Multi-input electric energy self-adaptive charging scheduling method

Abstract

The invention discloses a multi-input electric energy self-adaptive charging scheduling method, which relates to the technical field of charging scheduling, and comprises the following steps of: setting a power grid scheduling management area, acquiring the position of each charging node in the power grid scheduling management area in real time, constructing a charging scheduling network, and simultaneously, based on the constructed charging scheduling network, performing charging scheduling. Collecting electric energy data in each charging node in real time, and processing the collected electric energy data; after processing is completed, the processed electric energy data are analyzed in a data analysis mode, and self-adaptive charging scheduling is carried out according to an analysis result; after the self-adaptive charging scheduling is completed, the current of each charging node is integrated and corrected in a current integration and correction mode in the charging process of the user, and meanwhile, the charging power of the user is intelligently regulated and controlled in a resonance voltage gain mode based on the integrated and corrected current, so that the adaptability of multi-input electric energy self-adaptive charging is improved.

Description

Technical Field

[0001] This invention relates to the field of charging scheduling technology, and more specifically to an adaptive charging scheduling method with multiple input electrical energy. Background Technology

[0002] There is a shortage of pilot-scale platforms for electric charging products. It is particularly urgent to build a fully functional and resource-rich pilot-scale platform. This is not only an important measure to alleviate the supply and demand contradiction and optimize resource allocation, but also a key link to promote the healthy development of the electric charging industry and achieve industrial upgrading and transformation.

[0003] Existing technologies, such as the adaptive control method for electric vehicle groups participating in grid peak shaving disclosed in invention patent application CN119010147A, include: acquiring a first electric vehicle; determining whether a first historical charging divergence is within a preset divergence threshold; if not, analyzing a first travel chain to obtain a first travel block set; obtaining a first electric vehicle cluster with a first departure block as a constraint; acquiring a second electric vehicle; predicting a first path block set and a first destination block to obtain a first predicted charging characteristic; predicting a second path block set and a second destination block to obtain a second predicted charging characteristic; and performing grid peak shaving on a predetermined grid area. This solves the technical problems of inaccurate prediction of electric vehicle charging time and location, and unsatisfactory peak shaving effect in existing electric vehicle participation in grid peak shaving control.

[0004] As can be seen from the above solutions, current electric vehicle charging control methods often only regulate the charging location and the direction of power flow in the charging area, ignoring the current and charging power control issues caused by different electric vehicle charging devices, which has certain limitations. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive charging scheduling method for multiple input electrical energy sources, which solves the problems existing in the background technology.

[0006] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an adaptive charging scheduling method for multiple input electrical energy sources, specifically including the following steps:

[0007] S1. Set up a power grid dispatch management area, collect the location of each charging node within the power grid dispatch management area in real time, and build a charging dispatch network.

[0008] S2. Based on the constructed charging scheduling network, real-time power data of each charging node is collected, and the collected power data is processed to obtain processed power data.

[0009] The electrical energy data includes: voltage data, current data, electrical energy storage data, and charging consumption data;

[0010] S3. Analyze the processed power data using data analysis methods, and perform adaptive charging scheduling based on the analysis results, including the following steps:

[0011] S31. Based on the charging consumption data in the processed power data, estimate the charging consumption data in each charging node, and construct a charging demand model based on the estimated charging consumption data.

[0012] S32. Based on the constructed charging demand model and the processed power data of each charging node, adaptive charging scheduling is performed on user charging through an intelligent scheduling algorithm.

[0013] S4. After the adaptive charging scheduling is completed, the current of each charging node is integrated and corrected through the current integration and correction method during the user charging process to obtain the integrated and corrected current.

[0014] S5. Based on the integrated and corrected current, the user's charging power is intelligently controlled through the resonant voltage gain method.

[0015] Preferably, the step of setting up a power grid dispatch management area, collecting real-time data on the locations of each charging node within the power grid dispatch management area, and constructing a charging dispatch network includes the following steps:

[0016] Define the set of charging nodes within the power grid dispatch management area: N = {n1, n2, ..., n} n};

[0017] Where n represents the number of charging nodes, n n This represents the nth charging node;

[0018] Set the location data n for each charging node n Includes: charging node number, latitude data, and longitude data. n ={nId n ,lon n ,lat n};

[0019] Among them, nId n This represents the number of the nth charging node, lon. n lat represents the longitude data of the nth charging node. n This represents the latitude data of the nth charging node;

[0020] A charging scheduling network is constructed based on the location data of each charging node, and the data is saved.

