A flexible and mutually supportive power dynamic allocation collaborative management method and system
By acquiring real-time data from multiple energy sources and charging terminals, and generating correction and dynamic decisions, the problem of unreasonable power allocation in scenarios with multiple energy sources and multiple charging terminals is solved, achieving accurate and efficient power allocation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUXI POWER SUPPLY BRANCH OF STATE GRID JIANGSU ELECTRIC POWER CO LTD
- Filing Date
- 2025-01-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies cannot achieve precise and dynamic power allocation in scenarios with multiple energy sources and multiple charging terminals, resulting in unreasonable power allocation and an inability to meet complex and ever-changing energy supply and charging demands, leading to insufficient supply or idle power.
By acquiring the initialization status information of power allocation management, it is determined whether the real-time power value meets the required power value. If not, a correction command is generated and the correction strategy is invoked. Combined with the dynamic coordination strategy, a dynamic decision is generated to perform dynamic power allocation and collaborative management.
It improves the accuracy and timeliness of power allocation, ensures the rationality and stability of power allocation, and can dynamically adjust to adapt to changes in actual needs.
Smart Images
Figure CN119965843B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of energy management and power distribution technology, and in particular to a flexible and mutually supportive dynamic power distribution collaborative management method and system. Background Technology
[0002] In energy management and power distribution scenarios, the power allocation problem among multiple energy sources and charging terminals is particularly prominent. The contradiction between the demand for efficient and coordinated power allocation is also more pronounced. Achieving accurate, dynamic, and coordinated power allocation to better meet the complex and ever-changing energy supply and charging needs has become a crucial link in solving energy management and power distribution problems. Traditional energy power allocation methods are often rather extensive and localized, relying only on simple allocation strategies or limited evaluation indicators. They lack sufficient control over the overall situation of multiple energy sources and charging terminals, and lack in-depth analysis and utilization of real-time changing data. It is difficult to comprehensively and accurately assess the stability, reliability, and energy efficiency of the system. In terms of power allocation, there are unreasonable situations, resulting in insufficient power supply in some key areas and idle power in other areas. The formulation of power allocation schemes is relatively simple and fixed, and cannot well cope with the ever-changing reality of energy and charging demands.
[0003] In current technologies, power allocation methods cannot dynamically adjust allocation strategies according to actual supply and demand changes in complex scenarios with multiple energy sources and multiple charging terminals, resulting in technical problems of unreasonable power allocation in terms of quantity and time. Summary of the Invention
[0004] This application provides a flexible and mutually supportive dynamic power allocation collaborative management method and system. It involves acquiring initial power allocation management status information (including real-time available power from multiple energy sources), sequentially obtaining the power demand values of multiple charging terminals, and determining whether the real-time power value meets the demand. If it does, an energy priority strategy is invoked to generate a decision. Allocation management records are acquired, and an evaluation function is used to analyze and obtain the real-time fitness, determining whether it is within a predetermined threshold. If not, a correction command is generated to correct the real-time power value and the demand power value to obtain the predicted power. A dynamic collaborative strategy is invoked to analyze and generate a dynamic decision, thereby performing dynamic power allocation collaborative management, achieving the technical effect of improving the accuracy and timeliness of power allocation.
[0005] This application provides a flexible and mutually supportive power dynamic allocation and collaborative management method, including:
[0006] The system acquires initialization status information for power allocation management, including real-time available power, which includes multiple real-time power values from multiple energy resource modules. It then sequentially acquires multiple demand power values from multiple charging terminals and determines whether these real-time power values meet the demand power requirements. If they do, it invokes a predetermined energy priority strategy to generate a power allocation decision. Next, it acquires a power allocation management record, which is a record of the collaborative allocation of the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision. Finally, it introduces an allocation management evaluation function to analyze the power allocation management record to obtain real-time fitness, and determines whether the real-time fitness is within a predetermined fitness threshold. If not, it generates a correction instruction, and based on the correction instruction, it invokes an output power correction strategy to correct the multiple real-time power values to obtain predicted available power. It also invokes a demand power correction strategy to correct the multiple demand power values to obtain predicted demand power. Finally, it invokes a predetermined dynamic coordination strategy to analyze the predicted available power and the predicted demand power, generates a dynamic power allocation decision, and performs dynamic power allocation coordination management based on the dynamic power allocation decision.
[0007] This application also provides a flexible and mutually supportive power dynamic allocation and cooperative management system, including:
[0008] The system comprises the following modules: an initialization status information module, which acquires initialization status information for power allocation management, including real-time available power, which includes multiple real-time power values from multiple energy resource modules; a demand power value judgment module, which sequentially acquires multiple demand power values from multiple charging terminals and determines whether the multiple real-time power values meet the demand power requirements; a power allocation decision generation module, which, if the demand is met, retrieves a predetermined energy priority strategy to generate a power allocation decision; and a power allocation management record acquisition module, which acquires power allocation management records, which refer to the records of the coordinated allocation of the multiple energy resource modules to the multiple charging terminals based on the power allocation decision. The system includes: a power allocation management record for the terminal; a predetermined fitness threshold judgment module, which uses 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; a correction instruction generation module, which generates a correction instruction if the fitness is not within a predetermined fitness threshold, and uses the correction instruction to retrieve an output power correction strategy to correct the multiple real-time power values to obtain predicted available power, and retrieves a demand power correction strategy to correct the multiple demand power values to obtain predicted demand power; and a collaborative management module, which retrieves a predetermined dynamic collaborative strategy to analyze the predicted available power and the predicted demand power, generates a dynamic power allocation decision, and performs dynamic power allocation collaborative management based on the dynamic power allocation decision.
