Electric vehicle charging method and system with optimized power grid load

Abstract

The present invention relates to the field of electric energy management technology, specifically to a method and system for charging electric vehicles with optimized power grid load, comprising the following steps: monitoring the location information and power consumption data of multiple nodes of the power grid, collecting real-time current and voltage data of multiple nodes, combining the characteristics of the battery pack and the charging interface, analyzing the power load characteristics of each node, calculating the instantaneous power demand of each node, and generating a node real-time power demand table. In the present invention, by monitoring and analyzing the real-time current and voltage data of each node of the power grid, the instantaneous power demand of each node can be accurately grasped, and the real-time dynamic allocation of power resources can be realized. By adjusting the charging power and time, the pressure of the power grid during high-load periods is effectively reduced, while the overall utilization rate of electric energy is improved and energy waste is reduced. In addition, future load changes are predicted based on the load data trend, and a long-term allocation plan is formulated, thereby improving the overall stability of the power system and its ability to respond to market changes.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy management, and in particular to a method and system for charging an electric vehicle with optimized power grid load. Background Art

[0002] The field of power management technology refers to the technical means of comprehensive management of power production, transmission, distribution and consumption, aiming to improve the overall efficiency and stability of the power system. The field covers aspects such as power load dispatching, power quality monitoring, reasonable energy distribution and optimization of power grid operation, usually including smart grids, load management systems, energy storage equipment control and various forms of power demand response strategies. Through reasonable control means, power management can achieve a dynamic balance between power supply and demand, reduce peak-to-valley differences, reduce energy waste, and promote more effective integration of distributed energy and renewable energy. It is of great significance to improve the reliability of the power system, reduce energy consumption, optimize resource allocation and cope with complex and changing energy demands.

[0003] The grid load optimized electric vehicle charging method is to reasonably allocate power resources to avoid excessive load on the grid while meeting the charging needs of electric vehicles. Through intelligent control and load balancing, this method gives priority to charging when the grid load is low, and reduces the pressure on the grid by delaying or reducing the charging power when the load is high, thereby achieving an optimal match between the grid and charging needs. This method is widely used in electric vehicle charging stations and home charging scenarios, helping to reduce the pressure on the grid, improve the efficiency of power energy utilization, and support the effective integration of renewable energy.

[0004] Although existing technologies can perform power load dispatching and power quality monitoring, they often fail to adjust charging strategies in real time and predict future load demand, resulting in ineffective response during high-load periods of the power grid, which may cause grid instability and uneven resource allocation. The power resource allocation in existing methods is also relatively rigid and difficult to adapt to rapidly changing energy demand, affecting the efficient use of energy and the operation efficiency of the power grid. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an electric vehicle charging method and system with optimized grid load.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for charging an electric vehicle with optimized grid load, comprising the following steps:

[0007] S1: Monitor the location information and power consumption data of multiple nodes in the power grid, collect real-time current and voltage data of multiple nodes, combine the characteristics of the battery pack and the charging interface, analyze the power load characteristics of each node, calculate the instantaneous power demand of each node, and generate a real-time power demand table of the node;

[0008] S2: Based on the data in the real-time power demand table of the nodes, identify the nodes in the charging station whose power consumption exceeds the average level, record the load status of the nodes exceeding the average level, extract key power consumption parameters, summarize the load status data, and generate the fluctuation range of key nodes;

[0009] S3: Based on the fluctuation range of the key nodes, combined with the charging status of the battery pack and the charging interface, the current power configuration is set by analyzing the fluctuation data, the charging power and time of the key nodes are adjusted, the power resources are reallocated, and the optimized scheduling plan is generated;

[0010] S4: Implement the optimized dispatching scheme, monitor the implementation effect of the strategy, track the load response of the power grid, make real-time fine-tuning on the power output of the charging station node, use power load monitoring equipment to record the response status of multiple nodes after implementation, and generate adjusted real-time load monitoring data;

[0011] S5: Based on the real-time load monitoring data and combined with the power demand of the motor drive unit, the load change trend in the future time period is predicted, and the collected load data is trend analyzed to identify the peak and valley time periods, and a long-term power resource allocation plan is formulated according to the prediction results.

[0012] The node real-time power demand table specifically includes current data, voltage data, power load characteristics, and instantaneous power demand. The key node fluctuation range specifically includes load status and power consumption parameters. The optimized scheduling plan specifically includes charging power adjustment, charging time adjustment, and power resource reallocation. The adjusted real-time load monitoring data specifically includes multi-node response status and power output fine-tuning. The long-term power resource allocation plan specifically includes load change trend, peak time period identification, and valley time period identification.

