280 results about "Load model" patented technology

The invention discloses an electric power marketing management information platform daily electric power fitting method and related equipment, and relates to the technical field of electric power data management, and the method comprises the steps: obtaining historical load data, external environment data and equipment operation state data of a target region; constructing an initial load fitting model based on the historical load data; extracting a date characteristic factor according to a preset time classification rule, and dynamically correcting the initial load fitting model based on the date characteristic factor to generate a corrected load model; generating a multi-source fusion feature based on the external environment data and the equipment operation state data; and inputting the multi-source fusion features into the corrected load model, and outputting a target load prediction result.

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 relates to the technical field of source network load storage intelligent scheduling, and discloses a source network load storage AI intelligent scheduling method and system, and the system comprises a data collection module, a fluctuation analysis module, a constraint calculation module, a load modeling module, an energy storage analysis module, a decision engine module, and a safety correction module. Output data of a photovoltaic power station, a wind power plant and a traditional power plant are collected in real time through an Internet of Things sensor, and a multi-source heterogeneous energy data pool is constructed; aligning the corrected source load data with the energy storage state and the power grid operation parameters according to a time sequence to generate a four-dimensional optimization combination matrix; matching an optimal scheduling algorithm through a bionic search strategy, and analyzing the data matrix through the deep reinforcement learning model to generate a three-section scheduling instruction; and finally, the power grid control system executes power generation adjustment, load regulation and control and energy storage charging and discharging instructions. According to the system, the edge computing gateway is adopted to realize data acquisition, the new energy consumption capability and the power grid stability are improved, and the risk of source-grid load-storage collaborative failure is reduced.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention discloses a load management system based on a load model in the plastic product industry. The load management system comprises a load time sequence model building module which classifies and marks equipment according to operation behaviors of the equipment to obtain a load time sequence model; the data acquisition and fusion module acquires power, current, voltage and start-stop state data of equipment in real time through a monitoring point position, and generates a real-time load data set in combination with real-time production state data; the load prediction and adjustability evaluation module generates a load prediction curve by using a long short-term memory network, generates an adjustability score by adopting a support vector machine model, and determines an adjustable process list in combination with real-time production state data; and the scheduling execution module determines candidate adjustment equipment according to the load prediction curve, the adjustability score and the adjustable process list, constructs a multi-objective optimization function through a multi-objective optimization algorithm, and generates a load adjustment strategy by using a particle swarm optimization algorithm to execute scheduling on the equipment. According to the invention, the refinement level of load management is improved.

Disclosed are a risk assessment method for a distributed power distribution network considering a line fault and a device thereof, belonging to the field of risk assessment of faults of a power distribution network. The method includes the following steps: establishing a wind generating set output model, a photovoltaic module output model and a load model, and generating a wind generating set output data sample, a photovoltaic module output data sample and a load sample; establishing a short-term fault rate model of a wind generating set, a photovoltaic module and a line, and generating a system state sample; establishing an objective function and a constraint condition, and reconstructing the power distribution network to obtain the reconstructed power distribution network topology; carrying out the risk assessment and an entropy weight method.

The invention discloses a method and a system for identifying a power imbalance state of a power system under climate and weather changes. The method comprises the following steps: determining climate and weather environment data of a target region; determining a current climate and meteorological environment event of the target region; if the climate and meteorological environment event meets the preset type, triggering an electric power imbalance state identification process of a target electric power system in the target region; constructing a source side output model matched with the power supply side, and constructing a load model matched with the load side; constructing a source-load coupling prediction model of the target power system; inputting real-time power operation data of the target power system into the source-load coupling prediction model to obtain power gap data; obtaining an identification result of the current state of the target power system; and generating an imbalance state identification log of the target power system. According to the method and the system for identifying the electric power unbalance state of the electric power system under the climate and weather change, the electric power unbalance state is comprehensively evaluated from the source side and the load side.

