418 results about "Power model" patented technology

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

The invention discloses an equipment fault diagnosis method and system based on a large electric power model, and the method comprises the steps: collecting and fusing voltage and current waveform data and alarm log information in a short time window before and after a fault moment, achieving the context integration of a multi-dimensional feature vector through multi-level feature coding and semantic embedding, and achieving the fault diagnosis of a large electric power model. And relevant knowledge fragment retrieval is carried out in combination with a power field knowledge base. On the basis, relevant knowledge fragments, voltage-current waveform fusion feature coding vectors and fault alarm key information semantic embedding coding feature vectors are jointly embedded into a preset Prompt template and then are input into a diagnosis engine based on large model driving, so that intelligent analysis and report generation of fault types, reasons and key features are realized. Through the mode, the limitation of dependence on single data or rules traditionally is broken through, the accuracy, automation and interpretability of fault diagnosis under complex working conditions are remarkably improved, and a solid support is provided for intelligent and high-reliability equipment operation and maintenance.

The invention discloses an electromagnetic ultrasonic transducer rapid design method based on an S-SADE algorithm, and the method comprises the steps: constructing an electromagnetic ultrasonic transducer model and a joint simulation platform, obtaining initial sample data and various parameters, and initializing a population and a sample database; using samples in the database to construct an RBF proxy model; performing variation and crossover operation on the parent population individuals according to five variation strategies to obtain a filial population; using an RBF agent model to predict fitness values of offspring individuals, transmitting parameters of an optimal individual to an electromagnetic ultrasonic transducer model for simulation evaluation, and updating a database; selecting a most expected sub-population from the five sub-populations as a next-generation population; and outputting an optimal parameter design scheme. According to the adaptive differential evolution algorithm, appropriate variation strategies and control parameters can be selected in different evolution stages, the objective function values of population individuals are predicted through the proxy model instead of expensive simulation evaluation, and computing resources can be saved to a great extent.

A cooperative control optimization method suitable for a near-field fan wall micromodule, comprising the following steps: step 10: obtaining IT system scheduling data and air-conditioning system data of the near-field fan wall micromodule within a preset period of time, and separately establishing a micromodule server real-time power model, an air-conditioning system power model, and a rapid server temperature prediction model; and step 20: performing cooperative control on deployment of virtual machines and an air-conditioning system by using an optimization method of combining real-time joint optimization and timed decoupling optimization based on the micromodule server real-time power model, the air-conditioning system power model, and the rapid server temperature prediction model. The present invention provides a cooperative control optimization method suitable for a near-field fan wall micromodule, to resolve the technical problem that local hot spots and overcooling are prone to occur during energy-saving scheduling of a near-field fan wall micromodule.

The invention relates to the technical field of network security, and particularly discloses an edge device network threat detection method and system based on an electric power large model, and the method comprises the steps: capturing a network message sequence in real time, extracting a time sequence randomness feature and a semantic deviation feature from a time dimension and a protocol dimension, and carrying out the fusion to form a comprehensive threat feature vector; performing multi-dimensional feature analysis and time sequence modeling by adopting a lightweight electric power large model to realize millisecond-level threat assessment; establishing a multi-level response mechanism, dynamically triggering a differential protection strategy according to the threat level, and ensuring the reliability and consistency of response actions through digital signature and collaborative verification; according to the method, the complex network attack in the power edge equipment can be effectively identified, the threat detection accuracy and the system defense capability are improved, and the strict requirements of a power system on real-time performance and reliability are met.

The invention provides a photovoltaic power generation power prediction method, system and device and a storage medium, and relates to the technical field of photovoltaic power generation power prediction, and the method comprises the steps: collecting historical data and real-time data of a photovoltaic array region; performing synchronous preprocessing to generate a historical data set and a real-time data set; establishing an initial generation power model, and drawing a curve graph of generation power changing along with time; analyzing climate data and cloud cluster image data in the real-time data, judging the illumination condition of the photovoltaic array area, and calculating the moving direction and the moving speed of the photovoltaic array area under the illumination condition; calculating the actual illuminated area of the photovoltaic array area under each time node, introducing the information into the initial power generation power model for optimization, and generating new prediction data; and carrying out comparison verification on the optimized prediction data and subsequent actual generation power. According to the method, the prediction accuracy is remarkably improved, and the influence of environmental factors on the prediction result is effectively reduced.

