Flexible mutual-aid power dynamic allocation collaborative management method and system

Abstract

The invention discloses a flexible mutual-aid power dynamic distribution collaborative management method and system, and relates to the technical field of energy management and power distribution, and the method comprises the steps: obtaining the initialization state information of power distribution management, sequentially obtaining the required power values of a plurality of charging terminals, and judging whether the real-time power value can meet the required power value or not. And if yes, calling an energy priority strategy to generate a decision. Acquiring a distribution management record, analyzing the distribution management record by using an evaluation function to obtain real-time fitness, and judging whether the real-time fitness is within a preset threshold value or not; and if not, generating a correction instruction, correcting the real-time power value and the required power value to obtain predicted power, calling a dynamic collaborative strategy to analyze and generate a dynamic decision, and performing power dynamic allocation collaborative management according to the dynamic decision. The technical problem that power distribution is unreasonable in quantity and time due to the fact that an existing power distribution method cannot dynamically adjust a distribution strategy according to actual supply and demand changes is solved, and the technical effect of improving accuracy and timeliness of power distribution is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of energy management and power distribution, and in particular to a flexible and mutually beneficial power dynamic distribution collaborative management method and system. Background Art

[0002] In the scenario of energy management and power distribution, the power distribution problem between multi-source energy and multiple charging terminals is particularly prominent, and the contradiction between the demand for efficient and coordinated power distribution is relatively more prominent. Realizing accurate, dynamic and coordinated power distribution to better meet the complex and changing energy supply and charging needs has become a crucial link in solving energy management and power distribution. Traditional energy power distribution methods are often extensive and localized, relying only on simple distribution strategies or limited evaluation indicators. They have insufficient control over the overall situation of multi-source energy and multiple charging terminals, lack in-depth analysis and utilization of real-time changing data, and are difficult to comprehensively and accurately evaluate the stability, reliability and energy efficiency of the system. There are unreasonable situations in power distribution, resulting in insufficient power supply in some key areas and idle power in some areas. The formulation of power distribution plans is relatively simple and fixed, and cannot cope well with the actual situation of constantly changing energy and charging needs.

[0003] In the current related technologies, the power allocation method cannot dynamically adjust the allocation strategy according to the actual changes in supply and demand in complex scenarios with multiple energy sources and multiple charging terminals, resulting in technical problems such as unreasonable power allocation in quantity and time. Summary of the invention

[0004] The present application provides a flexible and mutually beneficial method and system for collaborative management of dynamic power allocation, which obtains power allocation management initialization status information (including real-time available power of multiple energy sources), obtains the required power values ​​of multiple charging terminals in turn, and determines whether the real-time power value can meet the required power value. If satisfied, call the energy priority strategy to generate a decision. Obtain the allocation management record, use the evaluation function to analyze the real-time fitness, and determine whether it is within the predetermined threshold. If not, generate a correction instruction, correct the real-time power value and the required power value to obtain the predicted power, call the dynamic collaborative strategy to analyze and generate a dynamic decision, and perform collaborative management of dynamic power allocation based on this, thereby achieving the technical effect of improving the accuracy and timeliness of power allocation.

[0005] The present application provides a flexible and mutually beneficial power dynamic allocation collaborative management method, including:

[0006] Acquire initialization state information of power allocation management, the initialization state information includes real-time available power, and the real-time available power includes multiple real-time power values ​​of multi-source energy resource modules; acquire multiple required power values ​​of multiple charging terminals in sequence, and determine whether the multiple real-time power values ​​meet the multiple required power values; if so, call a predetermined energy priority strategy to generate a power allocation decision; acquire a power allocation management record, the power allocation management record refers to an allocation management record for collaboratively allocating the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision; introduce an allocation management evaluation function to analyze the power allocation management record to obtain real-time fitness, and determine whether the real-time fitness is within a predetermined fitness threshold; if not, generate a correction instruction, and based on the correction instruction, call an output power correction strategy to correct the multiple real-time power values ​​to obtain predicted available power, and call a required power correction strategy to correct the multiple required power values ​​to obtain predicted required power; call a predetermined dynamic collaborative strategy to analyze the predicted available power and the predicted required power, generate a dynamic power allocation decision, and perform power dynamic allocation collaborative management according to the dynamic power allocation decision.

[0007] The present application also provides a flexible and mutually beneficial power dynamic allocation collaborative management system, including:

[0008] An initialization status information module, the initialization status information module is used to obtain the initialization status information of the power allocation management, the initialization status information includes the real-time available power, and the real-time available power includes multiple real-time power values ​​of the multi-source energy resource module; a required power value judgment module, the required power value judgment module is used to sequentially obtain multiple required power values ​​of multiple charging terminals, and judge whether the multiple real-time power values ​​meet the multiple required power values; a power allocation decision generation module, the power allocation decision generation module is used to call the predetermined energy priority strategy to generate a power allocation decision if it is satisfied; a power allocation management record acquisition module, the power allocation management record acquisition module is used to obtain the power allocation management record, the power allocation management record refers to the coordinated allocation of the multi-source energy resource module to the multiple charging terminals based on the power allocation decision The terminal's allocation management record; a predetermined fitness threshold judgment module, the predetermined fitness threshold judgment module is used to introduce an allocation management evaluation function to analyze the power allocation management record to obtain real-time fitness, and judge whether the real-time fitness is within a predetermined fitness threshold; a correction instruction generation module, the correction instruction generation module is used to generate a correction instruction if it is not within a predetermined fitness threshold, and based on the correction instruction, call the output power correction strategy to correct the multiple real-time power values ​​to obtain the predicted available power, call the demand power correction strategy to correct the multiple demand power values ​​to obtain the predicted demand power; a collaborative management module, the collaborative management module is used to call a predetermined dynamic collaborative strategy to analyze the predicted available power and the predicted demand power, generate a dynamic power allocation decision, and perform power dynamic allocation collaborative management according to the dynamic power allocation decision.

