93 results about "Energy dynamics" patented technology

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The invention provides a multi-stage embedded control equipment state sensing and energy cascade scheduling system. Comprising a master control decision center module, a distributed edge embedded node module, an equipment full-dimension state sensing module, an energy dynamic optimization scheduling module, a fault prediction and self-healing control module, a cross-protocol communication interconnection module and a man-machine cooperative command module. According to the invention, through constructing a three-layer time domain control chain of edge node nanosecond-level signal processing, cloud second-level optimization scheduling and equipment hour-level strategy presetting, seamless cooperation of turbine bearing pedestal micro-vibration monitoring and a power grid peak regulation strategy is realized, and real-time wavelet noise reduction preprocessing of embedded nodes is combined with cloud LSTM life prediction. A taboo search algorithm is driven to dynamically reconstruct a power supply scheme, and the pain point of control response lag in a high-fluctuation scene is solved.

The invention discloses a control method and system of a multi-source intelligent power manager based on 5G communication, and the method comprises the steps: obtaining multi-source heterogeneous power data of the multi-source intelligent power manager, constructing a dynamic energy topology through employing a multi-scale pulse fusion tensor decomposition algorithm, and generating a power state joint tensor with time-space alignment; inputting the power supply state joint tensor into a physically constrained adversarial prediction network, and outputting an energy dynamic balance vector including power supply margin prediction; performing multi-target dynamic game optimization on the energy dynamic balance vector, and outputting an anti-interference multi-source cooperative power supply strategy matrix; and inputting the multi-source cooperative power supply strategy matrix into a distributed cooperative control framework driven by edge computing, and finally outputting a multi-source intelligent control instruction set meeting low delay and high reliability. According to the embodiment of the invention, the flexibility and response speed of power management can be improved, and the stability and reliability of power supply are ensured.

The invention provides a port energy dynamic optimization method and system based on digital twinning, and relates to the technical field of port energy optimization. According to the method, the full-link closed-loop control of the port energy data is realized by constructing a physical-edge-virtual three-layer digital twin architecture, and the utilization rate of renewable energy sources is improved and the carbon emission is reduced through real-time data synchronization and multi-time scale optimization strategies. Through combination of edge layer preprocessing and a virtual layer physical constraint model, the problem that dynamic response is delayed and a physical rule is disjointed in a traditional method is solved.

The invention provides a method and system for controlling ventilation cooling type permafrost subgrade based on energy dynamic balance, wherein the method comprises the steps of constructing a heat absorption predicting model; and inputting an operating condition combination of a permafrost subgrade into the heat absorption predicting model to produce a predicted heat absorption for the permafrost subgrade, and controlling a cooling system to operate based on the predicted heat absorption. The heat absorption predicting model is constructed based on a seed point algorithm of formula matrix, which comprises the steps of producing a known fitting formula; establishing a formula matrix; and calculating a predictive fitting formula corresponding to a new seed point by known seed points in the formula matrix and an index formula, and then calculating the predicted heat absorption based on the predictive fitting formula.

The invention discloses a power grid multi-energy dynamic aggregation equivalence control method and system based on a knowledge graph, and relates to the technical field of power grid energy aggregation equivalence modeling, and the method comprises the following steps: obtaining power grid energy structure multi-source data, and constructing a power grid multi-class energy aggregation equivalence knowledge graph database; based on the electrical similarity between the knowledge graph database and the node residual voltage, quickly aggregating and dynamically regulating and controlling the multiple energy sources of the power grid, and constructing an aggregation equivalent model of the multiple energy sources of the power grid; performing power flow simulation on the aggregation equivalence model, obtaining evaluation data of the aggregation equivalence model, and controlling the structure and parameters of the aggregation equivalence model according to the evaluation data; according to the invention, flexible equivalence of the multiple types of energy sources of the power grid is carried out through a knowledge graph-based rapid real-time aggregation equivalence method considering the multiple types of energy sources of the power grid, so that the problem that centralized and distributed new energy sources and shearable load energy structures cannot be automatically and flexibly aggregated and equivalence to power grids of any voltage class rapidly and in real time is solved; and the method is not suitable for rapid real-time aggregation equivalence of multiple types of energy sources.