[0013] As a further solution of the present invention, the step of obtaining the node real-time power demand table is specifically as follows:

[0014] S111: receiving real-time data from a multi-node location monitoring system, including current and voltage values ​​of each node, and combining the power consumption data of the node to obtain real-time power data of each node;

[0015] S112: Compare the real-time power data of each node with the characteristic data of the battery pack and the charging interface, calculate the power load characteristics of the node, and obtain the power load characteristic value of each node;

[0016] S113: Using the power load characteristic value, adopt the formula:

[0017] ;

[0018] Calculate the instantaneous power demand of each node and obtain the real-time power demand table of the node;

[0019] in, Used to calculate the instantaneous power demand of each node, is the voltage at the node, is the node current, is the load characteristic factor obtained from the battery pack and charging interface characteristics, Represents a single node.

[0020] As a further solution of the present invention, the step of obtaining the fluctuation range of the key node is specifically as follows:

[0021] S211: Based on the node real-time power demand table, calculate the average power consumption of multiple nodes, determine the average power consumption level, and obtain the average power consumption index;

[0022] S212: Based on the average power consumption indicator, identify nodes whose power consumption exceeds the average level, record power data of the nodes, mark the nodes that exceed the standard, and generate a list of nodes that exceed the standard;

[0023] S213: Based on the list of nodes exceeding the standard, analyze the power data of the nodes exceeding the standard, extract key power consumption parameters including current and voltage, and the load status of the nodes, integrate all the data, and obtain the power load status information of the key nodes;

[0024] S214: Based on the power load status information of the key node, the formula is used:

[0025] ;

[0026] Calculate the power consumption fluctuation of key nodes and obtain the fluctuation range of key nodes;

[0027] in, Represents the node that exceeds the standard Real-time power consumption during the time period, represents the average power consumption, Represents the number of nodes that exceed the limit, It is used to calculate the average absolute deviation of node power consumption, which represents the deviation of multiple nodes' consumption from the average consumption.

[0028] As a further solution of the present invention, the step of obtaining the optimized scheduling solution is specifically as follows:

[0029] S311: Based on the fluctuation range of the key nodes, the charging status of the battery pack and the charging interface of each node is checked, and the current power supply and demand status of each node is evaluated through data comparison to generate a power configuration status result;

[0030] S312: According to the power configuration status result, the key nodes are screened by using the power consumption deviation, the deviation value of each key node to the average power is calculated, the charging power and time that need to be adjusted are determined, and a preliminary adjustment plan is obtained;

[0031] S313: Based on the preliminary adjustment plan, the formula is adopted:

[0032] ;

[0033] Adjust charging parameters of key nodes and calculate power adjustment plans;

[0034] in, is the current charging power, represents the difference from the average power, represents the average power of all nodes, is the adjustment factor, represents the key regulation parameters for optimizing load distribution in the power grid;

[0035] S314: According to the power adjustment plan, reallocate power resources, optimize power usage, and generate an optimized power dispatch plan.

[0036] As a further solution of the present invention, the steps for obtaining the adjusted real-time load monitoring data are specifically as follows:

[0037] S411: Implement the optimized scheduling scheme, deploy a power management system to monitor the power output and load response of each charging station node, and preliminarily generate real-time monitoring data results;

[0038] S412: extracting key power data from the real-time monitoring data results, including current, voltage and power parameters, and recording the response status of each node after the scheduling scheme is implemented through the power load monitoring device to generate a multi-node response data set;

[0039] S413: Analyze the multi-node response data set using the formula:

[0040] ;

[0041] Calculate the node power deviation ratio to obtain the power adjustment requirement of each node;

[0042] in, Representative Node The power deviation ratio is used to measure the difference between the actual power output of the node and the target power output. Representation Node The actual power output reflects the real-time power status of each node after the scheduling scheme is implemented. Indicates the preset target power output, which is the ideal power state set according to the optimized scheduling scheme;

[0043] S414: According to the power adjustment requirements of each node, the power output of the charging station node is fine-tuned in real time, the allocation of power resources is optimized, and the adjusted real-time load monitoring data is generated.

[0044] As a further solution of the present invention, the steps of formulating a long-term power resource allocation plan are specifically as follows:

[0045] S511: Analyze the adjusted real-time load monitoring data, combine the power demand of the multi-motor drive unit, evaluate the current power consumption mode, and generate a real-time power demand analysis result;

[0046] S512: extracting peak and valley time periods from the real-time power demand analysis results, calculating the fluctuation amplitude of power consumption using a power fluctuation analysis method, identifying key time periods, and generating power fluctuation analysis results;

[0047] S513: Analyze the power fluctuation analysis results and predict the load change trend in the future time period using the formula:

[0048] ;

[0049] Calculate the power adjustment requirements of each node and predict future power demand trends;

[0050] in, Represents the load change trend in the predicted future time period, represents the maximum power demand recorded during the analysis, represents the minimum power demand recorded during the analysis period, Represents the basic adjustment coefficient, which is used to adjust the initial value of the forecast trend. represents the sensitivity adjustment factor, which is used to amplify or reduce the impact of power demand differences on load trends;

[0051] S514: Formulate a long-term power resource allocation plan based on the future power demand trend and the power fluctuation analysis results.