Embodiments of the present application set forth a trajectory tracking control method for an exoskeleton robot, comprising: in a first stage, when an exoskeleton robot is not applied to a subject, determining system interference of a power system of the exoskeleton robot, and compensating an output parameter of a power model of the exoskeleton robot; in a second stage, when the exoskeleton robot is applied to a subject, isolating the system interference from a human-machine interaction torque of the second stage, and determining the human-machine interaction torque; inputting the human-machine interaction torque into a controller, and using the controller to determine an assistance of the power system of the exoskeleton robot.

The invention discloses a server power consumption monitoring method, a computer program product and a data center, and relates to the technical field of power consumption prediction.The method comprises the steps that a server power consumption model comprising a hardware basic power consumption model, a state switching loss model and a thermal hysteresis compensation power consumption model is constructed; the power of the central processing unit under the steady-state working condition, the power changed compared with the steady-state working condition under the state switching working condition and the power consumption increased due to heat accumulation can be predicted, and the sum of the three is the total power consumption of the server; compared with the prior art, the method has the advantages that power response lag caused by state switching working condition load sudden change and leakage current power consumption calculation errors caused by thermal lag are considered, the accuracy of real-time prediction of the server power is greatly improved, and the problem of inaccurate real-time power prediction of the server power model is solved.

The invention relates to the field of urban traffic management and supervision and the field of traffic simulation, and provides a human-vehicle interactive simulation method and system based on an improved social force model and application. The method comprises the following steps: S1, constructing an improved social force model; s2, constructing a decision-making model, wherein the decision-making model is used for determining a behavior mode when the pedestrian and the vehicle meet; and S3, giving initial basic data of pedestrians and vehicles, and performing interactive simulation on human-vehicle movement conditions by using the improved social force model and the decision model. The method can be applied to scenes where vehicles and pedestrians are crossed and mixed, such as intersections without traffic lights, including crossroads and multi-directional intersections. According to the method, the trueness of the model in a complex traffic environment is remarkably improved, and behavior decisions of pedestrians in an actual scene can be more accurately simulated, especially under the condition that potential conflicts occur between the pedestrians and vehicles; through the introduction of modular design and transfer learning, the model can quickly adapt to a dynamically changing environment.

The invention discloses an electric power model deployment method and device based on federated learning and knowledge distillation, and the method comprises the steps: carrying out the data division of a common data set, and obtaining a training set; constructing a teacher model, and training the teacher model through the training set; constructing a student model, and training the student model through the training set and the teacher model; and descending the student model to a power distribution network edge node based on a federated learning framework, training the student model through a local data set, deploying the student model to the power distribution network edge node for operation, and updating the student model regularly. The method and the device provided by the invention not only can effectively reduce model parameters to realize lightweight deployment of the power distribution network, but also can ensure the security of the model during data sharing.

The invention discloses a household micro-grid energy optimization control method based on meta reinforcement learning, which is used for researching the joint coordination problem of demand response containing renewable energy resource consumption and alternating current optimal power flow under the conditions of no building thermal power model and guarantee of user comfort demand. Modeling the problem of minimizing the operation cost of the household micro-grid as a Markov decision process; secondly, a Soft Actor Critic algorithm which is integrated with Normalization Flow is constructed, and then, a soft Actor Critic algorithm which is integrated with Normalization Flow is constructed; thirdly, adding a meta-learning method into the constructed algorithm training; the method has the advantages that on the premise that the comfort requirement of a user is guaranteed, the method has the larger running cost saving potential, and the method can quickly adapt to different family micro-grid topological structures and quickly adapt to new tasks.

