Method for optimizing configuration of multi-type energy storage microgrid capacity based on power wireless private network

Abstract

The present application relates to the technical field of micro-grid energy storage optimization, and relates to a multi-type energy storage micro-grid capacity optimization configuration method based on a power wireless private network.The present application calculates effective controllable confidence through communication delay data and packet loss rate, determines whether the communication quality is suddenly changed in combination with historical effective controllable confidence, extracts the rated power response speed and rated energy capacity of each type of energy storage when the communication quality is suddenly changed, determines the current energy distribution weight of each type of energy storage, determines the energy scheduling value based on energy output prediction data and load demand prediction data, calculates the target output energy in combination with the current charging and discharging capacity boundary of each energy storage unit, generates a capacity distribution instruction, extracts the actual output energy of each energy storage unit after the capacity distribution instruction is executed, generates an energy storage unit capacity abnormality report based on the capacity output error, realizes tracking and closed-loop verification of the execution effect of the capacity distribution instruction, and guarantees the power supply reliability of long-term operation of the micro-grid.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy storage optimization technology, and to a method for optimizing the capacity configuration of multi-type energy storage microgrids based on a power wireless private network. Background Technology

[0002] Microgrids achieve flexible energy dispatch and supply-demand balance by integrating multiple types of energy storage units. The effectiveness of their capacity optimization directly impacts the economic efficiency and reliability of microgrid operation. Since energy storage units transmit energy distribution commands via communication links, the communication quality affects their dispatch response capabilities. In microgrids with multiple types of energy storage operating collaboratively, how to utilize the fluctuating communication quality of power wireless private networks to achieve dynamic capacity optimization of various energy storage units has become a technical challenge for improving the accuracy of microgrid energy dispatch and operational stability.

[0003] In the prior art, Chinese Patent Publication No. CN120414617A discloses a method for allocating remaining energy storage capacity that is adapted to multiple application scenarios. This method is based on the multi-dimensional characteristics of scenario requirements, generates dynamic scenario clusters and constructs a pre-matching relation library through an improved clustering algorithm; combines a spatiotemporal joint cost model to comprehensively consider power deviation, capacity difference and geographical distance constraints to generate an initial allocation scheme; monitors the health of energy storage units, the rate of change of remaining capacity and communication status in real time to trigger an elastic reallocation mechanism, dynamically reduces the allocation ratio according to priority when the capacity is critical, and activates neighboring energy storage collaborative compensation when there is a sudden power fluctuation; predicts the capacity change trend through a reinforcement learning model and adjusts the allocation strategy in advance, outputs a dynamic optimization scheme and completes multi-dimensional verification.

[0004] The existing technology has the following problems: 1. Although the existing technology uses the communication status as the trigger condition for redistribution, it does not combine historical communication status for mutation detection and trend analysis. It cannot accurately assess the real-time impact of communication link fluctuations on the controllability of each energy storage unit. As a result, the energy allocation weight of the energy storage unit cannot be adjusted in time under the scenario of sudden changes in communication quality, which leads to a decrease in the reliability of capacity allocation command transmission and is prone to causing energy storage scheduling response delays or execution deviations.

[0005] 2. Existing technologies dynamically adjust the allocation ratio according to the overall matching degree priority during the flexible redistribution process. However, this priority determination is based only on the preset overall matching degree index and does not take into account the differences in power response speed and energy capacity of different types of energy storage units. This results in a mismatch between capacity allocation and the actual physical characteristics of energy storage units, causing the system response speed and capacity supply to fail to match the source load fluctuation demand, and reducing the collaborative utilization efficiency of multiple types of energy storage units.

[0006] 3. Existing technologies do not track, verify, or analyze the actual output energy of energy storage units after dynamically adjusting the allocation ratio. This results in a lack of closed-loop verification of capacity execution deviations, making it difficult to detect hidden faults in energy storage units in a timely manner. The continuous accumulation of deviations will affect the supply and demand balance of the microgrid, thereby increasing the safety hazards of microgrid operation and the risk of overload of energy storage equipment. Summary of the Invention

[0007] This invention aims to overcome the deficiencies in the prior art and provide a method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks. This method integrates dynamic monitoring of power wireless private network communication quality with optimized configuration of multi-type energy storage capacity, thereby improving the reliability of microgrid energy dispatch command transmission and the accuracy of energy storage resource allocation.

[0008] The technical solution adopted by the present invention to solve its technical problem is as follows: The present invention provides a method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private network, including: S1, based on the communication delay data and packet loss rate of each energy storage unit in the power wireless private network, calculate the effective controllable confidence of the current monitoring period, and determine whether the communication quality has changed abruptly by combining the historical effective controllable confidence of the previous monitoring period.