[0009] This application proposes a flexible and mutually supportive dynamic power allocation and collaborative management method and system. First, it acquires the initialization status information of power allocation management (including the real-time available power of multiple energy sources). Then, it sequentially acquires the power demand values of multiple charging terminals and determines whether the real-time power value can meet the demand. If it does, it invokes an energy priority strategy to generate a decision. Next, it acquires allocation management records and uses an evaluation function to analyze and obtain the real-time fitness, determining whether it is within a predetermined threshold. If not, it generates a correction command to correct the real-time power value and the demand power value to obtain the predicted power. Finally, it invokes a dynamic collaborative strategy to analyze and generate a dynamic decision, thereby performing dynamic power allocation and collaborative management, achieving the technical effect of improving the accuracy and timeliness of power allocation. Attached Figure Description
[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.
[0011] Figure 1 A flowchart illustrating a flexible and mutually supportive power dynamic allocation and collaborative management method provided in this application embodiment;
[0012] Figure 2 A schematic diagram of a flexible and mutually supportive power dynamic allocation and collaborative management system provided in this application embodiment;
[0013] Explanation of reference numerals in the attached diagram: Initialization status information module 10, demand 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, and collaborative management module 70. Detailed Implementation
[0014] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.
[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0016] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. 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 explicitly listed, but may include other steps or modules not explicitly 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 commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.
[0017] This application provides a flexible and mutually supportive dynamic power allocation and collaborative management method, such as... Figure 1 As shown, the method includes:
[0018] Step S100: Obtain the initialization status information for power allocation management. This initialization status information includes real-time available power, which comprises multiple real-time power values from the multi-source energy resource modules. Specifically, the initialization status information for 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 power acquisition parameters such as energy source type and acquisition frequency, and execute the power acquisition operation to obtain real-time power value sequence records of the multi-source energy resource modules. These real-time power value sequence records represent the output power of each energy module at a specific moment, reflecting its real-time power supply capability. Continue executing power acquisition to ensure comprehensive and accurate real-time available power information is obtained. Record the entire process of obtaining the initialization status information for power allocation management.
[0019] Step S200: Sequentially acquire multiple power demand values for multiple charging terminals, and determine whether the multiple real-time power values meet the multiple power demand values. Specifically, the process of acquiring the power demand values of multiple charging terminals and determining their relationship with the real-time power values refers to the following steps in the power allocation process: selecting multiple charging terminals to be evaluated, preparing power acquisition equipment, setting power acquisition parameters such as terminal type, charging speed requirements, and remaining battery power, performing power demand acquisition, and obtaining a sequence of power demand value records for multiple charging terminals. This sequence of power demand value records represents the power demand of each charging terminal under specific conditions, and its characteristic is that it reflects the power consumption needs of different terminals. Continue to perform power demand analysis to determine whether the multiple real-time power values can meet these power demand values. This determination process involves comparing the real-time available power with the power demand of each terminal, and its characteristic is that it determines 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 a key operation in power allocation. Select the energy priority strategy to be used, prepare relevant computing equipment, set strategy parameters such as energy cost weight, energy stability weight, and charging terminal importance weight, execute strategy retrieval, and 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, characterized by comprehensive consideration of multiple factors. Continue to execute strategy analysis to generate a power allocation decision. This decision clarifies the specific arrangement of power allocation from each energy resource module to each charging terminal, characterized by achieving reasonable energy allocation.
[0021] Step S400: Obtain power allocation management records. These records refer to the allocation management records used to collaboratively allocate the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision. Specifically, obtaining power allocation management records refers to key operations in power allocation. Select the scenario for which records are to be obtained, prepare the relevant acquisition equipment, set acquisition parameters such as record type and time range, and execute record acquisition to obtain power allocation management record data. This data represents the detailed situation of collaboratively allocating the multi-source energy resource modules to multiple charging terminals based on the power allocation decision, and its characteristic is a comprehensive presentation of the allocation status. Continue data acquisition to ensure complete and accurate records are obtained.
[0022] Step S500 involves 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. Specifically, the process of 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 is a key operation in power allocation management. This involves selecting the power allocation management record to be analyzed, preparing the relevant equipment for the evaluation function, setting the evaluation function parameters (such as evaluation dimensions and weighting coefficients), executing the evaluation function calculation, and obtaining real-time fitness data. The real-time fitness data represents the comprehensive evaluation result of the power allocation management record, characterized by quantifying the allocation effect. The fitness judgment continues, comparing the real-time fitness with a predetermined fitness threshold. The predetermined fitness threshold represents the standard range of the ideal power allocation state, characterized by setting an evaluation benchmark.
