174 results about "User charge" patented technology

The invention relates to a new energy charging pile dynamic scheduling method, system and device and a medium, and the method comprises the steps: collecting the real-time load data of a power grid, user charging request information, battery health state data and historical charging data, and carrying out the classification and aggregation of charging behavior characteristics, and generating behavior grouping labels; demand prediction and power safety boundary calculation are carried out based on a preset power prediction model in combination with the battery health state, and a power prediction result is formed; detecting power grid load conflicts and optimizing power distribution to generate a preliminary scheduling scheme; performing multi-target power optimization adjustment on the conflict points to generate a charging power scheme, and issuing and executing the charging power scheme; and finally, dynamically updating prediction model parameters according to charging pile feedback data. According to the method, the problems of neglect of battery health change, insufficient charging behavior distinguishing, response lag, weak collaborative optimization capability and the like in the prior art are solved, and collaborative optimization of power grid stability, equipment safety and charging efficiency is realized.

The invention relates to the technical field of community new energy control, and discloses an orderly and rapid charging new energy control method, which comprises the following steps: collecting photovoltaic output data, power grid voltage / current data, user charging behavior data and battery state parameters in real time; generating a dynamic charging strategy based on the power grid load data, the time-of-use electricity price information, the photovoltaic output fluctuation range and the battery health state evaluation result; generating a corresponding local optimization strategy based on the operation state data of each charging pile; an economic index and a technical index are calculated, and when any index exceeds a preset threshold value, a strategy adjustment instruction is triggered, and a charging mode switching threshold value is updated; an equipment health index is generated by combining inverter temperature, coil coupling efficiency and insulation impedance parameters with historical fault data, and maintenance early warning is triggered when the health index is lower than a safety threshold value. According to the invention, the electricity cost of the user can be reduced on the basis of improving the multi-energy collaborative optimization capability of the charging facility in the residential area.

The invention provides an electric vehicle charging resource planning and optimizing method, and aims to solve the problems of static performance, single target optimization, one-sided attraction evaluation, disjunction with traffic jam management and the like in existing planning. The method comprises the following steps: firstly, acquiring regional basic data, and establishing a charging station composite attraction evaluation model which comprehensively considers POI value, plot cost, estimated available charging pile quantity proportion and reputation parameters determined based on historical service data, so as to predict the probability of selecting each charging station by a user; and optimizing charging pile capacity configuration according to the predicted user selection probability and the total charging demand so as to minimize the combined cost of the construction cost and the average queuing waiting time of the users. The optimal charging site is screened and recommended through weighted aggregation of multiple indexes such as composite attraction, construction cost, queuing waiting time and the like. Scientific site selection and reasonable capacity configuration of the charging stations are achieved, urban traffic congestion is effectively relieved, balanced distribution of the charging flow is promoted, and the charging experience of users is improved.

The invention relates to the technical field of electric vehicle charging, in particular to a non-inductive guidance intelligent charging system and method for electric vehicle users, and the method comprises the steps: researching the charging behavior time-varying characteristics of private vehicles and commercial vehicles and a prediction model based on a user charging rule and a decision mechanism; the selection preference, waiting time, unit time cost and price sensitivity of the user to the charging pile are analyzed, and a charging behavior guiding difficulty and evaluation model is established; combining different service charges and load data to construct a service charge-load elastic model; a multi-period pricing mechanism facing peak load shifting and considering user space-time guidance is provided; establishing a power grid interaction analysis and dynamic grouping recommendation method under multiple scenes of the power grid; and in combination with SOC and real-time position information, a response capability prediction model is constructed, and the multi-element regulation and control requirements are met. According to the invention, new requirements can be met in time, and efficient operation of the charging service is ensured.

The invention relates to the technical field of charging management, and discloses an intelligent charging method and system based on a charging pile, and the method comprises the steps: collecting an identifier of a charging object connected with the charging pile, calling the historical charging data and safety evaluation information, obtaining a user charging instruction, and combining with a charging pile performance evaluation model, performing gradient analysis on the charging instruction to generate a test plan and a subsequent plan, performing testable charging according to the test plan, collecting test data to construct a test information flow, analyzing the test information flow to form a feedback factor matrix, obtaining guide information in combination with safety evaluation information, and substituting the guide information into the subsequent plan to realize final stable charging operation. According to the method, by introducing multi-dimensional data analysis and gradient control, a personalized and dynamic charging strategy is realized, the charging efficiency and safety are improved, the service life of the battery is prolonged, the risks of overcharging, overheating and the like are reduced, and the problem that in the prior art, a charging pile lacks individual difference adjustment of a charging object is solved.