[0052] A power grid load optimized electric vehicle charging system, the power grid load optimized electric vehicle charging system is used to execute the above power grid load optimized electric vehicle charging method, the system comprises:

[0053] The power grid monitoring module monitors the current and voltage of multiple nodes, records power consumption data, analyzes battery pack and charging interface characteristics, calculates the instantaneous power demand of each node, and generates a real-time power demand table for the nodes;

[0054] The load analysis module reads the data in the real-time power demand table of the node, calculates the average power consumption of multiple nodes, compares the demand of each node with the average value, identifies nodes with loads exceeding the average level, extracts key power consumption parameters, summarizes the load status, and generates fluctuation ranges for key nodes;

[0055] The scheduling optimization module obtains the fluctuation range and charging status of the key nodes, sets the power configuration, adjusts the charging power and time of the key nodes, reallocates power resources, and generates an optimized scheduling plan;

[0056] The real-time adjustment and long-term prediction module implements the optimized scheduling plan, monitors the load response of the power grid, adjusts the power output of the charging station node, records the node status after implementation, generates real-time load monitoring data, analyzes the load change trend based on the real-time load monitoring data, identifies the peak and valley time periods, and formulates a long-term power resource allocation plan.

[0057] Compared with the prior art, the advantages and positive effects of the present invention are:

[0058] In the present invention, by monitoring and analyzing the real-time current and voltage data of each node of the power grid, the instantaneous power demand of each node can be accurately grasped, and the real-time dynamic allocation of power resources can be realized. By adjusting the charging power and time, the pressure of the power grid during high-load periods is effectively reduced, while the overall utilization rate of electric energy is improved and energy waste is reduced. In addition, future load changes are predicted based on the load data trend, and a long-term allocation plan is formulated, thereby improving the overall stability of the power system and its ability to respond to market changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0060] Figure 2 A flowchart of the steps for obtaining the real-time power demand table of a node according to the present invention;

[0061] Figure 3 A flowchart of the steps for obtaining the fluctuation range of key nodes of the present invention;

[0062] Figure 4 A flowchart of the steps for obtaining the optimized scheduling scheme of the present invention;

[0063] Figure 5 A flowchart of the steps for obtaining the real-time load monitoring data adjusted by the present invention;

[0064] Figure 6 A flow chart of the steps for formulating a long-term power resource allocation plan for the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0066] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0067] Example: See Figure 1 The present invention provides a technical solution: a method for charging an electric vehicle with optimized power grid load, comprising the following steps:

[0068] S1: Monitor the location information and power consumption data of multiple nodes in the power grid, collect real-time current and voltage data of multiple nodes, combine the characteristics of the battery pack and the charging interface, analyze the power load characteristics of each node, calculate the instantaneous power demand of each node, and generate a real-time power demand table of the node;

[0069] S2: Based on the data in the node real-time power demand table, identify the nodes in the charging station whose power consumption exceeds the average level, record the load status of the nodes exceeding the average level, extract key power consumption parameters, summarize the load status data, and generate the fluctuation range of key nodes;

[0070] S3: Based on the fluctuation range of key nodes, combined with the charging status of the battery pack and the charging interface, the current power configuration is set by analyzing the fluctuation data, the charging power and time of key nodes are adjusted, the power resources are reallocated, and the optimized scheduling plan is generated;

[0071] S4: Implement the optimized dispatching plan, monitor the implementation effect of the strategy, track the grid load response, make real-time fine-tuning of the power output of the charging station nodes, use power load monitoring equipment to record the response status of multiple nodes after implementation, and generate adjusted real-time load monitoring data;

[0072] S5: Based on real-time load monitoring data and combined with the power demand of the motor drive unit, the load change trend in the future time period is predicted, and the collected load data is trend analyzed to identify the peak and valley time periods. A long-term power resource allocation plan is formulated based on the prediction results.

[0073] The real-time power demand table of the node specifically includes current data, voltage data, power load characteristics, and immediate power demand. The fluctuation range of key nodes specifically includes load status and power consumption parameters. The optimized scheduling plan specifically includes charging power adjustment, charging time adjustment, and power resource reallocation. The adjusted real-time load monitoring data specifically includes multi-node response status and power output fine-tuning. The long-term power resource allocation plan specifically includes load change trend, peak time period identification, and valley time period identification.

[0074] See also Figure 2 ,The specific steps for obtaining the node real-time power demand table are:

[0075] S111: receiving real-time data from a multi-node location monitoring system, including current and voltage values ​​of each node, and combining the power consumption data of the node to obtain real-time power data of each node;

[0076] When receiving real-time data from a multi-node location monitoring system, ensure that the current and voltage values ​​of each node are recorded accurately. This process involves real-time data transmission from the sensor to the data acquisition system, adjusting the data acquisition frequency and accuracy to meet the needs of the power system, and obtaining real-time power data of each node through instant analysis of the current and voltage data of each node. The data is used to update and reflect the power consumption status of each node in real time for real-time monitoring and data analysis.