The invention discloses a hydrogen fuel cell hybrid electric vehicle energy management method based on robust optimization, and the method comprises the following steps: building a vehicle model: building a longitudinal dynamic model of a hydrogen fuel cell hybrid electric vehicle, determining a vehicle demand power model, and building an evaluation function of instantaneous hydrogen consumption, constructing an output power model of the hydrogen fuel cell and the capacitor; performing uncertainty analysis on the hydrogen fuel cell hybrid electric vehicle, determining uncertain parameters in the vehicle model, and constructing an uncertainty set of the uncertain parameters; the method comprises the following steps: constructing an sLSTM vehicle speed prediction model through vehicle historical speed data, predicting a future driving speed, analyzing the uncertainty of the prediction model, and outputting a prediction speed error in a current prediction time domain; and according to the optimization target, constructing a target function for minimizing the hydrogen consumption, solving the target function by applying a robust optimization algorithm, and outputting a first control result of the optimal control sequence.

The invention discloses an electricity-carbon collaborative optimization method and system based on a carbon emission flow theory, and belongs to the technical field of optimization of power systems and energy management. The electricity-carbon collaborative optimization method based on the carbon emission flow theory comprises the following steps: constructing a regional power distribution network architecture comprising load nodes and power generation nodes, and establishing a power model of the load nodes; establishing a carbon emission flow model according to the power flow model; constructing an electricity-carbon collaborative optimization model based on the power model and the carbon emission flow model of the load node; predicting actual photovoltaic and wind power generation and load data in the power distribution network by using an LSTM network, collecting charging demand information of a charging station, and performing parameter assignment in the power transmission-carbon collaborative optimization model; and solving the electricity-carbon collaborative optimization model by using a solver, determining the optimal power flow of each load per hour, and calculating the carbon emission intensity and the carbon emission rate to obtain a final scheduling scheme.

The invention discloses a water supply pipe network leakage locating method based on a lightweight gradient elevator algorithm, and belongs to the technical field of intelligent water affair and pipe network monitoring. The method comprises the following steps: integrating hydraulic modeling basic information, and constructing an initial hydraulic model by utilizing hydraulic software; actual measurement values of key node pressure and main pipe flow under different working conditions are collected and compared with simulation values to be calibrated to reach the standard, and a high-precision hydraulic model is obtained; simulating each leakage working condition in different time periods on a selected potential leakage node / pipe section by using the calibration model, collecting pressure change data of a pressure monitoring node, and performing preprocessing and data division; the data set is used for training a LightGBM model, the LightGBM model learns the relevance of node pressure in spatial topology and the dynamic change characteristics of a time sequence at the same time, a spatial position differentiation weight mechanism is introduced, and accurate positioning of leakage points and quantitative estimation of leakage coefficients are achieved. According to the method, the pipe network leakage positioning precision and efficiency are remarkably improved, and support is provided for rapid and accurate leakage detection and leakage amount evaluation.

The invention provides an electricity-hydrogen coupling energy hub peak regulation potential evaluation method and system. The method comprises the following steps: obtaining new energy output of an energy hub and equipment parameters of each electricity-hydrogen coupling energy hub; on the basis of the new energy output and the parameters of the electricity-hydrogen coupling equipment, combining with a pre-constructed energy hub peak load regulation power model, and obtaining actual peak load regulation participation power of the electricity-hydrogen coupling equipment; on the basis of actual participation power of peak regulation of the electric-hydrogen coupling equipment, the new energy output and the equipment parameters in combination with a pre-constructed energy hub peak regulation target model, obtaining an optimal target value of peak regulation participated by the electric-hydrogen coupling equipment; according to the net load value and the total scheduling time of the energy hub corresponding to the optimal target value, evaluating the grid-connected peak regulation potential of the electricity-hydrogen coupling energy hub; according to the invention, the potential of the electro-hydrogen fusion energy hub in the peak regulation of the power grid is fully exerted through potential evaluation; the adjusting capability of the power grid is improved, and the stability and safety of power grid operation are improved.