The invention relates to the technical field of intelligent power grid and new energy automobile charging control, and discloses a low-carbon economical charging pile power dynamic regulation and control method, system and device and a medium, and the method comprises the steps: obtaining user charging parameters and preference weights, combining historical travel data to predict a departure probability, and screening an adjustable and controllable time period; collecting electricity price, carbon intensity and power grid state data in real time to calculate a dynamic regulation and control coefficient; performing standardized processing on external data and fusing user weights to construct a time period scoring function; selecting high-quality time periods to form a candidate set according to score sorting; solving an optimal power distribution scheme meeting target electric quantity and power constraint in the candidate set by taking minimization of carbon emission and power cost as a target; and finally, converting the scheme into a control instruction sequence to execute dynamic power regulation and control in real time. The method is used for optimizing electricity-carbon collaboration and user experience in the charging process.

The invention discloses an electric vehicle charging management method based on an HA-SAC algorithm, and the method comprises the steps: firstly, carrying out the modeling of an electric vehicle charging management optimization model through fully considering the life loss of a transformer and the charging preference of a user; modeling a charging management optimization problem as Markov Game (MG for short), taking each electric vehicle as an independent intelligent agent, constructing a state space according to information such as the charging state, the electricity price and the user preference, and taking the charging power as an action variable; then, on the basis of an HA-SAC algorithm, a local action network and a centralized evaluation network are constructed for each agent, and centralized training is carried out through global information; and finally, the trained strategy is deployed to each electric vehicle agent node, so that the electric vehicle agent node can autonomously decide a charging behavior only by depending on local information in actual operation, and distributed and privacy-protected cooperative charging control is realized.

The invention relates to the technical field of power system optimization, and discloses a charging load estimation and charging mode optimization method based on an electric vehicle. According to the method, a source load storage-medium voltage distribution network collaborative planning framework is initialized, and charging station geographical distribution and user charging behavior modes are integrated; collecting charging load sequence data in real time, and judging a charging load control state through a volatility analysis algorithm; when the charging load control is in an unstable state, synchronously analyzing voltage fluctuation characteristics and charging power change characteristics of the power distribution network, and generating a charging efficiency evaluation value; dividing the charging effect into three levels of high efficiency, conventional and low efficiency based on the evaluation value; for a conventional grade charging effect, a time sequence decomposition technology is adopted to extract a load trend component, and an overall load stability index is calculated; and fusing the charging efficiency evaluation value and the overall load stability index, and outputting a charging power adjustment instruction. The method effectively improves the operation stability and the charging efficiency of the power distribution network.

An electric vehicle charging station recommendation method and system, applied to the related field of charging station recommendation. The method comprises: acquiring user charging features and influence degree values of user charging evaluation features on user charging preferences, wherein the user charging evaluation features are constructed on the basis of the user's evaluation information on charging stations (S1); calculating the user's comprehensive scores for different charging stations on the basis of the influence degree values, and constructing a user preference matrix by using the user's comprehensive scores for different charging stations as elements (S2); and decomposing the user preference matrix by means of a singular value decomposition method, acquiring potential matching relationships between the user and charging station features, and recommending a charging station to the user on the basis of the potential matching relationships (S3). The method solves the problem in the prior art that charging stations cannot be personalizedly recommended for different users, thereby improving user experience.

The invention discloses an artificial intelligence Emsp super charging prediction and management system, and relates to the technical field of Emsp super charging, the charging demand and the power grid load change are predicted by obtaining the charging station equipment state, the user behavior, the power grid load and the environment data, and an optimized charging task distribution plan is calculated; the charging power switching rate is smoothly adjusted by the load scheduling module, and the instantaneous load change rate is reduced; the synchronous control module is used for realizing synchronization of frequency and phase of the charging equipment and the power grid; a coordination index is monitored in real time by means of an evaluation and dynamic regulation and control module, power distribution is dynamically adjusted under the incoordination condition, and the power of a high-load station is dispersed; real-time data and historical data are integrated through the control and storage module, an artificial intelligence model and a scheduling strategy are optimized, the risk that a power grid is unstable due to instantaneous load surge and harmonic interference is effectively reduced, the operation efficiency and safety of a charging network are improved, and meanwhile the charging experience of a user is improved.

