Ordered charging regulation and control method of direct current charging pile and related device

Abstract

The invention discloses an ordered charging regulation and control method of a direct current charging pile and a related device, and relates to the technical field of charging control, and the method comprises the steps: carrying out the charging time prediction based on the obtained vehicle condition data and charging demand data; carrying out charging priority analysis on the to-be-charged vehicle based on the vehicle condition data and the expected charging time; performing time-space characteristic analysis on the charging load based on the expected charging time and the charging priority sequence; performing power grid load condition prediction based on real-time power grid monitoring data in combination with expected charging time and time-space characteristic analysis data; constructing a power grid dispatching control strategy based on the power grid load condition prediction data and the charging priority sequence to generate an ordered charging strategy; and based on the ordered charging strategy, the charging module is controlled to carry out parameter dynamic adjustment in the ordered charging process of each to-be-charged vehicle. According to the invention, accurate energy management is realized while the vehicle charging task is ensured to be completed, and the power grid load is optimized.

Description

Technical Field

[0001] The present invention relates to the field of charging control technology, and in particular to an orderly charging control method for a DC charging pile and a related device. Background Art

[0002] At present, my country's new energy industry is in a stage of rapid development. The new energy vehicles derived from this, which use electricity as a driving energy source, have gradually become the most popular type of vehicles. The use of charging services has also increased year by year, and more and more DC charging piles are used to transmit electricity to new energy vehicles. A large number of DC charging piles are connected to the power grid, which will have a strong impact on the power grid. Conventional disorderly charging management can no longer meet the charging needs of vehicles. Therefore, it is extremely necessary to transform disorderly charging into orderly charging. In the orderly charging of vehicles with DC charging piles, the prediction of vehicle charging time is a very important part. At present, it is usually predicted by looking up the current in the table, but the prediction accuracy of this method is insufficient, resulting in the inability to regulate the orderly charging of vehicles well. At the same time, in the existing orderly charging regulation, there is a lack of analysis of the spatiotemporal characteristics of the charging load and the charging priority analysis, which makes it impossible to accurately predict the load of the power grid, so that the orderly charging process of the vehicle will increase the peak load of the power grid, reduce the power supply efficiency, and affect the safety and stability of the distribution network, resulting in the failure to achieve more effective orderly charging regulation. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a method and related devices for orderly charging control of a DC charging pile, which can achieve precise energy management while ensuring the completion of vehicle charging tasks, optimize power grid load, and achieve peak shaving and valley filling, so that the orderly charging control of the DC charging pile can achieve a more ideal effect.

[0004] In order to solve the above technical problems, the present invention provides an orderly charging control method for a DC charging pile, which is applied to a main control module and a charging module of a DC charging pile, wherein the main control module is connected to the charging module; the method comprises:

[0005] The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0006] Performing a charging priority analysis on the vehicles to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence;

[0007] Performing a spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain spatiotemporal characteristic analysis data;

[0008] Based on the real-time grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data, grid load condition prediction is performed to obtain grid load condition prediction data;

[0009] Constructing a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatching control strategy;

[0010] Based on the orderly charging strategy, the charging module is controlled to dynamically adjust parameters during the orderly charging process of each vehicle to be charged.

[0011] Optionally, the performing charging time prediction based on the vehicle status data and the charging demand data to obtain the expected charging time corresponding to each vehicle to be charged includes:

[0012] Calculate the charging preheating time and charging duration reference value of the battery of each to-be-charged vehicle based on the vehicle condition data and the charging demand data;

[0013] Acquire historical charging data of a DC charging pile, and complete the historical charging data to obtain completed historical charging data;

[0014] Clustering is performed based on the completed historical charging data to obtain a clustering result, and curve fitting is performed based on the clustering result using a least squares method to obtain a charging fitting curve;

[0015] A correction coefficient corresponding to the charging duration reference value is matched based on the charging fitting curve, and an expected charging time corresponding to each to-be-charged vehicle is determined based on the correction coefficient, the charging preheating time and the charging duration reference value.

[0016] Optionally, performing charging priority analysis on the vehicles to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence includes:

[0017] Determine a power impact factor of the DC charging pile based on the vehicle condition data and the expected charging time;

[0018] Perform power space analysis based on the vehicle status data and the expected charging time to obtain power space data for charging each vehicle to be charged;

[0019] Performing joint clustering based on the power space data, vehicle condition data and expected charging time to obtain a feature data set;

[0020] Based on the feature data set, a feature contribution matrix is ​​used to quantify the contribution of the charging demand priority to obtain a target feature contribution;

[0021] Based on the target characteristic contribution and the power impact factor, a charging priority analysis of the vehicles to be charged is performed using a preset total capacity constraint and a preset power allocation constraint to obtain a charging priority sequence.