[0009] The flexible and mutually beneficial power dynamic allocation collaborative management method and system proposed in this application first obtains the power allocation management initialization state information (including the real-time available power of multiple energy sources), and obtains the required power values ​​of multiple charging terminals in turn to determine whether the real-time power value can meet the required power value. If satisfied, call the energy priority strategy to generate a decision. Obtain the allocation management record, use the evaluation function to analyze the real-time fitness, and determine whether it is within the predetermined threshold. If not, generate a correction instruction, correct the real-time power value and the required power value to obtain the predicted power, call the dynamic collaborative strategy analysis to generate a dynamic decision, and perform power dynamic allocation collaborative management based on this, thereby achieving the technical effect of improving the accuracy and timeliness of power allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A schematic diagram of a flow chart of a flexible and mutually beneficial power dynamic allocation collaborative management method provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of a flexible and mutually beneficial power dynamic allocation collaborative management system provided in an embodiment of the present application;

[0013] Explanation of the accompanying drawings: initialization status information module 10, required power value judgment module 20, power allocation decision generation module 30, power allocation management record acquisition module 40, predetermined fitness threshold judgment module 50, correction instruction generation module 60, collaborative management module 70. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application embodiment provides a flexible and mutually beneficial power dynamic allocation collaborative management method, such as Figure 1 As shown, the method includes:

[0018] Step S100, obtain the initialization state information of power allocation management, the initialization state information includes real-time available power, and the real-time available power includes multiple real-time power values ​​of multi-source energy resource modules. Specifically, the initialization state information of power allocation management refers to the relevant data obtained at the beginning of power allocation. Select the power allocation scenario to be managed, prepare the power monitoring equipment, set the power acquisition parameters, such as energy source type, acquisition frequency, etc., perform the power acquisition operation, and obtain the real-time power value sequence record data of the multi-source energy resource module. The real-time power value sequence record data represents the output power of each energy module at a specific moment, and its characteristic is that it reflects the real-time energy supply capability. Continue to perform power acquisition to ensure that comprehensive and accurate real-time available power information is obtained. Record the entire process of obtaining the initialization state information of power allocation management.

[0019] Step S200, sequentially obtain multiple required power values ​​of multiple charging terminals, and determine whether the multiple real-time power values ​​meet the multiple required power values. Specifically, the process of obtaining the required power values ​​of multiple charging terminals and determining their relationship with the real-time power values ​​refers to selecting multiple charging terminals to be evaluated in the power allocation process, preparing power collection equipment, setting power collection parameters, such as terminal type, charging speed requirements, remaining battery power, etc., performing power demand collection, and obtaining required power value sequence record data of multiple charging terminals. The required power value sequence record data represents the power demand of each charging terminal under specific conditions, and its characteristic is that it reflects the power demand of different terminals. Continue to perform power demand analysis to determine whether multiple real-time power values ​​can meet these required power values. This judgment process represents the operation of comparing the real-time available power with the required power of each terminal, and its characteristic is to determine whether the power supply and demand are balanced.

[0020] Step S300, if satisfied, retrieve the predetermined energy priority strategy to generate a power allocation decision. Specifically, the process of retrieving the predetermined energy priority strategy to generate a power allocation decision if satisfied refers to the key operation in power allocation. Select the energy priority strategy to be applied, prepare relevant computing equipment, set policy parameters, such as energy cost weight, energy stability weight, charging terminal importance weight, etc., execute strategy retrieval, obtain predetermined energy priority strategy data, the predetermined energy priority strategy data represents the basis for determining the order and proportion of energy allocation according to preset rules and algorithms, and its characteristic is that multiple factors are comprehensively considered. Continue to perform strategy analysis and generate a power allocation decision. This decision represents the specific arrangement for allocating power to each charging terminal by each energy resource module, and its characteristic is to achieve reasonable allocation of energy.

[0021] Step S400, obtaining a power allocation management record, wherein the power allocation management record refers to an allocation management record for collaboratively allocating the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision. Specifically, the process of obtaining a power allocation management record refers to a key operation in power allocation. Select a scenario for the record to be acquired, prepare relevant acquisition equipment, set acquisition parameters, such as record type, time range, etc., execute record acquisition, and obtain power allocation management record data. The power allocation management record data represents the details of collaboratively allocating the multi-source energy resource modules to multiple charging terminals based on the power allocation decision, and is characterized by a comprehensive presentation of the allocation status. Continue to acquire data to ensure that complete and accurate records are acquired.

[0022] Step S500, introduce the allocation management evaluation function to analyze the power allocation management record to obtain real-time fitness, and judge whether the real-time fitness is within the predetermined fitness threshold. Specifically, the process of introducing the allocation management evaluation function to analyze the power allocation management record to obtain real-time fitness, and judging whether the real-time fitness is within the predetermined fitness threshold refers to the key operation in power allocation management. Select the power allocation management record to be analyzed, prepare the evaluation function related equipment, set the evaluation function parameters, such as evaluation dimension, weight coefficient, etc., execute the evaluation function operation, and obtain the real-time fitness data. The real-time fitness data represents the comprehensive evaluation result of the power allocation management record, and its characteristic is the quantitative evaluation of the allocation effect. Continue to perform fitness judgment, compare the real-time fitness with the predetermined fitness threshold, the predetermined fitness threshold represents the standard range of the ideal state of power allocation, and its characteristic is to set the evaluation benchmark.