The invention discloses a permanent magnet synchronous motor joint simulation method, which comprises the following steps of: firstly, establishing a permanent magnet synchronous motor finite element model in Maxwell, and defining material nonlinear characteristics and motion boundary conditions; a motor drive control circuit is constructed in Simplorer, the finite element model is imported through a Twinbuild tool, and an electromagnetic-circuit joint simulation environment is established; creating a motor multi-body dynamic model and an external load model in Adams, and respectively exporting the motor multi-body dynamic model and the external load model into an FMU format and an Adamssub format; and finally, establishing an electromagnetic-circuit-mechanism joint simulation platform in the Simulink, and executing joint simulation. Compared with traditional joint simulation, according to the method, through electromagnetic-circuit-mechanism three-field real-time interaction, the motor can sense the load sudden change working condition, then the torque and current of the motor are rapidly adjusted through the PI controller, the problems that in engineering practice, the external load of the motor is not real, and the abnormal load is detected are solved, and the reliability of the motor is improved. And the crossing from an ideal load to an actual load is realized.

The invention discloses a distributed power supply load model establishment method and system, and relates to the technical field of load model establishment, after an establishment system obtains power system information through an API interface, a topological graph model of a power system is established, each component in the power system is abstracted as a node and an edge in the topological graph model, and the topological graph model is established; after simulation software is used for conducting simulation detection on the topological graph model, a target value of the topological graph model is calculated through an objective function, an optimal path between nodes is updated through a path algorithm, current distribution of edges is adjusted through a graph cut algorithm, power loss is reduced, overload is avoided, the steps are repeated for multiple iterations, and when convergence conditions are met, the optimal path is obtained. And outputting the final topological graph model as a comprehensive load model. The system can optimize the targets at the same time through a graph optimization algorithm, improves the overall benefits of the system, and overcomes the defect that the complexity of the power system cannot be comprehensively considered in single target optimization.

The invention relates to the technical field of industrial robot fault diagnosis, and discloses a fault early warning and remote diagnosis method for an industrial robot electrical control system, and the method comprises the steps: obtaining a mechanical load parameter through a potential load model, an inertia load model and a basic dynamics model, obtaining an electrical state parameter through an electrical efficiency model, and carrying out the fault early warning and remote diagnosis. A correction factor is generated through a correction model in combination with the environment temperature, a dynamic current threshold value is calculated through a threshold value model based on the correction factor and a basic current threshold value, and finally health scores and fault early warning information are output through a diagnosis model in combination with the vibration acceleration and servo ring following errors. According to the method, mechanical, electrical and environmental multi-source parameters are fused, dynamic threshold adjustment and multi-dimensional health assessment are realized, normal working conditions and abnormal states are effectively distinguished, the fault early warning accuracy is remarkably improved, the false alarm rate is reduced, and a reliable remote diagnosis scheme is provided for the industrial robot.

The invention discloses a photovoltaic energy storage and charging integrated power station capacity optimization design method and system, and relates to the technical field of power system distributed photovoltaic energy storage planning, and the method comprises the steps: building an electric vehicle queue charging load model based on a preset parameter system describing user charging behaviors and probability distribution fitting; generating a photovoltaic charging load uncertainty scene of the target power station based on the generative adversarial network, the electric vehicle queue charging load model and the photovoltaic output power of the target power station; based on the photovoltaic charging load uncertainty scene, constructing a target robust optimization model; solving the target robust optimization model to obtain an optimal capacity planning result of the target power station; and based on the optimal capacity planning results of different target power stations, utilizing Kriging interpolation to obtain the coupling relationship between the charging behavior and the optimal capacity. The technical problem that the integration capability of the photovoltaic and battery energy storage system cannot be accurately determined in the prior art is relieved.