[0022] Optionally, the performing the spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain the spatiotemporal characteristic analysis data includes:

[0023] Obtaining the vehicle type of each vehicle to be charged, and determining a charging time constraint and a charging space constraint based on the vehicle type using an expected charging time and a charging priority sequence;

[0024] Based on the completed historical charging data, time series regression coefficient analysis and space regression coefficient analysis are performed to obtain the target time series regression coefficient and target space regression coefficient;

[0025] The charging load distribution of the DC charging pile is obtained, and the spatiotemporal characteristic analysis of the charging load is performed using a spatiotemporal joint algorithm model based on the charging time constraint condition, the charging space constraint condition, the target time series regression coefficient and the target space regression coefficient in combination with the charging load distribution to obtain spatiotemporal characteristic analysis data.

[0026] Optionally, the predicting of the load condition of the power grid based on the real-time power grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data to obtain the power grid load prediction data includes:

[0027] Performing power grid load fluctuation analysis based on the spatiotemporal characteristic analysis data to obtain power grid load fluctuation data;

[0028] The power grid load fluctuation data and the real-time power grid monitoring data are input into a power grid load prediction model to perform power grid load condition prediction and obtain power grid load condition prediction data.

[0029] Optionally, constructing a power grid dispatching control strategy based on the power grid load condition prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatching control strategy, includes:

[0030] Constructing corresponding control constraints based on the grid load prediction data and the charging priority sequence;

[0031] Acquire historical power grid dispatch control strategy data, and generate corresponding learning strategy data using a reinforcement learning algorithm based on the power grid load prediction data and the charging priority sequence;

[0032] Based on the learning strategy data, the historical power grid dispatching control strategy data is updated to obtain updated power grid dispatching control strategy data;

[0033] Determining a power grid dispatch control strategy using control constraints based on the updated power grid dispatch control strategy data;

[0034] Generate a charging sequence combination based on the power grid dispatch control strategy and the charging priority sequence, and construct an initial charging strategy based on the charging sequence combination;

[0035] The initial charging strategy is simulated and analyzed to obtain a simulation analysis result, and the initial charging strategy is adjusted based on the simulation analysis result to obtain an orderly charging strategy.

[0036] Optionally, the controlling the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy includes:

[0037] Based on the orderly charging strategy, the charging module is controlled to adaptively and dynamically adjust the charging power and charging time during the orderly charging process of each vehicle to be charged.

[0038] In addition, the present invention provides an orderly charging control device for a DC charging pile, which is applied to a main control module and a charging module of a DC charging pile, wherein the main control module is connected to the charging module; the device comprises:

[0039] Charging time prediction module: used for the main control module to obtain real-time power grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and to predict the charging time based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0040] Charging priority analysis module: used to perform charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence;

[0041] A spatiotemporal characteristic analysis module: used to perform spatiotemporal characteristic analysis of charging load based on the expected charging time and charging priority sequence, and obtain spatiotemporal characteristic analysis data;

[0042] A power grid load condition prediction module: used to predict the power grid load condition based on the real-time power grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data, and obtain power grid load condition prediction data;

[0043] An orderly charging strategy building module is used to build a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generate an orderly charging strategy based on the power grid dispatching control strategy;

[0044] Orderly charging process control module: used to control the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

[0045] In addition, the present invention provides an orderly charging control system for a DC charging pile, the system comprising a main control module and a charging module of the DC charging pile, the main control module being connected to the charging module, and the system being configured to execute the above-mentioned orderly charging control method for the DC charging pile.

[0046] In addition, the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned orderly charging control method of a DC charging pile.

[0047] In an embodiment of the present invention, the expected charging time corresponding to each vehicle to be charged is determined based on the correction coefficient and the charging preheating time and charging duration reference value generated by the vehicle status data and the charging demand data, which can effectively improve the prediction accuracy of the charging time. The charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time using the characteristic contribution analysis can enhance the dynamic management capability of the charging resources and improve the resource utilization efficiency. The spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence can provide sufficiently accurate and comprehensive data support for the subsequent grid load situation prediction through the spatiotemporal characteristic analysis. The grid load situation prediction based on the real-time grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data can improve the accuracy of the grid load situation prediction and avoid the large deviation between the obtained grid load situation prediction data and the actual situation. The grid dispatching control strategy is constructed based on the grid load situation prediction data and the charging priority sequence to generate an orderly charging strategy, which can control the charging module to dynamically adjust the parameters in the orderly charging process of each vehicle to be charged, and realize accurate energy management while ensuring the completion of the vehicle charging task, optimize the grid load, and realize peak shaving and valley filling, so that the orderly charging regulation of the DC charging pile can achieve a more ideal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 is a flow chart of an orderly charging control method for a DC charging pile in an embodiment of the present invention;