[0023] In a possible implementation, an allocation management evaluation function is introduced to analyze the power allocation management record to obtain real-time fitness, and determine whether the real-time fitness is within a predetermined fitness threshold. Step S500 further includes step S510, extracting the first evaluation dimension in the predetermined evaluation dimension, and the first evaluation dimension corresponds to the first dimension characterization indicator set. Specifically, the first evaluation dimension is extracted from the predetermined evaluation dimension, and the predetermined evaluation dimension covers important aspects such as stability dimension, reliability dimension and energy efficiency dimension. When extracting the first evaluation dimension, the system will select according to pre-set rules and priorities. The rules are based on the focus of the current power allocation, or are determined according to the operating status and needs of the system to determine the first evaluation dimension. Since each evaluation dimension has its corresponding characterization indicator set, the corresponding first dimension characterization indicator set can be clearly identified. The first dimension characterization indicator set is specially set to accurately measure and describe the first evaluation dimension, and includes indicators such as the amplitude, frequency, energy conversion rate, and energy loss rate of power fluctuations.

[0024] Step S520, traverse the first dimension characterization indicator set in the power allocation management record to obtain a first indicator parameter set. Specifically, after the first evaluation dimension is determined, the first dimension characterization indicator set corresponds to it. The indicator set is a series of quantitative or qualitative indicators used to specifically measure and describe the first evaluation dimension. The first dimension characterization indicator set is comprehensively traversed in the power allocation management record, and through the traversal operation, the data related to the indicator is screened and extracted from the power allocation management record, thereby forming a first indicator parameter set.

[0025] Step S530, the real-time fitness is obtained by evaluating and analyzing the first indicator parameter set according to the allocation management evaluation function. Specifically, after obtaining the first indicator parameter set, it is evaluated and analyzed according to the allocation management evaluation function. The evaluation function processes each parameter in the first indicator parameter set one by one, and different parameters are assigned different weights to reflect their importance in the evaluation. Considering the values, weights and mutual relationships of all parameters, a quantitative value that can reflect the current power allocation situation is calculated, that is, the real-time fitness. The real-time fitness is a comprehensive evaluation result of the power allocation status represented by the first indicator parameter set, which clearly shows whether the current power allocation performs well under a specific evaluation dimension and whether it has reached the expected standard or goal.

[0026] Step S540, wherein the predetermined evaluation dimensions include stability dimension, reliability dimension and energy efficiency dimension. Specifically, the predetermined evaluation dimensions include stability dimension, reliability dimension and energy efficiency dimension. For the stability dimension, the fluctuation in the power distribution process is mainly considered, including monitoring the amplitude, frequency and duration of power output changes. For example, observe whether the power can maintain a relatively stable state in different time periods, whether there are sudden large fluctuations or frequent small fluctuations. By collecting and analyzing these data, the stability performance of power distribution is evaluated. The reliability dimension focuses on evaluating the ability of the power distribution system to continue to operate normally under various conditions, involving considerations of equipment failure frequency, system recovery time, accuracy and integrity of data transmission, etc. For example, statistics within a certain period of time. The energy efficiency dimension focuses on analyzing the efficiency of energy utilization in the entire power distribution process. It is necessary to calculate the input-output ratio of energy and examine the energy loss during transmission, conversion and use. For example, the total input energy amount is compared with the actual energy amount that is effectively utilized, and the proportion of energy lost in the energy conversion link (such as the conversion from electrical energy to mechanical energy) is analyzed. In addition, the energy consumption on the energy transmission line is studied, which can comprehensively and deeply evaluate the performance and effect of the power distribution plan, thereby providing valuable basis for optimization and improvement.

[0027] In a possible implementation, the first indicator parameter set is evaluated and analyzed according to the allocation management evaluation function to obtain the real-time fitness, and step S530 further includes step S531, and the expression of the allocation management evaluation function is as follows: Wherein, f(x) refers to the real-time fitness of the power allocation decision x, and refers to the fitness of the stability dimension, the reliability dimension and the energy efficiency dimension in the predetermined evaluation dimension, respectively, a, b and c refer to the coefficient of variation of the stability dimension, the reliability dimension and the energy efficiency dimension, respectively, and a+b+c=1, A(x i ) refers to the index parameter corresponding to the i-th characterization index in the stability dimension, B(x j ) refers to the index parameter corresponding to the jth characterization index in the reliability dimension, C(x k ) refers to the index parameter corresponding to the kth characterization index in the energy efficiency dimension. Specifically, in the power allocation evaluation, the expression of the allocation management evaluation function is: Analyze each term in the function, f(x) represents the real-time fitness of the power allocation decision x, and comprehensively evaluates the power allocation effect. Where a is the coefficient of variation of the stability dimension, which reflects the importance of this dimension in the overall evaluation. It represents the sum of the index parameters corresponding to each characterization index in the stability dimension. During calculation, the index parameter A(x i ), then add them up and multiply them by the coefficient of variation a, Where b is the coefficient of variation of the reliability dimension, is the sum of the index parameters corresponding to each characterization index in the reliability dimension, and obtains the index parameter B(x j ) are accumulated and then multiplied by the coefficient of variation b. part, c is the coefficient of variation of the energy efficiency dimension, It is the sum of the index parameters corresponding to each characterization index in the energy efficiency dimension. First, obtain the index parameter C(x k ) are accumulated and multiplied by the coefficient of variation c. The three parts are added together to obtain the final real-time fitness f(x). In the calculation process, the sum of a, : and c is 1. By adjusting the values ​​of these three coefficients, the weights of stability dimension, reliability dimension and energy efficiency dimension in the evaluation can be changed according to actual needs and emphasis, so as to more accurately evaluate the pros and cons of power allocation decisions.