The invention discloses a power grid dispatching method in an energy storage participation scene. The method comprises the following steps: establishing a master-slave game system taking an energy storage end of a demand side of a power distribution network as an upper layer and an electrical load end of the demand side as a lower layer, wherein the master-slave game system comprises an upper-layer energy storage model and a lower-layer electrical load model; inputting the scheduling capacity and the scheduling ratio of the energy storage end into the upper-layer model, outputting electric quantity transmission cost parameters of the energy storage end and the electric load end, inputting the electric quantity transmission cost parameters into the lower-layer model, outputting the transmission electric quantity of the energy storage end and the electric load end after processing, and inputting the transmission electric quantity into the upper-layer model for circulation until a variable reaches a steady-state value, and obtaining the scheduling amount to realize the scheduling of the power grid. According to the method, dynamic collaborative optimization of energy storage and power utilization load side resources can be realized, the problems of low resource utilization rate and insufficient model precision in the prior art are solved, the overall optimization and scheduling flexibility of an energy system is remarkably improved, and the technical blank in the field of collaborative optimization and dynamic interaction in the prior art is filled.

The invention relates to the technical field of engineering monitoring, in particular to a bridge structure state monitoring method and system based on digital twinning, and the method comprises the steps: building a multi-source monitoring data matrix based on a data timestamp; establishing a geometric model, a material attribute model and a load model of a bridge structure based on the multi-source monitoring data matrix, and fusing to obtain a bridge digital twin initial model; and carrying out structural mechanical analysis based on the bridge digital twin initial model to obtain stress distribution data of each region of the bridge. Through real-time acquisition and analysis of multi-source monitoring data, the method can dynamically evaluate the structural state of the bridge and timely identify potential safety hazards, so that the safety of the bridge is improved; and through calibration of the initial model, the obtained digital twin precise model reflects the real state of the bridge, and a more accurate data basis is provided for subsequent structural analysis.

The invention discloses a power distribution network operation parameter optimization identification method based on optimal power flow calculation, and relates to the technical field of power distribution systems, and the method comprises the steps: collecting historical operation parameters of each node of a power distribution network, screening to obtain a data collection priority of an optimal power flow output index, and reasonably distributing node sampling frequencies according to the data collection priority; measuring and collecting operation parameters of the power distribution network according to the distributed sampling frequency, carrying out pre-detection on the collected data, and meanwhile, carrying out adaptive anomaly judgment and sampling frequency dynamic adjustment and carrying out multi-scale decomposition and sensitivity analysis on node residual errors, so as to realize optimal power flow calculation time window adjustment driven by dynamic parameters; and the load model parameters are verified to ensure the accuracy and reliability of the parameter identification result. Through combination of historical data, real-time measurement and residual sensitivity information, transient disturbance, medium-term fluctuation and long-term trend can be considered in the operation parameter identification of the power distribution network, and the online precision of a load model and line parameters is improved.

The invention discloses a source load impact and system bearing life evaluation method and system, equipment and a medium, and relates to the technical field of traction power supply, and the method comprises the steps: collecting data to recognize impact, building a source load model, analyzing an impact diffusion path, evaluating system bearing, fusing multi-dimensional damage, calculating the residual life of the equipment, and automatically updating parameters. And generating a closed-loop regulation and control strategy. The system comprises a data analysis and dynamic modeling module, a bearing evaluation and conduction analysis module, a damage coupling and life prediction module and a parameter correction and strategy generation module. The traction impact time domain-frequency domain-topology integrated quantization is realized, and the problem of impact time-space diffusion assessment is solved. A life model is constructed, and multi-dimensional stress damage is accurately mapped. The micro-simulation and on-line filtering technology is utilized to ensure that model parameters are adaptively updated along with working conditions, and the problem of disjunction between evaluation and actual conditions is solved. The method supports side cloud cooperation and closed-loop pre-control, effectively improves the safety margin of the power grid, and delays the aging of equipment.

The invention relates to the technical field of comprehensive energy system optimization scheduling, in particular to an energy-carbon excitation-based farm comprehensive energy system optimization scheduling method, which comprises the following steps: S1, constructing a comprehensive energy system covering an energy operator and farm users, providing electric heating energy by the operator, and supplying heat by the farm users through biogas power generation, the three types of loads are combined to construct an equipment and load model; s2, carbon potential is calculated based on a carbon emission flow theory, an energy-carbon price is formulated, and a stepped carbon transaction and segmented subsidy mechanism is adopted to stimulate a user to use low-carbon energy; s3, constructing a master-slave game model, performing upper-layer pricing excitation and lower-layer optimization response, and performing joint iterative solution by adopting a genetic algorithm and GUROBI; according to the invention, by constructing a master-slave game scheduling framework and introducing an energy-carbon joint pricing and stepped carbon excitation mechanism, collaborative optimization of system economy and a low-carbon target is realized, and user response enthusiasm and carbon emission reduction potential are effectively improved.