[0077] S112: Compare the real-time power data of each node with the characteristic data of the battery pack and the charging interface, calculate the power load characteristics of the node, and obtain the power load characteristic value of each node;

[0078] When comparing and analyzing power data with the characteristics of the battery pack and charging interface, it involves real-time processing of the real-time collected power data, including current and voltage data comparison. In this process, the power data of each node is matched with the characteristic data of the battery pack and charging interface. The key is to capture the power load characteristics, including real-time analysis of current and voltage data and real-time evaluation of battery performance, so as to calculate the power load characteristics of each node and obtain the power load characteristic value of each node.

[0079] S113: Using the characteristic value of power load, the formula is:

[0080] ;

[0081] Calculate the instantaneous power demand of each node and obtain the real-time power demand table of the node;

[0082] in, Used to calculate the instantaneous power demand of each node, is the voltage at the node, is the node current, is the load characteristic factor obtained from the battery pack and charging interface characteristics, Represents a single node.

[0083] formula:

[0084] ;

[0085] The benefit of the formula is that it can calculate the power demand of each node in real time based on the real-time monitored voltage and current and power load characteristic factors, increasing the system's response speed and accuracy to changing power demand.

[0086] Detailed explanation of the formula and the process of formula calculation and derivation:

[0087] Set the voltage of a node Volt, current Ampere, electrical load characteristic factor , then the instantaneous power demand of the node is:

[0088] watt;

[0089] This calculation process directly reflects the impact of voltage, current and load characteristic factors on the node's power demand. The results show that the node currently requires 522.5 watts of power to meet its operating needs.

[0090] See also Figure 3 , the specific steps for obtaining the fluctuation range of key nodes are:

[0091] S211: Calculate the average power consumption of multiple nodes based on the node real-time power demand table, determine the average power consumption level, and obtain the average power consumption index;

[0092] Based on the node real-time power demand table, the calculation of the node average power consumption starts with the real-time power data received from each node. The data includes the current and voltage data of each node. Through these data, the total power consumption of each node in the target time period can be calculated. Then the power consumption of all nodes is summed up and divided by the total number of nodes to obtain the average power consumption index. This process ensures the comprehensiveness and representativeness of the data, can truly reflect the power usage of the entire system, and provides basic data for the next step of analysis.

[0093] S212: Identify nodes whose power consumption exceeds the average level based on the average power consumption index, record power data of the nodes, mark the nodes that exceed the standard, and generate a list of nodes that exceed the standard;

[0094] From the real-time power demand table, by comparing the actual consumption of each node with the average power consumption index calculated previously, identify which nodes have power consumption exceeding the average level. This process involves reviewing the power consumption data of each node one by one and marking those nodes that exceed the average level. This step is automatically completed through programming. By setting thresholds, it is determined which nodes exceed the standard. The threshold covers the real-time power consumption data of the node, such as current, voltage, power load characteristics, etc. The threshold selection is based on the real-time monitored power consumption data, thereby automatically generating a list of all nodes that exceed the standard, which not only improves efficiency but also ensures the accuracy of the processing process.

[0095] S213: Based on the list of nodes exceeding the standard, analyze the power data of the nodes exceeding the standard, extract key power consumption parameters including current and voltage, and the load status of the nodes, integrate all the data, and obtain the power load status information of the key nodes;

[0096] Extracting key power consumption parameters from the power data of nodes that exceed the standard, including current and voltage as well as the real-time load status of the nodes, involves querying and summarizing these data from the database. During the analysis process, the current and voltage data need to be normalized to ensure that the data are comparable between differentiated nodes. Then, through this series of data processing steps, including data cleaning and screening, these data are integrated to obtain a power load status information table of the key nodes.

[0097] S214: Based on the power load status information of key nodes, the formula is:

[0098] ;

[0099] Calculate the power consumption fluctuation of key nodes and obtain the fluctuation range of key nodes;

[0100] in, Represents the node that exceeds the standard Real-time power consumption during the time period, represents the average power consumption, Represents the number of nodes that exceed the limit, It is used to calculate the average absolute deviation of node power consumption, which represents the deviation of multiple nodes' consumption from the average consumption.

[0101] formula:

[0102] ;

[0103] The benefit of the formula is that by calculating the average of the absolute difference between each node's power consumption and the average consumption, a measure is provided to identify and handle nodes with abnormal power consumption, which helps system operators to quickly identify nodes that need special attention so as to carry out targeted maintenance or optimization.

