685 results about "Virtual power" patented technology

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.

The invention relates to the technical field of data management, in particular to a low-altitude economic flight data management method and system based on edge computing, and the system comprises edge computing nodes deployed on an aircraft and a ground base station, a sensor for collecting multi-source data, a module for preprocessing the data, and an annular data queue caching mechanism. The system comprises a composite biological feature code generation module, a distributed storage edge node, an abnormal fluctuation feature extraction module, an equipment health state and trend analysis and calculation module, a virtual power supply manager, a main power supply, a standby power supply, an abnormal behavior monitoring module and a defense strategy library. After preprocessing and caching, feature codes are generated and encrypted distributed storage is carried out, dynamic resource adjustment is realized through state evaluation and a digital twinning and optimization algorithm, a neural network is utilized to manage a power supply, data security is guaranteed in combination with abnormal monitoring, and a whole-process management system is formed.

The invention relates to the technical field of virtual power plant operation monitoring, and discloses a virtual power plant distributed resource cluster anomaly monitoring system, method and equipment, and the system comprises a resource node data collection module, an anomaly trend score calculation module, an association graph model construction module, a single node influence calculation module and a comprehensive anomaly early warning output module. In the prior art, a single-point alarm mode depends on a fixed threshold value or a rule base, and especially under the conditions that distributed resources in a virtual power plant are complex in type and space coupling and controller sharing exist between nodes, accurate identification and dynamic early warning of trend anomalies, cooperative faults and potential propagation paths are difficult to realize. According to the method and the device, the multi-dimensional parameter modeling and the graph neural network anomaly propagation analysis are fused, so that the anomaly in the distributed resource cluster is identified and dynamically evaluated, and the early warning accuracy and the intelligent operation and maintenance efficiency of the virtual power plant are improved.

The invention relates to the technical field of energy storage power station combined operation and maintenance management, and provides a virtual power plant-oriented energy storage power station combined operation and maintenance management system, which comprises a dynamic sensing module for acquiring battery cluster temperature difference distribution, power converter switch transient characteristics and grid-connected point harmonic spectrum data of an energy storage power station in real time through a multi-dimensional sensor network; the collaborative decision-making module is used for dynamically generating a cross-station power interaction constraint rule based on the virtual power plant topological structure; the impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; the state balancing module is used for realizing charge state dynamic balance and power margin collaborative distribution among the multiple energy storage power stations; and the secure communication module is used for ensuring secure transmission of control instructions among the modules by adopting a layered encryption protocol. The operation stability, the power distribution flexibility and the equipment safety of the virtual power plant can be improved, and the service life of the energy storage equipment is prolonged.

The invention provides a charging and discharging control strategy optimization method and system of V2G applied to a virtual power plant. The method comprises the steps of constructing a collaborative optimization strategy of comprehensive income data based on electricity price change prediction and peak regulation demand prediction; based on the user behavior mode and the user historical charging behavior data, constructing a collaborative optimization strategy matched with the user demand; constructing a collaborative optimization strategy of battery health degree management based on the health degree quantitative model of the battery; and based on a collaborative optimization strategy of comprehensive income data, user demand matching and battery health degree management, a multi-target dynamic optimization model is constructed, and charging and discharging control strategy optimization of the V2G application in the virtual power plant is realized. According to the method, full-process cooperation of strategy customization-dynamic invitation-user response is realized under the guidance of the virtual power plant, multi-mode charging and discharging control strategy combination is realized, load aggregation is carried out by effectively utilizing the distributed energy storage characteristic of the electric vehicle group, and the power grid regulation capability is maximized.

The invention discloses a virtual power plant intelligent aggregation optimization control method for multi-type flexible resources, and the method comprises the steps: constructing a dynamic characteristic model of distributed resources, wherein the dynamic characteristic model comprises a photovoltaic output probability prediction model, an energy storage SOC-life coupling model, an electric vehicle behavior chain model, an adjustable load constraint model, and an industrial interruptible load model; an edge agent node calculates an adjustable potential interval of a resource cluster in real time and uploads the adjustable potential interval to a cloud end, a global optimization target is solved on the cloud end based on an improved sparrow search algorithm (ISSA), after a scheduling instruction is generated, model parameters are corrected in a rolling mode according to actual output deviation calculated in real time, and a scheduling result is obtained. And triggering a resource fault emergency strategy for prediction deviation and resource fault problems occurring in the operation process of the virtual power plant. Through an edge-cloud collaborative architecture and a multi-stage optimization strategy, accurate modeling, optimization aggregation and intelligent scheduling of distributed resources are realized.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

