825 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 relates to the technical field of virtual power plants, in particular to a multi-target scheduling optimization method and system for a virtual power plant to participate in a power market, and the method comprises the steps: carrying out the dynamic integration of distributed energy, an adjustable load and an energy storage unit in the virtual power plant, and constructing a resource model; establishing a distributed resource identity authentication and data storage mechanism; constructing a multi-objective optimization function, comprehensively considering a plurality of objectives such as resource utilization rate, carbon emission and load balance, and designing a dynamic weight adjustment mechanism; calculating a multi-objective optimization result, determining the optimal output and operation state of virtual power plant resources, and generating a virtual power plant optimization scheduling strategy; and cooperative optimization of internal resource scheduling and external power grid scheduling of the virtual power plant is realized by combining a game theory and a swarm intelligence algorithm. According to the method, the utilization efficiency of distributed resources can be improved, source-network load-storage coordination and new energy consumption are promoted, and the flexibility and comprehensive benefits of a power system are enhanced.

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 a self-adaptive intelligent power control method for an electric energy router in a photovoltaic power generation system. According to the method, key parameters of photovoltaic, energy storage and load are monitored in real time, a virtual power pool is constructed to quantify a multi-source power margin and dynamically distribute a priority sequence, energy storage output is dynamically adjusted in combination with a feed-forward compensation control strategy, frequency modulation deviation is periodically checked, weight optimization redistribution is triggered, and energy storage output is dynamically adjusted. Cooperative control of photovoltaic load reduction frequency modulation and DC bus voltage stability is realized, the multi-source cooperative frequency modulation response capability and the energy utilization rate are effectively improved, voltage fluctuation caused by power abrupt change is suppressed, and both the system operation economy and the power supply reliability are considered.

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

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 dispatching system and method, and particularly relates to the technical field of power dispatching. By monitoring the output characteristics of the distributed energy, whether nonlinear interference is generated or not is judged. And further analyzing the disturbance of the distributed energy on the power flow inversion path, judging the abnormal change of the coupling correlation degree between potential hidden resources, and evaluating the influence on the scheduling precision. And in combination with a node differentiation response mode of a distributed energy storage aggregation region and a flexible load community, analyzing the influence of a local grouping cooperation effect on the overall output boundary identification precision of the virtual power plant. Potential erosion of scheduling errors caused by demand side multi-dimensional elastic cooperation to scheduling precision is evaluated by analyzing spectral domain characteristics of demand side elastic gradients and transient disturbance under different spatial and temporal scales. Through comprehensive analysis, the internal regulation and control scheme of the virtual power plant is adjusted, and the overall scheduling efficiency and stability of the virtual power plant are improved.

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.

The invention discloses a virtual power plant path optimization system and method based on an electricity-carbon combination model, belongs to the technical field of virtual power plants, and aims to solve the problem of how to realize carbon emission minimization while ensuring stable operation of a power grid. The method comprises the following steps: collecting target data in real time through interfaces of power generation equipment, energy storage equipment, a load end and carbon emission monitoring equipment; performing data preprocessing on the collected target data, analyzing an energy consumption mode and a carbon emission trend based on the preprocessed target data, predicting a future power demand and a carbon emission level, and judging whether an abnormal trend exists or not; constructing a power-carbon combination model, and optimizing a power production path by using an objective function and constraint conditions; when an abnormal trend is detected, a scheduling scheme is formulated by optimizing a power production path; and executing the scheduling scheme, scheduling the power resources and the power generation equipment, and controlling coordinated operation among the power generation unit, the energy storage equipment and the power grid load in the virtual power plant.

The invention discloses a virtual power plant cloud edge collaborative optimization method and system based on improved federal learning, and the method comprises the steps: a cloud node of a virtual power plant builds an initial model, and transmits model parameters to an edge node; the edge node carries out low-rank adaptive decomposition on the model parameters, local data training is utilized to obtain updated model parameters, and the updated model parameters are uploaded to the cloud node; and the cloud node performs weighted aggregation on the uploaded model parameters and issues the aggregated model parameters to the edge node. Based on the federated learning framework, local data of the edge node does not need to be massively transmitted to the cloud node, the data transmission amount is reduced, and the data privacy is protected; meanwhile, a low-rank adaptive decomposition matrix is adopted at an edge node to train and update model parameters, so that the data transmission quantity of cloud-edge collaboration is further reduced, and the security of a book transmission process is improved; the weighted aggregation mode of the model parameters at the cloud is improved, and the rationality of cloud node global model aggregation is improved.

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 discloses a multi-type virtual power plant participation market regulation capacity optimization distribution method and system, and the method comprises the steps: constructing a power supply type, load type and energy storage type virtual power plant participation market operation characteristic model, and then combining a value-at-risk assessment CvaR model to obtain a value-at-risk assessment model; a refined virtual power plant capacity distribution model is established by taking maximum pumped storage income and minimum risk when the virtual power plant participates in capacity distribution of main and auxiliary markets as targets, constraint conditions are set for modeling, and finally, the virtual power plant is combined with an electric energy market demand, a peak regulation market demand and a frequency modulation market demand for joint optimization. And continuously detecting and optimizing through the virtual power plant capacity distribution model. According to the method, on one hand, execution of a power generation plan is guaranteed along with wind and light output, on the other hand, a reasonable capacity distribution mechanism in a multi-element market is designed according to different mechanisms of the current power market, services such as peak regulation, frequency modulation and standby are provided, and the optimal configuration problem of a virtual power plant in the power market is solved.