[0009] S2. When communication quality changes abruptly, extract the rated power response speed and rated energy capacity of each type of energy storage, and determine the current energy allocation weight of each type of energy storage by combining the effective controllable confidence level.

[0010] S3. Based on the energy output forecast data and load demand forecast data for the future monitoring period, determine the energy dispatch value, combine the current charging and discharging capacity boundary of each energy storage unit, calculate the target output energy of each type of energy storage under the current energy allocation weight, and generate the capacity allocation instructions for all energy storage units corresponding to each type of energy storage.

[0011] S4. Extract the actual output energy of each energy storage unit after executing the capacity allocation command, compare and determine the capacity output error, and generate an energy storage unit capacity anomaly report based on the capacity output error.

[0012] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention calculates the effective controllable confidence by weighted fusion of communication delay data and packet loss rate, and determines whether the communication quality has changed abruptly by combining the historical effective controllable confidence of the previous monitoring period, thereby realizing the quantitative evaluation and mutation identification of the communication link quality, effectively capturing the time-varying characteristics of the power wireless private network communication status, ensuring that the energy allocation weight is adjusted in time when the communication quality changes abruptly, improving the reliability of capacity allocation command transmission and the timeliness of energy storage unit scheduling response, and avoiding energy storage control failure caused by communication quality fluctuations.

[0013] (2) When communication quality changes abruptly, the present invention corrects the rated power response speed and rated energy capacity by combining effective controllable confidence to obtain the equivalent power response speed and equivalent energy capacity, and determines the current energy allocation weight of each type of energy storage based on the response priority coefficient and capacity priority coefficient, so as to ensure that the energy allocation weight matches the actual performance of the energy storage unit and improve the collaborative utilization efficiency and capacity allocation accuracy of multiple types of energy storage.

[0014] (3) Based on the energy output forecast data and load demand forecast data of the future monitoring period, the present invention determines the energy dispatch value, combines the current charging and discharging capacity boundary of each energy storage unit, calculates the target output energy of each type of energy storage and generates capacity allocation instructions, realizes the coordinated optimization of energy supply and demand forecast and energy storage capacity constraints, ensures the dynamic balance of energy supply and demand in the microgrid, improves the executability and dispatch accuracy of capacity allocation instructions, and reduces the risk of overcharging and over-discharging of energy storage.

[0015] (4) This invention extracts the actual output energy of each energy storage unit after executing the capacity allocation command, compares and determines the capacity output error, and statistically analyzes the proportion of abnormal capacity units of each type of energy storage based on the capacity output error. It then determines the type of energy storage with cluster capacity abnormalities and generates an energy storage unit capacity abnormality report. This enables the tracking and closed-loop verification of the execution effect of the capacity allocation command, and can promptly detect individual capacity deviations of energy storage units and cluster capacity abnormalities of type energy storage. This provides forward-looking data support for the operation and maintenance management of microgrid energy storage systems and ensures the long-term power supply reliability of microgrids. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the method steps of the present invention;

[0018] Figure 2 This is a schematic diagram illustrating the steps for determining the current energy allocation weights for various types of energy storage in this invention.

[0019] Figure 3 This is a schematic diagram illustrating the specific steps involved in generating an energy storage unit capacity anomaly report in this invention. Detailed Implementation

[0020] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. Furthermore, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0021] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0022] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0023] Please see Figure 1 As shown, the present invention provides a method for optimizing the capacity configuration of multi-type energy storage microgrids based on a power wireless private network, including: S1, based on the communication delay data and packet loss rate of each energy storage unit in the power wireless private network, calculating the effective controllable confidence of the current monitoring period, and determining whether the communication quality has changed abruptly by combining the historical effective controllable confidence of the previous monitoring period.

[0024] S2. When communication quality changes abruptly, extract the rated power response speed and rated energy capacity of each type of energy storage, and determine the current energy allocation weight of each type of energy storage by combining the effective controllable confidence level.

[0025] S3. Based on the energy output forecast data and load demand forecast data for the future monitoring period, determine the energy dispatch value, combine the current charging and discharging capacity boundary of each energy storage unit, calculate the target output energy of each type of energy storage under the current energy allocation weight, and generate the capacity allocation instructions for all energy storage units corresponding to each type of energy storage.

[0026] S4. Extract the actual output energy of each energy storage unit after executing the capacity allocation command, compare and determine the capacity output error, and generate an energy storage unit capacity anomaly report based on the capacity output error.