A collaborative resource allocation method for a virtual power plant cluster and a power distribution network comprises the steps that a virtual power plant cluster-power distribution network joint operation framework is constructed, a double-layer optimization model is constructed, and an upper-layer optimization model formulates an electric energy and carbon quota P2P transaction plan through a cooperative game between virtual power plants; the lower-layer optimization model solves the optimal power flow of the power distribution network based on the transaction plan and feeds back the electricity-carbon comprehensive price; and a cooperative game model is constructed, and the double-layer optimization model is solved by adopting an alternating direction multiplier algorithm, so that cooperative resource allocation is realized. According to the method, a joint operation framework of the virtual power plant cluster and the power distribution network is constructed, and a double-layer optimization model and an asymmetric Nash bargaining theory are combined, so that P2P transaction of electric energy and carbon quota is realized, and the optimal power flow of the power distribution network is optimized.

The invention discloses a virtual power plant green energy consumption cooperation method and system fusing digital twinning and reinforcement learning, and the method comprises the steps: building a green energy power plant digital twinning model through a simulation tool, collecting data in real time, and carrying out the normalization and abnormal value cleaning; inputting the data into the digital twinborn model, mapping the operation state of a physical system, and rehearsing the influence of different energy scheduling strategies on the green energy consumption rate and the power grid frequency in a virtual environment; optimizing and updating the energy scheduling strategy based on a near-end strategy optimization PPO algorithm; the optimized and updated energy scheduling strategy is fed back to a physical system to be executed, the strategy execution effect is monitored in real time, the digital twin model parameters are updated, and closed-loop control is formed; the method can solve the problem that the intermittency of renewable energy sources is not matched with the dynamic demand of the load.

The invention discloses a cloud side-end collaborative virtual power plant scheduling method and system based on dynamic task migration, relates to the technical field of virtual power plant resource scheduling, and solves the problem that a general method is difficult to meet the real-time scheduling requirement of a virtual power plant. According to the invention, task migration is realized through a three-level architecture of a cloud planning long-term strategy, regional collaboration of an edge layer and quick response of a terminal layer in combination with a dynamic load balancing algorithm; the first edge node locally processes regional collaborative tasks, reduces the communication frequency of the cloud server, reduces the end-to-end delay, and then combines federated learning to realize training parameter sharing between the edge nodes and avoid redundant data transmission, thereby effectively improving the response speed of the scheduling system, and improving the scheduling efficiency. And the real-time scheduling requirement of the virtual power plant can be better met.

The invention relates to the technical field of power system optimization scheduling, in particular to a virtual power plant resource optimization scheduling method based on multi-head self-calibration tensor factors. Comprising the following steps: S1, collecting virtual power plant data, preprocessing the virtual power plant data, and carrying out standardization, anomaly detection and interpolation on various heterogeneous resource data in a virtual power plant; s2, constructing a hierarchical gradient tensor network, executing multi-head self-calibration decomposition, and establishing a virtual power plant decision optimization model, and S3, designing a multi-head self-calibration decomposition mechanism according to the virtual power plant decision optimization model, and generating a virtual power plant resource optimization scheduling scheme. The technical problems that in existing virtual power plant large-scale heterogeneous resource scheduling, calculation complexity is high, memory occupation is large, and control precision and efficiency are difficult to balance can be solved.

The invention relates to the technical field of electric power control, in particular to a multi-virtual power plant collaborative scheduling method and system based on mixed games and carbon transactions. The method comprises the following steps: acquiring parameters of each virtual power plant, reading a carbon quota allocation scheme and a carbon transaction market price released by a power grid, and establishing a virtual power plant operation cost expression containing a carbon transaction cost; constructing a non-cooperative game model, and obtaining a preliminary optimal output strategy of each virtual power plant by taking minimization of the operation cost of the virtual power plant as a target; the operation state of the virtual power plant is detected, if the risk that renewable energy consumption is insufficient or the total carbon emission exceeds the standard exists, a cooperative optimization mechanism is triggered, a plurality of virtual power plants are selected to form a joint optimization group, and the joint optimization group is regarded as an independent participant to obtain an optimal output strategy again; and issuing the optimized scheduling instruction to each virtual power plant for execution according to the optimal output strategy, and participating in carbon market transaction according to the actual carbon emission and the quota difference in the settlement period.