[0021] Preferably, the process of collecting real-time power data from each charging node based on the constructed charging scheduling network and processing the collected power data to obtain processed power data includes the following steps:

[0022] S21. Collect all the power data of each charging node within one year in real time and store the power data in the same table;

[0023] S22. Merge the stored electrical energy data;

[0024] Set a time threshold and determine whether the two charging consumption data belong to one charging process based on the time interval between the two charging consumption data. If the time interval is less than the time threshold, the two charging consumption data belong to the same batch of charging and the second charging consumption data needs to be merged with the first charging consumption data.

[0025] If the time interval is greater than the time threshold, the two charging consumption data do not belong to the same batch of charging, and the charging consumption data will not be merged.

[0026] S23. Set the normal operating voltage and current values ​​for each charging node, and remove any abnormal data to obtain the removed energy data.

[0027] S24. Standardize the removed electrical energy data using data standardization methods to obtain processed electrical energy data.

[0028] Preferably, the process of standardizing the excluded power data using data standardization to obtain processed power data includes the following steps:

[0029] The data standardization formula is as follows:

[0030]

[0031] Where X represents the electrical energy data before standardization. This represents standardized electrical energy data; X max X represents the maximum value in the electrical energy data. min This represents the minimum value in the electrical energy data.

[0032] Preferably, the step of estimating the charging consumption data within each charging node based on the charging consumption data in the processed power data, and constructing a charging demand model based on the estimated charging consumption data, includes the following steps:

[0033] The charging consumption data in the processed power data was fitted using the linear least squares method.

[0034] Based on the timestamps of charging consumption data, the objective function coordinates are set as: (x1, y1), (x2, y2), ..., (x...). i ,y i );

[0035] Where y1,x1 represent the first set of charging consumption data and the corresponding timestamp, y i ,x i This represents the i-th set of charging consumption data and its corresponding timestamp;

[0036] The formula for the linear relationship between two adjacent sets of coordinates is: y = kx + b;

[0037] The linear least squares fitting formula is shown below:

[0038] f = v(y) i -kx i -b) 2 ;

[0039] Where f represents the fitted charging consumption data, k represents the slope parameter, b represents the linear parameter, and y i Let x represent the charging consumption data for the i-th group. i This represents the charging consumption data for group i;

[0040] Set the fitted charging consumption data as the estimated charging consumption data;

[0041] The charging demand model is defined as: Energy storage data - Estimated charging consumption data.

[0042] Preferably, the adaptive charging scheduling for users based on the constructed charging demand model and the processed power data of each charging node, using an intelligent scheduling algorithm, includes the following steps:

[0043] The charging demand of each charging node is calculated based on the charging demand model, and the calculated charging demand is sorted in descending order.

[0044] Based on the latitude and longitude data of each charging node in the charging scheduling network, the paths between each node are rasterized. At the same time, adaptive charging scheduling is performed using the A* algorithm according to the user's coordinate position in the raster space and the charging demand of each charging node.

[0045] Adaptive charging scheduling is performed by calculating the path cost between each charging node using the A* algorithm. Each charging node represents a target node, and the path between each target node is divided into smaller target nodes.

[0046] The path cost F(η) is calculated as follows:

[0047] F(η)=ρ(η)+h(η);

[0048] Where ρ(η) represents the actual cost from the current node to the target node, and h(η) represents the estimated cost from the current node to the target node;

[0049] If there exists a path whose estimated cost is less than or equal to the actual cost from the current node to the target node, then this path is defined as the optimal path.

[0050] Each time a small target node is reached, the current target node is set as the current node, and the next small target node is set as the new target node. The process is iterated continuously to find the optimal path until the last small target node is reached.

[0051] Adaptive charging scheduling is performed on users based on the calculated optimal path.

[0052] Preferably, after the adaptive charging scheduling is completed, the current of each charging node is integrated and corrected through a current integration correction method during the user charging process to obtain the integrated and corrected current, including the following steps:

[0053] S41. Collect the periodic output of the current of each charging node and the output control quantity and status quantity of each charging node.

[0054] Under the condition of a single input power, the single output prediction is calculated by the control variable method based on the collected periodic output and the output control and state variables of each charging node.

[0055] The single output value of each charging node is linearly related to the output control quantity and state quantity of the charging node.