[0050] Figure 2 is a flow chart of an orderly charging control method for a DC charging pile in another embodiment of the present invention;

[0051] Figure 3 Schematic diagram of the structure of the orderly charging control system of the DC charging pile in the embodiment of the present invention;

[0052] Figure 4 It is a schematic diagram of the structural composition of the orderly charging control device of the DC charging pile in the embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1

[0055] See also Figure 1 , Figure 1 : is a flow chart of an orderly charging control method for a DC charging pile in an embodiment of the present invention, the method is applied to a main control module and a charging module of a DC charging pile, the main control module is connected to the charging module; the method comprises:

[0056] S11: The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0057] In the specific implementation process of the present invention, the charging time prediction is performed based on the vehicle status data and the charging demand data to obtain the expected charging time corresponding to each vehicle to be charged, including: calculating the charging preheating time and the charging duration reference value of the battery of each vehicle to be charged based on the vehicle status data and the charging demand data; obtaining the historical charging data of the DC charging pile, and completing the historical charging data to obtain the completed historical charging data; clustering is performed based on the completed historical charging data to obtain the clustering results, and curve fitting is performed using the least squares method based on the clustering results to obtain a charging fitting curve; matching the correction coefficient corresponding to the charging duration reference value based on the charging fitting curve, and determining the expected charging time corresponding to each vehicle to be charged based on the correction coefficient, the charging preheating time and the charging duration reference value.

[0058] Specifically, the main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged. The real-time grid monitoring data includes the real-time current, real-time voltage and real-time load of the grid, etc. The vehicle status data includes the remaining battery power, battery capacity, temperature, power demand and state of charge of the vehicle, etc. The charging demand data includes the expected stay time and the expected battery state of charge value after charging, etc. Based on the vehicle status data and charging demand data, the charging preheating time and charging duration reference value of the battery of each vehicle to be charged are calculated, the battery temperature data in the vehicle status data is extracted, the preheating rate of the battery under low-temperature charging conditions is obtained, the charging preheating time is calculated according to the preheating rate using the battery temperature data combined with the preheating target temperature, the battery remaining capacity and state of charge in the vehicle status data are extracted, and the charging duration reference value is calculated according to the battery remaining capacity and state of charge. Acquire historical charging data of a DC charging pile, the historical charging data including charging time and corresponding charging percentage of various types of electric vehicles, etc., complete the historical charging data, perform correlation analysis on the charging time and charging percentage through the Pearson coefficient, obtain a target Pearson correlation coefficient, complete the historical charging data according to the target Pearson correlation coefficient, and obtain the completed historical charging data. Clustering is performed based on the completed historical charging data, and several historical charging curves are drawn according to the completed historical charging data, the initial slope of each historical charging curve is extracted, and the initial slope of a preset number of historical charging curves is randomly selected, and the initial slope is used as the initial centroid, and the distance between the initial slope and the initial centroid of each historical charging curve is calculated respectively, and the corresponding historical charging curves are clustered into a class with the smallest distance to form several clusters, and the centroids of several clusters are recalculated and used as new clustering standards, and the distances between each historical charging curve and the centroid of the cluster are recalculated to cluster again, and the above process is repeated until the centroids calculated twice before and after do not change, and the calculation is stopped to obtain a clustering result, and curve fitting is performed using the least squares method based on the clustering result to obtain a charging fitting curve. Based on the correction coefficient corresponding to the charging duration reference value matched with the charging fitting curve, a corresponding target charging fitting curve is determined from a plurality of charging fitting curves according to the charging duration reference value, a correction coefficient is determined according to the target charging fitting curve, and an expected charging time corresponding to each vehicle to be charged is determined based on the correction coefficient, the charging preheating time and the charging duration reference value, thereby realizing accurate prediction of the charging time of the electric vehicle.

[0059] S12: performing charging priority analysis on the vehicles to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence;

[0060] In the specific implementation process of the present invention, the charging priority analysis of the vehicles to be charged is performed based on the vehicle condition data and the expected charging time to obtain a charging priority sequence, including: determining the power influence factor of the DC charging pile based on the vehicle condition data and the expected charging time; performing power space analysis based on the vehicle condition data and the expected charging time to obtain power space data for charging each vehicle to be charged; performing joint clustering based on the power space data, vehicle condition data and expected charging time to obtain a feature data set; based on the feature data set, using a feature contribution matrix to quantify the contribution of the charging demand priority to obtain a target feature contribution; based on the target feature contribution and the power influence factor, using a preset total capacity constraint and a preset power allocation constraint to perform charging priority analysis on the vehicles to be charged to obtain a charging priority sequence.