The invention discloses a self-adaptive load balancing method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the hierarchical modeling based on different semantic granularities in response to a preset heterogeneous resource and service calculation demand, and constructing a global semantic computing power model; obtaining a first semantic vector representation of a resource node in the target heterogeneous resource cluster; performing similarity calculation according to the first semantic vector representation corresponding to each resource node and the service computing power semantic vector to obtain a similarity list; target resources in the target heterogeneous resource cluster are obtained through screening based on the similarity list; obtaining a comprehensive evaluation score corresponding to each resource node through fuzzy evaluation; and determining the scheduling priority of each resource node based on the comprehensive evaluation score so as to perform load balancing scheduling. The self-adaptive load balancing method can efficiently and accurately realize self-adaptive load balancing and can be widely applied to the technical field of data processing.

The invention relates to the technical field of hydropower station intelligent monitoring and dispatching, and discloses a hydropower station computer monitoring method based on domestic hardware, which comprises the following steps: dynamic system modeling: establishing nonlinear dynamic models, including water pump and generator power models, of equipment in a hydropower station, and describing the operation state of the equipment; the multi-objective optimization scheduling is used for calculating load scheduling of equipment based on a multi-objective optimization model of power consumption, equipment loss and system energy efficiency; and the fault prediction and early warning module is used for carrying out real-time prediction on the equipment state through Kalman filtering, carrying out fault mode prediction based on historical data and sending out fault early warning in time. According to the invention, through dynamic system modeling, multi-objective optimization scheduling, Kalman filtering fault prediction and H-infinity robust control, efficient scheduling, accurate fault early warning and robust control strategy optimization of hydropower station equipment are realized, and the operation efficiency and stability of the system are significantly improved.

The present invention discloses a method for flexible load control of distribution network substations under new energy access, comprising the following steps: S1, aggregation modeling of typical flexible loads of users in the substation, wherein the typical flexible loads include electric vehicle charging and discharging models, temperature control load power consumption models and energy storage equipment charging and discharging models; S2, flexible load dispatching of users in the substation for regional load aggregators; S3, joint dispatching of multiple flexible load groups of users in the substation; S4, day-ahead dispatching of distribution network substation loads with multi-agent game; S5, real-time control of substation loads with uncertainty of source, load and storage based on day-ahead dispatching; the present invention realizes active distribution network optimization dispatching based on flexible loads on multiple time scales, implements the use of flexible loads to smooth fluctuations of renewable energy, and solves the problem of difficulty in real-time dispatching caused by uncertainty of source, load and storage.

The invention relates to the technical field of power equipment fault detection, in particular to a power equipment fault diagnosis method and system based on a large power model, and the method comprises the steps: converting the multi-modal data of a power cable in operation into a feature vector, and inputting the feature vector into a pre-trained fault diagnosis model to obtain a preliminary diagnosis result and confidence; if the confidence coefficient is not lower than the threshold value, the preliminary diagnosis result is reserved; and if the confidence coefficient is lower than a threshold value, taking the feature vector as a current potential fault feature vector to perform secondary discrimination, namely obtaining a significance index by calculating the similarity and volatility of the current potential fault feature vector and a historical potential fault feature vector, analyzing a time change trend to obtain a cumulative trend index, and performing secondary discrimination on the cumulative trend index. And comprehensively determining a potential fault index through the significance index and the cumulative trend index, and determining a final diagnosis result according to the potential fault index. According to the scheme, the diagnosis accuracy of the fault diagnosis model on low-confidence potential faults and unknown faults is improved.

The invention discloses a flexible load control method and system for a multi-split variable frequency air conditioner and a medium. The method comprises the steps that operation data are collected; building an air conditioner power model: building the air conditioner power model based on the collected operation data; and air conditioner load response: when the power instruction is received, the air conditioner load response is started, and the flexible load control strategy is executed on the multi-split variable frequency air conditioner. According to the method, load control suitable for VRV air conditioners of different types and powers is achieved, the method is suitable for rooms of different state conditions, and the later maintenance cost is saved.