[0027] Considering that the communication link quality between the power wireless private network and the energy management and control center is affected by factors such as wireless channel interference and changes in equipment status, communication delay and packet loss rate exhibit time-varying fluctuation characteristics. Without real-time quantitative assessment and abrupt change identification of communication quality, it will be difficult to accurately grasp the real-time controllable status of energy storage units. This could lead to capacity allocation still being performed according to the original energy allocation weights even when communication quality deteriorates, resulting in dispatch command transmission failures or delayed energy storage response. Therefore, it is necessary to extract communication delay data and packet loss rate, perform weighted fusion, and construct an effective and controllable confidence index to achieve dynamic quantitative assessment of communication link quality.

[0028] Based on this, the specific implementation of the fusion calculation of the effective controllable confidence level for the current monitoring period in this invention includes: S11, extracting the communication delay data and packet loss rate of each energy storage node in the power wireless private network transmitting energy allocation instructions to the energy management and control center within the current monitoring period. The communication delay data is the time interval between the energy allocation instruction being sent from the energy management and control center and the energy storage node confirming receipt, and the packet loss rate is the ratio of the number of energy allocation instructions that the energy storage node failed to receive to the total number of sent instructions within the current monitoring period.

[0029] The current monitoring cycle is set according to the microgrid dispatch control cycle, for example, 5 minutes, to ensure that the communication quality monitoring frequency matches the energy dispatch control frequency.

[0030] S12. Compare the communication delay data of each energy storage node with the preset communication delay benchmark value, calculate the communication delay deviation, map the communication delay deviation to the preset delay confidence interval, and obtain the communication delay confidence.

[0031] Preferably, in a specific embodiment of the present invention, the communication delay deviation is calculated as follows: the ratio of the absolute value of the difference between the communication delay data and a preset communication delay benchmark value to the preset communication delay benchmark value is used as the communication delay deviation, characterizing the degree of deviation of the current communication delay from the benchmark state. The preset communication delay benchmark value is the rated communication performance index of the power wireless private network.

[0032] The method for mapping the communication delay deviation to a preset delay confidence interval is as follows: a piecewise linear mapping function is used to map the communication delay confidence interval to... Within the numerical range, when the communication delay deviation is 0, the communication delay confidence level takes the maximum value of 1; when the communication delay deviation is greater than or equal to the preset maximum allowable deviation, the communication delay confidence level takes the minimum value of 0; when the communication delay deviation is between 0 and the preset maximum allowable deviation, the communication delay confidence level decreases linearly as the communication delay deviation increases.

[0033] S13. Compare the packet loss rate data of each energy storage node with the preset packet loss rate benchmark value, calculate the packet loss rate deviation, and map the packet loss rate deviation to the preset packet loss rate confidence interval to obtain the packet loss rate confidence level. The preset packet loss rate benchmark value is the rated communication performance index of the power wireless private network. The packet loss rate deviation and packet loss rate confidence level are obtained using the same methods as the communication delay deviation and communication delay confidence level.

[0034] S14. Perform a weighted fusion calculation on the communication delay confidence and the packet loss rate confidence to obtain the effective controllable confidence for the current monitoring period.

[0035] Preferably, in a specific embodiment of the present invention, the formula for calculating the effective controllable confidence level is: .

[0036] In the formula, The effective and controllable confidence level for the current monitoring period. For communication delay confidence, For the confidence level of packet loss rate, The weighting coefficients for the confidence level of communication delay. Here are the weighting coefficients for the confidence level of the packet loss rate, and In this invention, All values ​​are set to 0.5. Implementers can also adjust the weight allocation according to the actual communication characteristics of the power wireless private network, for example, increasing it for latency-sensitive networks. The value of is important for improving packet loss sensitive networks. The value of .

[0037] This invention extracts communication delay data and packet loss rate of each energy storage unit in the power wireless private network, and calculates the effective controllable confidence level of the current monitoring period to achieve dynamic quantitative evaluation of communication link quality. It effectively captures the time-varying characteristics of the communication status of the power wireless private network and provides a reliable quantitative basis for subsequent judgment of communication quality abrupt changes.

[0038] Given the time-varying nature of communication quality in power wireless private networks, the effective controllable confidence level for a single monitoring period only reflects the current communication status. Without combining historical communication quality data for abrupt change identification, it will be difficult to distinguish between normal fluctuations and abnormal changes in communication quality, leading to frequent or delayed adjustments to energy allocation weights. Therefore, it is necessary to determine whether a sudden change in communication quality has occurred by comparing the change in effective controllable confidence level between the current and previous monitoring periods.

[0039] Based on this, the specific implementation of determining whether communication quality has a sudden change in this invention includes: S15, extracting the historical effective controllable confidence level of the previous monitoring period from the historical communication quality database of the energy management and control center, and performing a difference calculation between the effective controllable confidence level of the current monitoring period and the historical effective controllable confidence level to obtain the change in effective controllable confidence level. When the change in effective controllable confidence level is negative, it indicates that the communication quality is deteriorating; when the change in effective controllable confidence level is positive, it indicates that the communication quality is improving.