The invention belongs to the technical field of electromagnetic detection, and relates to an electromagnetic detection multi-level current emission system for a plateau environment and a control method. The control method comprises the steps of calculating load model impedance; analyzing an MFPWM control process, and determining an optimal control parameter by using an optimization target of transmitting spectrum intensity maximization; and MFPWM optimization control: realizing directional power distribution and independent regulation and control of each frequency component by adjusting the frequency and amplitude of each modulation signal component. A multi-level topological structure is adopted, high output voltage is achieved while switching voltage stress is reduced, and the problems that cost rises and system complexity is too high due to the fact that a traditional H-bridge inverter depends on high power supply voltage are solved; by matching with multi-frequency pulse width modulation optimization control, spectrum energy can be flexibly configured, high-frequency components of transmitted signals are enhanced, and the spectrum energy utilization rate and the signal-to-noise ratio are improved; grounding impedance changes in a high dynamic range can be accurately tracked, and the requirements of deep geological electromagnetic detection in an extreme environment are met.

The invention relates to the technical field of smart power grids, in particular to a detection signal design method and device for multi-injection-point and multi-load-node identification, and the method comprises the steps: calculating the static sensitivity of a plurality of to-be-identified load nodes corresponding to each target equipment injection node in a plurality of target equipment injection nodes based on steady-state operation data, establishing a detection signal optimization model taking a reactive detection signal as a decision variable by taking the total minimum load model parameter identification error of a plurality of to-be-identified load nodes as a target and combining static sensitivity, and introducing reactive energy constraints and signal structure constraints of a plurality of target equipment injection nodes and solving; generating an optimal reactive power detection signal waveform of each target equipment injection node, performing evaluation by using a target evaluation index, when an evaluation result meets a preset constraint condition, injecting the optimal reactive power detection signal waveform into the system, and after a response is generated, performing identification on a load model parameter by using the response, and obtaining an identification result of each load node to be identified.

The invention relates to the technical field of offshore wind power engineering, in particular to a collaborative optimization design method and system for an offshore wind turbine generator base, and the method comprises the steps: building an environment load model based on target sea area data, and generating a load working condition; a parameterized fan-base coupling model is constructed; aiming at minimizing the leveling cost per kilowatt-hour, and taking the structural strength and the inherent frequency as constraints; fan and base design variables are synchronously adjusted through multidisciplinary collaborative optimization, and candidate schemes are output; establishing a local high-precision model for the selected scheme to perform fatigue life verification, and if not, performing feedback optimization until a final scheme is obtained; and finally, based on the scheme, deploying the sensor and initializing the digital twin, realizing life prediction and graded early warning, and forming full life cycle closed-loop management.

The invention discloses a reliability evaluation method for a power distribution network containing network construction type energy storage and flexible resources, and the method comprises the steps: firstly constructing a wind power and photovoltaic combined output probability distribution model considering the output uncertainty and spatial-temporal correlation of new energy, and a typical load scene; then establishing a'operation-fault 'two-state shutdown model, and generating a system operation state through sequential Monte Carlo simulation; simulating the power distribution network according to the fault information, and analyzing the power output characteristics of the constructed network type energy storage and the voltage support effect of the constructed network type energy storage on the near-area distributed new energy; and finally, constructing an optimal load shedding model considering the network-forming type energy storage power support effect, calculating the fault consequence of the system, and calculating the reliability index of the system according to the fault consequence. According to the method, the improvement effect of network construction type energy storage on the reliability of the power distribution network is analyzed; and meanwhile, the accurate reliability evaluation capability is still kept in a high-permeability scene of the distributed power supply, and a theoretical support is provided for network construction type energy storage planning investment for improving the reliability of the power distribution network.