[0023] In one possible implementation, an allocation management evaluation function is introduced to analyze the power allocation management records to obtain real-time fitness, and it is determined whether the real-time fitness is at a predetermined fitness threshold. Step S500 further includes step S510, extracting a first evaluation dimension from the predetermined evaluation dimensions. The first evaluation dimension corresponds to a first dimension representation index set. Specifically, the first evaluation dimension is extracted from the predetermined evaluation dimensions. The predetermined evaluation dimensions cover important aspects such as stability, reliability, and energy efficiency. When extracting the first evaluation dimension, the system selects according to pre-set rules and priorities. The rules are based on the current focus of power allocation or determined according to the system's operating status and requirements. Since each evaluation dimension has its corresponding representation index set, the corresponding first dimension representation index set can be clearly identified. The first dimension representation index set is specifically designed to accurately measure and describe the first evaluation dimension, and includes indicators such as the amplitude and frequency of power fluctuations, energy conversion rate, and energy loss rate.
[0024] Step S520: The first dimension characterization index set is traversed through the power allocation management record to obtain the first index parameter set. Specifically, after determining the first evaluation dimension, there is a corresponding first dimension characterization index set. The index set is a series of quantitative or qualitative indicators used to specifically measure and describe the first evaluation dimension. The first dimension characterization index set is fully traversed through the power allocation management record. Through the traversal operation, data related to the indicators is filtered and extracted from the power allocation management record to form the first index parameter set.
[0025] Step S530: The real-time fitness is obtained by evaluating and analyzing the first set of indicator parameters according to the allocation management evaluation function. Specifically, after obtaining the first set of indicator parameters, it is evaluated and analyzed according to the allocation management evaluation function. The evaluation function processes each parameter in the first set of indicator parameters one by one, and different parameters are assigned different weights to reflect their importance in the evaluation. Considering the values, weights, and interrelationships of all parameters, a quantitative value that reflects the current power allocation situation is calculated, namely the real-time fitness. The real-time fitness is a comprehensive evaluation result of the power allocation situation represented by the first set of indicator parameters, clearly indicating 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 a stability dimension, a reliability dimension, and an energy efficiency dimension. Specifically, the predetermined evaluation dimensions include stability, reliability, and energy efficiency. For the stability dimension, the main consideration is the fluctuation during the power distribution process, including monitoring the amplitude, frequency, and duration of power output changes. For example, observing whether the power can maintain a relatively stable state over different time periods, and whether there are sudden large fluctuations or frequent small fluctuations. By collecting and analyzing this data, the stability performance of the power distribution is evaluated. The reliability dimension focuses on evaluating the ability of the power distribution system to operate continuously under various conditions, involving considerations such as equipment failure frequency, system recovery time, and the accuracy and integrity of data transmission. For example, statistically analyzing data over a certain period of time... The number of equipment failures is counted, the average time required for the system to fully recover from a failure is calculated, and data loss or errors are checked during transmission and processing. The energy efficiency dimension focuses on analyzing the utilization efficiency of energy in the entire power distribution process. It is necessary to calculate the energy input-output ratio, examine the energy loss during transmission, conversion, and use, such as comparing the total input energy with the actual amount of energy effectively utilized, analyzing the proportion of energy lost in energy conversion stages (such as the conversion from electrical energy to mechanical energy), and studying energy consumption on energy transmission lines. This allows for a comprehensive and in-depth evaluation of the performance and effectiveness of power distribution schemes, thus providing valuable basis for optimization and improvement.
[0027] In one possible implementation, the real-time fitness is obtained by evaluating and analyzing the first set of index parameters according to the allocation management evaluation function. Step S530 further includes step S531, where the expression of the allocation management evaluation function is as follows: Where f(x) refers to the real-time fitness of the power allocation decision x. and These refer to the fitness of the stability dimension, reliability dimension, and energy efficiency dimension in the predetermined evaluation dimensions, respectively. a, b, and c refer to the coefficients of variation of the stability dimension, reliability dimension, and 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 j-th characterization index in the reliability dimension, C(x) k () refers to the index parameter corresponding to the k-th characterization index in the energy efficiency dimension. Specifically, in power allocation assessment, the expression of the allocation management assessment function is: In the analysis function, f(x) represents the real-time fitness of the power allocation decision x, providing a comprehensive quantitative evaluation of the power allocation effect. For the terms in the expression... Where 'a' is the coefficient of variation of the stability dimension, reflecting the importance of this dimension in the overall evaluation. This represents the sum of the index parameters corresponding to each representation index in the stability dimension. During calculation, the index parameter A(x) corresponding to the i-th representation index in the stability dimension is obtained. i Then sum them up and multiply by the coefficient of variation α. In this context, 'b' represents the coefficient of variation for the reliability dimension. It is the sum of the index parameters corresponding to each characterization index in the reliability dimension. The index parameter B(x) corresponding to the j-th characterization index in the reliability dimension is obtained. j The results are accumulated and then multiplied by the coefficient of variation, b. In this part, c is the coefficient of variation for the energy efficiency dimension. It is the sum of the index parameters corresponding to each characteristic indicator in the energy efficiency dimension. First, obtain the index parameter C(x) corresponding to the Kth characteristic indicator in the energy efficiency dimension. k The values of a, c, and c are summed and multiplied by the coefficient of variation c. The sum of these three parts is used to obtain the final real-time fitness f(x). During the calculation process, the sum of a, c, and c is 1. By adjusting the values of these three coefficients, the weights of the stability, reliability, and energy efficiency dimensions in the evaluation can be changed according to actual needs and priorities, thereby more accurately evaluating the merits of power allocation decisions.