[0028] Step S600, if it is not, generate a correction instruction, and based on the correction instruction, call the output power correction strategy to correct the multiple real-time power values ​​to obtain the predicted available power, and call the demand power correction strategy to correct the multiple demand power values ​​to obtain the predicted demand power. Specifically, when it is determined that the real-time fitness is not within the predetermined fitness threshold range, the system will immediately start the correction mechanism, generate a correction instruction, and trigger subsequent correction operations. Based on the correction instruction, the system will call the output power correction strategy to correct the multiple real-time power values. The output power correction strategy is a set of pre-set rules and algorithms for adjusting and optimizing the real-time power values. When performing the correction, it will comprehensively consider various factors, such as the stability of the energy supply, the working status of the equipment, the current load conditions, etc., to adjust and correct the multiple real-time power values ​​to obtain the predicted available power. The system will also call the demand power correction strategy to correct the multiple demand power values. The output power correction strategy is a set of pre-set rules and algorithms for adjusting and optimizing the real-time power values. When performing the correction, it will combine the stability of the energy supply. Qualitative, working status of equipment, current load conditions, etc., through the analysis and calculation of factors, multiple real-time power values ​​are adjusted and corrected to obtain the predicted available power. The system will also call the demand power correction strategy to correct multiple demand power values. The demand power correction strategy is also based on a series of preset rules and algorithms, aiming to make reasonable adjustments to the demand power value according to the actual situation. When correcting the demand power value, factors such as the priority of the charging terminal, the urgency of the charging task, and the expected demand in the future are taken into account. After correction, the predicted demand power is obtained. Through the dual correction of the real-time power value and the demand power value, the power allocation can be more reasonable and optimized, in the hope that in subsequent operations, the predetermined fitness threshold can be reached or approached, so as to achieve more efficient and stable power allocation.

[0029] In a possible implementation, if it is not, a correction instruction is generated, and based on the correction instruction, the output power correction strategy is called to correct the multiple real-time power values ​​to obtain the predicted available power, and the demand power correction strategy is called to correct the multiple demand power values ​​to obtain the predicted demand power, and step S600 further includes step S610, extracting the first energy resource module from the multi-source energy resource module. Specifically, the first energy resource module is accurately extracted from the multi-source energy resource module, and the extraction process is based on specific rules or priorities, such as energy type, production efficiency and other factors.

[0030] Step S620, obtaining a first historical output power time series of the first energy resource module, wherein the first historical output power time series includes multiple time periods with output power identifiers. Specifically, obtaining a first historical output power time series of the first energy resource module, the historical data covers multiple time periods with output power identifiers, and records in detail the power output of the energy resource module in different time periods in the past.

[0031] Step S630, generating a first scatter plot of the multiple time periods with output power identifications, and performing curve fitting on the first scatter plot to obtain a first regression fitting curve. Specifically, based on historical data, multiple first scatter plots with output power identification time periods are generated, each scatter point represents an output power value of a specific time period, and a mathematical method is used to perform curve fitting on the first scatter plot to obtain a first regression fitting curve, which can reflect the trend and law of historical output power changes.

[0032] Step S640, predicting and analyzing the first regression fitting curve in combination with the real-time period to obtain a first predicted power value. Specifically, predicting and analyzing the first regression fitting curve in combination with the current real-time period, and calculating and obtaining the first predicted power value by analyzing the trend of the curve and the relationship with the real-time period, this value represents the power output prediction of the energy resource module in the current real-time period.

[0033] Step S650, according to the output power correction strategy, taking the average of the first predicted power value and the first real-time power value of the first energy resource module as the first predicted available power. Specifically, according to the preset output power correction strategy, the first predicted power value and the first real-time power value of the first energy resource module are averaged, and the average is determined as the first predicted available power, taking the predicted value and the real-time value into consideration to improve the accuracy and reliability of power prediction.

[0034] Step S660, the predicted available power is constructed based on the first predicted available power. Specifically, the first predicted available power is clarified, and the predicted available power data of other energy resource modules are widely collected. The data acquisition method is similar to the acquisition of the first predicted available power. According to the rules and order of module priority, importance or proportion in the system, the predicted available power of each module is integrated, and the data is standardized so that the data of different modules are unified in units and magnitudes, which is convenient for combination and calculation. Through a specific algorithm or calculation method, all predicted available power values ​​are accumulated and summarized, and the constructed predicted available power is carefully checked and verified. Once a problem is found, the calculation process is reviewed or the data is re-acquired for correction to ensure its accuracy and rationality, providing solid support for energy management and allocation decisions.

[0035] In a possible implementation, the first regression fitting curve is predicted and analyzed in combination with the real-time period to obtain a first predicted power value, and step S640 further includes step S641, performing spectral density analysis on the first regression fitting curve to obtain a first spectral density analysis result, and the first spectral density analysis result includes multiple time periods with spectral density identifiers. Specifically, the first regression fitting curve is subjected to spectral density analysis, and the power change pattern represented by the curve is converted into spectrum information. During the analysis process, the curve is subdivided and processed according to the time series, and the power spectral density value corresponding to each time point or time period is calculated to obtain a spectral density value. Each value is associated with a specific time period, and each time period is assigned a unique spectral density identifier to distinguish different power change characteristics, forming a first spectral density analysis result, which includes multiple time periods with clear spectral density identifiers. Each identifier not only represents the corresponding time period, but also contains important information such as the frequency characteristics and energy distribution of the power change in the time period, which is of great significance for further understanding the power output law of the energy resource module, predicting future power output, and performing effective power management and optimization.

[0036] Step S642, the real-time period is matched with the multiple periods with spectral density identifiers to obtain real-time spectral density. Specifically, after obtaining multiple periods with spectral density identifiers, the current real-time period is accurately matched with these periods, and key features of the real-time period, such as start time, end time, duration, etc., are extracted, and the features are compared with corresponding features of multiple periods with spectral density identifiers one by one.

[0037] Step S643, analyzing the real-time spectrum density to obtain the first predicted power value. Specifically, during the comparison process, the system will make a judgment according to a predetermined matching rule, which is based on the time overlap, the allowable range of the time interval, etc. For each time period with a spectrum density identifier, the similarity or matching degree between the real-time time period and it is calculated. Through continuous comparison and calculation, the time period that best matches the real-time time period is finally found among multiple time periods with spectrum density identifiers. Once the matching time period is determined, the corresponding spectrum density value can be obtained. This spectrum density value is the real-time spectrum density. The entire matching process requires efficient calculation and accurate judgment to ensure that the real-time spectrum density that accurately reflects the power characteristics of the real-time time period is obtained.

