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.
The distributed access with single-phase and uncertain generation of the renewable energy increase the risk of voltage unbalance limit violation in the distribution network. With the increasing penetration rate of the renewable energy generation, it is important to study the mitigation of the impacts of intermittent renewable energy generation on the risk of voltage unbalance limit violation in the distribution network. A shared energy storage allocation strategy and optimal operation method based on global sensitivity analysis (GSA) is proposed. First, a back propagation neural network (BPNN) based probabilistic voltage unbalance factor calculation model for the distribution network is constructed, and the risk index of the distribution network probabilistic voltage unbalance factor limit violation is defined, which can quickly and accurately quantify the impact of uncertain renewable energy generation on the risk of voltage unbalance limit violation in the distribution network. Then, a GSA method based on Wasserstein distance is proposed to identify the critical renewable energy sources affecting the distribution network voltage unbalance. Finally, the GSA-based shared energy storage allocation strategy and the rolling prediction optimization-based operation method of the shared energy storage are proposed. The effectiveness of the proposed method is verified through the simulation analysis of IEEE 123-bus distribution network.
Accurate electric vehicle load forecasting is the basis for maintaining the safe and economical operation of charging stations, and for supporting the planning and decision-making of new and expanded charging infrastructure. In order to improve the accuracy of the ultra-short-term load forecasting of charging stations, an ultra-short-term load forecasting method based on ensemble learning is proposed. First, aimed at the prediction accuracy and the response speed, the light gradient boosting machine (LightGBM) framework is utilized to build several basic regressors. Next, the basic regressors are integrated by using the adaptive boosting (Adaboost) method. Finally, by using hyperparameter adjustment and optimization, a dual-system for ultra-short-term load forecasting of charging stations named energy ensemble boosting-light gradient boosting machine (EEB-LGBM) is generated. The analysis of the numerical examples shows that the proposed model has a higher accuracy than the back propagation neural network (BPNN), convolutional neural networks-long short term memory (CNN-LSTM), autoregressive integrated moving average (ARIMA), and other load forecasting methods, which can greatly reduce the training time and the computing power requirements of the training platform.
In partial discharge(PD) detection, due to the simultaneous and constantly changing phenomenon of multiple discharge sources and on-site interference sources, it is difficult to effectively separate and identify multiple PD sources. An efficient adaptive efficient adaptive online data stream clustering algorithm (EAOStream) is proposed. The algorithm uses natural neighborhoods to create K-dimensional (KD) trees to improve the efficiency of querying neighbors. That is, the adaptive neighborhood radius and the area density are obtained through the characteristics of the flow data, which can search locally and form clusters, and realize the real-time online separation of multiple local discharge sources. The superiority of EAOStream is verified in the artificial data set and the real data set. After comparing EAOStream with the traditional DenStream and SE-Stream algorithms, it is applied to the pattern recognition of gas-insulated substation faults. Experimental test results show that the clustering accuracy of EAOStream in the real network intrusion detection, the forest cover type, and the multi-source PD signal data sets reaches 95.28%, 98.47%, and 97.23%, verifying the practicability and effectiveness of the algorithm in fault diagnosis of gas-insulated substations.
To achieve the double carbon goal of “carbon peaking and carbon neutrality”, the construction of the power system which is based on the green energy needs to be accelerated. With the growth of the system scale, the distributed green energy carbon trading mechanism and the carbon data management technology based on the blockchain technology can effectively encourage the development of green energy and become effective means for the implementation of low-carbon electricity. The accurate and real-time carbon emission calculation will further provide data support for the accuracy and security of carbon trading information. First, the current research status of green certificate trading and carbon asset management is introduced. Next, the adaptability analysis of the key technologies of the blockchain technology in the four directions of green electricity traceability, green certificate trading, carbon trading, and joint market of green certificate and carbon assets is performed. Afterwords, the specific mathematical models of carbon emission calculation is studied, and the data availability of carbon source traceability methods applicable to the blockchain architecture are discussed. Finally, some suggestions for the future development of carbon emission flow analysis are proposed.