[0040] S16. If the absolute value of the effective controllable confidence change is greater than the preset confidence change threshold, it is determined that a sudden change in communication quality has occurred, and the recalculation of energy allocation weights needs to be triggered; otherwise, it is determined that no sudden change in communication quality has occurred, and the energy allocation weights of each type of energy storage in the previous monitoring period are maintained to avoid frequent adjustments to energy allocation weights caused by normal fluctuations in communication quality, and to ensure the stability of capacity optimization configuration.

[0041] Preferably, in a specific embodiment of the present invention, the preset confidence change threshold can be determined by selecting the statistical distribution of the historical effective controllable confidence change within a set historical period (such as a week), for example, taking the 75th quantile of the distribution of the historical effective controllable confidence change as the preset confidence change threshold.

[0042] This invention calculates the effective controllable confidence level by weighted fusion of communication delay data and packet loss rate, and determines whether the communication quality has changed abruptly by combining the historical effective controllable confidence level of the previous monitoring period. This enables quantitative assessment and abrupt change identification of communication link quality, effectively captures the time-varying characteristics of the power wireless private network communication status, ensures timely adjustment of energy allocation weights when communication quality changes abruptly, improves the reliability of capacity allocation command transmission and the timeliness of energy storage unit scheduling response, and avoids energy storage control failure caused by communication quality fluctuations.

[0043] Considering the differences in power response speed and energy capacity among various types of energy storage units in a microgrid, if a fixed energy allocation weight is used when communication quality changes abruptly, energy storage units with severely degraded communication quality will be burdened with scheduling tasks beyond their controllable capabilities, while energy storage units with good communication quality will fail to fully utilize their regulation capabilities. It is necessary to modify the rated physical parameters of the energy storage units by incorporating effective controllability confidence, and to redetermine the energy allocation weights to ensure that the allocation strategy matches the actual controllable performance of the energy storage units.

[0044] Based on this, such as Figure 2 As shown, the specific implementation of determining the current energy allocation weight of each type of energy storage in this invention includes: S21, classifying all energy storage units in the microgrid by type, and extracting the rated power response speed and rated energy capacity of each type of energy storage. The types of energy storage include, but are not limited to: energy-type energy storage and power-type energy storage.

[0045] S22. Combining the effective controllable confidence of each energy storage unit in the current monitoring period, the rated power response speed and rated energy capacity of all energy storage units in each type of energy storage are corrected to obtain the equivalent power response speed and equivalent energy capacity.

[0046] The equivalent power response speed is the product of the effective controllable confidence level and the rated power response speed for the current monitoring period. The equivalent power response speed characterizes the available power response capability after considering the impact of communication quality. The lower the effective controllable confidence level, the smaller the equivalent power response speed, indicating that the available power response capability of the energy storage unit is weaker when communication quality deteriorates.

[0047] The equivalent energy capacity is the product of the effective controllable confidence level and the rated energy capacity for the current monitoring period. The equivalent energy capacity represents the available schedulable energy capacity after considering the impact of communication quality. The lower the effective controllable confidence level, the smaller the equivalent energy capacity, indicating that the energy storage unit has less available schedulable energy when communication quality deteriorates.

[0048] S23. Based on the maximum equivalent power response speed and maximum rated energy capacity of all types of energy storage, the response priority coefficient and capacity priority coefficient are obtained by comparison.

[0049] It should be noted that the response priority coefficient is calculated as follows: the ratio of the equivalent power response speed to the maximum equivalent power response speed is used as the response priority coefficient. The larger the response priority coefficient, the better the power response capability of the energy storage unit.

[0050] The capacity priority coefficient is calculated as the ratio of the equivalent energy capacity to the maximum rated energy capacity. The larger the capacity priority coefficient, the better the energy capacity of this type of energy storage.

[0051] S24. Analyze the response priority coefficients and capacity priority coefficients of each energy storage unit to obtain the current energy allocation weight of each type of energy storage.

[0052] Preferably, in a specific embodiment of the present invention, the step of obtaining the current energy allocation weight is as follows: S241, the response priority coefficient and capacity priority coefficient are arranged in descending order to obtain the response priority ranking and capacity priority ranking of each energy storage unit.

[0053] S242. Based on the response priority ranking and capacity priority ranking scores of each energy storage unit, calculate the response priority score and capacity priority score of all energy storage units in each type of energy storage, and calculate the average total priority score for each type of energy storage. The total priority score is the sum of the response priority score and the capacity priority score.