The invention provides a control method and device based on charging pile V2G, equipment and a medium. The method comprises the steps that power grid dynamic supply and demand parameters, vehicle state information and use plan information are collected in real time; constructing a multi-objective optimization model according to the dynamic supply and demand parameters of the power grid, the vehicle state information and the use plan information; setting constraint conditions in the multi-objective optimization model; wherein the constraint conditions comprise a vehicle state constraint, a time constraint, an electric quantity constraint, an equipment constraint and a power grid constraint; and solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy meeting constraint conditions, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution. According to the invention, power grid peak and valley load distribution can be obviously improved, user charging cost is reduced, the service life of a battery is prolonged, stable operation of the power distribution network is ensured, efficient response and safe controllability of vehicle-network cooperative scheduling are realized, and bidirectional requirements of the power grid and the user are considered while energy management efficiency is improved.

The invention provides a charging revenue improvement intelligent regulation and control method and system fusing user behaviors and operation data, and the method comprises the steps: carrying out the load prediction modeling of the charging time period distribution of each user group through a long-short-term memory network, and obtaining the charging revenue improvement intelligent regulation and control information of each user group according to the user group label and the time sequence data; obtaining an expected load distribution curve of each charging pile in the next 24 hours, and judging the load difference degree of the peak period and the valley period; real-time charging pile state information and user charging request data are obtained, if the predicted load distribution shows that the supply and demand mismatching degree in a certain time period exceeds a preset threshold value, a dynamic resource configuration mechanism is started, and the number of users needing load transfer and a target time period are determined; and according to the flexible user characteristics in the user group tag, a personalized off-peak charging suggestion is generated by adopting a differential incentive strategy, and an optimal charging period recommendation scheme for each user is obtained by calculating electricity price discounts and charging point rewards in different periods.

The invention relates to an intelligent charging control method and system for an electric vehicle battery and an intelligent charger, and the method comprises the steps: obtaining environment monitoring, user charging habits, mileage and other data, carrying out the compensation of missing data, estimating the health state of a storage battery through an SOH evaluation model, generating an optimized charging strategy through combining with the environment data, and executing the optimized charging strategy. The system comprises a data acquisition module, a data storage module, a missing data processing module, an analysis decision module and the like, and realizes data processing and strategy generation. And the intelligent charger receives the instruction to adjust the output power, and starts a default security policy when communication is interrupted. According to the scheme, the SOH evaluation precision is improved, the charging strategy is optimized, the reliability and safety of the system are enhanced, and the service life of the battery is prolonged.

The invention discloses a collaborative peak clipping dynamic scheduling optimization method based on a V1G and V2G hybrid electric vehicle. User charging demand parameters of an electric vehicle connected to a charging pile for charging are acquired in real time through a cloud platform, a target function of mixed integer linear programming and constraint conditions thereof are established according to the user charging demand parameters in combination with peak and valley electricity prices, and the load of the electric vehicle is predicted according to real-time electricity prices and load prediction. And solving the target function and the constraint condition thereof in the cloud platform to generate a peak clipping dynamic charging and discharging plan of the V1G and V2G vehicles, and issuing the charging and discharging plan to a charging controller of the charging pile through the cloud platform to realize cooperative charging adjustment of the V1G and V2G electric vehicles. The peak load of the transformer is effectively reduced, the service life of equipment is prolonged, and the power supply reliability is improved. The method is suitable for electric vehicle high-density scenes such as residential areas and commercial districts, and double optimization is provided for power grid peak clipping and user economy.

The invention relates to the technical field of electric vehicle charging, and discloses an electric vehicle competitive charging method and system, which assesses the charging priority of electric vehicle users based on a plurality of assessment indexes, and aims to minimize the user charging cost and maximize the user charging demand. And establishing a multi-target optimization mathematical model by taking the condition that the SOC of the battery of the electric vehicle after charging needs to meet a user expected value and the interruption frequency of the charging process cannot exceed 2 as constraint conditions, and obtaining an optimal competitive right charging queue of the electric vehicle users accessed to the charging pile by solving an optimal solution of the multi-target optimization mathematical model. The technical problems that the service life of a battery of the electric vehicle is affected due to the fact that the electric vehicle is charged disorderly, safe and stable operation of a power grid and the satisfaction degree of a user during traveling are affected, and the waiting time of the user of the electric vehicle is prolonged or charging fails due to frequent charging interruption are solved.