[0104] Detailed explanation of the formula and the process of formula calculation and derivation:

[0105] There are five nodes with power consumptions of The average power consumption is Substitute the formula to calculate the average of the absolute differences between multiple nodes and the average consumption:

[0106] ;

[0107] ;

[0108] ;

[0109] The results show that the average power consumption fluctuation range is 18.4. This value helps system operation and maintenance personnel analyze the fluctuations in power consumption of the entire charging station node.

[0110] See also Figure 4 , the specific steps for obtaining the optimized scheduling plan are:

[0111] S311: Based on the fluctuation range of the key nodes, the charging status of the battery pack and the charging interface of each node is checked, and the current power supply and demand status of each node is evaluated through data comparison to generate a power configuration status result;

[0112] Analyze the data of fluctuation range of key nodes and the real-time status of battery packs and charging interfaces at each node. This data comparison process involves the collection and processing of input data of the real-time monitoring system. Through the real-time data monitoring system, the current, voltage and time series data of multiple nodes are collected, and these data are preliminarily filtered to remove outliers or data points that exceed the preset threshold. This data processing not only ensures data quality, but also provides accurate basic data for subsequent analysis. Through statistical analysis methods, including calculating the mean and standard deviation of multi-node data, the current status of power demand and supply is determined. The generated power configuration status results describe the power usage of each node in differentiated time periods, including the difference in power usage during peak and trough periods. This information is crucial for power scheduling and management.

[0113] S312: According to the power configuration status result, the key nodes are screened by using the power consumption deviation, the deviation value of each key node to the average power is calculated, the charging power and time that need to be adjusted are determined, and a preliminary adjustment plan is obtained;

[0114] According to the power configuration status results, data analysis technology is used to analyze the power consumption data of multiple nodes, including using data mining methods to identify consumption patterns and anomalies, and calculating power deviations by comparing the actual power consumption of each node with the average value. Key nodes that require special attention are identified. The power adjustment of the nodes will be based on the degree of deviation from the average power, and a preliminary adjustment plan will be formulated with comprehensive reference to the operating efficiency and reliability of each node. The plan will indicate which nodes need to be adjusted first and the direction and magnitude of the adjustment. This not only improves the overall efficiency of the system, but also ensures stable power supply in key areas.

[0115] S313: Based on the preliminary adjustment plan, use the formula:

[0116] ;

[0117] Adjust charging parameters of key nodes and calculate power adjustment plans;

[0118] in, is the current charging power, represents the difference from the average power, represents the average power of all nodes, is the adjustment factor, represents the key regulation parameters for optimizing load distribution in the power grid;

[0119] formula:

[0120] ;

[0121] The formula is useful because it introduces an adjustment factor , the formula can flexibly adjust the charging power to match the actual changes in electricity demand, promoting the stable operation of the power grid and the optimal allocation of energy.

[0122] Detailed explanation of the formula and the process of formula calculation and derivation:

[0123] Set the current power 500kW, average power is 450kW, power deviation for kW, adjustment factor Set it to 0.1 and substitute it into the formula:

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] The results show that with slight adjustments, power can be increased to better match load demand and maintain network balance.

[0129] S314: According to the power adjustment plan, reallocate power resources, optimize power usage, and generate an optimized power dispatch plan.

[0130] Utilize the power adjustment plan to plan the power redistribution of each node, and implement power dispatch through the advanced power grid management system. The resource optimization plan after redistribution will be dynamically adjusted according to the actual power demand to ensure the charging efficiency of key nodes and the overall stability of the system. The optimized dispatch plan records the adjusted power and expected operating time of multiple nodes to ensure that each node can operate in the optimal state, thereby reducing energy waste and improving the response speed and reliability of the system.

[0131] See also Figure 5 , the specific steps for obtaining the adjusted real-time load monitoring data are as follows:

[0132] S411: Implement the optimized dispatching plan, deploy the power management system to monitor the power output and load response of each charging station node, and initially generate real-time monitoring data results;

[0133] After implementing the optimized dispatching plan, the power management system collects key parameters of current, voltage and power at each node and generates data in real time. These data are crucial for monitoring the load response of the entire power grid. The management system uses the data to evaluate the implementation effect of the dispatching plan and determine whether iterative adjustment or optimization is needed. This method allows operators to instantly understand the operating status of multiple nodes and respond to any changes that do not meet expectations, thereby ensuring the stable operation of the power grid and the continuity of power supply.

[0134] S412: extract key power data from the real-time monitoring data results, including current, voltage and power parameters, and record the response status of each node after the scheduling plan is implemented through the power load monitoring equipment to generate a multi-node response data set;

[0135] Extract key power data from real-time monitoring data results, and the power load monitoring equipment records the response status of each node after the scheduling plan is implemented. The recorded data includes current, voltage and power. The data is measured by the data acquisition system and then processed by data analysis. The data is sorted, filtered and compared to identify which nodes are abnormal or do not meet expectations. Data analysis at this stage is critical to ensure the effective management of the charging network. By analyzing real-time data, target nodes that need to be adjusted or paid attention to can be identified.