[0054] Preferably, in a specific embodiment of the present invention, the scoring method for the response priority score and the capacity priority score is as follows: scores are assigned in reverse order according to the ranking position, with the highest score assigned to the first ranked position, and the score decreasing as the ranking position decreases. For example, for n energy storage units, the first ranked unit is assigned n points, the second ranked unit is assigned n-1 points, and so on, with the nth ranked unit assigned 1 point.

[0055] S243. The ratio of the average priority total score to the sum of the average priority total scores of all types of energy storage is used as the current energy allocation weight for each type of energy storage.

[0056] When communication quality changes abruptly, this invention corrects the rated power response speed and rated energy capacity by combining effective and controllable confidence to obtain the equivalent power response speed and equivalent energy capacity. Based on the response priority coefficient and capacity priority coefficient, it determines the current energy allocation weight of each type of energy storage, ensuring that the energy allocation weight matches the actual performance of the energy storage unit, thereby improving the collaborative utilization efficiency of multiple types of energy storage and the accuracy of capacity allocation.

[0057] Given the time-varying nature of energy output and load demand in microgrids, capacity allocation based solely on the real-time energy status of the current monitoring period, without forecasting future energy supply and demand changes, leads to a mismatch between capacity allocation commands and actual future energy conditions. This can result in overcharging / over-discharging of energy storage or source-load imbalance. Therefore, it is necessary to extrapolate energy output and load demand for future monitoring periods to provide forward-looking data support for determining energy dispatch values.

[0058] Based on this, the specific implementation of determining the energy dispatch value in this invention includes: S311, extracting the energy output time-series data and load demand time-series data of the microgrid within the historical monitoring period, determining the data change rate and data fluctuation amplitude through a preset time window, and constructing an energy output prediction model and a load demand prediction model.

[0059] It should be noted that the rate of change of the data is the ratio of the difference between energy output or load demand in adjacent time windows to the time interval, representing the rate of change of energy output or load demand; the data fluctuation amplitude is the difference between the maximum and minimum values ​​of energy output or load demand in adjacent time windows, representing the fluctuation range of energy output or load demand.

[0060] Preferably, in a specific embodiment of the present invention, the energy output prediction model is constructed as follows: a coordinate system is constructed with time windows as the horizontal axis and energy output data as the vertical axis; based on the data change rate, data fluctuation amplitude and endpoint data values ​​of each time window, the least squares method is used to fit the energy output data curve of each time window; the energy output data curves of all time windows are connected and smoothed to generate the energy output prediction model.

[0061] Similarly, the load demand forecasting model is obtained by using the same method as the energy output forecasting model.

[0062] S312. Input the real-time energy output data and real-time load demand data of the current monitoring period into the energy output prediction model and the load demand prediction model respectively to obtain the energy output prediction data and load demand prediction data for the future monitoring period.

[0063] S313. Calculate the difference between the energy output forecast data and the load demand forecast data to obtain the energy dispatch value. The energy dispatch value is the difference between the energy output forecast data and the load demand forecast data.

[0064] Considering that the total energy regulation demand of various types of energy storage under the current energy allocation weight may exceed the charging and discharging capacity boundary of their cluster, without capacity boundary constraint determination and redistribution of excess energy, some energy storage units may be overcharged or over-discharged, or capacity allocation commands may fail to be executed. Therefore, it is necessary to determine the cluster charging and discharging capacity boundary to ensure that the target output energy does not exceed the controllable capacity limit of the energy storage cluster.

[0065] Based on this, the specific implementation of calculating the target output energy of various types of energy storage under the current energy allocation weight in this invention includes: S321, determining the energy supply and demand status of the microgrid based on the positive or negative value of the energy dispatch value, wherein the energy supply and demand status includes an energy deficit status and an energy surplus status. When the energy dispatch value is negative, it is determined to be an energy deficit status, requiring the energy storage unit to discharge; when the energy dispatch value is positive, it is determined to be an energy surplus status, requiring the energy storage unit to charge.

[0066] S322. Combining the current energy allocation weights of each type of energy storage, the energy dispatch value is split and calculated according to the current energy allocation weights to obtain the total energy regulation demand value corresponding to each type of energy storage. The splitting and calculation involves allocating the absolute value of the energy dispatch value according to the current energy allocation weight ratio of each type of energy storage to obtain the total energy regulation demand value that each type of energy storage needs to undertake.

[0067] S323. Based on the current charging and discharging capacity boundaries of all energy storage units corresponding to each type of energy storage, determine the cluster charging and discharging capacity boundaries of each type of energy storage under the corresponding energy supply and demand conditions.

[0068] It should be noted that the current charge and discharge capacity boundary is determined based on the current charged capacity and rated capacity of each energy storage unit. When the energy supply and demand state is an energy deficit state, the current discharge capacity boundary of the energy storage unit is the current charged capacity. When the energy supply and demand state is an energy surplus state, the current charging capacity boundary of the energy storage unit is the difference between the rated capacity and the current charged capacity.