[0028] Step S600: If not within the predetermined fitness threshold range, a correction command is generated, and based on the correction command, an output power correction strategy is invoked to correct the multiple real-time power values to obtain the predicted available power. A demand power correction strategy is then invoked 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 immediately initiates a correction mechanism, generates a correction command, and triggers subsequent correction operations. Based on the correction command, the system invokes an output power correction strategy to correct the multiple real-time power values. The output power correction strategy is a set of pre-defined rules and algorithms used to adjust and optimize the real-time power values. During correction, various factors are considered, such as the stability of energy supply, the operating status of equipment, and the current load, to adjust and correct the multiple real-time power values, thereby obtaining the predicted available power. The system also invokes a demand power correction strategy to correct the multiple demand power values. The output power correction strategy is a set of pre-defined rules and algorithms used to adjust and optimize the real-time power values. During correction, factors such as the stability of energy supply are considered. The system analyzes and calculates factors such as qualitative factors, equipment operating status, and current load conditions to adjust and correct multiple real-time power values, thereby obtaining the predicted available power. The system also retrieves a 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 reasonably adjust the demand power values according to the actual situation. When correcting the demand power values, factors such as the priority of charging terminals, the urgency of charging tasks, and expected demand in the future period are taken into account. After correction, the predicted demand power is obtained. Through the dual correction of real-time power values and demand power values, the power allocation can be more reasonable and optimized, in order to reach or approach the predetermined fitness threshold in subsequent operations and achieve more efficient and stable power allocation.
[0029] In one possible implementation, if the current state is not met, a correction instruction is generated, and based on the correction instruction, an output power correction strategy is retrieved to correct the multiple real-time power values to obtain the predicted available power. A demand power correction strategy is then retrieved to correct the multiple demand power values to obtain the predicted demand power. Step S600 further includes step S610, extracting the first energy resource module from the multi-source energy resource modules. Specifically, the first energy resource module is accurately extracted from the multi-source energy resource modules. The extraction process is based on specific rules or priorities, such as energy type, production efficiency, etc.
[0030] Step S620: Obtain the first historical output power time sequence of the first energy resource module. The first historical output power time sequence includes multiple time periods with output power identifiers. Specifically, the first historical output power time sequence of the first energy resource module is obtained. 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: Generate a first scatter plot of the multiple time periods with output power identifiers, and perform curve fitting on the first scatter plot to obtain a first regression fitting curve. Specifically, a first scatter plot of multiple time periods with output power identifiers is generated based on historical data. Each scatter point represents the output power value of a specific time period. Mathematical methods are used to perform curve fitting on this first scatter plot to obtain a first regression fitting curve. The curve can reflect the changing trend and pattern of historical output power.
[0032] Step S640: A first predicted power value is obtained by performing predictive analysis on the first regression fitting curve in conjunction with the real-time time period. Specifically, the first regression fitting curve is analyzed in conjunction with the current real-time time period. By analyzing the trend of the curve and its relationship with the real-time time period, the first predicted power value is calculated. This value represents the predicted power output of the energy resource module in the current real-time time period.
[0033] Step S650: According to the output power correction strategy, the average of the first predicted power value and the first real-time power value of the first energy resource module is taken 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 value is identified as the first predicted available power. This comprehensively considers both the predicted value and the real-time value to improve the accuracy and reliability of power prediction.
[0034] Step S660: Construct the predicted available power based on the first predicted available power. Specifically, the first predicted available power is defined, and predicted available power data from other energy resource modules are collected extensively. The data acquisition method is similar to that used for the first predicted available power. Based on rules and order such as module priority, importance, or proportion in the system, the predicted available power of each module is integrated. The data is standardized to ensure that the data from different modules are consistent in units and magnitudes, facilitating combination and calculation. Through a specific algorithm or calculation method, all predicted available power values are accumulated and summarized. The constructed predicted available power is carefully checked and verified. If any problems are found, the calculation process is re-examined or data is re-acquired and corrected to ensure its accuracy and rationality, providing solid support for energy management and allocation decisions.
[0035] In one possible implementation, a first predicted power value is obtained by combining the real-time time period with the predictive analysis of the first regression fitting curve. Step S640 further includes step S641, which involves performing spectral density analysis on the first regression fitting curve to obtain a first spectral density analysis result. The first spectral density analysis result includes multiple time periods with spectral density identifiers. Specifically, the spectral density analysis of the first regression fitting curve converts the power change pattern represented by the curve into spectral information. During the analysis, 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 the 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 the first spectral density analysis result. This result 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 power changes within that time period. This is of great significance for further understanding the power output pattern of the energy resource module, predicting future power output, and conducting effective power management and optimization.
