state feature vector is built. The support vector regression (SVR) model is trained and the regression prediction relationship between the state value and the capacitance is set. The model is optimized by using the particle swarm optimization (PSO) algorithm, which can be used for capacitance identification of the DC-link capacitor. Simulation and experiments results show that the proposed method can implement the accurate capacitance identification of the DC-link capacitor of the grid connected inverter, with an identification error of less than 0.95%. This method does not need to add hardware circuit and change the control algorithm, and has a certain practical value.
In view of the extensive geology of overlying soil and underlying rock in mountainous areas, a pile-anchor composite foundation has been used in transmission line tower engineering. To reveal the uplift bearing mechanism and supplement the calculation method of the uplift capacity and the bearing exertion coefficient k, the verification model of field test case was established by using the PLAXIS 3D finite element software to study this problem. Parameter studies were conducted on this foundation. The influence of elastic modulus and cohesion of rock and soil and foundation condition on coefficient k was studied. The results show that there is asynchronism of ultimate uplift limit state of upper and lower parts of the pile-anchor foundation. The bearing ratio and coefficient k of the upper and lower parts of the foundation are related to geology and foundation structure. In combination with the parameter study and the relevant analytical solution of the relationship between the uplift load and the displacement, a theoretical calculation method of the coefficient k considering the foundation weight was proposed. Three test cases were used to perform confirmatory calculations for this method. By comparing with field tests and numerical calculation results, the correctness of this method has been verified. This method provides a theoretical reference for the design and application of this type of new foundation.