[0069] The cluster charge / discharge capacity boundary is the sum of the current charge / discharge capacity boundaries of all energy storage units of the same type.

[0070] S324. If the total energy regulation demand is lower than the cluster charging and discharging capacity boundary, the total energy regulation demand will be used as the target output energy; otherwise, the cluster charging and discharging capacity boundary will be used as the target output energy, and the excess energy will be redistributed to other types of energy storage according to the current energy allocation weight.

[0071] Preferably, in a specific embodiment of the present invention, the method of redistributing the excess energy is as follows: the excess energy whose total energy regulation demand value exceeds the cluster charging and discharging capacity boundary is proportionally split according to the current energy allocation weight of other types of energy storage besides the current type of energy storage, the difference after splitting is added to the total energy regulation demand value of other types of energy storage, and the cluster charging and discharging capacity boundary constraints of other types of energy storage are re-verified until the total energy regulation demand value of all types of energy storage meets the corresponding cluster charging and discharging capacity boundary constraints.

[0072] Based on this, the specific implementation of generating capacity allocation instructions for all energy storage units corresponding to each type of energy storage in this invention includes: S331, extracting the target output energy of each type of energy storage, performing a ratio calculation between the target output energy and the number of energy storage units of the corresponding type of energy storage, and obtaining the initial capacity allocation value of each energy storage unit.

[0073] S332. Extract the current charge / discharge capacity boundary of each energy storage unit. If the initial capacity allocation value is less than or equal to the corresponding current charge / discharge capacity boundary, then use the initial capacity allocation value as the capacity allocation value of the energy storage unit.

[0074] S333. Conversely, the current charge / discharge capacity boundary is used as the capacity allocation value for the energy storage unit, and the excess capacity is transferred according to the remaining capacity of other energy storage units of the same type.

[0075] Preferably, in a specific embodiment of the present invention, the energy difference transfer method is as follows: the excess capacity of the initial capacity allocation value of a certain energy storage unit that exceeds its current charge and discharge capacity boundary is allocated according to the proportion of the remaining capacity of other energy storage units of the same type. The difference after allocation is added to the capacity allocation value of other energy storage units, and it is checked whether the capacity allocation value of other energy storage units exceeds its current charge and discharge capacity boundary. If it does, the iterative transfer continues until the capacity allocation value of all energy storage units meets the corresponding capacity boundary constraint.

[0076] S334. Summarize the capacity allocation values ​​of all energy storage units corresponding to each type of energy storage, generate the capacity allocation instructions for the corresponding energy storage units, and transmit them to each energy storage unit for execution via the power wireless private network.

[0077] This invention determines energy dispatch values ​​based on energy output forecast data and load demand forecast data for future monitoring periods. It combines the current charge and discharge capacity boundaries of each energy storage unit to calculate the target output energy of each type of energy storage and generate capacity allocation instructions. This achieves coordinated optimization of energy supply and demand forecasting and energy storage capacity constraints, ensures dynamic balance of energy supply and demand in microgrids, improves the executability and dispatch accuracy of capacity allocation instructions, and reduces the risk of overcharging and over-discharging of energy storage.

[0078] Considering that the actual execution performance of each energy storage unit after the capacity allocation command is issued may be affected by factors such as communication quality fluctuations, equipment aging, and ambient temperature, resulting in execution deviations, if the actual output energy is not tracked and verified, it will be impossible to detect hidden faults or accumulated execution deviations in the energy storage units in a timely manner, leading to a gradual deterioration of the microgrid's supply and demand balance. Therefore, it is necessary to extract the actual output energy of each energy storage unit after executing the capacity allocation command, compare and determine the capacity output error, and achieve closed-loop verification of the execution effect of the capacity allocation command.

[0079] Based on this, such as Figure 3 As shown, the specific implementation of determining the capacity output error in this invention includes: S41, extracting the actual output energy of each energy storage unit after executing the capacity allocation command, and performing a difference calculation between the actual output energy and the capacity allocation value in the capacity allocation command to obtain the energy output deviation value of each energy storage unit. When the energy output deviation value is positive, it indicates that the actual output energy is greater than the capacity allocation value; when the energy output deviation value is negative, it indicates that the actual output energy is less than the capacity allocation value.

[0080] S42. Calculate the ratio between the energy output deviation value and the capacity allocation value to obtain the capacity output error of each energy storage unit.

[0081] Considering that the capacity output error of a single energy storage unit may be caused by random factors, relying solely on individual capacity output errors for anomaly judgment may lead to misjudgment. However, if multiple energy storage units of the same type simultaneously exhibit capacity output anomalies, it indicates a clustered performance degradation or systemic control failure within that type of energy storage. Therefore, it is necessary to identify the types of energy storage with clustered capacity anomalies by statistically analyzing the percentage of units with capacity anomalies for each type, and generate energy storage unit capacity anomaly reports to achieve hierarchical diagnosis from individual anomalies to clustered anomalies.

