To investigate the characteristics of the responsive capacity(RC)variations of small-scale electric pas-senger vehicle clusters participating in power grid regulation,a responsive capacity evaluation model based on char-ging and discharging flexibility is proposed.This model aims to address the issues of insufficient response capacity and uneven state of charge(SOC)distribution within electric vehicle(EV)clusters under demand response sce-narios.Based on the responsive capacity model constrained by EV SOC and power limitations,an EV responsive capacity calculation model incorporating charging and discharging flexibility is established.By integrating travel chain rules,the travel patterns of three different types of EV user groups are simulated.The proposed concept of charging and discharging flexibility evaluates the influence of short-term and long-term responsiveness of small-scale electric passenger vehicles.Simulation results indicate that EVs can flexibly adjust their operations under demand variations.The proposed regulation strategy effectively mitigates locally high or low SOC levels,ensuring a more balanced SOC distribution within the regulated EV clusters.The study verifies the effectiveness of the charging and discharging flexibility-based responsive capacity evaluation model and regulation strategy for small-scale electric passenger vehicle clusters.
Power generation side energy storage plant can improve the reliability of renewable energy power genera-tion,and is conducive to the electrolysis of water to produce hydrogen for new energy consumption,so this paper proposes a method for optimizing the capacity allocation of hydrogen power microgrid considering shared energy stor-age for the capacity allocation of hydrogen microgrids.Firstly,a double-layer planning model for the hydrogen mi-crogrid is established by considering the cooperative alliance mechanism between renewable energy power stations and shared energy storage.In the double-layer planning model,the upper layer takes into account the complemen-tary characteristics and volatility of the typical scenarios of wind and photovoltaic power generation,and establishes an optimization model of the capacity allocation of the hydrogen microgrid by taking the net benefit of the partici-pants of the game in the cooperative alliance in the whole life cycle as the optimization objective;the lower layer takes the operating cost of the microgrid and the income from the leasing service fee of the shared storage as the op-timization objective.In the lower layer,microgrid operation cost and shared energy storage leasing service fee reve-nue are taken as the optimization objectives to optimally model the output plan and service fee price of shared ener-gy storage.The example analysis demonstrates that the cooperative gaming of the alliance between the energy stor-age plant and the renewable energy power plant can increase the annual value of the system net return by at least 18.74%.
In the integrated energy microgrid,shared energy power stations not only improve energy utilization effi-ciency,but also enhance the flexibility and reliability of the system.In response to the existing optimization sched-uling methods for integrated energy microgrids and shared energy storage that cannot balance economy,low-carbon,flexibility,and robustness,on the basis of analyzing the integrated energy microgrid system,a dual layer model based integrated energy microgrid shared energy storage optimization scheduling method is proposed.The upper lev-el model is constructed with the goal of comprehensively optimizing the costs of electricity,gas,equipment mainte-nance,flexible resource scheduling,and carbon trading in energy microgrids.The lower level model is constructed with the goal of achieving the comprehensive optimization of energy storage investment cost,energy storage charging and discharging loss cost,internal energy interaction cost,and external energy interaction cost.The two-layer mod-el is solved through the improved non-dominated sorting genetic algorithm III.The superiority of the proposed dual layer model for integrated energy microgrid shared energy optimization scheduling is verified through numerical ex-amples.The results indicate that,the proposed method ensures safety,flexibility,and robustness,comprehensive energy microgrid operating costs,and carbon emissions while ensuring safety and robustness,providing certain as-sistance for achieving the dual carbon goals.
In order to achieve the dual carbon goals and improve the consumption rate of renewable resources in ac-tive distribution network(ADN),while enhancing the economic operation of distribution system scheduling,this paper proposes a GCN-DDPG strategy that integrates graph convolutional network(GCN)and deep deterministic policy gradient(DDPG)algorithms.The strategy constructs an economic dispatching model for photovoltaic,load,and energy storage systems with the objective of minimizing the daily operating cost of ADN.The ADN scheduling problem is formulated as a Markov decision process(MDP)in deep reinforcement learning(DRL),where the state space,action space,and reward function of the system are defined.Furthermore,the DDPG algorithm is applied in DRL.The GCN is incorporated to enhance the ability of agent to represent the graph data of distribution network.The effectiveness and robustness of the model are verified in the modified IEEE 33-bus simulation system.Com-pared with the DDPG algorithm,the scheduling cost is reduced by 29%.The scheduling costs of DDPG increase by 27%,45%,and 51%,respectively in comparison with different topologies.