The invention provides a round parachute air-drop system and an air-drop landing point prediction method and device thereof, and a program product. The method comprises the following steps: establishing a fluid-solid coupling model in a round parachute opening process; establishing an unsteady aerodynamic model of the round umbrella by using the unsteady aerodynamic coefficient of the round umbrella; establishing a dynamic model of the round parachute air-drop system; establishing a first constraint on speed and a second constraint on force of the round umbrella dynamic model and the load dynamic model; and according to the round umbrella dynamic model, the load dynamic model, the constraint condition of the round umbrella air-drop system, the predetermined round umbrella basic parameters, the load mass, the air-drop height and the current wind speed, predicting the air-drop point of the round umbrella air-drop system. By performing fluid-solid coupling numerical simulation on the inflation process of the circular parachute system, the prediction accuracy of the air drop point of the circular parachute air drop system is improved.

The invention discloses an aerosol particle behavior analysis method and device, and the method comprises the steps: obtaining basic parameters of a fluid to be analyzed, the fluid to be analyzed being a mixture of non-condensable gas, water vapor and aerosol particles; constructing a geometric model of a computational domain of the fluid to be analyzed; meshing the geometric model to obtain a grid unit data set of the geometric model; acquiring a control equation, a pre-created wall condensation function, a turbulence model, a discrete phase model, a thermophoresis force model and a pre-created diffusion phoresis force function; based on the basic parameters and the grid unit data set, solving a control equation, a pre-established wall surface condensation function, a turbulence model, a discrete phase model, a thermophoresis force model and a pre-established diffusion phoresis force function through a solver, and obtaining working condition distribution data of the to-be-analyzed fluid in a computational domain; and analyzing the behavior of the aerosol particles based on the working condition distribution data. Therefore, the motion behavior of the aerosol particles can be accurately simulated.

The invention relates to the technical field of model parameter calibration, and provides a hydrodynamic model calibration method and system based on an improved particle swarm optimization, and the method comprises the steps: S1, obtaining the corresponding relation between the section information of a hydrodynamic model and the roughness; s2, defining a roughness parameter range and an allowable error range of each section; s3, initializing a particle swarm; s4, performing parallel scheduling hydrodynamic model calculation according to the matrix number of the particle swarm to obtain a simulation result data set; s5, performing time sequence comparison on a simulation result and an actual measurement result, and calculating an error value; s6, screening a calibration optimal solution, finding out a particle with the minimum error, and recording a corresponding roughness parameter; s7, judging whether the optimal solution is within an error range or not, if yes, directly executing the step S8, and if not, repeatedly executing the steps S4 and S5 until the optimal solution meets the condition; and S8, outputting a calibration optimal solution, and displaying a calibration result, an actual measurement result process line and optimal parameters. According to the invention, the calibration efficiency can be effectively improved.

The invention discloses a Tube MPC-based hybrid electric vehicle layered energy management method. The method comprises the following steps of performing longitudinal dynamic modeling on a hybrid electric vehicle, determining a vehicle demand power formula, constructing an evaluation function of equivalent fuel consumption, and constructing an output power model of a vehicle engine and a battery; carrying out uncertainty analysis on the energy management system, determining uncertain parameters in the vehicle model, quantifying the uncertainty of the uncertain parameters based on probability distribution, and constructing an uncertainty set of the uncertain parameters; on the basis of a dynamic programming algorithm, under the global working condition, the minimum amount of equivalent fuel consumption serves as a target function, an optimal control sequence of the engine is output, and an SOC reference trajectory is generated; and performing tracking solution on the control variables and the state variables in the uncertainty set by applying a Tube model predictive control (Tube MPC) method to obtain an optimal control sequence under uncertain factors, and outputting a first control variable.

The invention relates to a coastal city flood multi-system coupling evaluation method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the unified projection and time resolution normalization processing of the data; constructing an upstream river network one-dimensional hydrodynamic model, an urban drainage pipe network one-dimensional hydrodynamic model and an urban two-dimensional surface water dynamic model based on the multi-source data, and setting tide boundary conditions; coupling each model and the tide boundary; performing model calibration and error constraint on the coupled multi-dimensional hydrodynamic coupling model; constructing a composite scene, taking each group of scenes as an input boundary condition to drive the multi-dimensional hydrodynamic coupling model, and obtaining a flood evolution result; and performing risk assessment and sensitivity analysis based on a flood evolution result. According to the method, a unified river network-pipe network-earth surface-tide multi-scale coupling framework is constructed, so that the defect that a traditional single model or result superposition method is difficult to reflect multi-system interaction is effectively overcome.