The invention relates to the technical field of network public opinion monitoring, and discloses an emotion energy dynamic change-based public opinion inversion recognition method, which comprises the following steps of: constructing a weighted emotion potential energy field, and calculating effective emotion potential energy of nodes; and then calculating semantic field topological stress representing the viewpoint structure tearing degree and emotional potential energy field system entropy representing the energy distribution disorder degree, on this basis, constructing an entropy-stress two-phase state space and a dynamic hysteresis loop model, quantifying the social damping characteristics of the system, monitoring the system state in real time, and determining the state of the system. When the two-phase space yield judgment condition and the damping failure judgment condition are met at the same time, a public opinion inversion early warning signal and a risk probability index are output, the critical characteristics of public opinion system instability can be accurately recognized from the dimensions of structural mechanics and dynamics, and the false alarm rate of inversion early warning is effectively reduced.

The invention belongs to the technical field of electric power, and discloses a load prediction and energy dynamic scheduling method and system for a multi-area micro-grid. Comprising the following steps: acquiring the total load power, the energy storage state, the charging and discharging power and the load power of each transformer area in a forward first time period; inputting the total load power and the load power of each transformer area into a cloud LSTM model to obtain a load prediction baseline of each transformer area in a second time period in the future; constructing a multi-target scheduling model taking the minimization of the total cost and the minimization of the load change rate as targets, and solving the multi-target scheduling model based on a quantum chaos collaborative algorithm to obtain a prediction charging and discharging strategy; inputting the load prediction base line, the prediction charging and discharging strategy, the real-time energy storage state, the real-time charging and discharging power and the real-time load power deviation value into an edge end LSTM model to obtain the charging and discharging power adjustment amount in a third time period in the future; and obtaining a target charging and discharging strategy to carry out energy storage adjustment. According to the invention, energy scheduling can be carried out more accurately to meet actual requirements.

The embodiment of the invention provides a vertical shaft material impact energy monitoring method and system based on machine learning. An impact characteristic signal set of a vertical shaft buffer structure contact interface is continuously collected. And performing dynamic feature extraction processing on the impact feature signal set to generate an energy dynamic feature sequence reflecting the impact energy change process. And calling a pre-trained impact energy analysis model to carry out energy state analysis processing on the energy dynamic characteristic sequence, and generating an impact energy dynamic change curve related to the initial impact energy characteristic, the buffer dissipation energy characteristic and the residual energy characteristic obtained through analysis. And calling a pre-trained well wall damage prediction model, inputting the impact energy dynamic change curve and preset well wall material characteristic parameters into the well wall damage prediction model, generating a damage risk index of the well wall structure, and executing graded early warning regulation and control operation according to the damage risk index. Therefore, the energy detection accuracy of vertical shaft material impact and the safety of vertical shaft construction are improved.

The invention relates to the technical field of artificial intelligence and energy system operation control, and discloses an artificial intelligence-based energy dynamic aid decision-making method and system, and the method comprises the steps: collecting multi-source heterogeneous data of an energy system, and constructing an observation vector; decoupling the observation vector into an environmental causal factor and an environmental noise factor of a potential space by using a variational structured causal characterization model; pruning a causal factor based on an accumulated anti-fact regret value gradient to obtain effective decision information, generating an anti-fact trajectory by applying intervention to a noise factor to construct an expanded belief state set, and updating a control strategy by using a continuous anti-fact regret minimization algorithm on this basis; and finally, performing physical constraint verification on the sampling action and executing the physical constraint verification. Through causal decoupling and an anti-factual reasoning mechanism, decision robustness, interpretability and physical safety of an energy system in a dynamic non-stationary environment are effectively improved.

The present invention discloses a control method and system for a multi-source intelligent power manager based on 5G communication. The method includes: obtaining multi-source heterogeneous power data of the multi-source intelligent power manager, constructing a dynamic energy topology using a multi-scale pulse fusion tensor decomposition algorithm, and generating a time-space aligned power state joint tensor; inputting the power state joint tensor into a physically constrained adversarial prediction network, and outputting an energy dynamic balance vector containing a power supply margin prediction; performing multi-objective dynamic game optimization on the energy dynamic balance vector, and outputting an anti-interference multi-source collaborative power supply strategy matrix; inputting the multi-source collaborative power supply strategy matrix into a distributed collaborative control framework driven by edge computing, and finally outputting a multi-source intelligent control instruction set that meets low latency and high reliability. Utilizing the embodiments of the present invention, the flexibility and response speed of power management can be improved, and the stability and reliability of power supply can be ensured.