[0061] Specifically, the power impact factor of the DC charging pile is determined based on the vehicle status data and the expected charging time, and the corresponding power impact factor is matched in the database according to the remaining battery capacity and the expected charging time in the vehicle status data, and the power impact factor is the charging power impact value. Power space analysis is performed based on the vehicle status data and the expected charging time, and the power space reserved for ensuring the stability of the power grid system is used to determine the power space data that can be allocated to each vehicle to be charged according to the power demand in the vehicle status data and the expected charging time, that is, the power space data for charging each vehicle to be charged is obtained. Joint clustering is performed based on the power space data, vehicle status data and expected charging time, and the K-nearest neighbor algorithm is used for joint clustering, and the relevant data is assigned to the corresponding cluster to form a feature category, such as high power space-long charging time, that is, a feature data set is obtained. Based on the feature data set, the contribution of the charging demand priority is quantified using the feature contribution matrix, key features are extracted from the feature data set, and the feature weights of the key features are weighted and aggregated based on the feature weight aggregation model to obtain the target feature weight, and the feature contribution matrix is ​​constructed according to the target feature weight. The contribution of the charging demand priority is quantified according to the feature contribution matrix to obtain the target feature contribution. Based on the target feature contribution and the power impact factor, the charging priority analysis of the vehicles to be charged is performed using the preset total capacity constraint and the preset power allocation constraint. The preset total capacity constraint includes that the sum of the charging powers of the DC charging piles used shall not exceed the preset capacity, and the preset power allocation constraint allocates higher power to high-priority vehicles. According to the priority decision model, the charging priority analysis of the vehicles to be charged is performed using the preset total capacity constraint and the preset power allocation constraint in combination with the target feature contribution and the power impact factor to obtain a charging priority sequence. The analysis of the charging priority can enhance the dynamic management capability of the charging resources, ensure the efficient use of power resources and the orderly progress of the charging process.

[0062] S13: performing a spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain spatiotemporal characteristic analysis data;

[0063] In the specific implementation process of the present invention, the spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence is performed to obtain the spatiotemporal characteristic analysis data, including: obtaining the vehicle type of each vehicle to be charged, and determining the charging time constraint and the charging space constraint based on the vehicle type using the expected charging time and the charging priority sequence; performing a time series regression coefficient analysis and a space regression coefficient analysis based on the completed historical charging data to obtain the target time series regression coefficient and the target space regression coefficient; obtaining the charging load distribution of the DC charging pile, and performing the spatiotemporal characteristic analysis of the charging load based on the charging time constraint, the charging space constraint, the target time series regression coefficient and the target space regression coefficient in combination with the charging load distribution using a spatiotemporal joint algorithm model to obtain the spatiotemporal characteristic analysis data.

[0064] Specifically, the vehicle type of each vehicle to be charged is obtained, and the vehicle types include electric private cars, electric taxis, and official cars, etc., and the charging time constraints and charging space constraints are determined based on the vehicle type using the expected charging time and the charging priority sequence, and the longest charging delay time is determined according to the vehicle type and vehicle status data, and the charging time constraints are determined according to the longest charging delay time and the expected charging time, and the power loss of the charging pile transfer is determined according to the vehicle type and vehicle status data, and the charging space constraints are determined according to the power loss and the charging priority sequence. Based on the completed historical charging data, time series regression coefficient analysis and spatial regression coefficient analysis are performed, and charging feature vectors are extracted from the completed historical charging data. Time series impact analysis is performed based on the charging feature vectors to obtain time series impact analysis data, and time series regression coefficient analysis is performed based on the time series impact analysis data using the time series analysis algorithm model to obtain the target time series regression coefficient, and the spatial impact vector is extracted based on the completed historical charging data. The spatial regression coefficient analysis is performed based on the spatial impact vector combined with the preset error term and the preset spatial impact factor to obtain the target spatial regression coefficient. The charging load distribution of the DC charging pile is obtained, that is, the time scale distribution of the charging load. Based on the charging time constraint, charging space constraint, target time series regression coefficient and target space regression coefficient, the time and space joint algorithm model is used to analyze the time and space characteristics of the charging load in combination with the charging load distribution. When analyzing the time and space characteristics of the charging load, an error term is added to the time and space joint algorithm model to represent the random error or noise that the model fails to capture when analyzing the time and space characteristics. It can include the influence of factors in the data that are not considered by the model, or the random fluctuations of the data itself, and obtain the time and space characteristics analysis data. The time and space characteristics analysis is to analyze the time and space requirements of charging.

[0065] S14: Predicting the load condition of a power grid based on the real-time power grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data to obtain power grid load prediction data;

[0066] In the specific implementation process of the present invention, the grid load condition is predicted based on the real-time grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data to obtain grid load condition prediction data, including: performing grid load fluctuation analysis based on the spatiotemporal characteristic analysis data to obtain grid load fluctuation data; inputting the grid load fluctuation data and the real-time grid monitoring data into the grid load prediction model to perform grid load condition prediction to obtain grid load condition prediction data.