The invention discloses a coagulation and bipolar coagulation integrated control system for a surgical operating instrument, and relates to the field of medical equipment. The system comprises an interface module, an energy detection module, a storage module, a load model matching module and an energy output control module. According to the system, an energy detection signal is output to a surgical instrument before energy output, and a real-time biological impedance spectrum of a target object is obtained; determining a target model from pre-stored electrical load models according to the impedance spectrum, and calling a corresponding adaptive energy output strategy; and in the energy output process, the impedance change of the target object is monitored, and the energy parameters are dynamically adjusted, so that the energy output is automatically stopped after the impedance change conforms to the preset stable target state. The self-adaptive precise energy control based on the real-time state of the tissue is realized, the problems of tissue carbonization, adhesion or incomplete coagulation and the like are effectively avoided, and the operation safety and effect are improved.

The invention belongs to the technical field of energy optimization scheduling, and particularly relates to a comprehensive energy system robust optimization scheduling method and device based on regulatable ability, and the method comprises the steps: building a natural gas supply system management and storage model; establishing a power distribution network time shift regulation and control load model, a natural gas network time shift regulation and control load model and a power distribution network demand response load model; constructing an optimization objective function considering the operation cost of the power distribution network and the operation cost of the natural gas network; establishing a comprehensive energy system robust optimization model considering the load uncertainty based on the optimization objective function; and constructing a double-layer robust optimization algorithm, solving the worst operation scene by the sub-problem, optimizing the operation cost of the integrated energy system by the main problem, and solving to obtain an optimal scheduling scheme of the integrated energy system. According to the invention, the scheduling strategy can effectively deal with the complex operation problem of the power-gas coupling integrated energy system, and the flexibility and adaptability of the system are improved.

The invention is suitable for the technical field of traveling block hook testing, and provides a testing device for a traveling block hook and a testing method thereof.The testing device comprises a mounting assembly used for providing rigid support for the whole device and a testing assembly arranged on the mounting assembly, the test assembly is used for applying a simulation load to the hook assembly of the traveling block, the detection assembly is arranged on the mounting assembly and is used for carrying out image acquisition on the hook assembly of the traveling block, and the test assembly comprises a clamping unit and a load simulation unit which are oppositely arranged from top to bottom. The clamping unit comprises a suspension oil cylinder, a first hinge structure and a fixing seat, the load simulation unit comprises a load oil cylinder and a second hinge structure, a friction effect equivalent load is introduced through an equivalent load model, the defect that the friction force of a drilling tool is neglected in an existing test is overcome, the test load better fits the actual drilling working condition, and the test efficiency is improved. The accuracy of a detection result is improved.

The invention relates to the technical field of power system modeling and simulation, in particular to a load model identification error analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining the prediction output and the measurement output of a preset load model, and building a target function based on the error between the prediction output and the measurement output; decomposing the measurement output to obtain an actual output and a noise component of a preset load model; and based on the objective function and the actual output, establishing a linear model between the identification error of the preset load model and the noise component through a first-order approximation method, and performing linear regression according to the linear model to obtain an analysis result of the identification error. Therefore, by constructing the theoretical model of the load model identification error, the problems that the identification error is difficult to predict, the data processing strategy selection lacks theoretical guidance and the like in related technologies are solved, and a theoretical basis is provided for selecting an optimal load modeling data processing strategy.

The invention discloses a power distribution network comprehensive stability evaluation method and system based on load modeling, and the method comprises the steps: constructing a power distribution network differential load model with spatial-temporal characteristics according to the load classification and spatial-temporal change rule; based on the model, combining voltage stability, frequency stability, load fluctuation and external disturbance influence factors to determine a comprehensive system stability index of the power distribution network; establishing a relation model among the voltage, the load and the frequency; determining a sensitivity coefficient of load fluctuation to voltage and frequency based on the relation model; evaluating the stability of the power distribution network under the load fluctuation condition according to the sensitivity coefficient; determining a dynamic stability index of the power distribution network through the dynamic stability index; and performing comprehensive dynamic stability evaluation on the power distribution network through the comprehensive system stability index of the power distribution network, the stability of the power distribution network under the load fluctuation condition and the dynamic stability index of the power distribution network. And the stability of the power distribution network can be comprehensively evaluated.