The invention provides a system and a method for dynamically utilizing cold energy of a gas storage of a million-cube compressed air energy storage system. The method comprises the following steps: acquiring multi-source monitoring data to construct a basic data set; establishing an internal space-time dynamic model of the gas storage by applying a multi-scale thermal flow constraint space reconstruction method; a hierarchical control framework is developed to realize cooperative control of a throttle valve and a cold energy recovery device; and a working condition similarity enhancement method based on cold energy characteristics and a cold energy recovery control strategy self-adaptive generation method are constructed. According to the method, the technical problems of temperature field accurate modeling in the large gas storage, cold energy characteristic working condition identification, throttling-cold energy recovery cooperative control and the like are solved, the cold energy recovery efficiency is improved by 20-30%, and turbine inlet temperature fluctuation is reduced.

The invention relates to the technical field of off-grid energy control, and discloses a multi-energy coordination control method for an off-grid system. The method comprises the following steps: firstly, synchronously collecting operation state data of a photovoltaic array, a wind driven generator and an energy storage unit, and constructing a multi-energy operation state data set; establishing a power distribution characteristic space by combining power fluctuation historical data under different load working conditions; constructing and training an energy coordination decision model on the basis, extracting a system stability index, defining an energy coordination control level and establishing a mapping relation between the energy coordination control level and the stability index; acquiring load fluctuation parameters in real time, generating a working condition self-adaptive coordination instruction and determining a target control level; and when the target control level exceeds a preset threshold range, activating a multi-energy redistribution mechanism. According to the method, multi-energy dynamic coordination can be realized, the operation performance of an off-grid system under a complex working condition is improved, and the adaptability of the system to load fluctuation and energy supply change is enhanced.

The invention provides a fluctuation collaborative distributed intelligent healing power distribution network method and system. The method comprises the steps that power distribution network operation data and distributed energy dynamic characteristic data are collected in real time; calculating distributed energy real-time output power and line voltage deviation based on a distributed energy dynamic output model; if the line voltage deviation exceeds a threshold value, triggering a multi-agent negotiation mechanism; each agent selects an optimal cooperation strategy through message transmission and strategy updating; and executing self-healing control operation. By collecting the dynamic characteristic data of the distributed energy in real time and establishing the accurate dynamic output model, the power output of the intermittent power supply can be accurately predicted, and the problem of insufficient consideration of the volatility of the distributed energy in the prior art is effectively improved.

The invention relates to the technical field of geological disaster early warning, in particular to a landslide mechanism-data dual-drive early warning system based on a deformation work theory. According to the method, a mechanism driving model is constructed, and a geomechanical mechanism is quantified into a computable energy reference; the data acquisition module acquires real-time data to construct a data driving model, the energy dynamic calculation module analyzes data such as deformation power, and the data acquisition module and the energy dynamic calculation module cooperate to provide a more scientific and comprehensive basis for early warning; graded early warning signals are obtained through numerical analysis and matching, the landslide state can be judged more accurately, and early warnings of different grades are sent out in time; according to the method, corresponding emergency response operations such as data acquisition frequency adjustment, standby sensor starting, intelligent retaining wall hydraulic support system triggering and evacuation prompt playing are triggered according to the graded early warning signals, visual monitoring is performed according to the ratio of deformation power to damage power, and landslide risk management and control are enhanced.

The invention discloses an energy dynamic prediction distribution method and system for railway power supply safety. Monitoring regenerative braking energy, a traction load state, a residual electric quantity state of an energy storage device and a power grid electricity price state in the railway traction power supply system in real time; constructing a cost function taking power supply safety as a core; establishing a dynamic coupling prediction data model including a traction load, a residual electric quantity state of an energy storage device and a power grid electricity price, and designing a dynamic prediction algorithm considering an energy distribution priority; based on a dynamic prediction algorithm, a distribution coefficient among the bidirectional energy fusion device, the energy storage device and the grid-connected energy feedback device is corrected in a rolling manner; based on the distribution coefficient, an energy distribution path is selected, and dynamic prediction distribution of regenerative braking energy in the railway traction power supply system is completed; according to the method and system provided by the invention, regenerative braking energy can be optimally utilized, and the energy utilization efficiency of the power supply system is remarkably improved.