[0136] S413: Analyze the multi-node response data set using the formula:

[0137] ;

[0138] Calculate the node power deviation ratio to obtain the power adjustment requirement of each node;

[0139] in, Representative Node The power deviation ratio is used to measure the difference between the actual power output of the node and the target power output. Representation Node The actual power output reflects the real-time power status of each node after the scheduling scheme is implemented. Indicates the preset target power output, which is the ideal power state set according to the optimized scheduling scheme;

[0140] formula:

[0141] ;

[0142] The benefit of the formula is that it provides a direct way to quantify node performance deviations, allowing managers to adjust power output to match preset targets, thereby optimizing the energy efficiency and responsiveness of the entire power grid. Through this formula, the difference between the actual performance of each node and the established target can be determined, thereby guiding adjustment decisions.

[0143] Detailed explanation of the formula and the process of formula calculation and derivation:

[0144] Sets the actual power output of a node The target power output is 150kW is 100kW. Substituting into the formula, we get:

[0145] ;

[0146] The results show that the actual power output of the node is 50% higher than the target power and needs to be adjusted to avoid overload or resource waste.

[0147] S414: According to the power adjustment requirements of each node, the power output of the charging station node is fine-tuned in real time, the allocation of power resources is optimized, and the adjusted real-time load monitoring data is generated.

[0148] According to the power adjustment requirements of each node, the power output of the charging station node is fine-tuned in real time. At this stage, using the data obtained from the previous analysis, the power deviation ratio of each node, the operator or the automation system will adjust the output power settings of multiple nodes to ensure the operation of the entire system. Through dynamic adjustment strategies, including adjusting the tap position of the transformer or adjusting the output of the generator, it is ensured that all nodes can work in the optimal operating state. During this adjustment process, each adjustment action is recorded in the system log for post-analysis and continuous system optimization.

[0149] See also Figure 6 , the specific steps for formulating a long-term power resource allocation plan are:

[0150] S511: Analyze the adjusted real-time load monitoring data, combine the power requirements of the multi-motor drive unit, evaluate the current power consumption mode, and generate a real-time power demand analysis result;

[0151] Analyze the adjusted real-time load monitoring data and refine the assessment of the current power consumption pattern in combination with the power requirements of multiple motor drive units, including the data collection frequency, data processing method and dynamic adjustment strategy of the real-time monitoring system for power requirements. By analyzing the immediate power demand of each motor drive unit, high-consumption units are identified and marked for special monitoring. At the same time, an intelligent regulation system is deployed to adjust the power output in real time to ensure the optimization of the system's energy efficiency and generate real-time power demand analysis results.

[0152] S512: extracting peak and valley time periods from the real-time power demand analysis results, calculating the fluctuation amplitude of power consumption using a power fluctuation analysis method, identifying key time periods, and generating power fluctuation analysis results;

[0153] Peak and valley time periods are extracted from the real-time power demand analysis results. The power fluctuation analysis method is used to simulate the fluctuation of power consumption through algorithms based on previous data and real-time inputs to identify key periods of power demand, which correspond to industrial production activities, commercial operation peaks or residential power consumption peaks. The power fluctuation analysis results include statistical data for peak and valley time periods, providing decision support for power dispatching.

[0154] S513: Analyze the power fluctuation analysis results and predict the load change trend in the future time period using the formula:

[0155] ;

[0156] Calculate the power adjustment requirements of each node and predict future power demand trends;

[0157] in, Represents the load change trend in the predicted future time period, represents the maximum power demand recorded during the analysis, represents the minimum power demand recorded during the analysis period, Represents the basic adjustment coefficient, which is used to adjust the initial value of the forecast trend. represents the sensitivity adjustment factor, which is used to amplify or reduce the impact of power demand differences on load trends;

[0158] formula:

[0159] ;

[0160] The benefit of the formula is that the exponential model can effectively predict the load change trend in the future time period. and , matching differentiated load change rates and providing a more flexible adjustment mechanism for power grid management.

[0161] Detailed explanation of the formula and the process of formula calculation and derivation:

[0162] During the target test, (maximum power demand) is 5000 kW, (Minimum power requirement) is 3,000 kilowatts, determined through data analysis and , substitute into the formula to calculate:

[0163] ;

[0164] The results show that during the forecast period, the growth rate of electricity demand is 738.91, which means that measures need to be taken to adjust the power supply to cope with the load pressure during peak periods.

[0165] S514: Develop a long-term electricity resource allocation plan based on future electricity demand trends and electricity fluctuation analysis results.