The invention relates to the technical field of virtual power plant scheduling, and provides a virtual power plant multi-target optimization scheduling method and system fusing deep reinforcement learning, and the method comprises the steps: calculating the voltage sensitivity parameter of each power grid node in an access region of a virtual power plant through obtaining the short-circuit capacity data and real-time impedance spectroscopy measurement data of a grid-connected point of the virtual power plant; positioning a weak line section associated with the power grid node; according to the voltage sensitivity parameter and the information of the weak line section, a reactive power resource dynamic allocation strategy is generated, multi-target optimization scheduling of the virtual power plant is achieved, and the deep reinforcement learning agent aims at maximizing the voltage regulation compensation income and minimizing the equipment regulation loss; according to the method, rapid collaborative optimization scheduling of the virtual power plant in a complex power grid environment is realized, the problem of multi-target coordination caused by model mismatch, response lag and distributed resource dispersity in a traditional method is solved, and real-time response and adaptive adjustment are realized.

The invention belongs to the technical field of virtual power plants, and particularly relates to a distribution network virtual power plant aggregation control method based on a disperse complex adaptive system, which comprises the following steps: constructing a distribution network disperse complex adaptive system architecture comprising a tail end resource layer, a local autonomy layer and a global collaboration layer; setting a dynamic aggregation index of the virtual power plant, and calculating the dynamic aggregation index of the virtual power plant based on the operation data of the distributed resources in the local autonomous layer; a virtual power plant dynamic aggregation algorithm based on a greedy strategy is adopted, and a virtual power plant is formed through aggregation; a virtual power plant voltage control strategy based on key node selection is constructed, in the local autonomy layer, each virtual power plant preferentially controls internal resources to realize autonomy, and in the global collaboration layer, key node voltage in each virtual power plant is adjusted to realize collaboration control among the virtual power plants. According to the method, the adjustment potential of the distributed resources can be fully mined, so that the distributed resources can better participate in power grid adjustment, and resource utilization and power grid benefit maximization are realized.

The invention provides a circulation power optimization method and system for a three-phase dual-active bridge, and relates to the field of power electronics, the method comprises the following steps: determining an optimal backflow modulation strategy set corresponding to a hybrid modulation group, the hybrid modulation group comprising triangular wave modulation, trapezoidal modulation, synchronous modulation and extended phase shift modulation, the optimal backflow modulation strategy set corresponding to the hybrid modulation group comprises modulation modes and degree-of-freedom relationships corresponding to different transmission power per unit value ranges in different voltage conversion ratio ranges; determining a degree of freedom and power relationship set corresponding to the hybrid modulation group, wherein the relationship set comprises a relationship between the degree of freedom and power of different transmission power per unit values corresponding to each mode; the phase shift angle, the primary side duty ratio and the secondary side duty ratio are determined by combining the real-time voltage conversion ratio and the real-time virtual power per unit value, operation of the three-phase dual-active bridge is controlled, and the method has the advantages of reducing complexity and improving portability and efficiency.

The invention belongs to the technical field of novel electric power system operation control and trust management, and particularly discloses a virtual power plant full-process trusted aggregation method and system based on a hierarchical trust chain. Differentiated hierarchical trust chain models of a data acquisition link, a scheduling control link and a market transaction link are constructed respectively; through real-time credibility evaluation and adaptive weight adjustment, credibility calculation of multi-agent collaborative decision is realized. According to the invention, a trust network formed by a plurality of efficient, safe and transparent virtual power plant trust chains is constructed, solid technical support is provided for fair competition, intelligent scheduling and reliable operation of a power market, and efficient interaction and stable operation of a power system are ensured.

The invention discloses a stability criterion method considering a current amplitude limiting link for a network construction type energy storage system, and belongs to the field of stability control of a power system. According to the invention, the problem of virtual power angle switching after current saturation of the grid-forming converter is solved. The equivalent acceleration area and the equivalent deceleration area serve as the reflection of the energy increment and the energy decrement in the transient process of the converter, and the transient synchronization stability of the grid-connected system of the grid-forming type energy storage converter can be conveniently judged by comparing the acceleration area and the deceleration area or comparing the fault cut-off angle and the limit cut-off angle; the research result has a certain significance in improving the stability of the network construction type energy storage equipment.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant regulation and control method and system for realizing new energy consumption, and the system comprises a multi-source heterogeneous data collection module, an intelligent prediction analysis module, a resource aggregation modeling module, an optimization decision module, a block chain cooperation module, and a digital twinborn evaluation module. Multi-time-scale coupling prediction is carried out on new energy output and load demand through the deep space-time convolutional neural network, fluctuation and intermittency characteristics of new energy can be described, short-term and ultra-short-term prediction precision is improved, wind curtailment and light curtailment rate and load reduction risk are reduced, and the prediction efficiency is improved. According to the method, high matching between a virtual power plant scheduling plan and an actual operation condition is guaranteed, a flexible resource feature matrix is constructed, and distributed energy storage, interruptible load and electric vehicle multi-element resources are subjected to refined modeling and aggregation, so that a virtual unit capable of being efficiently scheduled can be formed, the resource utilization efficiency is improved, and the overall scheduling cost is reduced.