The invention belongs to the technical field of agricultural management, and particularly relates to a winter wheat growth period prediction and intelligent management method and system based on multi-source data, and the method comprises the steps: generating a continuous time sequence through multi-source heterogeneous data, and sequentially carrying out the space-time modeling and dynamic feature interaction processing of continuous time sequence features, so as to predict the growth period of winter wheat; wherein in the spatio-temporal modeling stage, the feature expression ability is enhanced from three aspects of time periodicity, seasonal gating and geospatial dependence; in the dynamic feature interaction stage, the characterization capacity of the time-varying interaction relation between winter wheat time sequence variables is effectively improved through energy dynamic coupling; after refined prediction of the daily growth process of winter wheat is completed, a key growth stage turning point is extracted, a prediction result is explained in a natural language form, and personalized agricultural management suggestions are generated to facilitate guidance of agricultural operation.

The present application relates to a kind of complex water level geological conditions under booster station jacket foundation construction method, belong to marine wind power engineering technical field.The method solves in deep water, torrent, hard seabed environment, booster station jacket foundation construction is prone to structural tilt due to environmental load, and the problem that stability is insufficient and construction precision is difficult to control.The method includes: stress analysis to jacket, calculate total overturning moment and carry out stability judgment;Submarine pre-investigation and leveling;Using GPS and cable wind rope cooperation, complete accurate installation and pre-leveling of jacket;According to specific order, steel pipe pile is inserted and sunk, and pile body stress real-time monitoring and energy dynamic control are implemented during sinking process;Jacket leveling is fixed, annular space grouting and pile top cutting are carried out.The method is mainly used for the safe, efficient construction of offshore wind farm booster station jacket foundation under complex hydrogeological conditions.

The invention relates to the technical field of index chuck control, in particular to a low-energy-consumption dynamic synchronous driving efficient index chuck control method which comprises the following steps: collecting initial information, setting driving parameters, and initializing the index precision of a control system; chuck operation state data are collected through a sensor, and the load change trend is predicted in combination with an intelligent AI model; according to the prediction result, a driving signal is generated through a multi-axis synchronous driving algorithm, a plurality of driving units are controlled to operate in a coordinated mode, and the position, speed and torque distribution are dynamically adjusted; and meanwhile, driving parameters are dynamically optimized in combination with real-time energy consumption and indexing task requirements. The system energy consumption can be effectively reduced, the indexing precision and efficiency are improved, and the system is suitable for various industrial machining scenes.

The present invention discloses a comprehensive energy economic environment dispatch optimization method and system. The method includes: obtaining basic data of the generator sets and loads in the comprehensive energy system, where the comprehensive energy system includes thermal power generator sets, renewable energy generator sets, and energy storage devices; constructing a dynamic economic environment dispatch model of the comprehensive energy system including thermal power, wind power, photovoltaic, and energy storage according to the basic data and the economic and environmental objectives to be considered; introducing crossover, mutation, and chain strategies to improve the optimization ability of the mayfly optimization algorithm when calculating the optimal result; combining the improved mayfly algorithm, the renewable energy output control strategy, and the comprehensive energy dynamic economic environment dispatch model to construct a comprehensive energy capacity allocation system, and further obtaining the optimal scheme for the economic environment dispatch of each power generation system. The method realizes the optimization of the economic and environmental indicators of the power system on the premise of ensuring the utilization rate and stable output of renewable energy.

The invention discloses an energy supply side optimization management system and an implementation method, and relates to the technical field of energy management. The method comprises the following steps: acquiring source end data of an energy supply side, analyzing the source end data through a multi-level protocol adaptation engine and a dynamic rule matching engine, and extracting various energy features and energy supply side GIS (Geographic Information System) information; constructing a multi-energy flow topology constraint matrix based on energy features, performing fine-grained geographic grid division based on energy supply side GIS information, and determining grid-level energy dynamic parameters; performing dynamic trend adjustment on the grid-level energy dynamic parameters in combination with the multi-energy-flow topology constraint matrix, and determining a grid-level energy dynamic prediction map; searching an optimal tradeoff solution set through a population aggregation and dispersion search algorithm; and carrying out constraint solution on the optimal tradeoff solution set in combination with the safe operation boundary, and determining an energy scheduling optimal solution. According to the invention, the accuracy and foresight of energy management are improved, the loss link is accurately traced, and the system is driven to continuously evolve towards the low-carbon and high-efficiency direction.