[0056] The periodic output of the single charging node current calculated based on the controlled variable method is shown below:

[0057]

[0058] Among them, C T Let z(k) represent the periodic interval with respect to T, where T represents time, z(k) represent the state quantity of the charging node at time k, z(k-1) represent the state quantity of the charging node at time k-1, I(k) represent the periodic output value of the current of a single charging node at time k, and u(k) represent the output control quantity of the charging node at time k.

[0059] The prediction formula for the current output of a single charging node cycle is shown below:

[0060]

[0061] in, Let a represent the predicted value of the current cycle output of a single charging node at time k+j. jThe periodic dynamic coefficient indicates that the current period is the j-th period;

[0062] S42. Predict and calculate the periodic output of the charging node under multiple input current conditions using the superposition principle;

[0063]

[0064] in, Let A represent the predicted value of the periodic output of the charging node at time k+1 under β input current conditions, and let A represent the set of periodic dynamic coefficients.

[0065] S43. The predicted values ​​of the periodic output of the charging node under multiple input current conditions are integrated and corrected through a dynamic optimization algorithm.

[0066] Preferably, the step of integrating and correcting the predicted values ​​of the charging node's periodic output under multiple input current conditions using a dynamic optimization algorithm includes the following steps:

[0067] S431. Add a correction coefficient to the periodic output of the charging node under multiple input current conditions and calculate iteratively;

[0068] The dynamic correction function for the charging node output is shown below:

[0069] J = qI(k+1) + λAΔu(k);

[0070] Where q represents the output error correction coefficient, λ represents the control quantity error correction coefficient, and J represents the dynamic correction function of the charging node output;

[0071] S432. Summarize and initialize the periodic output data of the current of each charging node. At the same time, based on the hybrid genetic algorithm, perform chromosome encoding on the periodic output data of the current of each charging node after initialization, and construct a population set.

[0072] Each chromosome is encoded to represent a set of periodic output data for the current of a charging node;

[0073] Set the population size and the maximum number of iterations d max .

[0074] S433. Based on the size of the constructed population set, the dynamic correction function for the output of the charging node is set as the fitness function;

[0075] Define the fitness function:

[0076]

[0077] Where Fit represents the fitness function, This represents the periodic output prediction value of the charging node current;

[0078] S434. Based on the calculated fitness, calculate the probability that the periodic output data of the charging node current of each group is selected as a genetic operator.

[0079] S435. Perform chromosome crossover operation on the selected genetic operator;

[0080] In the selection of genetic operators, a crossover point is randomly selected from the chromosome codes corresponding to the selected crossover points, and the chromosome code segments between the crossover points are interacted to form offspring chromosomes.

[0081] Fitness is calculated for all offspring chromosomes. If an offspring is better than its parent, the offspring chromosome is replaced by the parent chromosome; otherwise, if the parent is better, the parent is selected to continue the iterative operation.

[0082] S436. Iteratively calculate and output the integrated and corrected current;

[0083] Set an iteration threshold Y. Set the output chromosome code to remain unchanged after Y iterations or to terminate the hybrid genetic algorithm after reaching the set maximum number of iterations. Then, use the last output chromosome code as the integrated and corrected current.

[0084] Preferably, the intelligent regulation of user charging power based on the integrated and corrected current through resonant voltage gain includes the following steps:

[0085] Record the resonant periodic modes in each charging node, determine the corresponding resonant periodic modes based on the direction of the current after integration and correction, and intelligently adjust the user's charging power according to the resonant frequency of the corresponding resonant periodic mode after determining the corresponding resonant periodic modes.

[0086] The present invention also provides an adaptive charging scheduling method for multiple input electrical energy, comprising: a data acquisition module, a data processing module, a data analysis module, a current integration and correction module, and a power regulation module;

[0087] The data acquisition module is used to collect power data from each charging node within the power grid dispatch and management area;

[0088] The data processing module is used to process the collected electrical energy data to obtain processed electrical energy data.

[0089] The data analysis module is used to analyze the processed power data and perform adaptive charging scheduling based on the analysis results.

[0090] The current integration and correction module is used to integrate and correct the current during the user's charging process through a current integration and correction method to obtain the integrated and corrected current.

[0091] The power regulation module is used to intelligently regulate the user's charging power based on the integrated and corrected current through a resonant voltage gain method.