[0166] Based on the results of future electricity demand trends and electricity fluctuation analysis, a long-term electricity resource allocation plan is formulated, focusing on how to increase electricity supply during predicted peak periods and reduce electricity supply during predicted valley periods. By adopting a power grid management system, real-time control of multiple power sources and connection points is achieved, and power flow and load balance are optimized. Measures include starting backup generators, adjusting the output power of key power stations, using battery storage systems to absorb excess electricity, and storing electricity during low-load periods. A long-term electricity resource allocation plan is generated, which will respond to changes in actual electricity demand through dynamic adjustments to ensure the reliability and economy of power supply.

[0167] A power grid load optimized electric vehicle charging system, a power grid load optimized electric vehicle charging system is used to execute the above power grid load optimized electric vehicle charging method, the system comprises:

[0168] The power grid monitoring module monitors the current and voltage of multiple nodes, records power consumption data, analyzes battery pack and charging interface characteristics, calculates the instantaneous power demand of each node, and generates a real-time power demand table for the nodes;

[0169] The load analysis module reads the data in the node real-time power demand table, calculates the average power consumption of multiple nodes, compares the demand of each node with the average value, identifies nodes with loads exceeding the average level, extracts key power consumption parameters, summarizes the load status, and generates fluctuation ranges for key nodes;

[0170] The scheduling optimization module obtains the fluctuation range and charging status of key nodes, sets the power configuration, adjusts the charging power and time of key nodes, reallocates power resources, and generates an optimized scheduling plan;

[0171] The real-time adjustment and long-term prediction module implements the optimized dispatching plan, monitors the load response of the power grid, adjusts the power output of the charging station nodes, records the node status after implementation, generates real-time load monitoring data, analyzes the load change trend based on the real-time load monitoring data, identifies the peak and valley time periods, and formulates a long-term power resource allocation plan.

[0172] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for charging an electric vehicle with optimized grid load, characterized in that: The following steps are involved: Monitor the location information and power consumption data of multiple nodes in the power grid, collect real-time current and voltage data of multiple nodes, combine the characteristics of the battery pack and the charging interface, analyze the power load characteristics of each node, calculate the instantaneous power demand of each node, and generate a real-time power demand table of the node; Based on the data in the real-time power demand table of the nodes, identify nodes in the charging station whose power consumption exceeds the average level, record the load status of the nodes exceeding the average level, extract key power consumption parameters, summarize the load status data, and generate the fluctuation range of key nodes; Based on the fluctuation range of the key nodes, combined with the charging status of the battery pack and the charging interface, the current power configuration is set by analyzing the fluctuation data, the charging power and time of the key nodes are adjusted, the power resources are reallocated, and the optimized scheduling plan is generated; Implement the optimized dispatching scheme, monitor the implementation effect of the strategy, track the load response of the power grid, make real-time fine-tuning on the power output of the charging station nodes, use power load monitoring equipment to record the response status of multiple nodes after implementation, and generate adjusted real-time load monitoring data; Based on the real-time load monitoring data and combined with the power demand of the motor drive unit, the load change trend in the future time period is predicted, the collected load data is trend analyzed to identify the peak and valley time periods, and a long-term power resource allocation plan is formulated based on the prediction results.

2. The method for charging an electric vehicle with optimized power grid load according to claim 1, characterized in that: The node real-time power demand table specifically includes current data, voltage data, power load characteristics, and instantaneous power demand. The key node fluctuation range specifically includes load status and power consumption parameters. The optimized scheduling plan specifically includes charging power adjustment, charging time adjustment, and power resource reallocation. The adjusted real-time load monitoring data specifically includes multi-node response status and power output fine-tuning. The long-term power resource allocation plan specifically includes load change trend, peak time period identification, and valley time period identification.

3. The method for charging an electric vehicle with optimized power grid load according to claim 2, characterized in that: The steps for obtaining the node real-time power demand table are specifically as follows: Receive real-time data from a multi-node location monitoring system, including current and voltage values ​​of each node, and combine with the power consumption data of the node to obtain real-time power data of each node; According to the real-time power data of each node, the characteristic data of the battery pack and the charging interface are compared to calculate the power load characteristics of the node to obtain the power load characteristic value of each node; Using the power load characteristic value, the formula is adopted: ； Calculate the instantaneous power demand of each node and obtain the real-time power demand table of the node; in, Used to calculate the instantaneous power demand of each node, is the voltage at the node, is the node current, is the load characteristic factor obtained from the battery pack and charging interface characteristics, Represents a single node.