[0067] Specifically, the grid load fluctuation analysis is performed based on the spatiotemporal characteristic analysis data, the grid load sequence analysis is performed based on the charging load analysis model using the spatiotemporal characteristic analysis data to obtain the load sequence data, the bidirectional time series characteristics of the spatiotemporal characteristic analysis data are determined based on the load sequence data, the weights of the data characteristics of different time steps in the bidirectional time series characteristics are determined based on the preset attention mechanism, the charging load characteristic analysis is performed based on the charging load prediction model using the weights of the data characteristics of different time steps in the bidirectional time series characteristics to obtain the charging load characteristic analysis data, the grid load fluctuation analysis is performed based on the charging load characteristic analysis data combined with the peak time analysis and the trough time analysis to obtain the grid load fluctuation data. The grid load fluctuation data and the real-time grid monitoring data are input into the grid load prediction model to predict the grid load situation, and the grid load prediction model is a convergence model obtained by inputting the sample data set into the deep neural network model for training, and the grid load situation prediction data is obtained.

[0068] S15: constructing a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatching control strategy;

[0069] In the specific implementation process of the present invention, the grid dispatching control strategy is constructed based on the grid load condition prediction data and the charging priority sequence, and the orderly charging strategy is generated based on the grid dispatching control strategy, including: constructing corresponding control constraints based on the grid load condition prediction data and the charging priority sequence; obtaining historical grid dispatching control strategy data, and generating corresponding learning strategy data based on the grid load condition prediction data and the charging priority sequence using a reinforcement learning algorithm; updating the historical grid dispatching control strategy data based on the learning strategy data to obtain updated grid dispatching control strategy data; determining the grid dispatching control strategy based on the updated grid dispatching control strategy data using control constraints; generating a charging series combination based on the grid dispatching control strategy and the charging priority sequence, and constructing an initial charging strategy based on the charging series combination; performing simulation analysis on the initial charging strategy to obtain simulation analysis results, and adjusting the initial charging strategy based on the simulation analysis results to obtain an orderly charging strategy.

[0070] Specifically, corresponding control constraints are constructed based on the grid load prediction data and the charging priority sequence. The grid load condition and the charging priority sequence affect the grid power dispatch. Therefore, corresponding control constraints need to be constructed based on the grid load prediction data and the charging priority sequence. The historical grid dispatch control strategy data is obtained, and the corresponding learning strategy data is generated based on the grid load prediction data and the charging priority sequence using the reinforcement learning algorithm. The reinforcement learning algorithm uses the generated data to modify its own strategy data, and the corresponding value function is selected according to the grid load prediction data and the charging priority sequence. The approximate value function is calculated based on the corresponding value function using the Bellman equation, and the approximate value function is iteratively updated. Data interaction is performed based on the iteratively updated approximate value function to obtain the corresponding learning strategy data. The historical grid dispatch control strategy data is updated based on the learning strategy data, that is, the updated grid dispatch control strategy data is obtained by interacting with the historical grid dispatch control strategy data through the learning strategy data. Based on the updated power grid dispatch control strategy data, the power grid dispatch control strategy is determined by using the control constraint conditions, and the iteration of the updated power grid dispatch control strategy data is performed in a simulation environment with the control constraint conditions introduced until the preset number of iterations is reached, wherein the scheme execution data is obtained after each iteration, and the scheme execution data is added to the next iteration to obtain the power grid dispatch control strategy. A charging sequence combination is generated based on the power grid dispatch control strategy and the charging priority sequence, and a time-increasing charging sequence of the power demand response is generated according to the power grid dispatch control strategy and the charging priority sequence, and an initial charging strategy is constructed based on the charging sequence combination, and the charging sequence combination and the power grid dispatch control strategy are combined to form an initial charging strategy. The initial charging strategy is simulated and analyzed to obtain a simulation analysis result. The initial charging strategy is adjusted based on the simulation analysis result, and the initial charging strategy is deeply fed back and analyzed using a deep learning algorithm according to the simulation analysis result to obtain deep feedback analysis data, and the improvement point data of the initial charging strategy is determined according to the deep feedback analysis data, and the initial charging strategy is adjusted according to the improvement point data to obtain an orderly charging strategy.

[0071] S16: Based on the orderly charging strategy, the charging module is controlled to dynamically adjust parameters during the orderly charging process of each vehicle to be charged.

[0072] In the specific implementation process of the present invention, the charging module is controlled based on the orderly charging strategy to dynamically adjust parameters during the orderly charging process of each vehicle to be charged, including: based on the orderly charging strategy, the charging module is controlled to adaptively and dynamically adjust the charging power and charging time during the orderly charging process of each vehicle to be charged.

