69 results about "Charge model" patented technology

The invention discloses an electric vehicle intelligent scheduling method and system, and a storage medium, and relates to the technical field of electric vehicle charging. The method comprises the following steps: initializing training parameters of an upper-layer optimization module; initializing a training environment of an upper-layer optimization module; the training environment comprises a state space and an action space; determining a charging strategy adopted by each agent according to the state space and the Actor-Critic strategy network, and calculating a reward value corresponding to the charging strategy according to a reward function; a state space is updated, an upper layer optimization module optimizes a charging strategy through a PPO reinforcement learning algorithm, multiple times of training are carried out, and a charging model is obtained; the upper-layer optimization module provides a charging strategy for the to-be-charged electric vehicle through the charging model, and the lower-layer optimization module optimizes the charging cost and the user satisfaction degree through an objective function according to the charging strategy and distributes a charging station for the to-be-charged electric vehicle. According to the method, flexible resource scheduling and management can be realized, and the overall efficiency of the system is improved.

The invention relates to an electric vehicle V2G scheduling method considering consumption of photovoltaic power and off-peak power, and the method comprises the steps: building an EV disordered charging model based on a user driving behavior in a power distribution network system containing photovoltaic power generation; establishing peak clipping-oriented price excitation; combining the established EV disordered charging model and price excitation to construct a photovoltaic and EV cooperative power generation optimization scheduling model; solving the photovoltaic and EV cooperative power generation optimal scheduling model by adopting an improved multi-target particle swarm algorithm, and outputting to obtain an EV ordered charging and discharging strategy; and correspondingly controlling the charging and discharging behaviors of the EV according to the EV ordered charging and discharging strategy. Compared with the prior art, the method can reduce the load variance of the power grid, achieves the efficient utilization of photovoltaic and off-peak power, and improves the V2G income of the electric vehicle.

Disclosed is a method for constructing an optimal scheduling model of an integrated energy system considering virtual thermal storage, which relates to the technical field of electric power systems. The method includes: establishing an operation model of an apparatus, a demand response model of an energy load, a charging model of an electric vehicle, and an energy flow model of a heat supply network of the virtual thermal storage in the integrated energy system; processing, according to a second-order cone relaxation method, a direct current power flow model of a power distribution network, and constructing the optimal scheduling model of the integrated energy system by taking minimization of total cost of an energy operator as an objective, thereby coordinately and optimally scheduling a plurality of energy sources.

The invention relates to the technical field of transformer area charging, and particularly provides a transformer area ordered charging intelligent control method and system, a terminal and a storage medium, and the method comprises the steps: building bidirectional communication connection between a charging pile and a transformer area use and collection terminal; after the connection is established, acquiring charging information and real-time electricity price by the transformer area using and acquiring terminal; constructing an MLP fusion model, fusing multi-dimensional data in the charging information into a fusion feature vector based on the MLP fusion model, and training a CNN-LSTM load prediction model based on the fusion feature vector; a load prediction result is obtained, a charging mode is selected based on the predicted load result, the real-time electricity price and the charging information, a charging strategy is dynamically adjusted based on the charging model, and the charging mode comprises a standard charging mode, a timing mode and an intelligent charging mode. The beneficial effects of improving the system compatibility, enhancing the load prediction accuracy, improving the intelligent management level to optimize the user experience, reducing the communication cost, optimizing the charging load management and the like are achieved.

The invention discloses a hydrogen fuel cell optimization algorithm based on particle swarm optimization of an integrated system, and belongs to the technical field of micro-grid energy management. An existing hydrogen fuel cell has the problems of low fuel utilization rate, slow response speed and the like under a dynamic load condition. In order to solve the problem, a hybrid energy system topology consisting of a photovoltaic (PV), a proton exchange membrane fuel cell (PEMFC), a lithium ion battery (BAT) and a super capacitor (SC) is constructed, and mathematical models, including a photovoltaic output power model, a fuel cell voltage model and a cell state-of-charge model, of all energy units are established. A PI controller is designed for a fuel cell, and a proportional gain KP and an integral gain KI of the fuel cell are dynamically adjusted through a particle swarm optimization (PSO) algorithm. Specifically, PI parameters are coded into particle position vectors, a multi-target fitness function is constructed, and hydrogen consumption, system stability and oxygen utilization rate are comprehensively considered; and a dynamically adjusted inertia weight mechanism is adopted to guide particle search, and iterative optimization is realized. After optimization, parameters are written into a PI controller in real time, and the hydrogen and air flow is accurately adjusted. Meanwhile, a continuous monitoring mechanism is introduced, when the photovoltaic power fluctuation exceeds 10%, re-optimization is automatically triggered, and closed-loop control is formed. The method improves the fuel utilization rate and the reaction rate of the fuel cell, is suitable for the dynamic load micro-grid, and improves the overall efficiency and stability of the system.