It is of great significance for unmanned aerial vehicle(UAV) to replace manual inspection of power insulators. Aimed at the problem of limited computing power and storage resources of the UAV, an improved real-time target detection algorithm suitable for insulator drop string failure detection is proposed. Based on the YOLOv5s detection network, first, the PANet networks in neck are replaced with bi-directional feature pyramid network(BiFPN) to improve the feature fusion ability. Next, DIoU is used to optimize the loss function to optimize the model. The channel pruning and fine tuning of the γ coefficient generally improve the accuracy, speed, and deployment ability of the detection network. Finally, the image is enhanced at the network output to improve the availability of the algorithm. The proposed algorithm is tested under a specially expanded insulator fault data set. The results show that compared with the original YOLOv5s algorithm, the average accuracy of the proposed algorithm is improved by 3.91%, the detection speed is improved by 25.6%, and the model volume is reduced by 59.1%.
Existing calculation methods of carbon emission cannot well meet the needs of gradual refinement and real-time of carbon emission regions. In order to ensure the real-time and accuracy of carbon emissions responsibility allocation, a real-time calculation method of carbon emissions in urban regions is proposed. The improved K-means clustering algorithm is used to cluster and combine the operating periods and operating scenarios of the urban area energy load,so as to obtain the typical carbon emission characteristics. The regional unit electricity carbon emission is proposed as a carbon emission indicator, the operating period and scenario are classified, and the unit electricity carbon emission and the total carbon emission of urban regions for each cluster are calculated. The proposed algorithm is verified based on part of the historical data of energy consumption in the energy brain of a certain region in eastern China. The results show that the clustering method and carbon emission indicators can effectively calculate the total carbon emission of urban regions in real-time.
There exists the subsynchronous oscillation (SSO) instability risk in large-scale photovoltaic(PV) grid-connected systems with series compensation, which is generally explained by the negative damped oscillation theory. In this paper, the inter-photovoltaic harmonics due to maximum power point tracking (MPPT) control are used as the disturbance source and the large-scale PV grid-connected system with series compensation as the forced system. The forced oscillation theory is used to reveal the SSO mechanism of PV power generation based on the interaction between the perturbed MPPT and the series compensation grid-connected system, and verified in the PSCAD/EMTDC simulation platform. The results show that the perturbed MPPT-based PV inverter outputs interharmonic currents to the system due to the modulation coupling on the AC-DC side, which may lead to serious forced SSO problems when the interharmonic frequency is close to the frequency of inherent weakly damped mode of the system, causing a shock to the system stability. The simulation results verify the correctness of the proposed theory.
Aimed at the problem of large frequency deviation caused by the source load uncertainty and the communication delay in the interconnected power system, a frequency control strategy for interconnected power systems with time-delay considering energy storage regulation is proposed. An interconnected power grid model with time delay which includes a steam turbine generator, a wind turbine generator, and energy storage equipment is established. According to the area control error (ACE), the energy storage device coordinates the steam turbine generator to participate in the frequency control, and the modified particle swarm optimization (MPSO) algorithm is used to optimize the proportional integral derivative (PID) load frequency controller to realize the secondary frequency adjustment, which improves the frequency stability of the load frequency control (LFC) system in a certain time-delay interval. A fractional order PID (FOPID) controller is designed for the energy storage device to adjust the output power and smooth the source load fluctuation. The frequency control performance of the energy storage system is improved to further control the frequency deviation of the interconnected power system. Different working conditions are compared and analyzed on the MATLAB/Simulink platform to verify the effectiveness of the proposed frequency control strategy.