Smart electricity meters and other advanced metering infrastructures have the capability to collect data at ultra-high frequencies,which helps to more accurately depict the characteristics of electricity usage.However,this process also poses a risk of user privacy leakage.To address this issue,a decomposition-reconstruction technique is proposed,which generates privacy-preserving load curves from the original data while maintaining the basic trend and peak features.Through discrete wavelet transformation,the high-frequency load curve is decomposed into low-frequency basic load components and high-frequency variation components.Subsequently,load curves from differ-ent users are adjusted,shifted,and recombined in a random load curve generator according to certain rules to ob-tain a high-fidelity new load curve.Experiments on real data show that the new load curves generated by the pro-posed method can fully retain the statistical characteristics of the original data,ensuring the reliability of down-stream data mining tasks,while avoiding the privacy leakage risks from high-frequency metering data.
The grid-connected stability of offshore wind power transmission system via modular multilevel converter based high voltage direct current(MMC-HVDC)has attracted much attention in recent years.However,due to the decoupling effect of MMC-HVDC cables,the inertia of the offshore wind power transmission system via MMC-HVDC is missing.Meanwhile,the frequency coupling effect is also an important factor affecting the grid-connected stability of the offshore wind power transmission system via MMC-HVDC.Therefore,this paper aims to explore the frequency support effect of additional frequency control on the grid-connected system of offshore wind power through MMC-HVDC,complement the influence of active frequency support control on the AC admittance of MMC-HVDC,and improve the small disturbance stability analysis method of offshore wind power through MMC-HVDC under the frequency coupling effect.Firstly,an additional frequency control is put into the actual engineering model of the di-rect drive wind farm connected to the grid through MMC-HVDC,and the admittance models of the wind farm side converter station and the MMC converter station under the additional frequency control are established respectively.Secondly,the small disturbance stability analysis method of grid-connected systems under the influence of frequency coupling effect is explored.Then,the frequency coupling effect of the system is measured quantitatively,and the influence of additional frequency control on the system frequency coupling effect is concerned.Finally,a resonant suppression measure is proposed,and electromagnetic transient simulation is carried out by PSCAD/EMTDC to ver-ify the effectiveness of additional frequency control,the accuracy of the established impedance model,the correct-ness of grid-connected stability analysis and the effectiveness of the resonant suppression measure.
Under the new energy convergence of the novel power system,the harmonic content of the converter sta-tion increases,and the dry-type air-core smoothing reactor will face more complex operating conditions,and the en-trapped winding electrodynamic force caused by it will be an important cause of the reactor failure.This paper ana-lyzes the harmonic characteristics of the valve side of the converter station under the new energy collection of the no-vel power system,and studies the electrodynamic distribution characteristics of the encapsulation winding of the dry-type air-core smoothing reactor under the condition of DC and multiple harmonic superposition.Firstly,based on Fourier analysis,the harmonic characteristics of dry-type air-core smoothing reactor in operation are obtained.Then,the mathematical model of the reactor is established,and the electrodynamic distribution characteristics un-der different operating conditions are studied by the finite element analysis method of field-circuit coupling.The re-search shows the superposition of multiple harmonics makes the magnetic field and electrodynamic force of the reac-tor winding unevenly distributed,and the radial electrodynamic force of a layer is significantly higher than that of the adjacent layer winding.Under the action of alternating magnetic field,the overall radial electric force of the re-actor increases by 3 times,and the axial electrodynamic force increases by 4 times.The above research results can provide reference for insulation fault analysis and insulation structure improvement of smoothing reactors in novel power system.
Proactive preparation is a key condition for energy systems to respond to disaster events.Against this research background,this paper proposes an active method to enhance the resilience of integrated energy system by obtaining pre-disaster optimal scheduling strategies.Firstly,the paper analyzes the mathematical model of typhoon disasters,calculates the fault model of overhead lines within the system,and conducts fixed-point reinforcement based on the line failure rate.Then,the total storage capacity of electricity,heat,and natural gas within the dis-patch range from the first impact of the disaster to the worst-case scenario is defined as the preparedness index.To improve the readiness index,the cost of load reduction in the pre-disaster scheduling range is needed.Therefore,the paper constructs a Markov model of the integrated energy system,and adopts Actor-Critic algorithm to solve the optimal scheduling strategy of the system in disaster,which solves the multi-objective optimization problem between the readiness index and the load supply.Finally,through simulation verification,the optimized scheduling strategy can significantly improve the readiness index and resilience index of the system,and significantly reduce post disas-ter load losses.