[0036] Step S642: Match the real-time time period with the multiple time periods with spectral density identifiers to obtain the real-time spectral density. Specifically, after obtaining the multiple time periods with spectral density identifiers, accurately match the current real-time time period with these time periods, extract key features of the real-time time period, such as start time, end time, duration, etc., and compare each feature with the corresponding features of the multiple time periods with spectral density identifiers.
[0037] Step S643: Analyze the real-time spectral density to obtain the first predicted power value. Specifically, during the comparison process, the system will make judgments according to predetermined matching rules. The rules are based on time overlap, allowable time intervals, etc. For each time period with a spectral 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 spectral density identifiers. Once the matching time period is determined, the corresponding spectral density value can be obtained. This spectral density value is the real-time spectral density. The entire matching process requires efficient calculation and accurate judgment to ensure that the real-time spectral density that accurately reflects the power characteristics of the real-time time period is obtained.
[0038] In one possible implementation, if the current state is not met, a correction instruction is generated, and based on the correction instruction, an output power correction strategy is invoked to correct the multiple real-time power values to obtain the predicted available power. A demand power correction strategy is then invoked to correct the multiple demand power values to obtain the predicted demand power. Step S600 further includes step S670, extracting the first charging terminal from the multiple charging terminals. Specifically, the first charging terminal is precisely extracted from the multiple charging terminals based on its priority, charging order, or other preset conditions.
[0039] Step S680: Obtain the first charging state information of the first charging terminal, which includes the first charging current timing sequence and the first charging voltage timing sequence. Specifically, obtaining the first charging state information of the first charging terminal includes the first charging current timing sequence and the first charging voltage timing sequence. Through a dedicated monitoring device and data acquisition system, the charging current and voltage are measured and recorded in real time at certain time intervals and with a certain precision.
[0040] Step S690: Analyze the first charging current timing sequence and the first charging voltage timing sequence to obtain the first actual power value. Specifically, analyze the obtained first charging current timing sequence and the first charging voltage timing sequence, multiply the current and voltage values at each moment, and integrate or sum them to 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, the average of the first actual power value and the first demand power value of the first charging terminal is taken as the first predicted demand power. Specifically, the demand power correction strategy is defined, the calculated first actual power value and the preset first demand power value of the first charging terminal are obtained, the two power values are added together, and the sum is divided by 2 to calculate their average value, which 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 real power demand of the first charging terminal, providing a more reliable basis for subsequent power allocation and management decisions.
[0042] Step S6110: Construct the predicted demand power based on the first predicted demand power. Specifically, clarify the position and role of the first predicted demand power in the entire predicted demand power system, collect predicted demand power data from other charging terminals (the data acquisition method is similar to the process of acquiring the first predicted demand power), follow corresponding calculation and correction strategies, and integrate the first predicted demand power with the predicted demand power of other charging terminals according to predetermined rules and order. The rules are based on factors such as the priority of the charging terminals, the urgency of the charging task, and the importance of the terminals. It is necessary to standardize the predicted demand power data of different charging terminals to ensure that the data is consistent in terms of format, unit, and magnitude, so as to facilitate subsequent calculation and analysis. The predicted demand power values of all charging terminals are summarized and accumulated, and the constructed predicted demand power is comprehensively checked and verified to check the accuracy, completeness, and rationality of the data, ensuring that it can truly reflect the demand power of the entire system. If problems or anomalies are found, the calculation process needs to be reviewed again or relevant data needs to be reacquired for correction. Based on the first predicted demand power, a comprehensive and accurate predicted demand power is successfully constructed, providing strong support and basis for subsequent power resource allocation and management.
[0043] In one possible implementation, the first actual power value is obtained by analyzing the timing of the first charging current and the timing of the first charging voltage. Step S690 further includes step S691, reading a predetermined timing feature collection scheme. Specifically, the predetermined timing feature collection scheme is read, which clearly specifies the specific method for collecting the timing features of the first charging current and the first charging voltage, such as sampling once every certain time interval (assuming 1 second) and the collection time range (assuming 5 minutes), etc.
[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 sequence and the first charging voltage timing sequence are collected respectively. Specifically, according to the predetermined timing feature collection scheme, features are collected for the first charging current timing sequence and the first charging voltage timing sequence respectively. When collecting current features, key information such as the peak value, average value, fluctuation amplitude, and change trend of the current are extracted to form the first current feature set. For voltage features, similar feature values are obtained, such as the maximum value, minimum value, stable value, and rate of change of the voltage, to form the first voltage feature set.
[0045] Step S693: The first current feature set and the first voltage feature set are used as input information for the actual power prediction model to obtain the first actual power value. Specifically, the collected first current feature set and first voltage feature set are used as input information for the actual power prediction model, which is an intelligent model obtained by supervised learning of relevant data from a large number of historical charging state records based on neural network principles.
[0046] Step S694, wherein the actual power prediction model is an intelligent model obtained by effectively supervising the learning of data from historical charging state records based on neural network principles. Specifically, within the model, the input feature set data undergoes calculation and processing by multiple layers of neurons. The neurons are connected by weights, and based on the learned patterns and rules, complex calculations and mappings are performed on the input features, ultimately outputting a first actual power value. This value, combined with the characteristics of current and voltage and the intrinsic relationship learned by the model, accurately reflects the actual power status of the first charging terminal. This achieves effective prediction of the actual power of the charging terminal by relying on the standardization of the predetermined time-series feature collection scheme and the powerful learning and prediction capabilities of the neural network model.