The embodiment of the invention provides a virtual power plant intelligent scheduling method and system based on digital twinning, a storage medium, electronic equipment and a computer program product, and belongs to the technical field of digital power grids. The method comprises the following steps: establishing a corresponding action response training data set based on a digital twin proxy matrix constructed by power grid nodes corresponding to distributed power equipment participating in scheduling; generating constraint feedback information reflecting the feasibility of the current target control action based on a power grid operation constraint response model; taking the constraint feedback information as coupling information of a pre-trained scheduling strategy model, and executing scheduling strategy training based on the current target control action to obtain a scheduling strategy model with constraint perception capability; and generating a corresponding scheduling control action based on the scheduling strategy model. According to the scheme, the perception capability and the response capability of the scheduling strategy to the power grid operation constraint are improved, so that the distributed resource scheduling efficiency and the decision intelligent level are dynamically optimized on the premise of meeting the power grid stability.

The invention relates to the technical field of virtual power plants, and provides a partition virtual power plant optimization scheduling method and device based on reinforcement learning, and a medium, and the method comprises the steps: determining a power partition set based on the resource information of a virtual power plant; constructing a global optimization scheduling model, and establishing global constraint conditions about the global optimization scheduling model; constructing a partition optimization scheduling model, and establishing a partition constraint condition set; determining a global optimization scheduling strategy based on a near-end strategy optimization algorithm, the global optimization scheduling model and a global constraint condition; determining a partition optimization scheduling strategy based on a heterogeneous proxy near-end strategy optimization algorithm, the partition optimization scheduling model and the partition constraint condition set; and performing joint training, generating a cross-region optimal scheduling strategy, and determining a target scheduling method of each partition. According to the embodiment, the scheduling efficiency and flexibility of the virtual power plant in the multi-partition power system are improved by combining the global and partition optimization scheduling strategy, the resource configuration is optimized, and the energy consumption and the cost are reduced.

The invention relates to a multi-virtual power plant collaborative transaction method, system and device and a readable medium, and the method comprises the steps: constructing a multi-energy coupling model containing a gas turbine, an energy storage device, biomass power generation and a new energy vehicle, and generating a multi-source data set to quantify the carbon emission cost and charge and discharge constraints; a wind and light output joint probability distribution model is generated based on an F-vine Copula distribution function, and a severe scene set is screened through random sampling and scene closeness evaluation; designing a renewable energy source-load correlation coefficient and a green energy consumption compensation mechanism, and correcting a virtual power plant scheduling objective function; establishing a double-layer dynamic transaction mechanism, and iteratively optimizing an upper-layer dynamic electricity price and a lower-layer multi-source transaction electricity quantity; a multi-stage robust optimization algorithm is adopted to process the uncertainty of wind, light and electric vehicles, and a transaction strategy among multiple virtual power plants in a severe scene is generated, so that the problems of low-carbon excitation deficiency, insufficient multi-source uncertainty coping and the like are solved, and unification of economy, low-carbon property and robustness of collaborative scheduling transaction of the multiple virtual power plants is realized.

The invention provides a virtual power plant dynamic response scheduling method and system based on a cognitive spectrum network, which can improve the reliability, self-adaptive capability calculation efficiency and risk management and control capability of a virtual power plant communication system, and relates to the technical field of virtual power plants, the method comprises the following steps: establishing a cognitive spectrum self-organizing network; collecting current operation state data of each power resource under the virtual power plant and historical operation state data under different working conditions; obtaining a resource response characteristic of each power resource; outputting a preliminary optimization result; converting the preliminary optimization result into a credible optimization result; generating a system risk partition topology report; a market risk hedging strategy capable of being automatically executed is generated; and generating a final scheduling instruction containing the power set value, the execution time, the hedging operation and the risk constraint condition of each resource. According to the method, a feasible technical path is provided for large-scale efficient utilization of distributed resources under the background of the energy internet.