4. The method for charging an electric vehicle with optimized power grid load according to claim 3, characterized in that: The steps for obtaining the fluctuation range of the key nodes are specifically as follows: Based on the node real-time power demand table, calculate the average power consumption of multiple nodes, determine the average power consumption level, and obtain the average power consumption index; Based on the average power consumption index, identify nodes whose power consumption exceeds the average level, record power data of the nodes, mark the nodes that exceed the standard, and generate a list of nodes that exceed the standard; Based on the list of nodes exceeding the standard, the power data of the nodes exceeding the standard are analyzed, key power consumption parameters including current and voltage, as well as the load status of the nodes are extracted, and all data are integrated to obtain the power load status information of the key nodes; Based on the power load status information of the key nodes, the formula is adopted: ； Calculate the power consumption fluctuation of key nodes and obtain the fluctuation range of key nodes; in, Represents the node that exceeds the standard Real-time power consumption during the time period, represents the average power consumption, Represents the number of nodes that exceed the limit, It is used to calculate the average absolute deviation of node power consumption, which represents the deviation of multiple nodes' consumption from the average consumption.

5. The method for charging an electric vehicle with optimized power grid load according to claim 4, characterized in that: The steps for obtaining the optimized scheduling scheme are specifically as follows: Based on the fluctuation range of the key nodes, the charging status of the battery pack and the charging interface of each node is checked, and the current power supply and demand status of each node is evaluated through data comparison to generate a power configuration status result; According to the power configuration status result, the key nodes are screened by using the power consumption deviation, the deviation value of each key node to the average power is calculated, the charging power and time that need to be adjusted are determined, and a preliminary adjustment plan is obtained; Based on the preliminary adjustment plan, the formula is adopted: ； Adjust charging parameters of key nodes and calculate power adjustment plans; in, is the current charging power, represents the difference from the average power, represents the average power of all nodes, is the adjustment factor, represents the key regulation parameters for optimizing load distribution in the power grid; According to the power adjustment plan, power resources are reallocated, power usage is optimized, and an optimized power dispatching plan is generated.

6. The method for charging an electric vehicle with optimized power grid load according to claim 5, characterized in that: The steps for obtaining the adjusted real-time load monitoring data are specifically as follows: Implement the optimized dispatching scheme, deploy a power management system to monitor the power output and load response of each charging station node, and preliminarily generate real-time monitoring data results; Extract key power data from the real-time monitoring data results, including current, voltage and power parameters, and record the response status of each node after the scheduling plan is implemented through power load monitoring equipment to generate a multi-node response data set; To analyze the multi-node response data set, the formula is used: ； Calculate the node power deviation ratio to obtain the power adjustment requirement of each node; in, Representative Node The power deviation ratio is used to measure the difference between the actual power output of the node and the target power output. Representation Node The actual power output reflects the real-time power status of each node after the scheduling scheme is implemented. Indicates the preset target power output, which is the ideal power state set according to the optimized scheduling scheme; According to the power adjustment requirements of each node, the power output of the charging station node is fine-tuned in real time, the allocation of power resources is optimized, and adjusted real-time load monitoring data is generated.

7. The method for charging an electric vehicle with optimized power grid load according to claim 6, characterized in that: The steps of formulating a long-term power resource allocation plan are specifically as follows: Analyzing the adjusted real-time load monitoring data, combining the power requirements of the multi-motor drive unit, evaluating the current power consumption pattern, and generating a real-time power demand analysis result; Extracting peak and valley time periods from the real-time power demand analysis results, calculating the fluctuation amplitude of power consumption using a power fluctuation analysis method, identifying key time periods, and generating power fluctuation analysis results; Analyze the power fluctuation analysis results and predict the load change trend in the future time period using the formula: ； Calculate the power adjustment requirements of each node and predict future power demand trends; in, Represents the load change trend in the predicted future time period, represents the maximum power demand recorded during the analysis, represents the minimum power demand recorded during the analysis period, Represents the basic adjustment coefficient, which is used to adjust the initial value of the forecast trend. represents the sensitivity adjustment factor, which is used to amplify or reduce the impact of power demand differences on load trends; A long-term electricity resource allocation plan is formulated based on the future electricity demand trend and the electricity fluctuation analysis results.

8. An electric vehicle charging system with optimized grid load, characterized in that: According to a grid load optimized electric vehicle charging method according to any one of claims 1 to 7, the system comprises: The power grid monitoring module monitors the current and voltage of multiple nodes, records power consumption data, analyzes battery pack and charging interface characteristics, calculates the instantaneous power demand of each node, and generates a real-time power demand table for the nodes; The load analysis module reads the data in the real-time power demand table of the node, calculates the average power consumption of multiple nodes, compares the demand of each node with the average value, identifies nodes with loads exceeding the average level, extracts key power consumption parameters, summarizes the load status, and generates fluctuation ranges for key nodes; The scheduling optimization module obtains the fluctuation range and charging status of the key nodes, sets the power configuration, adjusts the charging power and time of the key nodes, reallocates power resources, and generates an optimized scheduling plan; The real-time adjustment and long-term prediction module implements the optimized scheduling plan, monitors the load response of the power grid, adjusts the power output of the charging station node, records the node status after implementation, generates real-time load monitoring data, analyzes the load change trend based on the real-time load monitoring data, identifies the peak and valley time periods, and formulates a long-term power resource allocation plan.

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