Abstract:伴随传统电力系统向新型电力系统转型升级，电源结构和负荷形态发生重大变化，“源荷”双侧随机性、波动性显著提高。叠加近年来极端天气因素影响，需求侧季节性负荷峰谷差加大，电力供应保障面临极大挑战，须进一步释放负荷资源参与电网调控。而当前负荷资源特性分析不够精细、负荷资源能力预测不够精准、多应用场景需求的大规模负荷资源调控不够高效、负荷管理体系不健全等因素制约了负荷资源灵活调控作用的发挥。

Abstract:In the context of the dual-carbon goal,virtual power plants,microgrids and other forms of prosumers have emerged on a large scale. Energy sharing among multiple prosumers can improve the overall economic efficiency and new energy consumption. A collaborative operation mechanism for energy sharing among multiple prosumers under the weak centralization model is proposed. The mechanism considers the data privacy of each prosumer. Firstly,an internal scheduling model of prosumers with multiple distributed resources is constructed. The model considers the volatility and randomness of load demand and new energy output,quantifying the risk of uncertainty based on conditional value at risk (CVaR). Then,a weakly centralized tariff iteration mechanism for multiple prosumers is proposed,which uses the supply and demand relationship to guide the tariff update. Meanwhile,considering the privacy protection of prosumers,the power data aggregation method is designed based on Paillier homomorphic encryption algorithm and secret sharing principle. The method is able to obtain the system supply and demand information while ensuring the privacy of all parties. Finally,the effectiveness and rationality of the mechanism proposed in this paper is verified by an arithmetic example,and the overall cost of multiple prosumers is reduced by 12.6% after energy sharing.

Abstract:Rural areas with weak distribution networks often suffer from a lack of energy supply reliability,which makes it difficult to widely implement multi-energy complementary systems connected to large-scale photovoltaic. Energy storage systems,as a link to the integration of new energy systems,can effectively improve the reliability of energy supply in such areas. To this end,a cooperative and optimal configuration strategy for rural multi-energy complementary system (RMES) energy storage for weak distribution networks is proposed. Firstly,the RMES architecture including biomass waste energy conversion system is clarified. Secondly,an energy storage configuration evaluation criterion is proposed using the cost of supply deficiency loss of the system as a judgment basis. Finally,the RMES multi-energy storage cooptimal allocation model is established to take into account the total life cycle cost of energy storage. The model is decoupled and transformed into a two-layer optimization model with two stages of planning operation,and solved by a hybrid strategy combining particle swarm algorithm and Gurobi solver. The analysis of the algorithm shows that the proposed energy storage configuration evaluation criteria effectively motivate RMES to deploy energy storage. In addition,the developed multi-variate energy storage configuration model enables RMES to show better economic and environmental benefits,and is superior in improving system reliability.

Abstract:Pumped hydro storage (PHS) is the most mature storage technology with the largest installed capacity. However,its power regulation rate is slow,and its adjustment flexibility is relatively lacking. Electrochemical storage,which is currently the fastest developing storage equipment,provides capability of flexible power regulation. A photovoltaic-battery-pumped hybrid energy storage system is constructed in this paper. The goal is to improve the flexibility of PHS by utilizling the complementary characteristics of different storage facilities. The power regulation capability of the unit based on the power oscillation interval description of pumped storage is described，and further defines the flexibility of PHS according to the regulation of single unit and the unit-commitmen of multiple units. Considering the flexibility enhancing constraints and the demand to improve the operational lifespan of PHS,a capacity optimization strategy for the battery in hybrid energy storage system is established. A case study is conducted using a PHS in Shandong province as an example. The findings reveal that configuring an electrochemical energy storage system with a rated power equal to 10% of the PHS capacity can improve the flexibility of the storage system by about 40%,and reduce generation start-up times by 14%. However,under the current electricity pricing structure,the peak-valley price gap needs to surplus 0.5 yuan per kW·h for the electrochemical storage facility to become profitable.