[0038] In a possible implementation, if it is not, a correction instruction is generated, and based on the correction instruction, the output power correction strategy is called to correct the multiple real-time power values ​​to obtain the predicted available power, and the demand power correction strategy is called to correct the multiple demand power values ​​to obtain the predicted demand power, and step S600 further includes step S670, extracting the first charging terminal from the multiple charging terminals. Specifically, the first charging terminal is accurately extracted from the multiple charging terminals, and the extraction is based on the priority, charging order or other preset conditions of the charging terminal.

[0039] Step S680, obtaining first charging status information of the first charging terminal, wherein the first charging status information includes a first charging current sequence and a first charging voltage sequence. Specifically, obtaining the first charging status information of the first charging terminal, including a first charging current sequence and a first charging voltage sequence, and measuring and recording the charging current and voltage in real time at a certain time interval and accuracy through a special monitoring device and a data acquisition system.

[0040] Step S690: Analyze the first charging current sequence and the first charging voltage sequence to obtain a first actual power value. Specifically, analyze the acquired first charging current sequence and first charging voltage sequence, multiply and integrate or sum the current and voltage values ​​at each moment, and thus obtain the first actual power value, which reflects the power consumption of the first charging terminal during the actual charging process.

[0041] Step S6100, according to the demand power correction strategy, take the average of the first actual power value and the first demand power value of the first charging terminal as the first predicted demand power. Specifically, clarify the demand power correction strategy, obtain the calculated first actual power value and the preset first demand power value of the first charging terminal, add the two power values, divide the sum by 2 to calculate their average value, and the average value is the first predicted demand power. During the calculation process, it is necessary to ensure that the units and magnitudes of the first actual power value and the first demand power value are consistent to ensure the accuracy of the calculation results. By obtaining the average value, considering the actual power consumption and the preset demand power, the first predicted demand power can more accurately reflect the actual power demand of the first charging terminal, and provide a more reliable basis for subsequent power allocation and management decisions.

[0042] Step S6110, the predicted power demand is established based on the first predicted power demand. Specifically, the position and role of the first predicted power demand in the entire predicted power demand system are clarified, and the predicted power demand data of other charging terminals are collected. The data acquisition method is similar to the process of acquiring the first predicted power demand, and the corresponding calculation and correction strategy is followed. According to the predetermined rules and sequence, the first predicted power demand is integrated with the predicted power demand of other charging terminals. The rules are based on the priority of the charging terminal, the urgency of the charging task, the importance of the terminal and other factors. It is necessary to standardize the predicted power demand data of different charging terminals to ensure that the data is consistent in format, unit and magnitude, so as to facilitate subsequent calculation and analysis. The predicted power demand values ​​of all charging terminals are summarized and accumulated, and the predicted power demand obtained is comprehensively checked and verified. The accuracy, completeness and rationality of the data are checked to ensure that it can truly reflect the power demand of the entire system. If problems or abnormalities are found, it is necessary to review the calculation process or re-acquire relevant data for correction. Based on the first predicted power demand, a comprehensive and accurate predicted power demand is successfully established, which provides strong support and basis for subsequent power resource allocation and management.

[0043] In a possible implementation, the first charging current timing and the first charging voltage timing are analyzed to obtain the first actual power value, and step S690 further includes step S691, reading a predetermined timing feature collection scheme. Specifically, the predetermined timing feature collection scheme is read, and the scheme clearly specifies a specific method for collecting the first charging current timing and the first charging voltage timing features, such as sampling once every certain time interval (assuming 1 second), and a collection time range (assuming 5 minutes).

[0044] Step S692, according to the predetermined timing feature collection scheme, the first current feature set and the first voltage feature set of the first charging current timing and the first charging voltage timing are respectively collected. Specifically, according to the predetermined timing feature collection scheme, the first charging current timing and the first charging voltage timing are respectively collected. When collecting the current features, key information such as the peak value, mean value, fluctuation amplitude, and change trend of the current are extracted to form the first current feature set. For the voltage features, similar feature values ​​are also obtained, such as the maximum value, minimum value, stable value, and change rate of the voltage, to form the first voltage feature set.

[0045] Step S693, using the first current feature set and the first voltage feature set as input information of an actual power prediction model to obtain the first actual power value. Specifically, the collected first current feature set and the first voltage feature set are used as input information of an actual power prediction model, and the actual power prediction model is an intelligent model obtained by supervised learning of relevant data in a large amount of historical charging state records based on the principle of neural networks.

[0046] Step S694, wherein the actual power prediction model is an intelligent model obtained by effectively supervising and learning the data in the historical charging state records based on the principle of neural network. Specifically, within the model, the input feature set data will be calculated and processed by multiple layers of neurons, and the neurons will be connected through weights. According to the learned patterns and rules, the input features will be complexly calculated and mapped, and the first actual power value will be finally output. The value combines the characteristics of current and voltage and the prediction result obtained by the internal relationship learned by the model, accurately reflecting the actual power situation of the first charging terminal, achieving the specification of the predetermined time series feature collection scheme and the powerful learning and prediction ability of the neural network model, and realizing the effective prediction of the actual power of the charging terminal.