To improve the voltage tracking and anti-disturbance performance of the LC inverter, a novel voltage-current dual-loop control strategy is proposed. First, the voltage loop is tuned to first-order inertia link by zero-pole cancellation based on virtual resistance, which restrains the overshoot during voltage tracking. Next, the hypo-time-optimal current-loop is adopted to enhance the response speed of the current loop, which suppresses the sudden change of transient voltage. Finally, the cause of overshoot during the voltage recovery period is analyzed and the overshoot is eliminated by the adaptive integrator initial value, which modifies the voltage waveform distortion under loading disturbance. Based on the traditional double-loop control, the voltage loop and the current loop are improved respectively by the proposed novel control strategy, which overcomes the shortcomings of step response and anti-load disturbance performance. The feasibility and effectiveness of this method are validated through simulations on MATLAB/Simulink.
Distributed energy storage (DES) wind turbine is an effective means to solve the problem of system frequency stability caused by large-scale wind power connection. In this paper, an inertial control method for DES wind farms based on model predictive control (MPC) is proposed.First, the linearized prediction model of the DES wind farm is established. Then, on this basis, in combination with the control framework of MPC, an optimization model and strategy of MPC inertial control are proposed considering the cost of energy storage loss and the balanced change of wind turbine rotor speed,in order to achieve the balanced change of wind turbine rotor speed during inertia control. The simulation results show that the proposed control strategy can effectively coordinate the active power output of the wind power generation unit and the energy storage system unit in the DES wind turbine, reduce the cost of charging and discharging loss of the energy storage system, and ensure that the rotational speed of all wind turbines in the wind farm tends to be average during the inertial control period, avoiding the problem of wind turbines exiting frequency regulation due to excessive reduction of the rotational speed of wind turbines. The inertial control strategy of the DES wind farm is beneficial to improve the frequency stability of the power grid, which is of great significance to ensure the safe operation of the power grid.
In order to improve the economy of microgrid construction and operation, and meet the personalized demand for reliability of various types of microgrids, an integrated flexible planning method for microgrid is proposed to adapt to multiple scenarios. Based on the judgment results of type and composition, a two-layer model including capacity planning and grid planning is established. The lower-level capacity planning takes the minimum operation cost of micro-source construction as the goal and adopts the mixed integer optimization algorithm to solve it. The upper layer grid planning takes the minimum cost of network construction and operation as the goal, and uses the particle swarm optimization algorithm to solve it. A closed-loop integrated planning system consisting of judgment, capacity planning, and grid planning is formed. The system can meet the personalized reliability requirements of different microgrids by flexibly adjusting the independent operation duration constraints and the load outage attention parameters. The example shows that the proposed method can effectively reduce the construction and operation cost of microgrid, and has a good adaptability to different types and components and different reliability requirements.
Low power efficiency is a critical factor that restricts marketization development of the vertical axis wind turbine (VAWT). The proposal of the trailing edge flap can change flow structure on blade surface, so as to improve the aerodynamic performance of VAWT. At present, the variation law of aerodynamic performance of different airfoil VAWT with trailing edge flaps is not clear. Based on the computational fluid dynamics (CFD) method and the shear stress transport (SST) model, a numerical simulation of 3 H-type VAWTs with different airfoils (NACA0018, NACA0021, and NACA0024) with separated trailing edge flap is conducted. It is found that the results of the validation case are in good agreement with experimental results, which verifies the reliability of this method. Afterwards, 3 basic airfoils and 5 groups of flap deflection angle (-16°, -8°, 0°, 8°, and 16°) parameters are selected to explore the difference in the aerodynamic performance of VAWTs. The results indicate that the positive flap deflection angle in the upwind region can effectively improve blade moment coefficient, and the negative flap deflection angle in the downwind region has a beneficial effect. For the negative flap, the degree of wind energy utilization affected by deflection is positively correlated with airfoil thickness, while for the positive flap, the opposite is true. The research results of this paper can provide an effective reference for application of trailing edge flaps of vertical axis wind turbines.