Against the backdrop of diversified hydrogen sources and the approaching"dual carbon"goals,a low-carbon optimization strategy considering the synergy between electric thermal flexible loads and blue-green hydrogen is proposed to reduce the carbon emissions of system,enhance the flexibility of hydrogen utilization,and as well as improve the capacity of new energy consumption.The characteristics of blue hydrogen and green hydrogen are ana-lyzed,and a production,storage,and utilization model for both is established.By analyzing carbon emissions and wind curtailment data under different green hydrogen proportion coefficients,the optimal proportion of green hydro-gen is obtained.Transferable and substitutable load models are introduced on the load side,and satisfaction indica-tors for users and electrolysis cell equipment are proposed to analyze the impact of flexible loads on the optimized operation of system.Under the output constraints of various equipments,a blue-green hydrogen economic low-car-bon optimization scheduling model considering flexible loads is constructed with the goal of minimizing the total cost of system.The analysis of case results under four scenarios shows that compared with Scenario 3,the optimization model proposed in this paper reduces the carbon emissions cost of system,wind curtailment,and total cost by 52.85%,26.22%,and 3.49%,respectively,which proves the effectiveness of the method proposed in this paper.
The operating time of ultra-high voltage circuit breakers exhibits significant dispersion.To address this issue,this paper conducts on-site experiments on 550 kV hydraulic operated circuit breakers.Subsequently,based on the experimental results,a random forest(RF)regression prediction model is established with control voltage,environmental temperature,and oil pressure as inputs.The improved whale optimization algorithm(WOA)is used to optimize the RF algorithm and improve its prediction accuracy.Due to the difficulty of deploying neural network algorithms in embedded devices and the relatively low accuracy of the prediction methods for circuit breaker closing time in practical engineering,this paper proposes a circuit breaker action time prediction and compensation method based on cloud edge collaborative computing architecture.The improved WOA-RF model is trained on big data in the cloud computer,and the constructed model is used to calculate node information.Then,based on the node in-formation,Hermite interpolation is used to calculate the closing time of the circuit breaker on the engineering site,and the closing data is continuously recorded and transmitted to the cloud database.The research results indicate that using this scheme can more accurately predict the action time of circuit breakers.
In response to the accuracy and efficiency issues of power system path planning,this paper proposes a planning method for novel power system path based on the DeepLabv3+network,which views the power system path planning problem as an image segmentation problem.On the basis of the DeepLabv3+method,the spatial at-tention mechanism is initially introduced to focus on more important information of the image,which is conducive to the restoration of image detail information.A feature reconstruction module is designed by introducing asymmetric convolution and depthwise separable convolution to reconstruct the feature information and obtain the output of the network.Furthermore,a new loss function is devised to optimize the features of different scales of the network.The test is conducted based on the Vaihingen dataset,and compared with the methods of SegNet,UNet,DANet,and DeepLabv3+,which shows that the proposed method has higher accuracy.The effectiveness of each module is veri-fied through ablation experiments.
Considering the limitations of conventional horizontal wiring method in grounding impedance measure-ment for substations due to wiring path issues,a new method for measuring ground impedance of grounding grid based on vertical deep well wiring is proposed in this paper.The principle of measuring the grounding impedance of grounding grids in vertical deep well wiring based on zero potential compensation method is discussed,and the method of using the difference in grounding grid potential changes as the zero potential compensation value to deter-mine the position of potential poles in vertical deep well wiring is presented.The technical feasibility and electrode arrangement scheme of the method are calculated and analyzed using CDEGS.The effectiveness of the proposed method is tested through an actual 35 kV substation engineering case.Simulation and actual measurement results show that the deeper the current pole is arranged,the larger the potential flatness range and the higher the accuracy of grounding impedance measurement.Placing the well at the edge of the grounding grid is slightly better than pla-cing it in the middle.The deviation between the method described in the actual substation grounding impedance measurement and the conventional horizontal wiring method is about 5.86%,and the deviation from the simulated calculation value is about 5.14%,both of which are within the acceptable error range of engineering,which indi-cates that the proposed method can be effectively applied to the measurement of grounding impedance of grounding grids in practical engineering.