Abstract:In order to improve the orderly control and flexible adjustment capabilities of large-scale distributed power sources connected to the distribution network,it is currently an effective method to reasonably divide the distributed power sources into clusters and use the cluster as the basic unit for regulation. To this end,a distributed power cluster partitioning method that considers the adjustable capacity and response speed of flexible resources is proposed. Firstly,the modularity index and the active power balance index are proposed to characterize the structural and power balance capability of clusters. Secondly,based on the analysis of the cluster's flexible response capability,and in view of the problem that it is difficult to quantify the impact of the flexible resource response capacity and response speed on the adjustment capability,it is proposed to take the cumulative area of deviation between the response curve of cluster output and the net load curve as the indicator of the flexible response capability of clusters,and the influence of the allocation of energy storage capacity on the delineation of clusters is also taken into account. Then,a cluster division objective function that comprehensively considers the modularity index,active power balance index,cluster flexible response capability index and energy storage unit allocation index is proposed,and this function is solved using an improved adaptive genetic algorithm. Finally,the proposed method is verified in the improved IEEE 33-node system and IEEE 69-node system. The results show that the cluster partitioning method based on the proposed comprehensive performance index can improve the cluster flexible response capability by more than 20% on the basis of ensuring the structural strength of the cluster,which verifies the effectiveness and superiority of the cluster partitioning method proposed in this paper.

Abstract:In view of the problems of less interaction between source-load entities,high carbon emission intensity,low wind-solar power consumption capacity,and poor overall operating efficiency of the regional integrated energy system (RIES),an optimization method is proposed for the economic low-carbon operation of the RIES taking into account the demand response of electricity-gas-thermal prices. Based on the common commodity attributes of electricity,natural gas,and thermal energy and the dispatchable value of multiple flexible loads,a price-demand response model of electric-gas-thermal is established,which effectively enhances the incentive effect of price signals on load participation in demand response. To fully tap into the low-carbon potential of the system,stepped carbon trading is introduced to improve the actual carbon emission model. With the minimum system operation cost as the optimization goal,the influence of different operation modes on the economy and low carbon of the system is studied. The case study results show that the operation optimization model of price demand response and stepped carbon trading can achieve economic low-carbon system operation and wind-solar power consumption capacity while achieving peak-shaving and valley-filling.

Abstract:A virtual power plant (VPP) is proposed to aggregate various resources to participate as a whole in both electricity and carbon trading markets. As the scale of VPPs continues to expand,they are transitioning from being price takers to price makers. To this end,this paper treats the VPP as a price maker and proposes a bi-level bidding strategy in the day-ahead electricity market,considering the impact of carbon trading. Firstly,an introduction and analysis of the day-ahead electricity market mechanism,considering carbon trading,are provided. Secondly,based on the Stackelberg game theory,a bi-level bidding model in the day-ahead electricity market is established with the VPP as the bidding entity. The upper-level model aims to maximize the anticipated profit of the VPP,while the lower-level model aims to minimize the system's clearing cost. Considering the uncertainty in wind farm output predictions within the VPP,operators are provided with two bidding strategies:risk-averse and opportunity seeker strategies based on the information gap decision theory (IGDT). Then,utilizing the strong duality theory,the Karush-Kuhn-Tucker (KKT) optimality conditions,and the big-M method,the bi-level model is simplified into a mixed-integer linear programming problem for resolution. Finally,an example is provided to illustrate the optimal bidding strategy and operation plan for the VPP,along with an analysis of how uncertainty in wind farm output predictions within the VPP affects the expected profit of the VPP. The example shows that VPPs can influence market prices through strategic bidding decisions. After considering carbon trading,the expected revenue of the VPP increased by 5.1% compared to the scenario without carbon trading.

Abstract:In the future new power system,the multi-agent trusted transaction and information security in the virtual power plant (VPP) will be complex and challenging. The features of blockchain technology,such as information security,distribution decision,smart contract and tamper-proof,provide a new idea for VPP multi-subject trusted transaction. This paper focuses on the benefits maximization formed by multi-aggregator inside VPPs and the efficiency of trusted transaction matching. Firstly,according to the different demands of transaction subjects,a master and multi-slave hierarchical interactive control architecture is established to integrate blockchain technology with multi-agent system. Secondly,a credible transaction matching mechanism is designed under the demand of multi-aggregator peer to peer transaction,and non-cooperative game model is introduced to ensure the interests of multi-aggregator subjects. Then,the blockchain-based distributed particle swarm optimization algorithm proposes a consensus algorithm improved by delegated proof-of-authority (DPoA) to improve the security of the transaction. Finally,the rationality and feasibility of the strategy proposed in this paper are verified by an example analysis,which effectively enhances the matching efficiency and security of multi-agent transactions.