[0047] In one possible implementation, a predetermined timing feature collection scheme is read, and step S691 further includes step S6911, wherein the predetermined timing feature collection scheme includes a time domain feature collection scheme, a frequency domain feature collection scheme, and a time-frequency domain feature collection scheme. Specifically, the predetermined timing feature collection scheme covers a time domain feature collection scheme, a frequency domain feature collection scheme, and a time-frequency domain feature collection scheme. The time domain feature collection scheme reflects the direct performance of the signal on the time axis. For example, for the first charging current timing and the first charging voltage timing, the amplitude values ​​of the current and voltage changing with time will be collected, including the maximum value, minimum value, average value, root mean square value, etc., and the parameters directly related to time such as the rise time, fall time, and duration of the current and voltage will be recorded. The frequency domain feature collection scheme converts the time domain signal to the frequency domain for analysis through methods such as Fourier transform. For the first charging current and voltage, For the voltage time series, the main frequency of its spectrum, the amplitude and phase of the harmonic components, the bandwidth and other characteristics will be calculated. Through the frequency domain characteristics, the distribution of different frequency components in the current and voltage signals can be understood. The time-frequency domain feature collection scheme combines the characteristics of the time domain and the frequency domain, and can simultaneously reflect the changes in the signal in time and frequency. For example, the wavelet transform and other methods are used to obtain the energy distribution, instantaneous frequency and other characteristics of the current and voltage signals on different time and frequency scales. The scheme comprehensively and deeply describes the characteristics of the first charging current time series and the first charging voltage time series from multiple angles, providing a rich and accurate information basis for subsequent analysis and processing.

[0048] Step S700, retrieve the predetermined dynamic coordination strategy to analyze the predicted available power and the predicted demand power, generate a dynamic power allocation decision, and perform collaborative management of dynamic power allocation according to the dynamic power allocation decision. Specifically, the system retrieves the predetermined dynamic coordination strategy from a preset storage location or database. The dynamic coordination strategy contains a series of clear priority setting rules. The rules determine the order of different charging terminals or power users in power allocation based on their importance, urgency, and criticality, process the relationship between the predicted available power and the predicted demand power, and input the acquired predicted available power and predicted demand power into the dynamic coordination strategy for in-depth analysis. During the analysis process, the strategy will comprehensively consider multiple factors, such as the priority of different charging terminals, the current load conditions of the power grid, the stability of energy supply, the power change trend in the future, etc., to generate a dynamic power allocation decision. The decision will clarify to which each charging terminal or power user should be allocated. The system will start the collaborative management mechanism of dynamic power allocation based on the generated dynamic power allocation decision, which involves precise regulation of power converters, controllers and other equipment to ensure that power is accurately allocated to the corresponding terminals or devices according to the decision. During the power allocation process, the system will continuously monitor the actual power allocation situation, compare and adjust it with the decision. If an emergency occurs, such as a sudden increase in demand at a certain terminal or a decrease in available power, the system will recalculate and adjust the allocation decision according to the dynamic collaborative strategy to ensure the efficiency, stability and safety of the entire power allocation process, and achieve a dynamic cycle of continuous analysis, decision-making, execution and adjustment, aiming to achieve the optimal power allocation and collaborative management effect.

[0049] The embodiment of the present application adopts the method of obtaining the initialization state information of power allocation management (including the real-time available power of multiple energy sources), obtaining the required power values ​​of multiple charging terminals in turn, and judging whether the real-time power value can meet the required power value. If satisfied, call the energy priority strategy to generate a decision. Obtain the allocation management record, use the evaluation function to analyze to obtain the real-time fitness, and judge whether it is within the predetermined threshold. If not, generate a correction instruction, correct the real-time power value and the required power value to obtain the predicted power, call the dynamic collaborative strategy analysis to generate a dynamic decision, and perform collaborative management of dynamic power allocation accordingly, thereby achieving the technical effect of improving the accuracy and timeliness of power allocation.

[0050] In the above, refer to Figure 1 A flexible mutual aid power dynamic allocation collaborative management method according to an embodiment of the present invention is described in detail. Figure 2 A flexible and mutually beneficial power dynamic allocation collaborative management system according to an embodiment of the present invention is described.

[0051] A flexible and mutually beneficial power dynamic allocation collaborative management system according to an embodiment of the present invention is used to solve the technical problem that the existing power allocation method cannot dynamically adjust the allocation strategy according to the actual supply and demand changes in complex scenarios with multiple energy sources and multiple charging terminals, resulting in unreasonable power allocation in quantity and time, thereby achieving the technical effect of improving the accuracy and timeliness of power allocation. A flexible and mutually beneficial power dynamic allocation collaborative management system includes: an initialization state information module 10, a demand power value judgment module 20, a power allocation decision generation module 30, a power allocation management record acquisition module 40, a predetermined fitness threshold judgment module 50, a correction instruction generation module 60, and a collaborative management module 70.

[0052] The initialization state information module 10 is used to obtain the initialization state information of the power allocation management, wherein the initialization state information includes the real-time available power, and the real-time available power includes multiple real-time power values ​​of the multi-source energy resource modules;

[0053] The required power value judgment module 20 is used to sequentially obtain multiple required power values ​​of multiple charging terminals, and judge whether the multiple real-time power values ​​meet the multiple required power values;

[0054] The power allocation decision generation module 30 is used to call a predetermined energy priority strategy to generate a power allocation decision if the conditions are met;

[0055] The power allocation management record acquisition module 40 is used to acquire a power allocation management record, wherein the power allocation management record refers to an allocation management record of collaboratively allocating the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision;

[0056] The predetermined fitness threshold judgment module 50 is used to introduce the allocation management evaluation function to analyze the power allocation management record to obtain the real-time fitness, and judge whether the real-time fitness is within the predetermined fitness threshold;

[0057] The correction instruction generation module 60 is used to generate a correction instruction if it is not in the state, and based on the correction instruction, retrieve the output power correction strategy to correct the multiple real-time power values ​​to obtain the predicted available power, and retrieve the required power correction strategy to correct the multiple required power values ​​to obtain the predicted required power;

[0058] The collaborative management module 70 is used to retrieve a predetermined dynamic collaborative strategy to analyze the predicted available power and the predicted required power, generate a dynamic power allocation decision, and perform collaborative management of dynamic power allocation according to the dynamic power allocation decision.