The present application belongs to the technical field of data processing, and particularly relates to a method and system for dynamic monitoring of energy carbon based on multi-source data fusion, which comprises the following steps: collecting high-frequency energy data and low-frequency emission data of a target area, performing frequency-raising processing on the low-frequency emission data by using an interpolation algorithm, and performing standardization processing on the two types of data to obtain energy sequences and emission sequences with unified time resolution; calculating the causal fluctuation confidence of the energy sequences at each time, and constructing a cumulative distance matrix in combination with the causal fluctuation confidence; searching for an optimal alignment path of the energy sequences and the emission sequences by using a dynamic time warping algorithm, and calculating a local slope index at each path point; dynamically adjusting the observation noise covariance of Kalman filtering according to the local slope index, performing smoothing correction on the aligned emission data, and outputting the energy carbon monitoring result. The present application solves the problem of distortion in calculation of carbon emission intensity caused by phase misplacement of multi-source data.

The invention relates to a hydrogen energy dynamic scheduling method and system considering source load uncertainty in an industrial park, and belongs to the technical field of energy scheduling, and the method comprises the steps: obtaining wind-solar power generation and hydrogen load historical data, and constructing a data set; obtaining an intrinsic mode function component through variational mode decomposition; constructing a modal feature matrix based on the components, and performing K-medoids clustering by adopting a dynamic time warping distance to generate a typical scene and probability; establishing a day-ahead scheduling optimization model, and making a hydrogen production and energy storage plan by taking total cost minimization as a target; and in actual operation, a real-time scheduling instruction is generated by minimizing the plan deviation and the equipment variation through the intra-day rolling optimization model. Through multi-scale optimization and scene generation, the scheduling precision, economical efficiency and real-time adaptability are improved, and the method is suitable for an electric-hydrogen coupling system in an industrial park.

A tunnel dual-energy clustering routing method based on multi-objective optimization comprises the steps that a three-dimensional space model of a single-tube tunnel is constructed and used for representing the geometric structure of the tunnel; the method comprises the following steps of: providing a multi-target clustering routing (TCR) algorithm and a dual energy dynamic supply (DETCR) algorithm of the multi-target clustering routing (TCR) algorithm, and providing a multi-target clustering routing (DETCR) algorithm of the multi-target clustering routing (TCR) algorithm and a dual energy dynamic supply (DETCR) algorithm of the multi-target clustering routing (DETCR) algorithm of the multi-target clustering routing (DETCR) algorithm of the multi-target clustering routing (TCR) algorithm of the multi-target clustering routing (TCR) algorithm; on the basis of a tribe competition and member cooperation algorithm (CTCM), a multi-target tribe competition and member cooperation algorithm (MOCTCM) is provided, the residual energy, the communication distance and the energy consumption of nodes, the election frequency of relay nodes (RN) and other factors are comprehensively considered, the optimal cluster head (Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, Optimal Cluster Head, Optimal Relay Node, optima-RN is selected, so that a decision basis with higher adaptability and global optimization capability is provided. According to the method, the problems of non-uniform node energy consumption and limited energy supply in a dynamic and energy-limited tunnel environment are effectively solved, the life cycle of the network is remarkably prolonged, and the energy efficiency level of the network is improved.

This invention discloses an energy regulation method and system for virtual power plants based on a predictive model, relating to the field of virtual power plant operation control. The method includes the following steps: collecting energy data from the virtual power plant; integrating wind power data and load data, and extracting features from the integrated data; constructing a preliminary energy dynamic balance prediction model based on predicted generation data of distributed energy sources and predicted demand data of load-side resources; using the preliminary energy dynamic balance prediction model to predict day-ahead load demand and distributed energy; adjusting the operating status of each distributed energy source and load-side resource in real time using the niche gray wolf optimization algorithm according to the preliminary dispatch strategy; dynamically adjusting and optimizing the energy dynamic balance prediction model; and re-predicting load demand and distributed energy using the optimized energy dynamic balance prediction model. This invention can respond to changes in the power system in advance, achieving more efficient energy dispatch.