[0047] In one possible implementation, the predetermined timing feature collection scheme is read. Step S691 further includes step S6911, whereby 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 encompasses time-domain feature collection, frequency-domain feature collection, and time-frequency-domain feature collection schemes. The time-domain feature collection scheme reflects the direct representation 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 changes over time are collected, including maximum, minimum, average, and root mean square values, etc., and parameters directly related to time, such as the rise time, fall time, and duration of the current and voltage, are recorded. The frequency-domain feature collection scheme converts the time-domain signal to the frequency domain for analysis using methods such as Fourier transform. For the first charging current and the first charging voltage timing... The timing sequence is calculated by analyzing the dominant frequency, amplitude and phase of harmonic components, bandwidth, and other characteristics of its spectrum. Through 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 and frequency domains, and can simultaneously reflect the changes of the signal in time and frequency. For example, by using wavelet transform and other methods, the energy distribution and instantaneous frequency of the current and voltage signals at different time and frequency scales can be obtained. The scheme comprehensively and deeply describes the characteristics of the first charging current timing sequence and the first charging voltage timing sequence from multiple perspectives, providing a rich and accurate information foundation for subsequent analysis and processing.
[0048] Step S700: A predetermined dynamic coordination strategy is retrieved to analyze the predicted available power and the predicted demand power, generating a dynamic power allocation decision. Power allocation is then dynamically managed based on this decision. Specifically, the system retrieves a predetermined dynamic coordination strategy from a preset storage location or database. This strategy includes a series of explicit priority setting rules. These rules determine the order of different charging terminals or electrical devices in power allocation based on their importance, urgency, and criticality. The relationship between predicted available power and predicted demand power is processed. The obtained predicted available power and predicted demand power are input into the dynamic coordination strategy for in-depth analysis. During this analysis, the strategy comprehensively considers multiple factors, such as the priority of different charging terminals, the current grid load, the stability of energy supply, and power change trends over a future period, generating a dynamic power allocation decision. This decision clarifies which charging terminals or electrical devices should be allocated to which power allocation is required. The system generates dynamic power allocation decisions, including power values, timing, and priority details. Based on these decisions, the system initiates a dynamic power allocation collaborative management mechanism, involving precise control of power converters, controllers, and other devices. This ensures that power is accurately allocated to the appropriate terminals or devices according to the decisions. During the power allocation process, the system continuously monitors the actual power allocation and compares and adjusts it against the decisions. If unforeseen circumstances occur, such as a sudden increase in demand or a decrease in available power at a terminal, the system recalculates and adjusts the allocation decisions based on the dynamic collaborative strategy. This ensures the efficiency, stability, and safety of the entire power allocation process, achieving a dynamic cycle of continuous analysis, decision-making, execution, and adjustment, ultimately aiming to achieve optimal power allocation and collaborative management results.
[0049] This application embodiment acquires power allocation management initialization status information (including real-time available power from multiple energy sources), sequentially acquires the power demand values of multiple charging terminals, and determines whether the real-time power value can meet the power demand. If it does, an energy priority strategy is invoked to generate a decision. Allocation management records are acquired, and an evaluation function is used to analyze and obtain the real-time fitness, determining whether it is within a predetermined threshold. If not, a correction instruction is generated to correct the real-time power value and the power demand value to obtain the predicted power. A dynamic coordination strategy is invoked to analyze and generate a dynamic decision, thereby performing dynamic power allocation and collaborative management, achieving the technical effect of improving the accuracy and timeliness of power allocation.
[0050] In the above text, refer to Figure 1 A flexible and mutually supportive power dynamic allocation and collaborative management method according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes a flexible and mutually supportive power dynamic allocation and collaborative management system according to an embodiment of the present invention.
[0051] According to an embodiment of the present invention, a flexible and mutually supportive dynamic power allocation collaborative management system solves the technical problem that existing power allocation methods cannot dynamically adjust allocation strategies according to actual supply and demand changes in complex scenarios with multiple energy sources and multiple charging terminals, resulting in unreasonable power allocation in terms of quantity and time. This achieves the technical effect of improving the accuracy and timeliness of power allocation. The flexible and mutually supportive dynamic power allocation collaborative management system includes: an initialization status 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 status information module 10 is used to obtain the initialization status information of power allocation management. The initialization status information includes real-time available power, and the real-time available power includes multiple real-time power values of the multi-source energy resource module.
[0053] The power demand value determination module 20 is used to sequentially acquire multiple power demand values of multiple charging terminals and determine whether the multiple real-time power values meet the multiple power demand values.
[0054] The power allocation decision generation module 30 is used to generate a power allocation decision by retrieving a predetermined energy priority strategy if the conditions are met.
[0055] The power allocation management record acquisition module 40 is used to acquire power allocation management records, which refer to the allocation management records for the multi-source energy resource modules to be collaboratively allocated 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 to determine whether the real-time fitness is at the predetermined fitness threshold.