The invention relates to a dust particle swarm field-induced charge model construction method, and belongs to the technical field of dust monitoring. According to the method, based on a field-induced charge mathematical model of single particles and SMD indexes, a field-induced charge mathematical model of multiple uniform spherical particles is constructed, and specifically, according to the charge quantity Qg of a dust particle swarm, the mass M1 of the dust particle swarm in a three-dimensional sphere shape and the mass M2 of the dust particle swarm in an n-dimensional sphere shape, the charge-to-mass ratio of the dust particle swarm is calculated; wherein SMD represents the average diameter of Sauter and is defined as the diameter corresponding to the ratio of the total volume to the total surface area of the particles. According to the method, a charge theory system which is more accordant with actual dust migration characteristics is established, and theoretical support is provided for iterative upgrading of technologies such as industrial dust removal, spraying and dust monitoring.

Receive, by a price calculation microservice, tenant user input from a particular tenant of a multi-tenant system. Generate, by the price calculation microservice, a particular charge model based on the tenant user input. Detect, by a usage microservice, a usage event associated with a customer of the particular tenant of the multi-tenant system. Receive, by a rating microservice, a rating request, the rating request being received in response to the detecting the usage event. Calculate, by the price calculation microservice in response to the receiving the rating request, a usage charge for the usage event based on the particular charge model. Store, by the rating microservice, the usage charge. Generate an invoice based on the usage charge stored by the rating microservice. Provide the invoice to the customer of the particular tenant of the multi-tenant system.

A preconditioning system (200) and a method (100) for optimal preconditioning of EVs in a depot. The method (100) includes determining a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of EV and determining a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration. The method also includes determining a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV and determining a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV. Further, the method also includes generating a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV and causing to display the determined charging profile on a screen of a charger (1000).

The invention discloses a multi-antenna charger charging scheduling method under energy budget constraint. The method comprises the steps of defining a network model, a charging model and a charging utility model; formalizing a multi-antenna unified control charger scheduling problem under the energy budget constraint; obtaining candidate directions with a limited number of multi-antenna chargers by using a dominating coverage set extraction method; converting a multi-antenna unified control charger scheduling problem under the energy budget constraint; and calling a multi-antenna charger antenna switch unified control scheduling algorithm under the energy budget constraint to calculate the direction and switch scheduling scheme of the multi-antenna charger. According to the invention, the direction and the on-off state of the multi-antenna charger are adjusted in multiple time periods of the whole charging process, so that the overall charging effect of the sensor is maximized in a limited energy scene, and the energy utilization efficiency of the multi-antenna charger when the multi-antenna charger executes a charging task is improved.

This invention discloses an online measurement device and method for coal dust parameters based on multi-physics field fusion and adaptive decoupling. The device includes: a co-location fusion sensing probe, which integrates a charge sensing module and a microwave resonant sensing module, the microwave resonant sensing module operating at a frequency of 1.0-5.0 GHz; an adaptive decoupling calculation unit, equipped with an adaptive decoupling algorithm, configured to simultaneously receive electrostatic and dielectric characteristic signals, identify coal type characteristic parameters online through a pre-stored dielectric characteristic-coal type parameter mapping relationship, substitute these parameters into the charge signal physical model, and decouple and calculate the estimated coal dust concentration and flow rate values ​​to eliminate the influence of coal type through the online parameter identification method; and a data communication unit for outputting the estimated coal dust concentration and flow rate values. This invention, through online identification of coal type characteristics using microwave dielectric measurement and dynamic correction of the charge model, fundamentally eliminates measurement drift caused by changes in coal type, achieving long-term calibration-free, high-precision online measurement.