This invention relates to the field of power system demand response technology, and provides a user demand response method based on master-slave game theory under cost control constraints. The method includes: establishing a demand response model using master-slave game theory, where electricity retailers design time-of-use pricing strategies, and electricity users respond based on a convex utility function; the utility function includes a quantification term for electricity usage satisfaction and a calculation term for electricity costs; the electricity retailers' revenue function includes four functions: user electricity costs, long-term market electricity purchase costs, current market electricity purchase costs, and conditional risk value, coupled with the characteristics of the electricity spot market and risk management requirements; proving the existence of a Nash equilibrium solution using backward induction; and optimizing the strategy based on a slime mold optimization algorithm. This invention provides important support for electricity retailers to formulate flexible pricing mechanisms and tap demand-side resources in terms of cost control for users with high response potential and optimization of peak-valley load differences.

The invention provides a charging management method, a charging management system and electronic equipment, and the method comprises the steps: obtaining the user charging information of a to-be-charged vehicle, and determining a user behavior coefficient; collecting state information of a charging environment, and determining an environment state coefficient based on the state information; acquiring safety information of the charging facility, and determining a facility safety coefficient based on the safety information; based on the user behavior coefficient, the environment state coefficient and the real-time working parameters of the to-be-charged vehicle, the charging adaptation degree of the to-be-charged vehicle under the real-time working parameters is determined; and correcting the reference charging current based on the charging adaptation degree and the facility safety coefficient to obtain a target charging current of the to-be-charged vehicle. In the mode, the reference charging current can be dynamically corrected by integrating the charging demand of the user, the environment state and the facility safety, so that the accurate adaptation of the charging strategy to the personalized demand of the user and the real-time health state of the battery is realized on the premise of ensuring the safety of the field facility and the stability of the power grid.

The embodiment of the invention provides a battery charging and discharging optimization method and device, equipment and a storage medium, and relates to the technical field of vehicles. The method comprises the following steps: acquiring battery attribute data and charging and discharging data of a vehicle in a preset time period, and determining a target charging and discharging type of the vehicle based on the battery attribute data, the charging and discharging data and a plurality of pre-acquired charging and discharging types, wherein the plurality of pre-acquired charging and discharging types are obtained by clustering the attribute data and the charging and discharging data of the plurality of vehicles based on the charging and discharging behavior characteristics by adopting a condensation chromatography clustering algorithm. And according to the determined target charging and discharging type, obtaining a charging and discharging strategy of the vehicle and pushing the charging and discharging strategy to the vehicle end. By means of the method, the problem of adaptability of a fixed charging and discharging strategy and user charging and discharging behavior individuation in traditional battery management is effectively solved, the service life of the battery is further prolonged, and the use experience of the user is improved.

This invention discloses an AI intelligent charging control method, system, and mobile terminal. The method includes: collecting user charging data and marking records of continuous charging for more than a first duration after the battery is fully charged as valid charging records; activating an AI charging strategy when the cumulative number of valid records reaches a preset number and the current battery level is higher than a preset threshold; calculating pre-charging time, disconnection time, and recharging time based on the preset number of valid records; pausing charging when the battery level reaches a first preset level; and resuming charging to full charge when the recharging time is triggered. The system includes corresponding modules. This invention achieves intelligent pause and precise recharging by autonomously learning user charging habits, automatically fully charging the battery before the user disconnects it, effectively reducing the battery's immersion time in the high-charge range and extending battery cycle life. Simultaneously, through mechanisms such as screen brightness detection and low-temperature protection, it considers both emergency use needs and charging safety, achieving fully autonomous intelligent control without manual user intervention, thus improving the user experience.

The application relates to the technical field of electric digital processing, in particular to a pure electric vehicle replacement quantity prediction method and device. The method comprises the following steps: for each pure electric vehicle in a pure electric vehicle set, acquiring user charging behavior data, average temperature of a region where the vehicle is located and current battery parameters; determining an actual attenuation rate of the battery according to the current battery parameters; determining a remaining service life of the battery according to the actual attenuation rate and a current health degree of the battery; determining a charging index according to the user charging behavior data; determining a replacement confidence degree of each pure electric vehicle according to the charging index, the remaining service life of the battery and the average temperature of the region where the vehicle is located; and predicting a replacement quantity of the pure electric vehicles in the pure electric vehicle set according to the replacement confidence degree of each pure electric vehicle. The application scientifically and reasonably predicts the replacement quantity of the pure electric vehicles based on the user charging behavior data, the battery parameters and the temperature.