[0059] The specific configuration of the predetermined fitness threshold judgment module 50 will be described in detail below. As described above, the allocation management evaluation function is introduced to analyze the power allocation management record to obtain the real-time fitness, and to determine whether the real-time fitness is within the predetermined fitness threshold. The predetermined fitness threshold judgment module 50 may further include: a first evaluation dimension extraction unit is used to extract the first evaluation dimension in the predetermined evaluation dimension, and the first evaluation dimension corresponds to the first dimension characterization indicator set; a first indicator parameter set acquisition unit is used to traverse the first dimension characterization indicator set in the power allocation management record to obtain the first indicator parameter set; a real-time fitness acquisition unit is used to evaluate and analyze the first indicator parameter set according to the allocation management evaluation function to obtain the real-time fitness; a predetermined evaluation dimension unit is used, wherein the predetermined evaluation dimension includes a stability dimension, a reliability dimension, and an energy efficiency dimension.

[0060] The real-time fitness is obtained by evaluating and analyzing the first indicator parameter set according to the allocation management evaluation function. The real-time fitness acquisition unit may further include: the allocation management evaluation function subunit uses the following expression for the allocation management evaluation function: Wherein, f(x) refers to the real-time fitness of the power allocation decision x, and refers to the fitness of the stability dimension, the reliability dimension and the energy efficiency dimension in the predetermined evaluation dimension, respectively, a, b and c refer to the coefficient of variation of the stability dimension, the reliability dimension and the energy efficiency dimension, respectively, and a+b+c=1, A(x i ) refers to the index parameter corresponding to the i-th characterization index in the stability dimension, B(x j ) refers to the index parameter corresponding to the jth characterization index in the reliability dimension, C(x k ) refers to the indicator parameter corresponding to the kth characterization indicator in the energy efficiency dimension.

[0061] Next, the specific configuration of the correction instruction generating module 60 will be described in detail. As described above, if it is not in, a correction instruction is generated, and based on the correction instruction, the output power correction strategy is called to correct the multiple real-time power values ​​to obtain the predicted available power, and the demand power correction strategy is called to correct the multiple demand power values ​​to obtain the predicted demand power. The correction instruction generation module 60 may further include: a first energy resource module extraction unit is used to extract the first energy resource module in the multi-source energy resource module; a first historical output power timing acquisition unit is used to obtain the first historical output power timing of the first energy resource module, and the first historical output power timing includes multiple time periods with output power identification; a first regression fitting curve fitting unit is used to generate a first scatter plot of the multiple time periods with output power identification, and curve fit the first scatter plot to obtain a first regression fitting curve; a first predicted power value prediction unit is used to predict and analyze the first regression fitting curve in combination with the real-time time period to obtain a first predicted power value; a mean acquisition unit is used to take the mean of the first predicted power value and the first real-time power value of the first energy resource module according to the output power correction strategy as the first predicted available power; a predicted available power component unit is used to form the predicted available power based on the first predicted available power.

[0062] Among them, the first regression fitting curve is predicted and analyzed in combination with the real-time time period to obtain a first predicted power value, and the first predicted power value prediction unit may further include: a first spectral density analysis result acquisition subunit is used to perform spectral density analysis on the first regression fitting curve to obtain a first spectral density analysis result, and the first spectral density analysis result includes multiple time periods with spectral density identification; a time period matching subunit is used to match the real-time time period in the multiple time periods with spectral density identification to obtain a real-time spectral density; the first predicted power value acquisition subunit is used to analyze the real-time spectral density to obtain the first predicted power value.

[0063] Among them, the correction instruction generation module 60 may further include: a first charging terminal extraction unit is used to extract the first charging terminal among the multiple charging terminals; a first charging status information acquisition unit is used to obtain the first charging status information of the first charging terminal, and the first charging status information includes a first charging current timing and a first charging voltage timing; a first actual power value analysis unit is used to analyze the first charging current timing and the first charging voltage timing to obtain a first actual power value; a first predicted demand power acquisition unit is used to take the average of the first actual power value and the first demand power value of the first charging terminal as the first predicted demand power according to the demand power correction strategy; a predicted demand power assembly unit is used to assemble the predicted demand power based on the first predicted demand power.

[0064] Among them, the first charging current timing and the first charging voltage timing are analyzed to obtain the first actual power value, and the first actual power value analysis unit may further include: a scheme reading subunit is used to read the predetermined timing feature collection scheme; a feature set collection subunit is used to collect the first current feature set and the first voltage feature set of the first charging current timing and the first charging voltage timing according to the predetermined timing feature collection scheme; an input information subunit is used to use the first current feature set and the first voltage feature set as input information of an actual power prediction model to obtain the first actual power value; an intelligent model acquisition subunit is used, wherein the actual power prediction model is an intelligent model obtained by supervised learning of data in historical charging status records based on the principle of neural networks.

[0065] Among them, the predetermined timing feature collection scheme is read, and the scheme reading subunit further includes: a collection scheme microunit is used for the predetermined timing feature collection scheme including a time domain feature collection scheme, a frequency domain feature collection scheme and a time-frequency domain feature collection scheme.

[0066] A flexible and mutually beneficial power dynamic allocation collaborative management system provided in an embodiment of the present invention can execute a flexible and mutually beneficial power dynamic allocation collaborative management method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0067] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0068] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A flexible and mutually beneficial power dynamic allocation collaborative management method, characterized in that: include: Acquire initialization state information of power allocation management, wherein the initialization state information includes real-time available power, and the real-time available power includes multiple real-time power values ​​of multi-source energy resource modules; sequentially acquiring a plurality of required power values ​​of a plurality of charging terminals, and determining whether the plurality of real-time power values ​​meet the plurality of required power values; If it is satisfied, the predetermined energy priority strategy is called to generate a power allocation decision; Acquire a power allocation management record, wherein the power allocation management record refers to an allocation management record of collaboratively allocating the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision; Introducing an allocation management evaluation function to analyze the power allocation management record to obtain real-time fitness, and determining whether the real-time fitness is within a predetermined fitness threshold; If not, generate a correction instruction, and based on the correction instruction, call the output power correction strategy to correct the multiple real-time power values ​​to obtain the predicted available power, and call the demand power correction strategy to correct the multiple demand power values ​​to obtain the predicted demand power; A predetermined dynamic coordination strategy is retrieved to analyze the predicted available power and the predicted required power, a dynamic power allocation decision is generated, and dynamic power allocation coordination management is performed according to the dynamic power allocation decision.

