A distributed coordination control method and system of a direct-current micro-grid energy storage system

Abstract

The application provides a kind of distributed coordination control method and system of direct current micro grid energy storage system, it is related to direct current micro grid energy storage control technical field, this method includes: monitoring the real-time electrical state of distributed energy storage unit;According to the deviation degree of state of charge and rated operation interval, and the voltage deviation accumulation amount and power regulation frequency in historical operation data, the coordination control priority of each unit is determined;According to the difference of bus voltage and neighbor, the voltage consistency error is determined, and according to the difference of output current and ideal current sharing reference value, the current sharing error is determined;When bus voltage deviates from the rated interval, according to the voltage consistency error, current sharing error and priority, the droop control reference voltage compensation amount is iteratively calculated and the droop controller reference value is corrected.The scheme of the application can realize the dynamic allocation of priority voltage and current double target collaborative convergence, thereby improving the actual operation stability of distributed energy storage system.

Description

Technical Field

[0001] This application relates to the field of DC microgrid energy storage control technology, and more specifically, to a distributed coordinated control method and system for a DC microgrid energy storage system. Background Technology

[0002] In DC microgrids, distributed energy storage units operate collaboratively in parallel, which is crucial for maintaining stable bus voltage and ensuring reliable power supply. Droop control is widely used because it does not require high-speed communication. However, in scenarios with multiple energy storage units in parallel, there is an inherent contradiction between bus voltage recovery and the equal distribution of output current according to capacity ratio. Traditional fixed-coefficient droop control cannot meet both objectives at the same time, so a distributed secondary regulation mechanism needs to be introduced to achieve coordinated control of voltage and current.

[0003] However, existing distributed secondary control methods typically rely solely on instantaneous state of charge or single-sample information to determine the regulation priority of each energy storage unit. This fails to effectively integrate historical operational data such as the long-term cumulative effect of voltage deviations and the fatigue frequency of power regulation. Consequently, a significant amount of regulation pressure is continuously concentrated on a few energy storage units, accelerating their aging and degradation, while other units remain under light load or idle for extended periods. Furthermore, voltage consistency errors and current sharing errors are assigned fixed regulation weights, making it impossible to dynamically adjust the proportion of voltage recovery and current sharing tasks based on the actual state of each unit. This results in frequent disruptions to the converged current sharing accuracy during voltage rises, and vice versa, ultimately leading to system oscillations or steady-state deviations. Therefore, achieving coordinated convergence of voltage and current objectives through dynamic priority allocation to improve the actual operational stability of distributed energy storage systems has become a challenging problem for the industry. Summary of the Invention

[0004] This application provides a distributed coordinated control method and system for a DC microgrid energy storage system, which can achieve dual-objective coordinated convergence of voltage and current with dynamic priority allocation, thereby improving the actual operational stability of the distributed energy storage system.

[0005] In a first aspect, this application provides a distributed coordinated control method for a DC microgrid energy storage system, comprising the following steps: The real-time electrical status of multiple distributed energy storage units under their respective charging and discharging power is monitored, and the real-time electrical status includes at least bus voltage, output current and state of charge. The deviation between the current state of charge of each energy storage unit and the rated operating range is detected. Then, based on the deviation and historical operating data, the cumulative voltage deviation and power regulation frequency of each energy storage unit are extracted to determine the coordination control priority of each energy storage unit. The voltage consistency error is determined based on the difference between the real-time bus voltage and the neighboring unit, and the current sharing error is determined based on the difference between the real-time output current and the ideal current sharing reference value obtained by distributed iteration. When the bus voltage is detected to deviate from the rated operating range, the droop control reference voltage compensation amount of each energy storage unit is calculated through distributed iteration based on the voltage consistency error, current sharing error and the coordination control priority of each energy storage unit, and the reference value of the droop controller is corrected until the bus voltage is restored to the rated allowable range and the output current of each energy storage unit is evenly distributed according to the capacity ratio.