The invention relates to a power transformation equipment operation and maintenance task intelligent arrangement method and system, equipment and a medium, and relates to the technical field of electric power. The method comprises the steps of obtaining real-time operation data and historical operation and maintenance data of power transformation equipment, constructing input information of a large electric power model by adopting a Prompt technology enhanced by a knowledge graph, and predicting an operation and maintenance task by utilizing the large electric power model; and generating an operation and maintenance task arrangement scheme, performing dynamic optimization of the operation and maintenance task arrangement scheme based on reinforcement learning, generating and issuing an operation and maintenance work ticket, and feeding back task execution state and equipment state information to the large electric power model in real time to optimize the operation and maintenance task arrangement scheme. Compared with the prior art, the intelligent arrangement of the operation and maintenance tasks of the power transformation equipment is realized, the operation and maintenance efficiency and the equipment reliability are improved, the human resource consumption is reduced, and the scientificity of operation and maintenance decision is enhanced.

The invention relates to the technical field of power distributed resource regulation and control, in particular to a peak regulation market electric quantity declaration method and system for distributed resources, electronic equipment and a medium. The method comprises the following steps: firstly, acquiring real-time operation data and peak regulation market policy data of load side distributed resources; constructing a double-constraint generation power model according to the real-time operation data and peak regulation market policy data; constructing a distributed resource aggregation model based on the double-constraint power generation power model; extracting an unsteady variable from the resource aggregation state and the electric quantity declaration reference value; according to the unsteady variables, an interval optimization logic solution is adopted to obtain an optimal electric quantity declaration strategy; and finally generating an electric quantity declaration scheme participating in the peak regulation market. Through a double-constraint generation power model and interval optimization logic, the problem of insufficient peak regulation market income optimization caused by neglecting adjustable load characteristics and wind and light abandoning punishment in the prior art is solved, and the adaptability and reliability of an electric quantity declaration strategy in a complex market environment are improved.

The invention discloses a flood storage and detention area flood diversion process simulation method and device based on a hydrodynamic model, and the method comprises the steps: obtaining the landform, river section, dike engineering parameters, and real-time hydrological and meteorological data of a flood storage and detention area, constructing a basic parameter library, and storing the basic parameter library in a data input module; constructing a hydrodynamic model calculation module by taking a hydrodynamic equation as a theoretical basis on the basis of the basic database, and combining a finite element volume method; receiving a numerical calculation result, analyzing real-time parameters through a dynamic simulation module so as to match actual hydrological parameters, and performing comparison under multiple scenes; and transmitting the hydrological parameters and the comparison data to a result output module, and outputting key parameters of the flood diversion process and a risk assessment result. The method has the advantages that high-frequency coupling of a hydrodynamic model, multi-source dynamic data, a cross-domain model and an engineering control system is achieved, and the problems of data lag, model isolation and control disjunction in traditional simulation are solved.

The invention provides a power system frequency modulation optimization method in extreme weather. The method comprises the following steps: establishing a wind power model and a photovoltaic power model; constructing a typical extreme weather scene by adopting an improved fuzzy C-means clustering algorithm; establishing a frequency modulation optimization model taking the quasi-steady-state frequency deviation, the system regulation time and the comprehensive cost of the generator set as objective functions; constructing constraint conditions; and solving the optimization model based on a multi-target fox-monkey optimization algorithm. According to the invention, the improved fuzzy C-means clustering algorithm is adopted to construct a typical extreme weather scene, and the sample membership degree is evaluated through the comprehensive distance composed of the Euclidean distance and the included angle cosine distance, so that the accuracy of scene construction is improved; the frequency safety and economy of the system are comprehensively considered, and comprehensive optimization of various performance indexes is realized; a multi-target fox-monkey optimization algorithm is adopted for solving, and the algorithm is prevented from falling into local optimum; the accuracy of system frequency stability evaluation is improved, and a more reliable guarantee is provided for safe operation of the system in extreme weather.

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.