Aimed at the problem of large torque ripple and low efficiency caused by the poor current tracking effect of switched reluctance motor (SRM), a torque ripple reduction method of switched reluctance motor based on a five-level converter is proposed. A novel five-level converter with A and C phases or B and D phases sharing the same bridge arm is designed. Compared with the traditional converter, the control is more flexible and the cost is lower. According to the nonlinear model of SRM, the conduction region is reclassified, and the phase output torque with a large inductance change rate is preferred to avoid excessive peak current at the beginning of commutation. Aimed at the problem of poor winding current tracking ability when the speed and load conditions change greatly, the direct instantaneous torque control (DITC) method based on the five-level converter is proposed, and the DITC conduction rules at low and high speeds are designed. According to the current speed, the torque error, and the rotor position, the appropriate conduction mode is selected to make sure that the motor can run stably at a low speed and the current can track the required value in time at a high speed, so as to realize the reduction of torque ripple. The simulation and experimental results show that compared with the traditional DITC, the control strategy can reduce the torque and current ripple in a wide speed range and improve the dynamic characteristic of the torque.
In order to achieve China’s “30·60” decarbonization goal, the green and low-carbon transformation of the energy system is the fundamental support; the construction of new-type power system is the key step, and the green certificate is the important voucher to reflect the green value of renewable energy. Currently, the distribution mechanism of green certificates in China is oversimplified, which neither effectively measures the variability of green values generated by different types of renewable energy, nor balances the coordinated development of renewable energy. Therefore, to differentiate the exchange mechanism of green certificates by different types of renewable energy power in this paper, an evaluation index system is established, which describes the difference between green certificates, considering the comprehensive value of renewable energy, and an evaluation model is built with the criteria importance by using the intercriteria correlation (CRITIC) method, the entropy weight method, and the technique for order preference by similarity to an ideal solution (TOPSIS) method. Under the development scenario of peaking carbon emissions before 2030, the impact of the differentiated distribution model on the green incomes of centralized photovoltaic distributed photovoltaic power, onshore wind power, and offshore wind power is analyzed. Moreover, the development plan of renewable energy is modified in consideration of the effect of the differentiated distribution model, and policy suggestions on green certificates are proposed accordingly. The results show that the differentiated distribution model of green certificates is practical to provide corresponding decision-making support to the construction and improvement of green certificates trading mechanism in China.
The system of offshore direct-drive wind farm connected to the power grid via voltage source converter based high voltage direct current (VSC-HVDC) transmission consists of several converters, which have different time scale control loops and complex dynamic characteristics. Based on an example case with two direct-drive wind farms and VSC-HVDC transmission system, the sub/super-synchronous oscillation modes of the system and its relationship with current control loops are studied by state space analysis. The research shows that there are three dominant modes related to the current control of the converter in the system, which are the oscillation mode between wind farms and the offshore converter station, the mode between the offshore wind farms, and the mode between the onshore converter station and the alternating current (AC) system. The modes at the wind farm side are decoupled from the mode between the onshore converter station and the AC system. The relevant control parameters of the converters and the operating conditions have an important impact on the stability of the three modes. The oscillation caused by the single dominant mode may spread to the other side of VSC-HVDC, which means it is necessary to identify the root cause of oscillation in order to design the suppression strategy. The research results is of guidance to the understanding of the dynamic characteristics of offshore wind power grid-connected systems via VSC-HVDC, parameter design, and oscillation suppression.
Based on the relation between battery energy storage systems (BESSs) planning and operation, a multi-objective optimal allocation model that takes into account both economic and technical requirements is established, and a bi-level optimization structure is constructed to ensure effective planning and high-efficient operation of BESSs. In the inner layer, a peafowl optimization algorithm (POA) is employed to solve the BESSs charge-discharge operation strategy with the purpose of BESSs operation benefit maximization. In the outer layer, a multi-objective peafowl optimization algorithm (MOPOA) is devised to solve the Pareto solution set of BESSs siting and sizing scheme, which aims at minimizing BESSs cost, as well as voltage fluctuation and load fluctuation in distribution network. Furthermore, a typical scenario set is obtained via the clustering algorithm considering uncertain operating conditions. The simulation is performed based on the extended IEEE-33 bus system. The results show that the proposed algorithm achieves a trade-off between local search and global search, thus obtains a high-quality solution. It can obtain a more widely distributed and uniform Pareto front, which not only achieves the best investment benefit, but also improves voltage quality and power stability.