[0057] The correction instruction generation module 60 is used to generate a correction instruction if the current condition is not met, and to retrieve the output power correction strategy based on the correction instruction to correct the multiple real-time power values to obtain the predicted available power, and to retrieve the demand power correction strategy to correct the multiple demand power values to obtain the predicted demand 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 demand power, generate a dynamic power allocation decision, and perform dynamic power allocation collaborative management based on the dynamic power allocation decision.
[0059] The specific configuration of the predetermined fitness threshold judgment module 50 will be described in detail below. As mentioned above, the power allocation management record is analyzed using an allocation management evaluation function to obtain the real-time fitness, and it is determined whether the real-time fitness is at a predetermined fitness threshold. The predetermined fitness threshold judgment module 50 may further include: a first evaluation dimension extraction unit for extracting a first evaluation dimension from the predetermined evaluation dimensions, the first evaluation dimension corresponding to a first dimension representation index set; a first index parameter set acquisition unit for traversing the first dimension representation index set in the power allocation management record to obtain a first index parameter set; a real-time fitness acquisition unit for evaluating and analyzing the first index parameter set according to the allocation management evaluation function to obtain the real-time fitness; and a predetermined evaluation dimension unit, 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 set of indicator parameters according to the allocation management evaluation function. The real-time fitness acquisition unit may further include: an allocation management evaluation function subunit using the following expression for the allocation management evaluation function: Where f(x) refers to the real-time fitness of the power allocation decision x. and These refer to the fitness of the stability dimension, reliability dimension, and energy efficiency dimension in the predetermined evaluation dimensions, respectively. a, b, and c refer to the coefficients of variation of the stability dimension, reliability dimension, and 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 j-th characterization index in the reliability dimension, C(x) k ) refers to the index parameter corresponding to the k-th characterization index in the energy efficiency dimension.
[0061] The specific configuration of the correction instruction generation module 60 will be described in detail below. As described above, if not in a state of flux, a correction instruction is generated, and based on the correction instruction, an output power correction strategy is invoked to correct the multiple real-time power values to obtain the predicted available power. A demand power correction strategy is invoked 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 for extracting a first energy resource module from the multi-source energy resource modules; a first historical output power time series acquisition unit for acquiring the first historical output power time series of the first energy resource module, the first historical output power time series including multiple time periods with output power identifiers; a first regression fitting curve fitting unit for 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; a first predicted power value prediction unit for performing predictive analysis on the first regression fitting curve in conjunction with the real-time time periods to obtain a first predicted power value; a mean acquisition unit for taking the mean 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 according to the output power correction strategy; and a predicted available power component unit for constructing the predicted available power based on the first predicted available power.
[0062] The unit for predicting the first predicted power value by combining the real-time time period with the first regression fitting curve for predictive analysis can further include: a first spectral density analysis result acquisition subunit for 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 multiple time periods with spectral density identifiers; a time period matching subunit for matching the real-time time period among the multiple time periods with spectral density identifiers to obtain a real-time spectral density; and a first predicted power value acquisition subunit for analyzing the real-time spectral density to obtain the first predicted power value.
[0063] The correction instruction generation module 60 may further include: a first charging terminal extraction unit for extracting a first charging terminal from the plurality of charging terminals; a first charging state information acquisition unit for acquiring first charging state information of the first charging terminal, the first charging state information including a first charging current timing sequence and a first charging voltage timing sequence; a first actual power value analysis unit for analyzing the first charging current timing sequence and the first charging voltage timing sequence to obtain a first actual power value; a first predicted demand power acquisition unit for taking 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; and a predicted demand power construction unit for constructing the predicted demand power based on the first predicted demand power.
[0064] The analysis of the first charging current timing sequence and the first charging voltage timing sequence to obtain the first actual power value may further include: a scheme reading subunit for reading a predetermined timing feature collection scheme; a feature set collection subunit for collecting the first current feature set and the first voltage feature set of the first charging current timing sequence and the first charging voltage timing sequence respectively according to the predetermined timing feature collection scheme; an input information subunit for using the first current feature set and the first voltage feature set as input information for the actual power prediction model to obtain the first actual power value; and an intelligent model acquisition subunit, wherein the actual power prediction model is an intelligent model obtained by supervised learning of data in historical charging state records based on neural network principles.
[0065] The reading of the predetermined timing feature collection scheme, the scheme reading subunit further includes: a collection scheme micro-unit 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] The flexible and mutually supportive power dynamic allocation collaborative management system provided in this embodiment of the invention can execute the flexible and mutually supportive power dynamic allocation collaborative management method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
[0067] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. 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 each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.