Abstract:The access of wind power reduces the carbon emissions of the power system from the generation side,and the introduction of demand response to eliminate wind abandonment provides a new idea for carbon reduction from the load side. Considering wind power and demand response comprehensively,based on the theory of carbon emission flow of power system,a day-ahead and day-in two-stage low carbon optimal dispatching method for power grid is proposed. Firstly,the carbon emission flow theory of power system is analyzed,and the node carbon potential model of load side is established. Then,the flexible load is divided into two types,transferable load and reducible load. Based on the carbon potential model of load nodes,the response mechanism of calling these two types of loads to reduce carbon is designed. On this basis,the source load coordination day-ahead optimal scheduling model considering low-carbon and economic is established. Based on model predictive control,the day-ahead optimal scheduling model is solved,and the day-ahead scheduling results are changed through feedback correction. Finally,the simulation results of the improved PJM-5 node system and IEEE 300-node system show that the proposed optimal scheduling method can effectively promote the flexible load to absorb wind power,and reduce wind abandonment,and achieve the goal of load side carbon reduction.

Abstract:Permanent magnet synchronous motor (PMSM) has been widely used in industry due to the advantages of high efficiency,high power density and high reliability. In this paper,a finite-control-set model-free predictive current control (FCS-MFPCC) based on extended control set is proposed for permanent magnet synchronous motor drives. At first,the mathematical model of PMSM is analyzed and the principle of finite-control-set model predictive current control (FCS-MPCC) is elaborated. Then,the traditional FCS-MFPCC based on linear extended state observer (LESO) is introduced. In order to tackle the issue of dissatisfactory steady-state performance of the traditional FCS-MFPCC,a control-set extension method based on the discrete space vector modulation (DSVM) is utilized,by which,the number of voltage vectors can be increased to 25. Following this,a fast optimization strategy is proposed to solve the problem of high computation burden caused by the extended control set. The principle and procedure of implementation of the strategy is explained in detail. Finally,the effectiveness and superiority of the proposed control strategy are verified by comparing the traditional and the proposed FCS-MFPCC methods based on a 500 W PMSM experimental test rig. The results show that the proposed control strategy can significantly enhance the steady-state performance of the system. The total harmonic distortion of stator current can be reduced from 10.07% to 6.48%.

Abstract:With the development of new power systems,the protection and control tasks of distribution networks have become increasingly complex. When intelligent terminals are employed to handle these tasks,the requirements for balancing resource supply and demand have become move stringent. Therefore,a collaborative task allocation method for protection and control intelligent terminals (PCIT) in distribution networks considering elastic allocation of resources is proposed in this paper. Firstly,the technical architecture of multi-terminal collaboration is elaborated,and the container-based elastic resource model and task processing model within the terminal are established. Secondly,a two-layer model is proposed to optimize the cooperative assignment of protection control tasks among terminals and the elastic scheduling of resources. The implicit enumeration method is used to solve the model,so as to give full play to the flexibility of resources and improve the performance of task processing. Finally,the arithmetic example verifies the feasibility and advancement of the proposed method. The occupancy rate of computing resources of each intelligent terminal is reduced by about 28.85%,and the average processing delay of tasks is reduced by about 4.12%.

Abstract:Under the ‘dual-carbon' goal,industrial parks,as the most important and extensive carriers of industrial systems,must undergo low-carbon transformation. In this context,this paper establishes a bi-level optimal dispatching model of low-carbon industrial park considering flexible scheduling of high-energy-consuming enterprises' production workshops. The model fully exploits the flexible adjustment ability of high-energy-consuming enterprises,obtains the optimal low-carbon scheduling plan of the park by flexibly adjusting the production scheme of high-energy-consuming enterprises to realize the coordinated operation of the industrial park and high-energy-consuming enterprises. Firstly,the upper layer establishes an optimal scheduling model with the objective of minimizing the total operating cost,taking into account carbon trading and tradable green certificate costs. Then,the lower layer focuses on the flexible scheduling problem of the high-energy-consuming enterprise's production workshops within the industrial park,aiming to minimize the maximum completion time and cost. The cost savings achieved by enterprise scheduling at the operational layer are used as subsidies. The upper and lower layers continuously coordinate and schedule plans to achieve the optimal objective. Finally,the feasibility and effectiveness of the proposed model are verified through case studies. The proposed approach not only reduces production costs and improves production efficiency but also effectively promotes load balancing,realizing the low-carbon operation of the industrial park.