Considering the high-randomness and the low-economic-benefit characteristics of the offshore wind-power-integrated multi-microgrid, a two-stage optimal scheduling method considering the uncertain power of source and load is proposed to improve the operation profits of offshore wind-power-integrated multi-microgrid. The proposed two-stage optimal scheduling method consists of a day-ahead stage and an hour-ahead stage. In the day-ahead stage, the proposed method is based on the forecast data of the wind power and the load demand, which considers the distribution characteristics of the prediction errors. A stochastic optimization model is established to determine the unit committee of the diesel generators and the state-of-charge of the battery storages, so as to maximize the expected daily operation income. A deterministic optimization model is established based on the decisions from the day-ahead optimization relying on the hour-ahead forecast data of the wind power output and load demand. By optimizing the power of the diesel generators, wind turbines and battery energy storages, the operation income of each hour is maximized. Finally, a simulation model is established to verify the proposed method based on the prediction data of sources and loads in wind-power-integrated multi-microgrid. The simulation results show that compared with the conventional schedule strategies, the proposed two-stage optimal scheduling method can achieve a higher income and a higher overall consumption rate of the wind power.
Aimed at the problem of the inertia power allocation due to different indexes when multiple optical storage units are running together, a cooperative control strategy of multiple optical storage units is proposed by using adaptive virtual inertia control as a means to improve power quality. According to the charging and discharging characteristics of the battery, the inertia provided by the system is adjusted. As high-frequency disturbance occurs in the system, the super capacitor is the first choice to provide inertia support. As low-frequency disturbance occurs in the system, the battery provides inertia power support, and the distance algorithm of the superior and inferior solutions is introduced. As cooperative control is performed, indicators such as the allowable power fluctuation range of the converter and the allowable power fluctuation range of the energy storage device are selected as the evaluation reference, the coordination among multi virtual synchronous generator (VSG) units in multi-index comprehensive evaluation is realized. Finally, an AC system with multi VSG units is built on the experiment platform, and the effectiveness of the proposed control strategy is verified.
Flexible all-solid-state supercapacitors (FASS) are energy supplies for wearable electronic devices and power devices. Graphene nanosheets have unique two-dimensional (2D) structures, strong mechanical properties, and an excellent electrical conductivity, which are widely used in paper-like flexible electrodes. The essential feature of the double-layer electric performance for the simple graphene nanosheet-based FASS restricts the improvement of their capacitive performance and practical applications. FASS based on the ultralarge graphene nanosheets and the ultrathin boron nitride (BN) nanosheets are investigated. The nacre-like structures could efficiently integrate both merits of pseudocapacitive BN nanoflakes and conducting graphene, thereby exhibiting an excellent electrochemical performance in FASS. After 5000 charge-discharge cycles, the highest areal specific capacitance of FASS reaches 325.4 mF/cm2, with a high capacity retention rate of about 86.2% and a high energy density of 22.8 W·h/kg (1 W·h=3.6 kJ) at a power density of 85.7 W/kg.
Currently, the application of floating photovoltaics in the ocean is mainly restricted by the cost of submarine cables and special buoys. It will show a high degree of applicability if the energy is consumed by the unmanned management systems on ocean farms and in other scenarios. The grid system formed by the floating photovoltaics can satisfy the early warning requirements of the sudden weather changes on ocean farms. Due to the strong follow-up of the photovoltaic output model to random weather changes, based on the spatial-temporal correlation analysis of large-area photovoltaics, hardware, distance, time delay, and weather, a similar power station fusion estimation relationship is established. Based on the long short-term memory (LSTM) algorithm, the ultra-short-term prediction value of the time sequence tracking of similar power stations can be used to estimate the early warning of the status of target similar power stations. The city-scale data was used to verify the feasibility of the proposed idea, which shows that the framework can complement traditional research deficiencies.