[0073] Specifically, based on the orderly charging strategy, the charging module is controlled to adaptively and dynamically adjust the charging power and charging time during the orderly charging process of each vehicle to be charged, thereby ensuring the completion of the vehicle charging task while achieving precise energy management, optimizing the grid load, and achieving peak shaving and valley filling, so that the orderly charging regulation of the DC charging pile can achieve a more ideal effect.

[0074] In an embodiment of the present invention, the expected charging time corresponding to each vehicle to be charged is determined based on the correction coefficient and the charging preheating time and charging duration reference value generated by the vehicle status data and the charging demand data, which can effectively improve the prediction accuracy of the charging time. The charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time using the characteristic contribution analysis can enhance the dynamic management capability of the charging resources and improve the resource utilization efficiency. The spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence can provide sufficiently accurate and comprehensive data support for the subsequent grid load situation prediction through the spatiotemporal characteristic analysis. The grid load situation prediction based on the real-time grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data can improve the accuracy of the grid load situation prediction and avoid the large deviation between the obtained grid load situation prediction data and the actual situation. The grid dispatching control strategy is constructed based on the grid load situation prediction data and the charging priority sequence to generate an orderly charging strategy, which can control the charging module to dynamically adjust the parameters in the orderly charging process of each vehicle to be charged, and realize accurate energy management while ensuring the completion of the vehicle charging task, optimize the grid load, and realize peak shaving and valley filling, so that the orderly charging regulation of the DC charging pile can achieve a more ideal effect.

[0075] Embodiment 2

[0076] See also Figure 2 , Figure 2 : is a flow chart of an orderly charging control method of a DC charging pile in another embodiment of the present invention, the method is applied to a main control module and a charging module of a DC charging pile, the main control module is connected to the charging module; the method comprises:

[0077] S201: The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0078] S202: Analyzing the charging priority of the vehicle to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence;

[0079] S203: Acquire the vehicle type of each vehicle to be charged, and determine the charging time constraint and the charging space constraint based on the vehicle type using the expected charging time and the charging priority sequence;

[0080] S204: performing time series regression coefficient analysis and space regression coefficient analysis based on the completed historical charging data to obtain a target time series regression coefficient and a target space regression coefficient;

[0081] S205: Obtain charging load distribution of the DC charging pile, and perform spatiotemporal characteristic analysis of the charging load using a spatiotemporal joint algorithm model based on the charging time constraint, the charging space constraint, the target time series regression coefficient, and the target space regression coefficient in combination with the charging load distribution to obtain spatiotemporal characteristic analysis data;

[0082] S206: Predicting the load condition of a power grid based on the real-time power grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data to obtain power grid load prediction data;

[0083] S207: constructing a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatching control strategy;

[0084] S208: Controlling the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

[0085] In an embodiment of the present invention, the expected charging time corresponding to each vehicle to be charged is determined based on the correction coefficient and the charging preheating time and charging duration reference value generated by the vehicle status data and the charging demand data, which can effectively improve the prediction accuracy of the charging time. The charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time using the characteristic contribution analysis can enhance the dynamic management capability of the charging resources and improve the resource utilization efficiency. The spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence can provide sufficiently accurate and comprehensive data support for the subsequent grid load situation prediction through the spatiotemporal characteristic analysis. The grid load situation prediction based on the real-time grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data can improve the accuracy of the grid load situation prediction and avoid the large deviation between the obtained grid load situation prediction data and the actual situation. The grid dispatching control strategy is constructed based on the grid load situation prediction data and the charging priority sequence to generate an orderly charging strategy, which can control the charging module to dynamically adjust the parameters in the orderly charging process of each vehicle to be charged, and realize accurate energy management while ensuring the completion of the vehicle charging task, optimize the grid load, and realize peak shaving and valley filling, so that the orderly charging regulation of the DC charging pile can achieve a more ideal effect.

[0086] Embodiment 3

[0087] See also Figure 3 , Figure 3 3 is a schematic diagram of the structure of the orderly charging control system of the DC charging pile in the embodiment of the present invention, the system includes a main control module 31 and a charging module 32 of the DC charging pile, the main control module 31 is connected to the charging module 32, and the system is configured to execute the orderly charging control method of the DC charging pile in the above embodiment.

[0088] In the specific implementation of the present invention, the main control module 31 has functions such as intelligent control and communication, and integrates advanced algorithms. The main control module can realize the control of the operation of the charging module and the closure of the contactor, the monitoring and adjustment of the output voltage and current, and the heat dissipation adjustment in the cabin. Based on the dual-processor design of the microcontroller + microprocessor, more complex data operations and more efficient and flexible application designs can be realized, and multiple module control modes such as full matrix, dual matrix, and ring can be supported to adapt to different application scenarios. The charging module 32 converts the input power into a stable DC power that the battery can accept, and then connects the fuse, shunt, DC contactor and charging gun to charge the electric vehicle.