Ensuring the integrity and consistency of data across multiple nodes is a challenge in distributed storage systems,especially in the event of network partitioning or node failures.To this end,a distributed storage method for privacy protection of power energy data is proposed for cloud edge collaboration.Considering the operating envi-ronment of cloud edge collaboration technology,a distributed storage space is built.Cloud edge collaboration utili-zes the centralized management capabilities of the cloud to ensure data consistency,while local storage on the edge provides fast data access and recovery capabilities.On this basis,web crawler technology is adopted to obtain pow-er energy privacy data.Symmetric encryption,asymmetric encryption,and other methods are used to encrypt the power energy data to be stored in a hierarchical manner.Utilizing cloud edge collaboration technology to schedule power energy privacy data in parallel,and implementing distributed storage tasks for power energy data privacy pro-tection by encrypting data and writing it into the selected storage space.Through performance testing,it is conclu-ded that the proposed storage method significantly reduces data loss and tampering rates,and significantly improves storage throughput in both scenarios with and without attacks.
Existing methods for fault identification of low-voltage distribution transformer meter mainly relies on ex-pert experience or simple indicator-based methods,which results in issues such as low intelligence,low automation and inefficient manual troubleshooting.Moreover,the multi-periodicity characteristics of the measuring data for transformer meter are not utilized.A fault identification method of the transformer meter wiring based on Boruta and TimesNet model is proposed in this paper.Firstly,the Boruta algorithm is used to screen the original features and select features that have significant contributions to the target variables.The TimesNet model is then introduced to convert one-dimensional time series into two-dimensional tensors and the series features are captured within and across periods by multi-scale parallel convolution.In addition,this paper improves the TimesNet model based on prior knowledge,and complements the extracted periodic information.An example analysis based on the measured data of 10 kV station area in Shanghai shows that the proposed method can achieve more than 98%fault identifica-tion accuracy for 4 types of main faults(including normal state).
The programmable quantum voltage standard(PJVS)is a type of quantum voltage standard capable of generating AC waveforms accurately.This paper systematically summarizes the research progress of PJVS.The bas-ic principles of PJVS are elaborated,focusing on the sources of transient errors and methods for error suppression.The development history of PJVS chips is reviewed,the fabrication of low-temperature superconducting Josephson junction arrays,the core characteristics of PJVS chips,and the research progress of PJVS chips at home and abroad are briefly summarized.The main application of PJVS are sorted out and summarized,the setup,key indicators,and practical application scenarios of quantum voltmeters,impedance bridges,and quantum current measurements are specifically analyzed.This paper also deeply explores the differential sampling method of PJVS and compara-tively analyzes the two technical routes of differential sampling and subsampling.It conducts a comparative study on the two-terminal and four-terminal impedance bridges based on PJVS and summarizes their application status in va-rious countries.It analyzes the advantages and disadvantages of current standards based on PJVS and single-elec-tron tunneling schemes,and introduces the programmable quantum current standard based on Ohm's law.Finally,this paper prospects the future development trend of PJVS from two dimensions of the breakthrough of core underly-ing chip technologies and the optimization of system integration.
The promotion of electric power energy sharing transactions plays a crucial role in facilitating the con-sumption of clean energy and achieving a green,low-carbon transition.However,this novel transaction model may significantly impact traditional power markets and pose challenges to the economic and secure operation of distribu-tion networks.To explore the effects of energy sharing transaction strategies on modern distribution network,this paper considers the participation of power load aggregators in peer-to-peer transactions and establishes a distribution network-load aggregator game framework based on dual fixed-point mapping.Within this framework,this paper ex-amines the equilibrium states of energy sharing transactions between the distribution network and load aggregators,as well as among the load aggregators themselves.By modeling the objectives and decision spaces of the stakeholde-rs of load aggregators,an equivalent unified optimization decision model for the load aggregator group is derived.The Lagrange multiplier relaxation method is proposed to achieve distributed decoupling of the unified problem,thereby ensuring the privacy and fairness of the behaviors of participants in the energy transactions.Furthermore,the study introduces a modeling approach for the distribution network transaction strategy based on a dual fixed-point mapping model and demonstrates the existence and uniqueness of the equilibrium state of transaction strategies between the distribution network and load aggregators through game theory.To effectively solve this issue,the e-quivalence between the energy sharing mechanism of the load aggregator group and the distributed optimization problem is analyzed,and an algorithm for solving the distribution network energy sharing transaction strategy is pro-posed.Through simulation experiments based on a 56-node example,the reliability of the proposed dual fixed point model and algorithm in effectively describing the energy sharing trading strategy of the distribution network is veri-fied.Meanwhile,the scheme realizes a balance between the economic operation of the distribution network and the profit of load aggregators.
