79 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 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 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 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 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 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.

The invention relates to the technical field of multi-dimensional energy dynamic regulation and control, in particular to an energy panoramic visual dynamic control method based on multi-source data fusion, and the method comprises the steps: needed electricity consumption judgment, power station information extraction, predicted generating capacity calculation, clean energy generating capacity judgment, clean energy power station confirmation and combustion energy power station confirmation. According to the invention, through centralized multi-energy coupled electric energy monitoring, when the power supply quantity of the power grid does not meet the power demand, the electric quantity generated by other energy sources of the power grid is dispatched; and the clean energy power station or the combustion energy power station for supplementary power supply is confirmed by combining the electric energy transmission loss condition of the clean energy power station and the power generation efficiency condition of the power generation equipment in the combustion energy power station, so that low energy utilization efficiency is avoided, the power supply reliability is improved, and multi-energy coupling scheduling is realized. And complementarity among different energy sources is reflected, and limitation on flexibility and activeness of the energy market is avoided.

The invention provides a network construction type equipment electromagnetic dynamic modeling method and system considering a network energy storage state, and belongs to the technical field of power system dynamic modeling. The method comprises the following steps: establishing a circuit link electromagnetic scale dynamic model which simultaneously considers instantaneous power transmission and instantaneous energy dynamic states of inductance and capacitance elements according to a grid-connected system topology of a network construction converter; establishing a control link electromagnetic scale dynamic model of the network construction converter; the circuit link electromagnetic scale dynamic model and the control link electromagnetic scale dynamic model are combined, and a network construction converter electromagnetic scale dynamic nonlinear model representing system network energy dynamics is constructed; and carrying out linearization processing on the nonlinear model at a steady-state working point to obtain the electromagnetic scale dynamic small signal model of the network construction converter. According to the method, the coupling effect of network energy dynamic and converter control under the electromagnetic time scale is depicted, and the defects of a traditional steady-state model in broadband oscillation analysis are overcome.

The application discloses a rape whole growth cycle monitoring method and system based on three-dimensional point cloud reconstruction, relates to the field of intelligent agriculture and computer vision technology, collects three-dimensional point cloud data at multiple key growth periods of rape, constructs a semantic growth graph after pretreatment; the graph is input into a pre-trained time sequence model, the energy flow among source nodes, library nodes and channel nodes is simulated based on the energy dynamic balance principle, the future growth state of the plant is deduced, and growth monitoring data is finally output. The application realizes nondestructive monitoring of rape from the bud stage to the silique stage through three-dimensional point cloud technology, and overcomes the problems of low efficiency and strong destructiveness of traditional methods. Through the construction of the semantic growth graph, the organ recognition and segmentation problem is solved. Further, a calculable model is constructed by combining the source-library theory, photosynthetic product flow and organ competition are simulated, the biomass is accurately predicted before the silique forms, and the phenomenon of abortion is recognized, so that the leap from morphological monitoring to growth mechanism deduction is realized.

The application discloses an economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method, which comprises the following steps: framework design of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method, establishment of an upper-layer environment-economy evolution game planning model, establishment of a lower-layer energy dynamic pricing strategy master-slave game model and establishment of a multi-energy transformation coupling hub model. The method can consider the conflict relationship between economic benefits and environmental benefits in the planning problem, consider the dynamic characteristics of energy prices, balance the conflicting interests between micro energy network operators and users, improve the planning efficiency and economic benefits of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning, effectively reduce the total planning cost, balance the peak-valley difference of user load, improve the energy utilization efficiency, reduce the emission of greenhouse gas CO2 and air pollutants NO x , and realize the environmental value. The method has the advantages of scientific rationality, strong applicability and good effect.

The invention discloses an industrial park multi-energy dynamic complementary scheduling method and system based on multi-source data, and belongs to the technical field of energy scheduling. The method comprises the following steps: an acquisition module, a first determination module, a second determination module, a constraint determination module and a scheduling scheme determination module. According to the method, the power change rate is predicted in real time and quantified into the dynamic stability margin, then the dynamic stability margin is used as the rigid constraint to be embedded into the economic dispatching optimization model, the standby capacity is reserved in advance in the dispatching decision-making stage to prevent risks, the balance of economy and safety is converted into economic optimization within the safety boundary, and the economic optimization efficiency is improved. According to the method, the safety requirement is improved from a soft target to a hard constraint, active beforehand defense of an electric energy quality problem is realized, economic optimal scheduling is realized while the system stability is guaranteed, and the problem that voltage sag and frequency fluctuation are difficult to effectively suppress in renewable energy fluctuation processing in the prior art is solved.

The present invention discloses a cross-domain data access trusted control system and method, which relates to the field of data security technology. The method collects energy dynamic coupling data in real time by setting up collection points and implementing edge gateway devices in the power grid system. The method can effectively monitor the energy exchange of the power grid system and perform preprocessing in real time. By standardizing the energy dynamic coupling data, an accurate standard energy dynamic coupling data set can be generated to provide reliable data support for the subsequent regional energy dynamic coupling intensity index ECI and data access disturbance response score DSR. This process combines multi-dimensional data of energy fluctuations, dispatch response offsets and disturbance spectra, which can help the power grid dispatch system accurately identify potential risks, thereby reducing dispatch errors and system crashes caused by data access errors, and effectively improving the security and stability of the power grid system.

The present invention discloses a method and system for controlling the dynamic aggregation equivalent of multiple energy sources in a power grid based on a knowledge graph, which relates to the technical field of power grid energy aggregation equivalent modeling, and includes the following steps: obtaining multi-source data of the power grid energy structure, and constructing a knowledge graph library for the aggregation equivalent of multiple types of energy in the power grid; based on the electrical similarity between the knowledge graph library and the residual voltage of the node, quickly aggregating and dynamically regulating multiple types of energy in the power grid, and constructing an aggregation equivalent model of multiple energy sources in the power grid; simulating the flow of the aggregation equivalent model, obtaining evaluation data of the aggregation equivalent model, and controlling the structure and parameters of the aggregation equivalent model according to the evaluation data; the present application performs flexible equivalence of multiple types of energy in the power grid by considering the rapid and real-time aggregation equivalent method of multiple types of energy in the power grid based on the knowledge graph, so as to solve the problem that centralized, distributed new energy and load-shedding energy structures cannot be automatically and flexibly aggregated to any voltage level power grid in real time, and is not suitable for rapid and real-time aggregation equivalent of multiple types of energy.

The invention relates to the technical field of intelligent control of an energy system, and discloses an AI energy dynamic optimization control system and method based on an intelligent agent. The method comprises the following steps: extracting energy network equipment attributes and topology, and constructing a multi-dimensional data cube; and real-time sensing data and a flexible constraint rule are synchronously injected. And driving the intelligent agent to carry out incremental semantic annotation on the data stream, generating an energy event with a space-time mark, and compiling the energy event into a source load feature portrait. And according to the portrait, activating a matched regulation and control mode sub-graph in the strategy knowledge graph, generating a dynamic control parameter, and loading the dynamic control parameter to an actuator. According to the method, real-time deep semantic understanding of the operation state of the energy system and dynamic self-adaptive accurate regulation and control based on historical experience are realized, and the intelligent level and the operation efficiency of complex energy network control are improved.