The application belongs to the technical field of magnetic parameter measurement, and discloses a device and a calculation method for testing magnetic force in each direction between permanent magnets. The device uses an electric sliding table and a vertical sliding table to construct a three-dimensional motion platform, and collects the magnetic force of a tile-shaped permanent magnet under different spatial displacements in real time through a sensor. The device can not only meet the force testing needs of the inner / outer rotor permanent magnet under the axial, radial and tangential misalignment states, but also can adapt to different sizes of the inner / outer rotor permanent magnet by adjusting the clamp. Meanwhile, the magnetic force calculation method proposed by the application takes the equivalent magnetic charge model under the cylindrical coordinate system as the theoretical basis, and accurately solves the three-dimensional magnetic force of the inner / outer rotor permanent magnet under any spatial relative position through quadruple integration. With the help of the testing device and the calculation model proposed by the application, the precise measurement and theoretical verification of the magnetic force in each direction of the permanent magnet can be realized, the calculation is simple, and the degree of coincidence is high. The testing and calculation method has engineering universality and convenience.

The invention discloses a community alternating current charging pile load optimization method, which comprises the steps of obtaining historical basic load data, alternating current charging pile information, electricity price information and user orders of a target community, constructing a neural network prediction model, and further predicting current load data; constructing an ordered charging model containing an ordered charging target function and constraints according to the predicted load data and the lithium battery charging characteristics; searching an optimal solution of the model through a preset optimization algorithm, and generating an optimized charging scheme; and outputting the optimized charging scheme to the target alternating current charging pile and executing the optimized charging scheme through the target alternating current charging pile. According to the community alternating current charging pile load optimization method, on the premise that a user does not need to provide battery parameters, scheduling can be implemented according to charging time, electric quantity requirements and other basic information, off-peak charging is guided through an electricity price mechanism, the overload risk of a transformer is reduced, and the service life of the transformer is prolonged. Unification of power grid load balance, user cost optimization and safe and stable charging process is realized.

The invention relates to the technical field of electric energy storage, in particular to a virtual power plant optimization operation method and system based on energy storage capacity configuration regulation, and the method comprises the steps: building a comprehensive power generation model according to equipment information, obtaining a charge state model and a charge constraint condition of a lithium battery, and obtaining a compressed air energy storage principle model of compressed air energy storage equipment; calculating the daily generating capacity according to the real-time environment change parameters by using the comprehensive generating model; obtaining daily power output, and calculating a difference value between the power generation amount and the power output to obtain a power regulation amount change curve; according to the charge state model, the charge constraint condition and the compressed air energy storage principle model, capacity prediction operation based on a short period and a whole period is carried out on the electric power regulation variable change curve, and lithium battery recommended capacity and compressed air energy storage recommended capacity are obtained; and performing energy storage capacity configuration on the virtual power plant according to the lithium battery recommended capacity and the compressed air energy storage recommended capacity. The operation efficiency of the virtual power plant can be optimized.

The invention provides a battery module state estimation method considering functional state dynamic characteristics, and relates to the technical field of battery management. The method comprises the following steps: collecting characteristic parameters of a single battery, and calculating polarization voltage in the battery; according to the factors affecting the state estimation of the battery module, a state-of-charge model representing the short-time scale characteristic of the battery and a state-of-health model representing the long-time scale characteristic of the battery are constructed respectively; establishing a functional state dynamic model of the single battery and a state estimation model under a dual-time scale; and expanding the functional state dynamic model of the single battery and the state estimation model under the dual-time scale to a battery module level, and establishing the functional state dynamic model of the battery module and the state estimation model under the dual-time scale so as to calculate the state estimation value and the functional state of the battery module. According to the invention, the accuracy and dynamic response capability of battery state estimation are effectively improved, and a basis is provided for long-term health management of the battery.

The invention belongs to the technical field of energy regulation and control, and provides an energy storage regulation and control decision method and system based on energy dynamic balance, and the method comprises the steps: constructing an energy storage equipment feature set, carrying out the numerical conversion of state features in the energy storage equipment feature set, and obtaining key energy storage features; the method comprises the following steps: constructing an equivalent state-of-charge model, an intra-day time-phased capacity measurement and calculation model and a system power model of an energy storage system, setting balance conditions of the energy storage system, and carrying out quantitative solution on key energy storage characteristics to obtain quantitative data of energy storage nodes; according to the quantized data and an energy balance grading rule, an alternative regulation and control scheme is determined through a multi-energy storage system regulation and control model; and setting regulation and control constraint conditions, constructing a regulation and control cost model of the energy storage system participating in the power grid, and screening the alternative regulation and control schemes to obtain an energy storage regulation and control decision scheme. According to the scheme, the regulation and control precision is effectively improved, the energy dynamic balance of the energy storage node network is realized, and the regulation and control effect is better.