[0092] The beneficial effects of this invention are as follows:

[0093] (1) This invention sets up a power grid dispatch management area, collects the location of each charging node in the power grid dispatch management area in real time and constructs a charging dispatch network. At the same time, based on the constructed charging dispatch network, it collects the power data in each charging node in real time and processes the collected power data. After processing, it analyzes the processed power data through data analysis and performs adaptive charging dispatch based on the analysis results. After adaptive charging dispatch is completed, the current of each charging node is integrated and corrected through current integration correction during the user charging process. At the same time, based on the integrated and corrected current, the user charging power is intelligently controlled through resonant voltage gain, which improves the adaptability of adaptive charging with multiple input power.

[0094] (2) This invention improves the reliability of power data processing by collecting all power data of each charging node within one year, and merging, filtering and standardizing the collected power data.

[0095] (3) This invention fits the charging consumption data in the processed power data using the linear least squares method to determine the charging demand of each charging node. At the same time, based on the determined charging demand of each charging node, the charging demand of users is scheduled using a path algorithm, which improves the rationality of charging scheduling.

[0096] (4) This invention reduces current loss in a multi-input power environment by integrating and correcting the current of each charging node through current integration correction during the user charging process, and provides the optimal control current for each user's different charging devices, thus ensuring the safety of the charging devices.

[0097] (5) This invention determines the corresponding resonant periodic mode based on the direction of the integrated and corrected current by recording the resonant periodic modes in each charging node, and intelligently regulates the charging power based on the determined resonant periodic modes, thus ensuring the real-time nature of intelligent regulation of charging power. Attached Figure Description

[0098] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0099] Figure 1 This is a schematic diagram of the adaptive charging scheduling method for multiple input electrical energy according to the present invention. Detailed Implementation

[0100] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0101] In a specific embodiment of the present invention,

[0102] Reference Figure 1 As shown, the present invention provides an adaptive charging scheduling method for multiple input electrical energy sources, comprising:

[0103] S1. Set up a power grid dispatch management area, collect the location of each charging node within the power grid dispatch management area in real time, and build a charging dispatch network.

[0104] S2. Based on the constructed charging scheduling network, real-time power data of each charging node is collected, and the collected power data is processed to obtain processed power data.

[0105] The electrical energy data includes: voltage data, current data, electrical energy storage data, and charging consumption data;

[0106] S3. Analyze the processed power data using data analysis methods, and perform adaptive charging scheduling based on the analysis results, including the following steps:

[0107] S31. Based on the charging consumption data in the processed power data, estimate the charging consumption data in each charging node, and construct a charging demand model based on the estimated charging consumption data.

[0108] S32. Based on the constructed charging demand model and the processed power data of each charging node, adaptive charging scheduling is performed on user charging through an intelligent scheduling algorithm.

[0109] S4. After the adaptive charging scheduling is completed, the current of each charging node is integrated and corrected through the current integration and correction method during the user charging process to obtain the integrated and corrected current.

[0110] S5. Based on the integrated and corrected current, the user's charging power is intelligently controlled through the resonant voltage gain method;

[0111] Furthermore, referring to Figure 1As shown, setting up a power grid dispatch management area, collecting real-time data on the locations of each charging node within the power grid dispatch management area, and constructing a charging dispatch network includes the following steps:

[0112] Define the set of charging nodes within the power grid dispatch management area: N = {n1, n2, ..., n} n};

[0113] Where n represents the number of charging nodes, n n This represents the nth charging node;

[0114] Set the location data n for each charging node n Includes: charging node number, latitude data, and longitude data. n ={nId n ,lon n ,lat n};

[0115] Among them, nId n This represents the number of the nth charging node, lon. n lat represents the longitude data of the nth charging node. n This represents the latitude data of the nth charging node;

[0116] Furthermore, a charging scheduling network is constructed and stored based on the location data of each charging node;

[0117] Furthermore, referring to Figure 1 As shown, based on the constructed charging scheduling network, real-time power data is collected from each charging node, and the collected power data is processed to obtain the processed power data, including the following steps:

[0118] S21. Collect all the power data of each charging node within one year in real time and store the power data in the same table;

[0119] S22. Merge the stored electrical energy data;

[0120] Set a time threshold and determine whether the two charging consumption data belong to one charging process based on the time interval between the two charging consumption data. If the time interval is less than the time threshold, the two charging consumption data belong to the same batch of charging and the second charging consumption data needs to be merged with the first charging consumption data.

[0121] If the time interval is greater than the time threshold, the two charging consumption data do not belong to the same batch of charging, and the charging consumption data will not be merged.