Abstract:With the gradual increase of the proportion of renewable energy in the power system,the risk of frequency fluctuation in the power system increases. Flywheel and lithium battery can complement each other as hybrid energy storage applied in the primary frequency regulation of the grid,effectively solving the problem of system frequency fluctuation. In order to give full play to the respective frequency regulation advantages of flywheel and lithium battery,a primary frequency regulation (PFR) strategy for battery-flywheel hybrid energy storage based on adaptive state of charge (SOC) is proposed. Firstly,a weight allocation PFR is proposed,containing three control methods,namely,positive and negative virtual inertia control and virtual droop control. Secondly,the parameters of the PFR are corrected by using the SOC of the doubly-fed flywheel and the Li-ion battery,so as to improve the PFR capability of hybrid energy storage near the threshold of SOC. Finally,the control strategy of the hybrid energy storage with the other control strategies is compared with that of this paper in simulation under the frequency regulation scenarios. The results show that the SOC fluctuation range of the energy storage system of the strategy proposed in this paper is minimum. The battery will not be overcharged and overdischarged,and the system frequency fluctuation is kept no more than ±0.2 Hz,which proves that the proposed strategy can improve the frequency stability of the grid.

Abstract:With the large-scale integration of wind power into the grid,the inertia and disturbance resistance of the power system are gradually declining,leading to an increasingly prominent frequency stability issue. An adaptive frequency support control strategy for wind farms based on rotor kinetic energy is proposed. Firstly,a participation factor is proposed,which determines the degree of wind farm participation in frequency support based on the average rotor speed of the wind farm and adaptively adjusts the inertia response parameters and frequency droop control parameters of wind farms. Based on the real-time available rotor kinetic energy of wind turbines,the frequency modulation power of wind farms is dynamically allocated. The frequency support ability of wind turbines with more rotor kinetic energy is fully utilized and excessive participation of low-speed wind turbines is avoided in frequency modulation. After the frequency support is completed,a staged rotor speed recovery method is used to restore the rotor speed of the wind turbines. Simulation results show that the proposed strategy can adaptively adjust the active output power based on the operating status of the wind farms and the available rotor kinetic energy of the wind turbines while achieving effective support for the system frequency and avoiding secondary frequency drop.

Abstract:Developing offshore wind power is an important measure to achieve the goal of ‘dual carbon'. DC submarine cables are important devices in offshore wind power transmission projects,and the research on the ampacity of submarine cables plays an important role in promoting the large-scale development of offshore wind power. In recent years,the research on the ampacity of high-voltage DC submarine cables has considered relatively single marine environmental factors，and the limitation of temperature difference of insulation layer has not fully be considered. The article establishes an electric-thermal-current coupling model of the 500 kV DC submarine cable and the seawater system. Under different laying methods for single and bipolar submarine cables,the effects of vertical ocean currents (currents flowing perpendicular to the length direction of the cable) flow velocity,limitation of temperature difference of insulation layer and the the different spacing of the bipolar cables on the ampacity are studied. The results show that considering the temperature difference limitation 20 ℃ of the insulation layer comprehensively has a smaller ampacity compared with the situation only considering the temperature limitation 70 ℃ of the wire core,and direct burial laying has a smaller impact on the ampacity compared to any other laying method. It is also found that the ampacity of bipolar submarine cables increases with the increase of the distance between the two poles. When the flow rate is 0.1 m/s,the vortex have a small improvement effect on the ampacity of the submarine cable. The electric field flips around a temperature difference of 6 ℃ in the insulation layer. The research results can provide important guidance and reference for the selection of laying methods,and even for the prediction or evaluation of ampacity.