The secondary circuit involves a large number of electrical parameters and signals,which may contain noise and have complex correlations with each other.Faults are often difficult to directly observe and their temporal characteristics are difficult to extract,making hidden fault detection difficult.To this end,a method for online de-tection of hidden fault states in secondary circuit of substation relay protection based on recurrent neural network is proposed.A secondary circuit state detection framework is constructed,and the electronic transformers are used to obtain the working state set information of secondary equipment to analyze its reliability.The recurrent neural net-works are used to construct an online detection model for hidden fault states in secondary circuits,and the recurrent neural networks have significant advantages in processing time series data.On this basis,it determines the initial weight value of the online fault state detection model,updates the learning factor of the model training,and outputs the hidden fault state of the secondary circuit.The experimental results show that the F1 scores of the secondary circuit hidden fault state detection under the proposed algorithm are all around 0.981,and the convergence speed of this method is the fastest,indicating that the proposed hidden fault state detection method is highly efficient.
The integration of distributed new energy changes the power flow and increases system uncertainty in dis-tribution network.Aiming at the reliability evaluation problem of distribution network with high penetration rate of new energy,an improved sequential Monte Carlo simulation method is explored to evaluate its reliability.Firstly,the output power models of wind and photovoltaic power generation are discussed based on the probability distribu-tion in distribution network.Then,the adaptive importance sampling method is used in the Monte Carlo simulation process in order to reduce the sample variance and improve the computation efficiency of reliability indicators.Sim-ulation experiments show that the proposed method can improve the computational accuracy and efficiency of relia-bility indicators,and high penetration rate of new energy can improve the operation reliability of distribution net-work to some extent.
In the actual inspection process of overhead line insulators,the unmanned aerial vehicles need to process a large amount of different types of data in real time,which increases the difficulty of insulator defect detec-tion.Therefore,a research on unmanned aerial vehicle intelligent detection of insulator defects in overhead lines based on multi-source data is proposed.Combining compressive sensing algorithm and wavelet transform algorithm,we design pixel fusion rules for high-frequency and low-frequency regions of the image to be fused,and complete multi-source data fusion.By fusing different types of images,the real-time and accurate comprehensive insulator in-formation can be obtained.A multi-scale residual network(MSRN)is added to the input of the traditional YOLOv5 network to optimize the image perception ability of the overhead line insulator defect detection model.The coordi-nate attention mechanism is utilized to improve the attention of model to insulator defect features and optimize defect recognition performance.The CIoU-Loss function is used to generate bounding boxes for defect identification to im-prove model detection accuracy.The experiment shows that the YOLOv5 overhead line insulator defect detection model established by the proposed method has good convergence ability and can accurately identify different types of overhead line insulator defects,which is helpful for the safe and stable operation and efficient maintenance of the power system,thereby helping to achieve the"dual carbon"goal.
In response to the problems of non-standard data flow,missing transfer processes,and data disconnec-tion caused by system updates in the anomaly management of distribution network equipment in novel power system,this paper studies a distribution network equipment anomaly management system based on multi-system interaction.Based on the topology analysis of equipment anomaly in the distribution network,the overall architecture of the sys-tem and the software and hardware configurations at each level are constructed.Then,a distribution network equip-ment anomaly library model is established for unified storage of data,and a graph data forwarding mechanism be-tween multiple systems is constructed to optimize the management process of distribution network equipment anoma-ly.The effectiveness of the proposed system is verified through practical application in a city-level power company.The proposed system can improve the work efficiency and correctness of equipment anomaly management by about 34.4%and 6.9%respectively,which effectively enhances the operation and maintenance efficiency of the distri-bution network.