[0122] S23. Set the normal operating voltage and current values ​​for each charging node, and remove any abnormal data to obtain the removed energy data.

[0123] S24. Standardize the removed power data using data standardization methods to obtain processed power data;

[0124] The data standardization formula is as follows:

[0125]

[0126] Where X represents the electrical energy data before standardization. This represents standardized electrical energy data; X max X represents the maximum value in the electrical energy data. min This represents the minimum value in the electrical energy data;

[0127] Furthermore, referring to Figure 1 As shown, the process of estimating charging consumption data at each charging node based on the processed power data, and constructing a charging demand model based on the estimated charging consumption data, includes the following steps:

[0128] The charging consumption data in the processed power data was fitted using the linear least squares method.

[0129] Based on the timestamps of charging consumption data, the objective function coordinates are set as: (x1, y1), (x2, y2), ..., (x...). i ,y i );

[0130] Where y1,x1 represent the first set of charging consumption data and the corresponding timestamp, y i ,x i This represents the i-th set of charging consumption data and its corresponding timestamp;

[0131] The formula for the linear relationship between two adjacent sets of coordinates is: y = kx + b;

[0132] The linear least squares fitting formula is shown below:

[0133] f = v(y) i -kx i -b) 2 ;

[0134] Where f represents the fitted charging consumption data, k represents the slope parameter, b represents the linear parameter, and y i Let x represent the charging consumption data for the i-th group. i This represents the charging consumption data for group i;

[0135] Furthermore, the fitted charging consumption data is set as the estimated charging consumption data;

[0136] Set the charging demand model as follows: Energy storage data - Estimated charging consumption data;

[0137] Furthermore, referring to Figure 1 As shown, based on the constructed charging demand model and the processed power data of each charging node, the intelligent scheduling algorithm for adaptive charging scheduling of users includes the following steps:

[0138] The charging demand of each charging node is calculated based on the charging demand model, and the calculated charging demand is sorted in descending order.

[0139] Furthermore, the paths between each node are rasterized based on the latitude and longitude data of each charging node in the constructed charging scheduling network. At the same time, adaptive charging scheduling is performed using the A* algorithm based on the user's coordinate position in the raster space and the charging demand of each charging node.

[0140] Adaptive charging scheduling is performed by calculating the path cost between each charging node using the A* algorithm. Each charging node represents a target node, and the path between each target node is divided into smaller target nodes.

[0141] The path cost F(η) is calculated as follows:

[0142] F(η)=ρ(η)+h(η);

[0143] Where ρ(η) represents the actual cost from the current node to the target node, and h(η) represents the estimated cost from the current node to the target node;

[0144] If there exists a path whose estimated cost is less than or equal to the actual cost from the current node to the target node, then this path is defined as the optimal path.

[0145] Each time a small target node is reached, the current target node is set as the current node, and the next small target node is set as the new target node. The process is iterated continuously to find the optimal path until the last small target node is reached.

[0146] Furthermore, adaptive charging scheduling is performed on user charging based on the calculated optimal path;

[0147] Furthermore, referring to Figure 1 As shown, after the adaptive charging scheduling is completed, the current of each charging node is integrated and corrected through current integration and correction during the user charging process. The integrated and corrected current includes the following steps:

[0148] S41. Collect the periodic output of the current of each charging node and the output control quantity and status quantity of each charging node.

[0149] Under the condition of a single input power, the single output prediction is calculated by the control variable method based on the collected periodic output and the output control and state variables of each charging node.

[0150] The single output value of each charging node is linearly related to the output control quantity and state quantity of the charging node.

[0151] The periodic output of the single charging node current calculated based on the controlled variable method is shown below:

[0152]

[0153] Among them, C T Let z(k) represent the periodic interval with respect to T, where T represents time, z(k) represent the state quantity of the charging node at time k, z(k-1) represent the state quantity of the charging node at time k-1, I(k) represent the periodic output value of the current of a single charging node at time k, and u(k) represent the output control quantity of the charging node at time k.

[0154] The prediction formula for the current output of a single charging node cycle is shown below:

[0155]

[0156] in, Let a represent the predicted value of the current cycle output of a single charging node at time k+j. j The periodic dynamic coefficient indicates that the current period is the j-th period;

[0157] S42. Predict and calculate the periodic output of the charging node under multiple input current conditions using the superposition principle;

[0158]

[0159] in, Let A represent the predicted value of the periodic output of the charging node at time k+1 under β input current conditions, and let A represent the set of periodic dynamic coefficients.