Abstract:The advantages of fast charging speed and high efficiency of DC charging piles make the connection and cutting of electric vehicle (EV) loads relatively frequent. However,as a DC system,DC charging pile has the characteristics of low inertia and weak damping. In order to reduce the impact of EV load connection and cutting on the DC side voltage disturbance,a DC charging pile control method based on improved virtual inertia (VI) control is proposed. This paper firstly introduces VI control by analogy with virtual synchronous machines (VSM) technology. Secondly,the VI control is improved by using command filter backstepping integral sliding mode (CFBISM) control,in which the integral sliding mode (ISM) control is used to optimize the VI control to enhance the robustness of the system,and the command filter backstepping control is used to reconfigure the key parameters to avoid the computational inflation of the controller,while the ISM control is used to improve the current inner loop to further enhance the control effect. The stability of the proposed control strategy is then demonstrated by the Lyapunov stability criterion. Finally,through comparative simulation,it is verified that the DC-side voltage fluctuation can be limited within 2 V,the dynamic response speed is improved by about 0.1 s,and the perturbation of the key parameters in the steady state is limited to about 1 V,thus the superiority of the fast response speed and good robustness of the control proposed in this paper are verified by simulation.

Abstract:Lithium ion batteries are widely used in chemical energy storage systems. However,due to their inherent heat generation characteristics,thermal runaway has become a major safety hazard for chemical energy storage power plants. Therefore,optimizing the design of a battery thermal management system to effectively avoid thermal runaway is crucial for the safe operation of chemical energy storage systems. A new type of parallel serpentine flow channel liquid cooling plate with both series turn back and parallel branch structures is designed. Through simulation experiments,the effects of the flow channel structure of the liquid cooling plate,the layout of the liquid cooling system,and the inlet flow velocity of the coolant on the maximum temperature,temperature uniformity,and inlet and outlet pressure drop are studied to optimize the liquid cooling system. The results show that,under the same coolant inlet flow rate,the maximum temperature of the new channel is reduced by 0.284 9 K,and the temperature difference within the module is reduced by 0.466 3 K compared with the traditional parallel flow channel. The inlet and outlet pressure drop is reduced by 40.18% compared with the traditional serpentine flow channel. Based on the parallel serpentine flow channel liquid cooling plate,the optimal layout scheme for the liquid cooling system is the injection of coolant at the two split ports and the staggered arrangement of the liquid cooling plate. Different liquid cooling plates have different flow velocity settings. The inlet flow velocity of the two liquid cooling plates is set to 0.1 m/s,and the inlet flow velocity of the central liquid cooling plate is set to 0.2 m/s. Compared with the same flow velocity of 0.2 m/s for the four plates,the temperature difference within the battery module group is reduced by 13.62%,the inter column temperature difference is reduced by 82.59%,and the energy consumption is reduced by 44.87%,achieving an optimization effect of ‘cost reduction and efficiency enhancement'. A reasonable flow channel structure,staggered cold plate layout,and differentiated inlet flow rate design can optimize the liquid cooling system of battery modules and increase the safety of battery module operation.

Abstract:Against the backdrop of rapid growth in electric vehicle holdings,there is a growing demand for the construction of electric vehicle (EV) charging stations. To improve the charging station operation economy,an optimal scheduling method of EV charging stations with access to shared energy storage is proposed. Firstly,the operation modes of distributed energy storage and shared energy storage are analyzed,and the two-layer optimal scheduling model of EV charging station considering different operation modes of energy storage is constructed by taking the lowest annual investment cost and annual operating cost of charging station as the upper level objective function and the lowest typical daily operation cost as the lower level objective function. Secondly,three EV charging stations with differentiation are simulated and different energy storage operation modes in multiple scenarios are compared. Sensitivity analyses are then conducted on this basis for the effects of shared energy storage service fees and EV discharge losses on dispatching results. Finally,the example shows that shared energy storage access has a positive effect on reducing the operating costs of charging stations,and that reasonable pricing of the energy storage services and the cost of EV discharge losses can reduce the operating costs of charging stations.