[0068] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand 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 this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order 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 supportive dynamic power allocation and collaborative management method, characterized in that, include: Obtain initialization status information for power allocation management, the initialization status information including real-time available power, the real-time available power including multiple real-time power values of the multi-source energy resource module; The system sequentially acquires multiple power demand values from multiple charging terminals and determines whether the multiple real-time power values meet the multiple power demand values. If satisfied, retrieve the predetermined energy priority strategy to generate a power allocation decision; Obtain power allocation management records, which refer to allocation management records for the coordinated allocation of the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision; An allocation management evaluation function is introduced to analyze the power allocation management records to obtain the real-time fitness, and it is determined whether the real-time fitness is at a predetermined fitness threshold. If not, a correction instruction is generated, and based on the correction instruction, an output power correction strategy is retrieved to correct the multiple real-time power values to obtain the predicted available power. A demand power correction strategy is retrieved to correct the multiple demand power values to obtain the predicted demand power. The system retrieves a predetermined dynamic coordination strategy to analyze the predicted available power and the predicted demand power, generates a dynamic power allocation decision, and performs dynamic power allocation coordination management based on the dynamic power allocation decision. This includes: Extract the first evaluation dimension from the predetermined evaluation dimensions, where the first evaluation dimension corresponds to the first dimension characterization index set; The first dimension characterization index set is traversed through the power allocation management record to obtain the first index parameter set; The real-time fitness is obtained by evaluating and analyzing the first set of indicator parameters according to the allocation management evaluation function; The predetermined evaluation dimensions include stability, reliability, and energy efficiency. The expression for the allocation management evaluation function is as follows: ; in, This refers to the power allocation decision. The aforementioned real-time fitness, , and These refer to the fitness of the stability dimension, the reliability dimension, and the energy efficiency dimension in the predetermined evaluation dimensions, respectively. , and These refer to the coefficients of variation for the stability dimension, the reliability dimension, and the energy efficiency dimension, respectively. , This refers to the 1st stability dimension. The indicator parameters corresponding to each characterization indicator This refers to the first of the reliability dimensions. The indicator parameters corresponding to each characterization indicator This refers to the first energy efficiency dimension. The indicator parameters corresponding to each characterization indicator.
2. The flexible and mutually supportive power dynamic allocation and collaborative management method according to claim 1, characterized in that, include: Extract the first energy resource module from the multi-source energy resource modules; Obtain the first historical output power time sequence of the first energy resource module, wherein the first historical output power time sequence includes multiple time periods with output power identifiers; Generate a first scatter plot of the multiple time periods with output power identifiers, and perform curve fitting on the first scatter plot to obtain a first regression fitting curve; The first predicted power value is obtained by performing predictive analysis on the first regression fitting curve in conjunction with the real-time time period. According to the output power correction strategy, the average of the first predicted power value and the first real-time power value of the first energy resource module is taken as the first predicted available power. The predicted available power is constructed based on the first predicted available power.
3. The flexible and mutually supportive power dynamic allocation and collaborative management method according to claim 2, characterized in that, include: Spectral density analysis is performed on the first regression fitting curve to obtain the first spectral density analysis result, which includes multiple time periods with spectral density labels. The real-time spectral density is obtained by matching the real-time time period among the multiple time periods with spectral density identifiers; The first predicted power value is obtained by analyzing the real-time spectral density.
4. The flexible and mutually supportive power dynamic allocation and collaborative management method according to claim 1, characterized in that, include: Extract the first charging terminal from the plurality of charging terminals; Obtain the first charging status information of the first charging terminal, the first charging status information including the first charging current timing and the first charging voltage timing; The first actual power value is obtained by analyzing the timing sequence of the first charging current and the timing sequence of the first charging voltage. According to the power demand correction strategy, the average of the first actual power value and the first power demand value of the first charging terminal is taken as the first predicted power demand. The predicted demand power is constructed based on the first predicted demand power.
5. The flexible and mutually supportive power dynamic allocation and collaborative management method according to claim 4, characterized in that, include: Read the predetermined timing feature collection scheme; 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 sequence and the first charging voltage timing sequence are collected respectively. The first current feature set and the first voltage feature set are used as input information for the 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 from historical charging status records based on neural network principles.
6. The flexible and mutually supportive power dynamic allocation and collaborative management method according to claim 5, 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.
7. A flexible and mutually supportive power dynamic allocation and collaborative management system, characterized in that, The system is used to implement the flexible and mutually supportive power dynamic allocation and collaborative management method according to any one of claims 1-6, the system comprising: An initialization status information module is used to obtain initialization status information for power allocation management. The initialization status information includes real-time available power, which includes multiple real-time power values from the multi-source energy resource module. The power demand value determination module is used to sequentially acquire multiple power demand values of multiple charging terminals and determine whether the multiple real-time power values meet the multiple power demand values. A power allocation decision generation module is used to generate a power allocation decision by retrieving a predetermined energy priority strategy if the conditions are met. A power allocation management record acquisition module is used to acquire power allocation management records, which refer to the allocation management records for collaboratively allocating the multi-source energy resource modules to the multiple charging terminals based on the power allocation decision. A predetermined fitness threshold determination module is used to introduce an allocation management evaluation function to analyze the power allocation management record to obtain the real-time fitness, and determine whether the real-time fitness is at a predetermined fitness threshold. A correction instruction generation module is used to generate a correction instruction if the current state is not in use, and to retrieve an output power correction strategy based on the correction instruction to correct the multiple real-time power values to obtain the predicted available power, and to retrieve a demand power correction strategy to correct the multiple demand power values to obtain the predicted demand power. The collaborative management module is used to retrieve a predetermined dynamic collaborative strategy to analyze the predicted available power and the predicted demand power, generate a dynamic power allocation decision, and perform dynamic power allocation collaborative management based on the dynamic power allocation decision.