2. According to claim 1, a flexible and mutually beneficial power dynamic allocation collaborative management method is characterized in that: include: Extracting a first evaluation dimension from the predetermined evaluation dimensions, wherein the first evaluation dimension corresponds to a first dimension representation indicator set; Traversing the first dimension characterization indicator set in the power allocation management record to obtain a first indicator parameter set; Evaluating and analyzing the first indicator parameter set according to the allocation management evaluation function to obtain the real-time fitness; Wherein, the predetermined evaluation dimensions include stability dimension, reliability dimension and energy efficiency dimension.

3. According to claim 2, a flexible and mutually beneficial power dynamic allocation collaborative management method is characterized in that: The expression of the allocation management evaluation function is as follows: Wherein, f(x) refers to the real-time fitness of the power allocation decision x, and refers to the fitness of the stability dimension, the reliability dimension and the energy efficiency dimension in the predetermined evaluation dimension, respectively, a, b and c refer to the coefficient of variation of the stability dimension, the reliability dimension and the energy efficiency dimension, respectively, and a+b+c=1, A(x i ) refers to the index parameter corresponding to the i-th characterization index in the stability dimension, B(x j ) refers to the index parameter corresponding to the jth characterization index in the reliability dimension, C(x k ) refers to the indicator parameter corresponding to the kth characterization indicator in the energy efficiency dimension.

4. The flexible and mutually beneficial power dynamic allocation collaborative management method according to claim 1, characterized in that: include: Extracting a first energy resource module from the multi-source energy resource modules; Acquire a first historical output power time sequence of the first energy resource module, wherein the first historical output power time sequence includes a plurality of time periods having output power identifiers; Generating a first scatter plot of the multiple time periods with output power identifiers, and performing curve fitting on the first scatter plot to obtain a first regression fitting curve; Performing a prediction analysis on the first regression fitting curve in combination with the real-time period to obtain a first predicted power value; According to the output power correction strategy, taking the average of the first predicted power value and the first real-time power value of the first energy resource module as the first predicted available power; The predicted available power is established based on the first predicted available power.

5. A flexible and mutually beneficial power dynamic allocation collaborative management method according to claim 4, characterized in that: include: Performing spectral density analysis on the first regression fitting curve to obtain a first spectral density analysis result, wherein the first spectral density analysis result includes a plurality of time periods with spectral density identifiers; Matching the real-time time period in the multiple time periods with spectral density identifiers to obtain real-time spectral density; The real-time spectral density is analyzed to obtain the first predicted power value.

6. The flexible and mutually beneficial power dynamic allocation collaborative management method according to claim 1, characterized in that: include: extracting a first charging terminal from among the plurality of charging terminals; Acquire first charging state information of the first charging terminal, where the first charging state information includes a first charging current sequence and a first charging voltage sequence; Analyzing the first charging current timing and the first charging voltage timing to obtain a first actual power value; According to the required power correction strategy, taking the average of the first actual power value and the first required power value of the first charging terminal as the first predicted required power; The predicted required power is established based on the first predicted required power.

7. A flexible and mutually beneficial power dynamic allocation collaborative management method according to claim 6, characterized in that: include: Reading a predetermined timing feature collection plan; According to the predetermined timing feature collection scheme, a first current feature set and a first voltage feature set of the first charging current timing sequence and the first charging voltage timing sequence are respectively collected and obtained; Using the first current feature set and the first voltage feature set as input information of an actual power prediction model to obtain the first actual power value; The actual power prediction model is an intelligent model obtained by supervised learning of data in historical charging status records based on the principle of neural network.

8. A flexible and mutually beneficial power dynamic allocation collaborative management method according to claim 7, characterized in that: The predetermined time series feature collection scheme includes a time domain feature collection scheme, a frequency domain feature collection scheme and a time-frequency domain feature collection scheme.

9. A flexible and mutually beneficial power dynamic allocation collaborative management system, characterized in that: The system is used to implement a flexible and mutually beneficial power dynamic allocation collaborative management method according to any one of claims 1 to 8, and the system comprises: An initialization state information module, the initialization state information module is used to obtain initialization state information of power allocation management, the initialization state information includes real-time available power, and the real-time available power includes multiple real-time power values ​​of multi-source energy resource modules; A power demand value judgment module, the power demand value judgment module is used to sequentially obtain multiple power demand values ​​of multiple charging terminals, and judge whether the multiple real-time power values ​​meet the multiple power demand values; A power allocation decision generation module, wherein if the conditions are met, the power allocation decision generation module is used to call a predetermined energy priority strategy to generate a power allocation decision; A power allocation management record acquisition module, the power allocation management record acquisition module is used to acquire a power allocation management record, the power allocation management record refers to an allocation management record of collaboratively allocating the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision; A predetermined fitness threshold judgment module, the predetermined fitness threshold judgment module is used to introduce an allocation management evaluation function to analyze the power allocation management record to obtain real-time fitness, and judge whether the real-time fitness is within a predetermined fitness threshold; A correction instruction generation module, the correction instruction generation module is used to generate a correction instruction if it is not in, and based on the correction instruction, call the output power correction strategy to correct the multiple real-time power values ​​to obtain the predicted available power, and call the demand power correction strategy to correct the multiple demand power values ​​to obtain the predicted demand power; A collaborative management module is used to call a predetermined dynamic collaborative strategy to analyze the predicted available power and the predicted required power, generate a dynamic power allocation decision, and perform collaborative management of dynamic power allocation according to the dynamic power allocation decision.

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