The invention provides a dynamic charging mode for a battery swap station, which comprises a dynamic charging architecture and a dynamic charging device, and a plurality of data acquisition systems, a dynamic pricing model, a user-battery matching optimization module and a block chain transparent charging system are arranged between the dynamic charging architecture and the dynamic charging device. According to the invention, by fusing the Internet of Things and the block chain technology, the charging mode of the battery swap station is redefined, the battery health degree, the user behavior and the environmental factors are incorporated into a dynamic pricing system, the spanning from extensive charging to accurate value exchange is realized, and the fairness, economy, intelligence and transparency of charging of the battery swap station are improved.

The invention discloses a ship underwater water pressure field depth conversion method, which comprises the steps of constructing a ship electrostatic field conversion model based on a point charge model; the influence of the interface effect on a ship pressure signal under the shallow sea environment condition is equivalent to the influence of the interface effect on a ship corrosion related electrostatic field, and a model considering the influence of the medium effect on the ship electrostatic field is established; according to the electrostatic field model, a parallel equivalent source is constructed for a ship water pressure field along the longitudinal directions of a port and a starboard of a ship body based on a source-sink model, and a three-dimensional flow field problem is converted into an equivalent source model distribution problem; establishing a pressure signal inversion method of a point source on the basis of considering the influence of an interface effect on a ship pressure field signal and a source-sink model; the pressure signal model is solved, and then the pressure value of any field point is inverted; a multi-source coupling inversion system with physical interpretability is established, and the signal reconstruction precision under different water depth conditions is remarkably improved by introducing a constraint optimization algorithm; and the actual engineering requirements are met.

The invention relates to the technical field of power system optimization scheduling, and discloses an energy collaborative optimization method for a virtual power plant to participate in multiple power services, which comprises the following steps: step A, modeling optical storage and charging resources in the virtual power plant based on fuzzy opportunity constraints to obtain an optical storage and charging model and a resource operation characteristic model; b, evaluating the aggregation adjustment capability of the virtual power plant based on the Minkowski diagram and the optical storage charging model to obtain an aggregation adjustable domain of the virtual power plant; and step C, based on the resource operation characteristic model and the virtual power plant aggregation adjustable domain, establishing an energy collaborative optimization model for the virtual power plant to participate in the multi-element power service, solving the energy collaborative optimization model, and based on a solving result, constructing a joint declaration strategy for the virtual power plant to participate in the multi-element power service.

The invention discloses a method, a system, equipment and a medium for optimizing light storage and charging cooperative scheduling of a power distribution network in a mountainous area city, and belongs to the technical field of light storage and charging scheduling of the power distribution network, and the method comprises the steps: predicting the photovoltaic power generation power and the load power; establishing an uncertainty set; establishing a photovoltaic power generation model, an energy storage system model and an electric vehicle charging model; constructing a multi-objective optimization model comprising an operation cost objective function, a network loss objective function and a power balance constraint condition; solving the multi-objective optimization model by adopting a robust optimization method to obtain a scheduling scheme; performing rolling optimization on the scheduling scheme by adopting a model prediction control strategy, and updating a control decision according to the real-time operation state of the power distribution network; and outputting the charging and discharging power control quantity of the energy storage system and the charging power control quantity of the electric vehicle charging pile. According to the method, the operation cost, the network loss and the power balance constraint are comprehensively considered, so that the overall performance optimization of the system is realized.

The application discloses a charging remaining time confirmation method, device and vehicle, relates to the battery charging technical field, and discloses a charging remaining time confirmation method, which comprises the following steps: obtaining current charging data of a battery pack, wherein the current charging data comprises a charging rate and an SOC value; obtaining a temperature rising charging time and a temperature rising charging end SOC value based on the current charging data and a first charging model; obtaining a constant temperature charging time based on the temperature rising charging end SOC value and a second charging model; and confirming a charging remaining time based on the temperature rising charging time and the constant temperature charging time.According to the basic characteristics of the temperature in the low-temperature fast charging process, the charging remaining time is calculated in sections, the fast and accurate calculation of the fast charging remaining time is realized in combination with a fitting method and given data, and the overcomplicated calculation and the model building and calculation of heat generation, heat conduction and heat dissipation are avoided.