The invention provides a bidding method for virtual power plants participating in a joint market based on a master-slave non-cooperative game. The bidding method comprises the following steps: constructing a double-layer game framework in which multiple virtual power plants participate in a day-ahead joint market; carrying out Wasserstein distance uncertainty modeling on the wind power, the photovoltaic power and the electric load power; based on the multi-source load power uncertainty set, an upper-layer multi-virtual power plant two-stage distribution robust bidding model is constructed, and the bidding model comprises a first objective function with the minimum sum of pre-scheduling cost for making a bidding strategy in a day-ahead stage and rescheduling cost expectation in an intra-day stage and a corresponding first class of constraint conditions; constructing a lower-layer joint market clearing model; the clearing model comprises a second objective function with the minimum day-ahead electricity-peak regulation-hydrogen combined market energy purchase cost and a corresponding second class of constraint conditions; performing equivalent conversion on the bidding model and the clearing model; and solving the converted bidding model and the converted clearing model to obtain a bidding strategy.

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 provides an electric energy meter PCB function test method and device, a medium and equipment, and the method comprises the steps: obtaining a multi-channel signal source, automatically adjusting the characteristics of an input signal based on a feedback signal of an electric energy meter through a self-adaptive algorithm, and determining the consistency of the working characteristics of the input signal and the working characteristics of the electric energy meter; a virtual power grid environment simulation model is constructed, through integrated power simulation software and dynamic interference simulation, simulation signal source automatic adjustment signal characteristics are input into the virtual power grid environment simulation model based on the integrated power simulation software to synthesize a composite signal, and load fluctuation of an electric energy meter in actual operation is simulated based on the composite signal. Determining an electric energy meter test data set; according to the invention, the fault mode of the electric energy meter can be automatically identified, the test data can be analyzed in real time, and fault diagnosis and optimization suggestions can be automatically given. The intelligent diagnosis mode is far better than a traditional manual detection method, and the speed and accuracy of fault diagnosis can be remarkably improved.

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 relates to a virtual power plant adjustable capacity boundary construction method considering conditional value-at-risk, and the method comprises the steps: firstly constructing a virtual power plant electric energy dispatching model based on time-of-use electricity price guidance by taking the minimum electricity purchasing cost of a virtual power plant spot market as an objective function; constructing a virtual power plant regulation capacity boundary depicting model by taking maximum output and minimum output of the virtual power plant in the regulation time period as objective functions; the maximum output and the minimum output of the virtual power plant in the adjustment period respectively correspond to the upper limit and the lower limit of the adjustment capacity boundary of the virtual power plant; solving the virtual power plant electric energy scheduling model to obtain a scheduling strategy of the virtual power plant in the electric energy market; and finally, according to a scheduling strategy of the virtual power plant in the electric energy market, performing global optimization on the virtual power plant adjustment space capacity description model to obtain an adjustable capacity boundary of the virtual power plant in an adjustment time period. The invention is beneficial to improving the flexibility of the power system, promoting the green and low-carbon development of the power industry and optimizing the energy structure.

The invention discloses a resident household electrical appliance electrical load prediction method and device based on deep learning, and a medium. The method comprises the steps of collecting resident user electrical appliance load data; constructing a training set according to the collected data; building a multi-task prediction model with a long-time and short-time view field based on deep learning, wherein the multi-task prediction model is used for predicting whether electric equipment works or not and predicting an electric load of the equipment; designing a three-stage training strategy of the multi-task prediction model, and training the model by adopting the training set; and using the trained multi-task prediction model to predict the working state and the electrical load of each electrical device of each resident user accessed to the virtual power grid in a future short period. According to the method, the multi-task prediction model is established, the long-time and short-time key information of the historical power consumption data can be efficiently extracted through the three-stage strategy training model, and the method has the multi-task associated feature extraction capability, dynamically adapts to different scenes and accurately predicts the working state and load change of the power consumption equipment.

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.

A multi-virtual power plant collaborative optimization scheduling method considering energy sharing comprises the steps of constructing a master-slave game model, constructing an upper-layer game leader scheduling center model, constructing a lower-layer game follower multi-virtual power plant collaborative optimization scheduling model and a multi-virtual power plant income distribution model, and solving the models by adopting a particle swarm optimization algorithm and a Cplex solver. According to the invention, the scheduling center based on energy sharing and the master-slave game model of the multiple virtual power plants are provided, the scheduling center is used as a superior game leader, and the multiple virtual power plants are guided to perform optimal scheduling by dynamically adjusting the electricity price, so that energy sharing among the multiple virtual power plants is stimulated; effective interaction between the multiple virtual power plants and an upper-layer mechanism is achieved through electricity price adjustment; according to the method, the benefit relationship among the multiple virtual power plants is deeply studied, and the problem of benefit damage of part of main bodies in the collaborative operation process is solved based on the multi-virtual power plant collaborative operation benefit distribution model of the Shapley value method, so that the collaborative operation relationship of the multiple virtual power plants can be stably maintained.

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.