The invention belongs to the technical field of energy regulation and control, and provides an energy storage regulation and control decision method and system based on energy dynamic balance, and the method comprises the steps: constructing an energy storage equipment feature set, carrying out the numerical conversion of state features in the energy storage equipment feature set, and obtaining key energy storage features; the method comprises the following steps: constructing an equivalent state-of-charge model, an intra-day time-phased capacity measurement and calculation model and a system power model of an energy storage system, setting balance conditions of the energy storage system, and carrying out quantitative solution on key energy storage characteristics to obtain quantitative data of energy storage nodes; according to the quantized data and an energy balance grading rule, an alternative regulation and control scheme is determined through a multi-energy storage system regulation and control model; and setting regulation and control constraint conditions, constructing a regulation and control cost model of the energy storage system participating in the power grid, and screening the alternative regulation and control schemes to obtain an energy storage regulation and control decision scheme. According to the scheme, the regulation and control precision is effectively improved, the energy dynamic balance of the energy storage node network is realized, and the regulation and control effect is better.

The invention provides an energy carbon dynamic prediction system and method, and relates to the technical field of energy carbon prediction. The method comprises the following steps: acquiring a historical power consumption data set transmitted by a plurality of acquisition terminals; the historical power consumption data set comprises historical power consumption data sequences of multiple dimensions; processing the historical power consumption data set through clustering analysis to set a cluster label corresponding to each acquisition terminal; and updating the power utilization data set in real time, inputting the cluster label of each cluster and the power utilization data set into the trained prediction model, and obtaining an energy carbon prediction result of each cluster. According to the method, the historical power consumption data sequence is processed through clustering analysis to generate the cluster label, so that the depicting capability of the power consumption mode is enhanced; the cluster labels and the real-time power consumption data are combined, and the trained prediction model is input, so that dynamic prediction of different power consumption modes can be realized, and the accuracy and the real-time performance of energy carbon prediction are improved.

The invention provides a multi-objective optimal compromise robust optimization scheduling method for an integrated energy system, and the method comprises the steps: constructing an electric-thermal-hydrogen integrated energy system frame, sequentially constructing a hydrogen energy dynamic utilization model, a green hydrogen certificate transaction method and a chance constraint distribution robustness model, and carrying out the calculation of the optimal compromise robust optimization scheduling of the integrated energy system based on the ordering of approximate ideal solutions and a multi-objective decision theory. Establishing a multi-target optimal compromise distributed robust model of the integrated energy system; and finally, solving the multi-target optimal compromise distributed robust model of the integrated energy system by adopting a non-dominated sorting genetic algorithm based on local search. After the hydrogen energy dynamic utilization link is introduced, the operation characteristics of hydrogen energy can be effectively reflected, and the wind power absorption level and the energy utilization efficiency can be effectively improved; the multi-target optimal compromise robust low-carbon optimization not only considers the low carbon, economy and robustness of the system, but also significantly improves the solving efficiency and optimization result of the system.

The invention discloses a converter control method and device considering alternating current side phase angle feedback and a storage medium, and belongs to the field of novel power grid formation control. The method comprises the following steps: aiming at a direct current-alternating current power converter, considering the condition that the direct current-alternating current power converter is connected with an infinite bus or a dynamic center inertia (COI) power grid, establishing a converter model comprising explicit representation of energy dynamics, a direct current link, an LC output filter, a transmission line and the like, and determining a closed-loop kinetic equation under a hybrid angle control (HAC) strategy; based on the model and a control strategy, the existence, the stability and the boundaries of balance points of the closed-loop system are analyzed, and corresponding parameter conditions are given; considering the safety constraint of the power converter, designing current limit control compatible with hybrid angle control (HAC), and analyzing the stability of the current limit control; an implementation method of hybrid angle control (HAC) in practical application is researched and designed, the robustness and behaviors of the HAC under different working conditions are analyzed, and verification is carried out through a numerical example.

A comprehensive elasticity evaluation method for a power distribution network relates to the technical field of power systems, and comprises the following steps: S1, constructing a multi-source space-time cube model of the power distribution network, and dividing a geographic area into hexagonal grid units; s2, establishing a multi-energy dynamic game model, distributing energy weights based on an improved Shapley value, and realizing multi-agent collaborative optimization through a weight attenuation mechanism driven by output deviation; s3, deploying an edge-cloud collaborative digital twin system, and compressing the equipment model by adopting a model pruning technology; s4, calculating a spatial heterogeneity Gini coefficient, a time persistence toughness index and an energy alternative green elastic equivalent; s5, outputting a dynamic decision support scheme based on a B / S architecture visualization platform; according to the method, the space-time evolution characteristics of the disaster events are combined with the dynamic matching of the contribution degree of the multi-energy main body, so that the problems of static weight distribution defect of elastic evaluation and response lag of multi-energy cooperative scheduling in the prior art are solved.