The invention relates to a power demand side response carbon emission reduction effect quantification method. The method comprises the following steps: S1, obtaining user side data and power grid side data; s2, local data preprocessing is carried out through an edge computing node, and then the data are uploaded to a cloud database; s3, according to the preprocessed user side data, obtaining an adjustment load capacity; s4, calculating a dynamic carbon emission factor through a real-time power generation curve and a power grid power generation structure in combination with the preprocessed power grid side data, thereby evaluating marginal carbon emission change caused by demand response, dynamically calculating the real-time carbon emission factor, and dynamically updating EF (t) by using power grid power generation structure data; and S5, calculating a baseline and actual carbon emission according to the baseline load model, the adjusted load data and the EF (t), and finally obtaining an emission reduction effect. According to the invention, dynamic marginal carbon emission factor evaluation and high-precision carbon emission reduction quantification are realized.

The invention relates to the technical field of power distribution, and discloses a POE modular switch power distribution method and device and computer equipment, and the method comprises the steps: collecting a port power consumption data set of a POE modular switch; performing inter-port power consumption correlation modeling based on the port power consumption data set to obtain a dynamic load model; performing multi-standard handshake protocol analysis and power consumption prediction on the access equipment according to the dynamic load model to obtain an equipment power consumption prediction result; performing fast weight allocation based on the equipment power consumption prediction result to obtain a port power allocation scheme; and performing multi-port aggregation and power pooling management on the high-power demand ports according to the port power distribution scheme, and generating a power rerouting control instruction, thereby effectively solving the technical problems of low power utilization rate and non-uniform power supply of the traditional static distribution method, and remarkably improving the power management efficiency of the whole system.

The invention relates to the technical field of electrical load analysis, and discloses an electrical load model modeling method and system based on time-sharing electric quantity. The method comprises the following steps: based on intelligent electric meter time-sharing electric quantity acquisition and equipment start-stop frequency identification, extracting peak-valley normal-period basic load characteristics by adopting a time-sharing electric quantity empirical mode decomposition algorithm, and obtaining equipment start-stop load correlation characteristics by combining a time-sharing variational mode decomposition algorithm with an equipment start-stop state matrix; a time-sharing hyper-parameter optimization algorithm is used to carry out load prediction in each time period, and finally a start-stop frequency influence weight mechanism is established through a time-sharing load state fusion algorithm to obtain a time-sharing electric quantity load modeling result. The method solves the problems that an existing time-sharing electric quantity load modeling method lacks equipment start-stop state sensing, modeling precision between time periods is unbalanced, and time period switching continuity is poor.

The invention relates to the technical field of power grid planning, in particular to an intelligent transformer area clustering method considering operation characteristics of distributed resources, and the method comprises the steps: collecting operation data of multiple types of transformer areas, and building an operation characteristic parameter set of the multiple types of distributed resources; generating a characteristic fingerprint database through principal component analysis dimension reduction and clustering processing, and calculating characteristic similarity between different resource characteristics; constructing classification aggregation models of various types of resources, and fusing the classification aggregation models into a transformer area comprehensive load model; according to the topological structure of the power distribution network and the comprehensive load model, calculating an inter-station composite electrical distance index, and extracting load characteristic similarity; and performing two-dimensional clustering by taking minimization of the composite electrical distance and maximization of the load characteristic similarity as double targets, and outputting a transformer area aggregation scheme. According to the method, the problem of collaborative modeling of dynamic characteristic differences of multiple types of resources and electrical coupling strength between the transformer areas is effectively solved, and the transformer area aggregation precision and the engineering adaptability in a distributed energy scene are remarkably improved.