This invention relates to the field of charging station operation optimization and resource allocation technology, and particularly to a comprehensive operation management method and system for charging stations based on multi-dimensional user segmentation. The comprehensive operation management method for charging stations based on multi-dimensional user segmentation includes the following steps: S1: Acquiring user charging demand data and user value-added demand data, and constructing a complete mutual information set based on the user charging demand data and user value-added demand data; S2: Determining a first mutual information set and a second mutual information set based on the complete mutual information set, performing multi-dimensional user segmentation based on the first mutual information set and the second mutual information set, and constructing a charging station operation dimension combination library. This invention integrates user charging and value-added demand data, constructs a complete mutual information set for multi-dimensional segmentation, introduces directional demand conversion indicators, and dynamically matches strategies to achieve adaptive adjustment of resource allocation and continuous optimization of the strategy library through a closed loop.

The present application relates to the field of new energy vehicle direct current fast charging and charging power distribution control technology, and discloses a kind of intelligent charging pile charging power distribution management method.Connection charging user number is numbered, the remaining amount of charge needed is obtained according to the difference between target electric quantity and current electric quantity and a bidding factor is generated, the number of power modules and charging guns, maximum output power and connection relationship are initialized, each user charging power and module number are proportionally distributed according to the bidding factor sorting and combined with the total power available to the system, so that the power distribution takes into account the urgency, fairness and efficiency;And through temperature model and connection detection, module over-temperature peeling and smooth hot plug are realized, and single-gun and system power upper limit constraint and power scaling are realized, then according to the number of users and bidding, a unified settlement price is determined, the actual charging quantity is charged and the user and module state is updated, the full user is removed, the state information is transmitted, the cycle settlement closed loop and the next cycle safe continuous scheduling are realized.

The application discloses a user charging and changing behavior multi-dimensional feature space-time quantification method considering cross-domain factors, relies on user travel law, charging and changing service level, power grid price level, traffic network data, and meteorological factors of the place, and objectively quantifies the charging behavior of the user. In the method, not only the influence of the user charging behavior on the surrounding road traffic is proposed, the blank in the field is made up, but also the influence of the charging behavior on the area is fully considered, the traditional single evaluation dimension is transcended, the basic ability construction of vehicle-road-network coordination optimization can be better supported, the scientificity and precision of charging and changing infrastructure planning are improved, the user behavior modeling theory is enriched, and cross-domain technology integration is promoted.

This application relates to a method, apparatus, vehicle, and storage medium for assessing the driving life of a vehicle battery. The method includes: acquiring the current charging mode, current temperature, and current energy throughput of the battery to be assessed; inputting the current charging mode, current temperature, and current energy throughput into a pre-built driving aging model to obtain the capacity degradation rate of the battery to be assessed, wherein the pre-built driving aging model is trained using the charging mode, charging temperature, and energy throughput of multiple target vehicles distributed in multiple target regions; and assessing the driving life of the battery to be assessed based on the capacity degradation rate to obtain the assessment result of the driving life of the battery to be assessed. This solves the problems of limited empirical models for assessing battery driving life in related technologies, which are often singular and do not consider the impact of user charging habits on battery life, leading to low accuracy in battery driving life assessment, and improves the accuracy of battery driving life prediction.

This invention discloses a real-time power scheduling control method for charging chips, relating to the field of power scheduling technology for new energy vehicle charging piles. The method includes the following steps: Step S1, real-time acquisition of the operating parameters of the charging equipment, including the voltage fluctuation value of the input grid, the output current of the charging interface, and the temperature of the charging chip. This invention utilizes real-time collected multi-source data on grid voltage fluctuations, battery SOC / temperature, and chip operating conditions, combined with a multi-objective optimization model with dynamic weight allocation. This balances charging efficiency and grid load while minimizing battery life loss. A priority scheduling strategy is adopted to consider differences in user charging modes and battery states. Millisecond-level fine power control is achieved through the coordinated adjustment of PWM duty cycle and switching frequency, satisfying personalized charging needs while improving overall energy efficiency. A dual safety defense is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, intelligently reconfiguring the power allocation scheme in case of grid anomalies or battery overheating.