Abstract:Overhead ground wire (OGW) is an important equipment to prevent transmission wire from being struck by lightning. When OGW is struck by lightning,the sliding motion of arc root causes different damage mechanism. Therefore,it is necessary to analyze the sliding motion of arc root first,so as to provide data support of sliding displacement for the research of damage mechanism. In this paper,the sliding motion model of arc root along OGW is established based on the arc chain modeling method. By analyzing the main influence range of the electromagnetic force,the space boundary of modeling is narrowed,so the calculation amount is greatly reduced. Then,the model is verified by the needle-wire discharge experiment in the laboratory. Finally,based on the established model,the factors affecting the sliding displacement of arc root are analyzed,including thermal buoyancy,return stroke and continuous current. The results show that the effect of thermal buoyancy can be ignored under the action of return stroke. The sliding displacement of arc root is positively correlated with the wavelength time,but not with the wave head time. However,under the action of continuous current,thermal buoyancy affects the evaluation result of arc root sliding displacement,and the maximum error is more than 25%.

Abstract:In severe convective weather,transmission lines are prone to lightning strikes,wind swings, rain flashes and other faults that threaten the safe operation of the power grids. To overcome the problem that the existing nowcasting cannot fully meet the demand for refined weather forecasts for transmission line risk warning,a deep-learning-based nowcasting model of severe convective is constructed in this paper. The model is built by using meteorological radar echo image,assimilated data of wind speed and rainfall,lightning location data of the power grid and it is used to carry out early warning of transmission line risks. First,an long short-term memory (LSTM) network-based forecasting model of severe convective meteorological elements is constructed by taking the meteorological radar echo and its time-series extrapolation data as inputs,and assimilated data of wind speed and rainfall,lightning density,lightning current intensity as outputs. Then,combined with the wind,rain,and lightning forecast output from the model,the risk of wind swing,lightning strike,and tower collapse in the transmission corridor grid is evaluated. The fault probability of the transmission line is calculated in a comprehensive way for risk early warning. Finally,a case of successful early warning of severe convective weather processes in a province in September 2023 is demonstrated,which demonstrates the ability of the proposed method to improve the risk early warning capability of transmission lines under severe convective weather.

Abstract:Iron-based soft magnetic composites have the characteristics of high magnetic permeability,high magnetic induction strength and low iron loss. It is of great significance to study the vibration and noise characteristics of this material reactor for the design of vibration and noise reduction. In this paper,based on the finite element simulation model,the vibration and noise distributions of the reactors under normal operating conditions are calculated and analyzed through the magnetic field-structure-acoustic multiphysics coupling. The noise measurement system of the reactor is built,and the noise signals of the silicon steel core,hybrid iron core,and iron-based soft magnet core reactor are measured separately for experimental verification. The results show that the noise of the hybrid iron core and iron-based soft magnetic core reactors is significantly lower than that of silicon steel reactors. The hybrid reactor is least affected by current changes The simulation data shows that the magnetostriction to Maxwell force noise ratio of silicon steel core reactor is 1.15. Magnetostriction occupies the largest proportion. The main reason for the low noise of iron-based soft magnetic core reactor is that the magnetostrictive effect is negligible.

Abstract:At present,with the vigorous development of power Internet of Things,how to sustain and supply energy for a long time has become a technical problem that tens of thousands of sensors need to face. Triboelectric nanogenerator (TENG),as a new high efficiency energy supply technology,has a very broad application prospect in the field of sensor self-power supply. In this paper,based on the working principle of triboelectricity,a multi-layer elastic TENG energy harvesting device for transmission line breeze vibration is designed by using the vertical contact of the multi-layer elastic structure to separate the TENG,and the transmission line breeze vibration state is simulated by the output performance test platform of the vibration energy collector. The basic electrical characteristics of energy collectors at different frequencies and amplitudes are tested,and combined with power management strategies,a self-power supply system for vibration energy acquisition for transmission line breeze vibration is constructed,which realized efficient acquisition and conversion of wide-band vibration energy and completed energy supply for commercial sensors. Finally,the validity and feasibility of the vibration energy collection device designed in this paper are verified by the vibration reduction model of the transmission line. The vibration energy acquisition self-power supply system designed in this paper can effectively convert the broadband vibration energy of the transmission line into electric energy,and has the characteristics of simple structure and good economy,which can provide a new solution to solve the power supply problem of sensors for power Internet of Things.