The present invention discloses a method and device for constructing an open circuit voltage-state of charge model of a battery, and relates to the technical field of battery management systems. When constructing an OCV-SOC model of a battery based on an SVR model, the present invention continuously performs binary segmentation and binary search on the initial search space of the parameters to be optimized in each dimension of the SVR model, so that the optimal search subspace can be quickly located from the large-scale initial search space of the parameters to be optimized, and then further performs detailed parameter optimization based on the optimal search subspace with a particle optimization algorithm, which greatly reduces the amount of calculation in the construction of the OCV-SOC model. At the same time, the binary segmentation and binary search of the initial search space can comprehensively evaluate the parameter performance of each search subspace in the large-scale initial search space, and can accurately determine the target search space of the PSO algorithm, avoiding the possibility of losing the optimal parameters, and improving the accuracy of the construction of the OCV-SOC model.

The invention provides an energy storage planning method and system considering a new energy proportion evolution path, and the method comprises the steps: employing a Logistic growth model, and generating a new energy permeability evolution path driven by a carbon neutralization target; constructing a thermal power-energy storage multi-stage collaborative optimization model containing thermal power generating unit transformation cost and thermal power generating unit fire coal cost, and constructing a state-of-charge dynamic model about self-discharge rate and efficiency attenuation by taking a lithium ion battery as an energy storage carrier; constructing a first target function by taking the minimum total cost of system operation as a target; setting a multi-stage cooperative constraint condition for the first objective function; and constructing a multi-stage energy storage planning model according to the first objective function and the multi-stage coordination constraint condition, and solving the multi-stage energy storage planning model. According to the embodiment of the invention, the method can achieve the dynamic adaptation of a renewable energy high-permeability system and energy storage planning, can improve the economical efficiency of energy storage configuration, and provides reliable technical support for a novel power system.

This invention discloses a method for establishing and extracting a charge model for HEMT transistors based on GaN technology, comprising: obtaining the gate-channel voltage based on externally input gate voltage, drain potential, and source potential; obtaining the gate charge based on gate charge density, number of gate elements, gate length, and width of a single gate element; obtaining the drain-source potential difference based on the drain potential and source potential; establishing a charge model based on the gate-channel voltage, gate charge, and drain-source potential difference to characterize the correlation between charge value and drain-source voltage after the transistor device is turned on; and differentiating the charge value of the charge model to establish a capacitance model to characterize the correlation between gate-source capacitance, gate-source voltage, and drain-source voltage. This invention supports the charge / capacitance description of HEMT transistors based on GaN technology, especially the description of the relationship between gate-source capacitance, gate-source voltage, and drain-source voltage after the channel is turned on, with high model accuracy.

An orderly charging control method for electric vehicles based on dynamic time-of-use electricity price, which relates to an orderly charging control method for electric vehicles. The present invention aims to solve the problems of weak pertinence of the existing orderly charging model for electric vehicles and difficulty in solving intelligent optimization algorithms. The present invention proposes an orderly charging model for electric vehicles, sets up an objective function that meets actual needs and constraint conditions that conform to the actual situation, uses the Monte Carlo method to predict the charging load of electric vehicles, and proposes a dynamic time-of-use electricity price strategy based on the day-ahead load prediction results. Combining with the improved NSGA-II algorithm to solve the model, the formulation of an orderly charging strategy for electric vehicles that simultaneously considers the optimization of charging costs and load variance is realized. The present invention belongs to the technical field of new energy vehicle charging.

This invention discloses a reverse simulation method and platform for the dynamic operation of an electric vehicle's powertrain. The method includes building a physical simulation model of the electric vehicle's powertrain, including a charging model, a temperature model, a battery model, a vehicle dynamics model, and an energy transfer model. Injecting collected real-world driving data into the model allows for rapid reverse simulation of the energy transfer, consumption, and battery degradation of an electric vehicle over a long period of real-world driving. This method enables electric vehicle consumers and manufacturers to quickly simulate and evaluate the vehicle's energy transfer process and the impact of vehicle usage factors on energy consumption and battery life.