[0082] Based on this, the specific implementation of generating an energy storage unit capacity anomaly reports in this invention includes: S43, recording energy storage units with capacity output errors greater than a preset capacity error threshold as capacity anomaly units, and extracting the corresponding energy storage type of the capacity anomaly units. The preset capacity error threshold is determined based on the statistical distribution of historical capacity output errors, for example, taking the 95th quantile of the historical capacity output error distribution as the preset capacity error threshold.

[0083] S44. Count the number of capacity abnormal units for each type of energy storage, and calculate the ratio of the number of capacity abnormal units to the total number of energy storage units of the corresponding type to obtain the proportion of capacity abnormal units.

[0084] S45. When the proportion of units with abnormal capacity exceeds a preset abnormality percentage threshold, it is determined that there is a cluster capacity abnormality for this type of energy storage. An energy storage unit capacity abnormality report is generated, which includes the type of energy storage with cluster capacity abnormalities and the capacity output error of each unit with abnormal capacity. The preset abnormality percentage threshold is set according to the microgrid operation safety requirements, for example, 20%.

[0085] This invention extracts the actual output energy of each energy storage unit after executing the capacity allocation command, compares and determines the capacity output error, and statistically analyzes the proportion of abnormal capacity units for each type of energy storage based on the capacity output error. It then identifies the types of energy storage with cluster capacity anomalies and generates an energy storage unit capacity anomaly report. This enables the tracking and closed-loop verification of the execution effect of the capacity allocation command, and can promptly detect individual capacity deviations of energy storage units and capacity anomalies of energy storage clusters of different types. This provides forward-looking data support for the operation and maintenance management of microgrid energy storage systems and ensures the long-term power supply reliability of microgrids.

[0086] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0087] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0088] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0089] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0090] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for capacity optimization configuration of multi-type energy storage microgrid based on power wireless private network, characterized in that, include: Based on the communication delay data and packet loss rate of each energy storage unit in the power wireless private network, the effective controllable confidence level of the current monitoring period is calculated, and the communication quality is determined by combining the historical effective controllable confidence level of the previous monitoring period. When communication quality changes abruptly, the rated power response speed and rated energy capacity of each type of energy storage are extracted, and the current energy allocation weight of each type of energy storage is determined by combining the effective controllable confidence level. Based on the energy output forecast data and load demand forecast data for the future monitoring period, the energy dispatch value is determined. Combined with the current charging and discharging capacity boundary of each energy storage unit, the target output energy of each type of energy storage under the current energy allocation weight is calculated, and the capacity allocation instructions for all energy storage units corresponding to each type of energy storage are generated. Extract the actual energy output of each energy storage unit after executing the capacity allocation command, compare and determine the capacity output error, and generate an energy storage unit capacity anomaly report based on the capacity output error.

2. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 1, characterized in that, The method for calculating the effective and controllable confidence level of the current monitoring period is as follows: Extract communication delay data and packet loss rate of each energy storage node in the power wireless private network during the current monitoring period for transmitting energy distribution instructions to the energy management and control center; The communication delay data of each energy storage node is compared with the preset communication delay benchmark value to calculate the communication delay deviation. The communication delay deviation is then mapped to the preset delay confidence interval to obtain the communication delay confidence. The packet loss rate data of each energy storage node is compared with the preset packet loss rate benchmark value to calculate the packet loss rate deviation. The packet loss rate deviation is then mapped to the preset packet loss rate confidence interval to obtain the packet loss rate confidence. The communication delay confidence and packet loss rate confidence are weighted and fused to obtain the effective controllable confidence for the current monitoring period.

3. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 2, characterized in that, The steps for determining whether the communication quality has changed abruptly are as follows: Extract the historical effective controllable confidence level from the previous monitoring period, and calculate the difference between the effective controllable confidence level of the current monitoring period and the historical effective controllable confidence level to obtain the change in effective controllable confidence level; If the absolute value of the effective controllable confidence change is greater than the preset confidence change threshold, it is determined that a sudden change has occurred in communication quality; otherwise, it is determined that no sudden change has occurred in communication quality, and the energy allocation weights of each type of energy storage are maintained as in the previous monitoring period.

4. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 1, characterized in that, The method for determining the current energy allocation weight of each type of energy storage is as follows: All energy storage units in the microgrid are classified by type, and the rated power response speed and rated energy capacity of each type of energy storage are extracted. By combining the effective controllable confidence of each energy storage unit in the current monitoring period, the rated power response speed and rated energy capacity of all energy storage units in each type of energy storage are corrected to obtain the equivalent power response speed and equivalent energy capacity. Based on the maximum equivalent power response speed and maximum rated energy capacity of all types of energy storage, the response priority coefficient and capacity priority coefficient are obtained by comparison. The response priority coefficients and capacity priority coefficients of each energy storage unit are ranked and analyzed to obtain the current energy allocation weight of each type of energy storage.

5. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 4, characterized in that, The steps for obtaining the current energy allocation weights for each type of energy storage are as follows: The response priority coefficients and capacity priority coefficients are arranged in descending order to obtain the response priority ranking and capacity priority ranking of each energy storage unit. Based on the response priority ranking and capacity priority ranking scores of each energy storage unit, the response priority scores and capacity priority scores of all energy storage units in each type of energy storage are statistically analyzed, and the average total priority score of each type of energy storage is calculated. The ratio of the average priority total score to the sum of the average priority total scores of all types of energy storage is used as the current energy allocation weight for each type of energy storage.

6. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on a power wireless private network according to claim 1, characterized in that, The method for determining the energy dispatch value is as follows: Extract energy output time-series data and load demand time-series data of microgrid within the historical monitoring period, determine the data change rate and data fluctuation amplitude by dividing the data into preset time windows, and construct energy output prediction model and load demand prediction model. Input the real-time energy output data and real-time load demand data of the current monitoring period into the energy output prediction model and load demand prediction model respectively to obtain the energy output prediction data and load demand prediction data for the future monitoring period. The energy dispatch value is obtained by calculating the difference between the energy output forecast data and the load demand forecast data.

7. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 6, characterized in that, The method for calculating the target output energy of each type of energy storage under the current energy allocation weight is as follows: The energy supply and demand status of the microgrid is determined based on the positive or negative value of the energy dispatch value, which includes energy shortage status and energy surplus status. By combining the current energy allocation weights of each type of energy storage, the energy dispatch value is split and calculated according to the current energy allocation weights to obtain the total energy regulation demand value corresponding to each type of energy storage. Based on the current charging and discharging capacity boundaries of all energy storage units corresponding to each type of energy storage, the cluster charging and discharging capacity boundaries of each type of energy storage under the corresponding energy supply and demand conditions are determined. If the total energy regulation demand is lower than the cluster charging and discharging capacity boundary, the total energy regulation demand will be used as the target output energy; otherwise, the cluster charging and discharging capacity boundary will be used as the target output energy, and the excess energy will be redistributed to other types of energy storage according to the current energy allocation weight.

8. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 7, characterized in that, The method for generating capacity allocation instructions for all energy storage units corresponding to each type of energy storage is as follows: Extract the target output energy of each type of energy storage, and calculate the ratio between the target output energy and the number of energy storage units of the corresponding type of energy storage to obtain the initial capacity allocation value of each energy storage unit. Extract the current charge / discharge capacity boundary of each energy storage unit. If the initial capacity allocation value is less than or equal to the corresponding current charge / discharge capacity boundary, then use the initial capacity allocation value as the capacity allocation value of that energy storage unit. Conversely, the current charge / discharge capacity boundary is used as the capacity allocation value for the energy storage unit, and the excess capacity is transferred according to the remaining capacity of other energy storage units of the same type. The capacity allocation values ​​for all energy storage units corresponding to each type of energy storage are summarized, and the capacity allocation instructions for the corresponding energy storage units are generated.

9. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 8, characterized in that, The method for determining the capacity output error through comparison is as follows: Extract the actual output energy of each energy storage unit after executing the capacity allocation command, and calculate the difference between the actual output energy and the capacity allocation value in the capacity allocation command to obtain the energy output deviation value of each energy storage unit. The capacity output error of each energy storage unit is obtained by calculating the ratio between the energy output deviation value and the capacity allocation value.

10. The method for optimizing the capacity configuration of multi-type energy storage microgrids based on power wireless private networks according to claim 9, characterized in that, The method for generating an abnormal energy storage unit capacity report is as follows: Energy storage units with capacity output errors greater than a preset capacity error threshold are recorded as capacity abnormal units, and the corresponding energy storage types of capacity abnormal units are extracted. The number of capacity-abnormal units for each type of energy storage is counted, and the ratio of the number of capacity-abnormal units to the total number of energy storage units of the corresponding type is calculated to obtain the proportion of capacity-abnormal units. When the proportion of abnormal capacity units exceeds the preset abnormal proportion threshold, it is determined that there is a cluster capacity abnormality for this type of energy storage. An energy storage unit capacity abnormality report is generated by taking the type of energy storage with cluster capacity abnormality and the capacity output error of each abnormal capacity unit.

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