The invention provides a W-algorithm-based mobile phone real-time charging optimization method and system in a dynamic environment, and relates to the technical field of mobile phone charging optimization, and the method comprises the steps: building a unique identifier association between a to-be-charged device and an available charging resource; acquiring an equipment use state, subsequent schedule information, an equipment association relationship and a dynamic environment variable; calculating a comprehensive priority score based on a W algorithm; outputting resource scheduling decisions for different resource scenes; continuously tracking equipment parameters, electric quantity and resource state changes; and optimizing the basic weight of the priority evaluation index. According to the invention, by combining the W algorithm, the priority evaluation and the scenarized scheduling strategy are integrated, so that the accurate adaptation of the charging resources is realized; based on a dynamic recalculation mechanism of triggering conditions such as electric quantity change, schedule adjustment and resource abnormity, and continuous iterative optimization of weight factors, the real-time performance, the adaptability and the resource utilization rate of charging scheduling in a dynamic environment are improved, the equipment waiting time is effectively shortened, and the charging experience of a user is improved.

The application provides a charging pile remote regulation and control method and system based on a cloud platform, and belongs to the technical field of new energy infrastructure and smart grid technology.The method comprises the following steps: collecting historical charging data of charging pile users and historical operation data of power grids in the regions where the users are located, generating user charging original data sets and power grid original operation data sets; and using a data mining algorithm to construct a user behavior and power grid state double-dimensional model and generate double-dimensional model data; through the construction of the user behavior and power grid state double-dimensional model, the user charging habits and the real-time operation characteristics of the power grid can be accurately captured, detailed and comprehensive data support is provided for the regulation and control of the charging pile, the problem of inaccurate regulation and control caused by single data dimension in the traditional mode is effectively avoided, and the scientificity and rationality of the charging regulation and control decision are improved.

The invention discloses an electric vehicle charging load optimization method and system based on an interactive energy mechanism, and the method comprises the steps: collecting the charging demands and response parameters of a user, building a nonlinear optimization model with the maximization of the comprehensive revenue of a service provider as a target, carrying out the linearization of the model through reconstructing a normalized multi-parameter decomposition method, and carrying out the solving, and controlling the power output of the charging pile according to the result, and monitoring the power grid and vehicle state dynamic optimization in real time. The constraint condition covers the charging power, the state of charge, the power balance and the like. The interactive energy mechanism establishes a linear relation between the power reduction amount and the market clearing price through the maximum response compensation price parameter submitted by the user, and charging flexibility adjustment is achieved. The system comprises a data acquisition module, a model establishment module, a conversion module, a solving module and a control feedback module. The method improves the distribution uniformity of the power grid load, reduces the calculation complexity, balances the benefits of service providers and users, and is suitable for an electric vehicle charging load optimization scene.

The application provides an orderly charging method, device and equipment of an electric vehicle and a storage medium. It relates to the technical field of big data. The method comprises the following steps: taking multiple charging powers of the electric vehicle at different time points as particles of an initial population of an NSGA2 algorithm, and substituting the particles into a target optimization function of the lowest charging cost and a target optimization function of user charging satisfaction; obtaining an external non-dominated solution set by calculation; evaluating the evolution state of the NSGA2 algorithm according to the probability value of the external non-dominated solution set, including a convergence state and a diversity state; improving the diversity by population conversion if the algorithm is in the diversity state; obtaining a particle with the minimum potential energy value and calculating a global optimal value; selecting a compromise solution as a target charging power in the global optimal value; and performing orderly charging of the electric vehicle according to the target charging power. The method reduces the charging cost of the user and improves the charging satisfaction.

The invention discloses a charging load regulation and control method, platform, system and device for a charging station. The method comprises the steps of obtaining power data of a charging gun in a charging state in the charging station; according to the power data of the charging gun and the SOC value of the battery in the charging state, predicting and calculating to obtain a charging power parameter value in a preset regulation and control interval; and regulating and controlling the current charging load of the charging station according to the regulation and control plan parameter value and the charging power parameter value issued by the power grid dispatching system. According to the invention, the charging power parameter value of the charging gun is predicted, and the power limit value of the charging gun is calculated, so that the load of the charging station is accurately regulated and controlled, and the charging expectation of a user and the operation limit of a power grid are met. The technical effects of realizing non-inductive response and improving the participation heat of the charging operator in the power grid demand response are achieved.