The invention provides a virtual power plant control method based on a multi-objective optimization algorithm. The virtual power plant control method comprises the steps of collecting real-time operation data of distributed energy equipment through a virtual power plant control center; establishing a dynamic prediction model, and predicting output fluctuation characteristics and load change characteristics of each distributed energy device in a future time period; constructing a multi-time scale collaborative optimization model; solving the multi-time-scale collaborative optimization model by adopting an improved chaotic particle swarm algorithm, and generating an equipment control parameter set; dynamically adjusting a weight coefficient matrix of the optimization model according to the deviation degree of the real-time operation data and the prediction data; the robustness of the control parameters is verified through Monte Carlo simulation, and the next step is executed when the verification passing rate is larger than or equal to 95%; and issuing a control instruction set including a time sequence control instruction and a power adjustment threshold to each distributed energy device. According to the invention, efficient scheduling and precise control of the virtual power plant can be realized, and the power tracking precision, economical efficiency and stability of distributed energy can be improved.

The invention discloses a virtual power plant aggregation method based on node carbon potential and Zonotop, and relates to the field of virtual power plants, the method considers a second-order cone optimal power flow, designs a partition aggregation method of a virtual power plant according to the node carbon potential and an electrical position and based on a partition carbon balance principle, and performs external characteristic identification on the aggregated virtual power plant through the Zonotop. According to the method, the carbon emission attribute is fused into the resource aggregation process, and the single economic target limitation of a traditional virtual power plant is broken through; a zoning carbon balance principle is provided, and regions are dynamically divided based on a modularity index and a carbon balance potential index, so that local carbon self-consistency and global carbon benefit collaboration are realized.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a message indicating first and second sets of power control parameters associated with a first transmission reception point (TRP) and a second TRP. The UE may generate a first power headroom report (PHR) for a first uplink transmission associated with a first reference signal resource index using the first set of parameters, the first reference signal an actual transmission or a reference (e.g., virtual) transmission, and a second PHR for a reference uplink transmission associated with a second reference signal resource index using the second set of parameters. The second PHR may be a virtual PHR because of the reference uplink transmission, and the first PHR may be a virtual PHR if the first uplink transmission is a reference uplink transmission. The UE may transmit a message including the first and second PHRs.

The invention discloses a distributed virtual power plant game optimization scheduling method, and belongs to the field of optimization scheduling. According to the method, aiming at the characteristics that the distributed power supplies are distributed dispersedly and have randomness and volatility when the large-scale distributed power supplies are accessed to the network, multi-virtual power plant coordinated scheduling under the non-cooperative game is reasonably carried out, the economic benefit of multi-virtual power plant operation is improved, and energy utilization is fully optimized; considering the influence of the renewable energy prediction error along with the time scale, and establishing a multi-virtual power plant multi-time scale optimization scheduling model based on distributed model prediction control; taking a single virtual power plant as a whole to participate in the electricity market, and establishing a multi-virtual power plant non-cooperative game model; the two-way auction mechanism is applied to the electricity market, and the two-way auction process is analyzed through a non-cooperative game model; by considering distributed virtual power plant optimization scheduling under the competition relationship, the operation economy and transaction electric quantity are optimized, and flexible interaction and reasonable electric energy distribution between virtual power plants are realized.

The invention provides a low-voltage ride-through control method for a grid-forming type grid-connected converter based on reactive power synchronization, and belongs to the technical field of converter low-voltage ride-through control. When the voltage of a power grid drops, a current amplitude limiting module in the power grid sends a control switching signal to trigger low-voltage ride-through control, and the low-voltage ride-through control is switched to a reactive power synchronization mode; a reactive power synchronous control gain KQs is calculated according to an inner loop gain coefficient Kcp, a current amplitude limiting value, a power grid voltage Vg and a grid side inductance Lgi, a virtual power angle reference value is generated by combining a set reactive power direct current component reference value QN, so that a power angle delta is stabilized in a range close to a normal working point, and a control effect is verified by comparing with a traditional method. According to the method, the system can quickly recover to a normal working state regardless of the duration of the fault and the severity of the fault, meanwhile, the system can effectively work under different power grid fault levels, and the robustness of the system is further enhanced.

The invention discloses a digital twinning-based power grid dynamic frequency adaptive regulation and control method, system, equipment and medium, and belongs to the technical field of power grid dynamic frequency regulation and control, and the method comprises the steps: collecting power grid operation data through a data collection device disposed at a physical power grid key node; transmitting power grid operation data to a digital twinning construction and operation platform, and synchronously updating a virtual power grid state in real time; a digital twinning construction and operation platform is used for obtaining the operation state and dynamic parameters of the power grid; predicting a frequency dynamic response track of the power grid in a disturbance or expected scene based on the power grid operation state and the dynamic parameters; adjusting control parameters of resources participating in frequency regulation and control according to the frequency dynamic response track; and issuing the control parameters to a corresponding device of the physical power grid for execution through the control instruction execution interface. The frequency dynamic quality of the power grid is remarkably improved, and the frequency stability margin and the overall operation safety are improved.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.