The present application relates to electric vehicle charging technical field, especially to electric vehicle charging method, device and related equipment based on time-of-use electricity price, method includes: according to the travel data of electric vehicle constructs electric vehicle load model;Based on the electric vehicle load model, with the minimum peak-valley difference of total power load as objective function, the ordered charging model of electric vehicle is constructed;Establish the constraint condition corresponding to the ordered charging model of electric vehicle;Based on the objective function and the constraint condition, the ordered charging model of electric vehicle is solved by artificial bee colony algorithm.The present application can reduce the peak-valley difference of power grid, slow down the power grid load fluctuation caused by electric vehicle disorderly charging, improve the stability of power grid, and guide electric vehicle users orderly charging.

The present invention discloses a method for evaluating the rapid cook-off of explosives. The specific steps are as follows: establish a rapid cook-off calculation model with an air domain for simulation according to the rapid cook-off test system; the rapid cook-off calculation model with an air domain includes: a test projectile model, an air domain, a fuel inlet, and a fuel outlet; establish a rapid cook-off calculation model without an air domain, and the rapid cook-off calculation model without an air domain only includes a test projectile model; finally, obtain the scatter correspondence relationship between the heat flux boundary and the diameter of the corresponding charge model; according to any diameter of the charge model and the calculated heat flux boundary, load the heat flux boundary onto the outer wall surface of the test projectile model for calculation, obtain the internal temperature change, ignition time, and ignition temperature of the charge model, and evaluate the rapid cook-off characteristics of the explosives; the present invention can optimize the existing rapid cook-off calculation with an air domain and realize the evaluation of the rapid cook-off of explosives without an air domain.

A method for scheduling electric vehicle charging based on two-dimensional graphic filling, specifically a method for scheduling electric vehicle charging based on two-dimensional graphic filling; the present invention first obtains charging history data of each existing scheduling area and municipal electricity consumption history data of each scheduling area, and constructs a charging model and a power consumption model based on the acquired historical data, and then fills the constructed charging model and power consumption model in a two-dimensional graphic filling manner, and finally outputs a filling effect diagram, which is used to schedule electric vehicle charging; the present invention can combine municipal electricity consumption and electric vehicle charging, and reasonably schedule charging for each scheduling area on the basis of avoiding exceeding the municipal electricity consumption limit, thereby ensuring the safety of municipal electricity consumption and reasonably planning the scheduling effect of electric vehicle charging. In addition, it can also be continuously updated to ensure the rationality of charging scheduling.

The present invention discloses a charging station pricing and charging method based on a robust reward function of multi-agent reinforcement learning. By using the robust reward function to handle the uncertainty of electric vehicle behavior, on the basis of historical data, the optimal pricing and charging decision of the charging station are realized. Multi-agent deep reinforcement learning is used to mine the non-cooperative game actions of the charging station, realizing the efficient coordination of the distribution network - electric vehicle. A Markov process with uncertain states is adopted to construct a robust reward function in the worst-case scenario; the policy difference is converted into a reward difference through the total variation distance. Considering the operation constraints and optimization objectives of the power grid and the charging station, an optimal pricing and charging model of the charging station based on the robust reward function of multi-agent deep reinforcement learning is constructed.

The invention discloses an EV charging and integrated energy system capacity configuration optimization method considering seasonal characteristics. The method comprises the following steps: constructing an electric vehicle load access and departure IES time model and a time slot division model; on the basis of the established IES time model and time slot division model, introducing a dynamic urgency index, and establishing an electric vehicle coordinated charging model with a minimum load peak-valley difference as a target; constructing an IES equipment model and constraints, and generating a source-load seasonal scene; on the basis of the established IES equipment model and constraints, a two-stage optimization model with the minimum IES equipment capacity configuration cost as the target is established by adopting an electric load obtained by an electric vehicle coordinated charging model; and carrying out optimization solution on the established two-stage optimization model by considering coordinated charging of the electric vehicle. According to the method, the peak-valley difference of the load can be effectively reduced, and the light abandoning amount and the carbon dioxide emission amount of the system are effectively reduced; the configuration capacity of each device in the integrated energy system IES is reduced, and the device construction cost of the integrated energy system IES is reduced.