[0089] at the same time, Figure 3 The orderly charging control system of the DC charging pile shown does not constitute a limitation on all components, and may include more or fewer components than shown in the figure, or combine certain components. The specific implementation method can be referred to the above embodiment, which will not be repeated here.

[0090] In an embodiment of the present invention, the expected charging time corresponding to each vehicle to be charged is determined based on the correction coefficient and the charging preheating time and charging duration reference value generated by the vehicle status data and the charging demand data, which can effectively improve the prediction accuracy of the charging time. The charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time using the characteristic contribution analysis can enhance the dynamic management capability of the charging resources and improve the resource utilization efficiency. The spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence can provide sufficiently accurate and comprehensive data support for the subsequent grid load situation prediction through the spatiotemporal characteristic analysis. The grid load situation prediction based on the real-time grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data can improve the accuracy of the grid load situation prediction and avoid the large deviation between the obtained grid load situation prediction data and the actual situation. The grid dispatching control strategy is constructed based on the grid load situation prediction data and the charging priority sequence to generate an orderly charging strategy, which can control the charging module to dynamically adjust the parameters in the orderly charging process of each vehicle to be charged, and realize accurate energy management while ensuring the completion of the vehicle charging task, optimize the grid load, and realize peak shaving and valley filling, so that the orderly charging regulation of the DC charging pile can achieve a more ideal effect.

[0091] Embodiment 4

[0092] See also Figure 4 , Figure 4 : is a structural schematic diagram of an orderly charging control device for a DC charging pile in an embodiment of the present invention, the device is applied to a main control module and a charging module of a DC charging pile, the main control module is connected to the charging module; the device comprises:

[0093] Charging time prediction module 41: used for the main control module to obtain real-time power grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and to perform charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0094] The charging priority analysis module 42 is used to perform charging priority analysis on the vehicles to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence;

[0095] The spatiotemporal characteristic analysis module 43 is used to perform spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence, and obtain spatiotemporal characteristic analysis data;

[0096] A power grid load condition prediction module 44 is used to predict the power grid load condition based on the real-time power grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data, and obtain power grid load condition prediction data;

[0097] An orderly charging strategy building module 45 is used to build a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generate an orderly charging strategy based on the power grid dispatching control strategy;

[0098] The orderly charging process control module 46 is used to control the charging module to dynamically adjust parameters in the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

[0099] In the specific implementation process of the present invention, the specific implementation methods of the device items can be referred to the above embodiments, which will not be repeated here.

[0100] In an embodiment of the present invention, the expected charging time corresponding to each vehicle to be charged is determined based on the correction coefficient and the charging preheating time and charging duration reference value generated by the vehicle status data and the charging demand data, which can effectively improve the prediction accuracy of the charging time. The charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time using the characteristic contribution analysis can enhance the dynamic management capability of the charging resources and improve the resource utilization efficiency. The spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence can provide sufficiently accurate and comprehensive data support for the subsequent grid load situation prediction through the spatiotemporal characteristic analysis. The grid load situation prediction based on the real-time grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data can improve the accuracy of the grid load situation prediction and avoid the large deviation between the obtained grid load situation prediction data and the actual situation. The grid dispatching control strategy is constructed based on the grid load situation prediction data and the charging priority sequence to generate an orderly charging strategy, which can control the charging module to dynamically adjust the parameters in the orderly charging process of each vehicle to be charged, and realize accurate energy management while ensuring the completion of the vehicle charging task, optimize the grid load, and realize peak shaving and valley filling, so that the orderly charging regulation of the DC charging pile can achieve a more ideal effect.

[0101] A computer-readable storage medium provided in an embodiment of the present invention stores a computer program on the computer-readable storage medium, and when the program is executed by a processor, the orderly charging control method of a DC charging pile in any one of the above embodiments is implemented. Wherein, the computer-readable storage medium includes but is not limited to any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. That is, the storage device includes any medium that can store or transmit information in a readable form by a device (for example, a computer, a mobile phone), which can be a read-only memory, a disk or an optical disk, etc.

[0102] In addition, the above is a detailed introduction to the orderly charging control method and related devices of a DC charging pile provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for orderly charging control of a DC charging pile, characterized in that: A main control module and a charging module applied to a DC charging pile, wherein the main control module is connected to the charging module; the method comprises: The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged; Performing a charging priority analysis on the vehicles to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence; Performing a spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain spatiotemporal characteristic analysis data; Based on the real-time grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data, grid load condition prediction is performed to obtain grid load condition prediction data; Constructing a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatching control strategy; Based on the orderly charging strategy, the charging module is controlled to dynamically adjust parameters during the orderly charging process of each vehicle to be charged.

2. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The charging time prediction based on the vehicle status data and the charging demand data to obtain the expected charging time corresponding to each vehicle to be charged includes: Calculate the charging preheating time and charging duration reference value of the battery of each to-be-charged vehicle based on the vehicle condition data and the charging demand data; Acquire historical charging data of a DC charging pile, and complete the historical charging data to obtain completed historical charging data; Clustering is performed based on the completed historical charging data to obtain a clustering result, and curve fitting is performed based on the clustering result using a least squares method to obtain a charging fitting curve; A correction coefficient corresponding to the charging duration reference value is matched based on the charging fitting curve, and an expected charging time corresponding to each to-be-charged vehicle is determined based on the correction coefficient, the charging preheating time and the charging duration reference value.

3. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The performing charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence includes: Determine a power impact factor of the DC charging pile based on the vehicle condition data and the expected charging time; Perform power space analysis based on the vehicle status data and the expected charging time to obtain power space data for charging each vehicle to be charged; Performing joint clustering based on the power space data, vehicle condition data and expected charging time to obtain a feature data set; Based on the feature data set, a feature contribution matrix is ​​used to quantify the contribution of the charging demand priority to obtain a target feature contribution; Based on the target characteristic contribution and the power impact factor, a charging priority analysis of the vehicles to be charged is performed using a preset total capacity constraint and a preset power allocation constraint to obtain a charging priority sequence.

4. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The performing of spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain spatiotemporal characteristic analysis data includes: Obtaining the vehicle type of each vehicle to be charged, and determining a charging time constraint and a charging space constraint based on the vehicle type using an expected charging time and a charging priority sequence; Based on the completed historical charging data, time series regression coefficient analysis and space regression coefficient analysis are performed to obtain the target time series regression coefficient and target space regression coefficient; The charging load distribution of the DC charging pile is obtained, and the spatiotemporal characteristic analysis of the charging load is performed using a spatiotemporal joint algorithm model based on the charging time constraint condition, the charging space constraint condition, the target time series regression coefficient and the target space regression coefficient in combination with the charging load distribution to obtain spatiotemporal characteristic analysis data.

5. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The method of performing grid load condition prediction based on the real-time grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data to obtain grid load condition prediction data includes: Performing power grid load fluctuation analysis based on the spatiotemporal characteristic analysis data to obtain power grid load fluctuation data; The power grid load fluctuation data and the real-time power grid monitoring data are input into a power grid load prediction model to perform power grid load condition prediction and obtain power grid load condition prediction data.

6. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The step of constructing a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatching control strategy, includes: Constructing corresponding control constraints based on the grid load prediction data and the charging priority sequence; Acquire historical power grid dispatch control strategy data, and generate corresponding learning strategy data using a reinforcement learning algorithm based on the power grid load prediction data and the charging priority sequence; Based on the learning strategy data, the historical power grid dispatching control strategy data is updated to obtain updated power grid dispatching control strategy data; Determining a power grid dispatch control strategy using control constraints based on the updated power grid dispatch control strategy data; Generate a charging sequence combination based on the power grid dispatch control strategy and the charging priority sequence, and construct an initial charging strategy based on the charging sequence combination; The initial charging strategy is simulated and analyzed to obtain a simulation analysis result, and the initial charging strategy is adjusted based on the simulation analysis result to obtain an orderly charging strategy.

7. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The method of controlling the charging module based on the orderly charging strategy to dynamically adjust parameters during the orderly charging process of each vehicle to be charged includes: Based on the orderly charging strategy, the charging module is controlled to adaptively and dynamically adjust the charging power and charging time during the orderly charging process of each vehicle to be charged.

8. An orderly charging control device for a DC charging pile, characterized in that: A main control module and a charging module applied to a DC charging pile, wherein the main control module is connected to the charging module; the device comprises: Charging time prediction module: used for the main control module to obtain real-time power grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and to predict the charging time based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged; Charging priority analysis module: used to perform charging priority analysis of the vehicle to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence; A spatiotemporal characteristic analysis module: used to perform spatiotemporal characteristic analysis of charging load based on the expected charging time and charging priority sequence, and obtain spatiotemporal characteristic analysis data; A power grid load condition prediction module: used to predict the power grid load condition based on the real-time power grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data, and obtain power grid load condition prediction data; An orderly charging strategy building module is used to build a power grid dispatching control strategy based on the power grid load prediction data and the charging priority sequence, and generate an orderly charging strategy based on the power grid dispatching control strategy; Orderly charging process control module: used to control the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

9. An orderly charging control system for a DC charging pile, characterized in that: The system comprises a main control module and a charging module of a DC charging pile, wherein the main control module is connected to the charging module, and the system is configured to execute the orderly charging control method of a DC charging pile according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the orderly charging control method for a DC charging pile according to any one of claims 1 to 7.

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