Abstract:Before putting into operation,ultra-high voltage transformers need to undergo strict handover tests to ensure their safe and reliable operation. The grading capacity of the grading ring on the transformer will affect the accuracy of the handover test. When there are problems with high altitude environments or grading ring structures,their grading ability will change. It is particularly important to study and design grading rings that can have good grading ability in different situations. This article first uses COMSOL software to build a three-dimensional model of the withstand voltage test circuit of a 500 kV transformer. Secondly,the influence of changes in the pipe diameter and ring spacing of the equalizing ring on the equalizing capacity of the equalizing ring is studied. Finally,the variation of equalizing capacity of the equalizing ring in a rough state is studied. It is found that as the diameter of the equalizing pipe and the ring diameter increase or the distance between the rings decreases,the equalizing ability of the equalizing ring will be enhanced,with the greatest impact brought by the change in pipe diameter. When the grading ring has a rough surface,its grading ability will significantly decrease. Among them,the grading ability decreases most significantly when adhering to conical particles,and decreases least when concave,indicating that the smoother the surface of the grading ring,the better the grading ability. This study can provide some assistance for the design of grading rings for high altitude ultra-high voltage transformers.

Abstract:Aiming at the problem of insufficient accuracy in wind power prediction caused by the lack of target meteorological forecast data,a wind power prediction method based on spatial correlation and Stacking ensemble learning is proposed in this paper. Firstly,the spatial correlation between the target wind farm and adjacent meteorological stations is analyzed. The delay time is determined based on the extreme points of the correlation coefficient,and a wind speed time-shift dataset is constructed. Secondly,the Stacking ensemble method is used to integrate multiple algorithms to predict the wind power of the target wind farm from multiple data observation perspectives. It leverages the complementary advantages of different algorithms,enhancing overall generalization ability. Additionally,it can effectively balance search time and model performance by adopting the particle swarm optimization algorithm to search for model hyperparameters. Finally,the effectiveness and accuracy of the proposed method have been verified by the measured data from a wind farm in East China. The results show that it can effectively improve the wind power prediction accuracy in the case of missing data by considering the information bias at different locations with the data input and prediction models.

Abstract:Virtual power plants,as a comprehensive energy network with multi-energy flow interconnection,have become an important player in China's accelerated pursuit of its dual carbon goals. However,it is difficult to coordinate internal resources with low-carbon emission when facing challenges such as tight coupling of multi-energy flows,subjectivity of traditional carbon trading model parameters and difficulty of online optimization with high-dimensional dynamic parameters. To address these issues,this paper proposes a virtual power plant multi-energy low-carbon dispatching method that integrates the attention mechanism (AM) and soft actor-critic (SAC) algorithm. Firstly,based on the random carbon flow characteristics of virtual power plants,an improved stepped carbon trading mechanism based on Bayesian optimization is established for dynamic parameters. Next,an economic benefit and carbon emission-based objective function is constructed for the decoupling model of multi-energy flows in virtual power plants. Considering the high-dimensional nonlinearity and real-time updating of weight parameters in this model,the improved SAC deep reinforcement learning algorithm with integrated attention mechanism is used to solve it in a continuous action space. Finally,simulation analysis and comparative experiments are conducted to verify the feasibility of the proposed method and its efficiency compared with SAC algorithm.

Abstract:Accurate short-circuit fault current calculation is important for device selection,parameter design and protection research of flexible DC grid. To solve the problem that existing fault current calculation methods do not consider the pole-to-pole coupling of DC lines,a short-circuit fault current calculation method based on phase-mode transformation is proposed. First,the equivalent fault models of the converter station and DC line are established. Then,the phase-mode transformation is used to achieve electrical decoulping of positive and negative poles,and the node impedance matrix of the DC grid in the mode-domain is obtained. Subsequently,combining the fault boundary conditions of the DC grid,the fault current of the DC grid is calculated in the complex frequency-domain,and the fault current in the time-domain is obtained by using vector fitting. Finally,the proposed fault current calculation method is verified by PSCAD/EMTDC. The simulation results show that the proposed method can accurately calculate the DC fault currents after the DC fault occurs and before the converter station blocks.