[0160] S43. The predicted values ​​of the periodic output of the charging node under multiple input current conditions are integrated and corrected through a dynamic optimization algorithm.

[0161] S431. Add a correction coefficient to the periodic output of the charging node under multiple input current conditions and calculate iteratively;

[0162] The dynamic correction function for the charging node output is shown below:

[0163] J = qI(k+1) + λAΔu(k);

[0164] Where q represents the output error correction coefficient, λ represents the control quantity error correction coefficient, and J represents the dynamic correction function of the charging node output;

[0165] S432. Summarize and initialize the periodic output data of the current of each charging node. At the same time, based on the hybrid genetic algorithm, perform chromosome encoding on the periodic output data of the current of each charging node after initialization, and construct a population set.

[0166] Each chromosome is encoded to represent a set of periodic output data for the current of a charging node;

[0167] Furthermore, the population size and the maximum number of iterations d are set. max .

[0168] S433. Based on the size of the constructed population set, the dynamic correction function for the output of the charging node is set as the fitness function;

[0169] Define the fitness function:

[0170]

[0171] Where Fit represents the fitness function, This represents the periodic output prediction value of the charging node current;

[0172] S434. Based on the calculated fitness, calculate the probability that the periodic output data of the charging node current of each group is selected as a genetic operator.

[0173] The probability of selecting the periodic output data of the current of each charging node is set to p(w). g );

[0174]

[0175] Where G is the population size, Fit(w) g ) represents the fitness of the current cycle output data of the g-th charging node, w g This represents the periodic output data of the current at the g-th charging node;

[0176] S435. Perform chromosome crossover operation on the selected genetic operator;

[0177] In the selection of genetic operators, a crossover point is randomly selected from the chromosome codes corresponding to the selected crossover points, and the chromosome code segments between the crossover points are interacted to form offspring chromosomes.

[0178] Fitness is calculated for all offspring chromosomes. If an offspring is better than its parent, the offspring chromosome is replaced by the parent chromosome; otherwise, if the parent is better, the parent is selected to continue the iterative operation.

[0179] S436. Iteratively calculate and output the integrated and corrected current;

[0180] Set an iteration threshold Y. Set the output chromosome code to remain unchanged after Y iterations or to terminate the hybrid genetic algorithm when it reaches the set maximum number of iterations. Then, use the chromosome code of the last output as the integrated and corrected current.

[0181] Furthermore, referring to Figure 1 As shown, the intelligent regulation of user charging power based on the integrated and corrected current, through the resonant voltage gain method, includes the following steps:

[0182] Record the resonant periodic modes in each charging node, determine the corresponding resonant periodic modes based on the direction of the current after integration and correction, and intelligently adjust the user's charging power according to the resonant frequency of the corresponding resonant periodic modes after determining the corresponding resonant periodic modes.

[0183] In one specific embodiment, the adaptive charging scheduling method for multiple input electrical energy further includes: a data acquisition module, a data processing module, a data analysis module, a current integration and correction module, and a power regulation module;

[0184] The data acquisition module is used to collect power data from each charging node within the power grid dispatch and management area;

[0185] The data processing module is used to process the collected electrical energy data to obtain processed electrical energy data.

[0186] The data analysis module is used to analyze the processed power data and perform adaptive charging scheduling based on the analysis results.

[0187] The current integration and correction module is used to integrate and correct the current during the user's charging process through a current integration and correction method to obtain the integrated and corrected current.

[0188] The power regulation module is used to intelligently regulate the user's charging power based on the integrated and corrected current through a resonant voltage gain method.

[0189] It should be noted that the above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An adaptive charging scheduling method for multiple input electrical energy sources, characterized in that, Includes the following steps: S1. Set up a power grid dispatch management area, collect the location of each charging node within the power grid dispatch management area in real time, and build a charging dispatch network. S2. Based on the constructed charging scheduling network, real-time power data of each charging node is collected, and the collected power data is processed to obtain processed power data. The electrical energy data includes: voltage data, current data, electrical energy storage data, and charging consumption data; S3. Analyze the processed power data using data analysis methods, and perform adaptive charging scheduling based on the analysis results, including the following steps: S31. Based on the charging consumption data in the processed power data, estimate the charging consumption data in each charging node, and construct a charging demand model based on the estimated charging consumption data. S32. Based on the constructed charging demand model and the processed power data of each charging node, adaptive charging scheduling is performed on user charging through an intelligent scheduling algorithm. S4. After the adaptive charging scheduling is completed, the current of each charging node is integrated and corrected through the current integration and correction method during the user charging process to obtain the integrated and corrected current. S5. Based on the integrated and corrected current, the user's charging power is intelligently controlled through the resonant voltage gain method.

2. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 1, characterized in that, The process of setting up a power grid dispatch management area, collecting real-time data on the locations of each charging node within that area, and constructing a charging dispatch network includes the following steps: Define the set of charging nodes within the power grid dispatch management area: N = {n1, n2, ..., n} n }; Where n represents the number of charging nodes, n n This represents the nth charging node; Set the location data n for each charging node n Includes: charging node number, latitude data, and longitude data. n ={nId n ,lon n ,lat n }; Among them, nId n This represents the number of the nth charging node, lon. n lat represents the longitude data of the nth charging node. n This represents the latitude data of the nth charging node; A charging scheduling network is constructed based on the location data of each charging node, and the data is saved.

3. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 1, characterized in that, The constructed charging scheduling network collects real-time power data from each charging node and processes the collected power data to obtain the processed power data, including the following steps: S21. Collect all the power data of each charging node within one year in real time and store the power data in the same table; S22. Merge the stored electrical energy data; Set a time threshold and determine whether the two charging consumption data belong to one charging process based on the time interval between the two charging consumption data. If the time interval is less than the time threshold, the two charging consumption data belong to the same batch of charging and the second charging consumption data needs to be merged with the first charging consumption data. If the time interval is greater than the time threshold, the two charging consumption data do not belong to the same batch of charging, and the charging consumption data will not be merged. S23. Set the normal operating voltage and current values ​​for each charging node, and remove any abnormal data to obtain the removed energy data. S24. Standardize the removed electrical energy data using data standardization methods to obtain processed electrical energy data.

4. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 3, characterized in that, The process of standardizing the removed power data using data standardization to obtain processed power data includes the following steps: The data standardization formula is as follows: Where X represents the electrical energy data before standardization. This represents standardized electrical energy data; X max X represents the maximum value in the electrical energy data. min This represents the minimum value in the electrical energy data.

5. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 1, characterized in that, The process of estimating charging consumption data at each charging node based on the processed power data and constructing a charging demand model based on the estimated charging consumption data includes the following steps: The charging consumption data in the processed power data was fitted using the linear least squares method. Based on the timestamps of charging consumption data, the objective function coordinates are set as: (x1, y1), (x2, y2), ..., (x...). i ,y i ); Where y1,x1 represent the first set of charging consumption data and the corresponding timestamp, y i ,x i This represents the i-th set of charging consumption data and its corresponding timestamp; The formula for the linear relationship between two adjacent sets of coordinates is: y = kx + b; The linear least squares fitting formula is shown below: f=v(y i -kx i -b) 2 ; Where f represents the fitted charging consumption data, k represents the slope parameter, b represents the linear parameter, and y i Let x represent the charging consumption data for the i-th group. i This represents the charging consumption data for group i; Set the fitted charging consumption data as the estimated charging consumption data; The charging demand model is defined as: Energy storage data - Estimated charging consumption data.

6. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 1, characterized in that, The adaptive charging scheduling for users based on the constructed charging demand model and the processed power data of each charging node, using an intelligent scheduling algorithm, includes the following steps: The charging demand of each charging node is calculated based on the charging demand model, and the calculated charging demand is sorted in descending order. Based on the latitude and longitude data of each charging node in the charging scheduling network, the paths between each node are rasterized. At the same time, adaptive charging scheduling is performed using the A* algorithm according to the user's coordinate position in the raster space and the charging demand of each charging node. Adaptive charging scheduling is performed by calculating the path cost between each charging node using the A* algorithm. Each charging node represents a target node, and the path between each target node is divided into smaller target nodes. The path cost F(η) is calculated as follows: F(η)=ρ(η)+h(η); Where ρ(η) represents the actual cost from the current node to the target node, and h(η) represents the estimated cost from the current node to the target node; If there exists a path whose estimated cost is less than or equal to the actual cost from the current node to the target node, then this path is defined as the optimal path. Each time a small target node is reached, the current target node is set as the current node, and the next small target node is set as the new target node. The process is iterated continuously to find the optimal path until the last small target node is reached. Adaptive charging scheduling is performed on users based on the calculated optimal path.

7. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 1, characterized in that, After the adaptive charging scheduling is completed, the current of each charging node is integrated and corrected through a current integration and correction method during the user charging process. The integrated and corrected current includes the following steps: S41. Collect the periodic output of the current of each charging node and the output control quantity and status quantity of each charging node. Under the condition of a single input power, the single output prediction is calculated by the control variable method based on the collected periodic output and the output control and state variables of each charging node. The single output value of each charging node is linearly related to the output control quantity and state quantity of the charging node. The periodic output of the single charging node current calculated based on the controlled variable method is shown below: Among them, C T Let z(k) represent the periodic interval with respect to T, where T represents time, z(k) represent the state quantity of the charging node at time k, z(k-1) represent the state quantity of the charging node at time k-1, I(k) represent the periodic output value of the current of a single charging node at time k, and u(k) represent the output control quantity of the charging node at time k. The prediction formula for the current output of a single charging node cycle is shown below: in, Let a represent the predicted value of the current cycle output of a single charging node at time k+j. j The periodic dynamic coefficient indicates that the current period is the j-th period; S42. Predict and calculate the periodic output of the charging node under multiple input current conditions using the superposition principle; in, Let A represent the predicted value of the periodic output of the charging node at time k+1 under β input current conditions, and let A represent the set of periodic dynamic coefficients. S43. The predicted values ​​of the periodic output of the charging node under multiple input current conditions are integrated and corrected through a dynamic optimization algorithm.

8. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 7, characterized in that, The process of integrating and correcting the predicted values ​​of the charging node's periodic output under multiple input current conditions using a dynamic optimization algorithm includes the following steps: S431. Add a correction coefficient to the periodic output of the charging node under multiple input current conditions and calculate iteratively; The dynamic correction function for the charging node output is shown below: J = qI(k+1) + λAΔu(k); Where q represents the output error correction coefficient, λ represents the control quantity error correction coefficient, and J represents the dynamic correction function of the charging node output; S432. Summarize and initialize the periodic output data of the current of each charging node. At the same time, based on the hybrid genetic algorithm, perform chromosome encoding on the periodic output data of the current of each charging node after initialization, and construct a population set. Each chromosome is encoded to represent a set of periodic output data for the current of a charging node; Set the population size and the maximum number of iterations d. max . S433. Based on the size of the constructed population set, the dynamic correction function for the output of the charging node is set as the fitness function; Define the fitness function: Where Fit represents the fitness function, This represents the periodic output prediction value of the charging node current; S434. Based on the calculated fitness, calculate the probability that the periodic output data of the charging node current of each group is selected as a genetic operator. S435. Perform chromosome crossover operation on the selected genetic operator; In the selection of genetic operators, a crossover point is randomly selected from the chromosome codes corresponding to the selected crossover points, and the chromosome code segments between the crossover points are interacted to form offspring chromosomes. Fitness is calculated for all offspring chromosomes. If an offspring is better than its parent, the offspring chromosome is replaced by the parent chromosome; otherwise, if the parent is better, the parent is selected to continue the iterative operation. S436. Iteratively calculate and output the integrated and corrected current; Set an iteration threshold Y. Set the output chromosome code to remain unchanged after Y iterations or to terminate the hybrid genetic algorithm after reaching the set maximum number of iterations. Then, use the last output chromosome code as the integrated and corrected current.

9. The adaptive charging scheduling method for multiple input electrical energy sources according to claim 1, characterized in that, The intelligent regulation of user charging power based on the integrated and corrected current, through resonant voltage gain, includes the following steps: Record the resonant periodic modes in each charging node, determine the corresponding resonant periodic modes based on the direction of the current after integration and correction, and intelligently adjust the user's charging power according to the resonant frequency of the corresponding resonant periodic mode after determining the corresponding resonant periodic modes.

10. An adaptive charging scheduling method for multiple input electrical energy as described in claims 1-9, characterized in that, Also includes: The system includes a data acquisition module, a data processing module, a data analysis module, a current integration and correction module, and a power regulation module. The data acquisition module is used to collect power data from each charging node within the power grid dispatch and management area; The data processing module is used to process the collected electrical energy data to obtain processed electrical energy data. The data analysis module is used to analyze the processed power data and perform adaptive charging scheduling based on the analysis results. The current integration and correction module is used to integrate and correct the current during the user's charging process through a current integration and correction method to obtain the integrated and corrected current. The power regulation module is used to intelligently regulate the user's charging power based on the integrated and corrected current through a resonant voltage gain method.

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