[0006] In some embodiments, detecting the deviation of the current state of charge of each energy storage unit from its rated operating range specifically includes: For each energy storage unit, obtain the upper and lower limits of the preset rated state of charge for that energy storage unit, and compare the current state of charge with the upper and lower limits. If the current state of charge is within the rated operating range defined by the upper and lower limits, the deviation is set to zero. If the current state of charge is higher than the upper limit, the difference between the current state of charge and the upper limit is calculated, and the ratio of the difference to the width of the rated operating range is used as the deviation. If the current state of charge is lower than the lower limit, the difference between the lower limit and the current state of charge is calculated, and the ratio of this difference to the width of the rated operating range is used as the deviation.

[0007] In some embodiments, determining the coordinated control priority of each energy storage unit by extracting the cumulative voltage deviation and power regulation frequency of each energy storage unit based on the deviation and historical operating data specifically includes: For each energy storage unit, acquire the historical operating data of that energy storage unit; Based on historical operating data, the cumulative deviation of the bus voltage from the rated value is counted as the voltage deviation cumulative amount, and the number of output power regulation actions is counted as the power regulation frequency. Based on the accumulated voltage deviation and power regulation frequency, the regulation demand index of the energy storage unit is determined; Based on the fuzzy reasoning mechanism, the deviation degree corresponding to the energy storage unit and the regulation demand index are used as inputs. Fuzzy reasoning is performed through a preset fuzzy rule base to obtain the regulation membership priority of the energy storage unit. The coordination control priority of each energy storage unit is determined by sorting them according to the control priority of each unit.

[0008] In some embodiments, determining the regulation demand index of the energy storage unit based on the accumulated voltage deviation and power regulation frequency specifically includes: The accumulated voltage deviation and the power adjustment frequency are normalized to obtain the first intermediate value and the second intermediate value. The coupling strength coefficient is determined based on the remaining cycle life of the energy storage unit. The shorter the remaining cycle life, the larger the coupling strength coefficient, and the value of this coefficient is between 0 and 1. Using the coupling strength coefficient as a nonlinear fusion factor, cross-excitation calculations are performed on the first intermediate quantity and the second intermediate quantity to obtain the regulation demand index of the energy storage unit.

[0009] In some embodiments, using the coupling strength coefficient as a nonlinear fusion factor, cross-excitation calculations are performed on the first intermediate quantity and the second intermediate quantity to obtain the regulation demand index of the energy storage unit, specifically including: Multiply the first intermediate quantity by the second intermediate quantity to obtain the cross product term; Using the coupling strength coefficient as the exponent, the cross-product term is transformed by a power function to obtain the nonlinear excitation term; The linear combination of the first intermediate quantity and the second intermediate quantity is superimposed on the nonlinear excitation term to generate the regulation demand index of the energy storage unit, so that when both the first intermediate quantity and the second intermediate quantity are at a high level, the regulation demand index exhibits superlinear growth.

[0010] In some embodiments, determining the voltage consistency error based on the difference between the real-time bus voltage and the neighboring unit specifically includes: Collect the current real-time bus voltage values ​​of this energy storage unit and all neighboring units; The validity of neighbor voltages is determined, and neighbor voltages that are interrupted or have not been updated due to timeout are replaced with predicted estimates based on historical data to obtain a set of valid neighbor voltages. The voltage consistency error is obtained by calculating the deviation between the voltage of this energy storage unit and the arithmetic mean of the effective neighbor voltage set.

[0011] In some embodiments, determining the current sharing error based on the difference between the real-time output current and the ideal current sharing reference value obtained through distributed iteration specifically includes: Each energy storage unit iteratively exchanges rated capacity and real-time output current information with its neighboring units; A globally unified ideal current sharing reference value is obtained through distributed consensus computation convergence. This ideal current sharing reference value reflects the expected current allocated according to the capacity ratio. The initial current deviation is obtained by comparing the real-time output current of this unit with the ideal current sharing reference value. The gain coefficient of the initial current deviation is adjusted according to the system operating mode to obtain the current average error.

[0012] In some embodiments, the bus voltage and output current of each energy storage unit are acquired by voltage sensors and Hall current sensors, respectively.

[0013] In some embodiments, the state of charge of each energy storage unit is obtained by the battery management system using the ampere-hour integration method combined with terminal voltage lookup table correction.

[0014] Secondly, this application provides a distributed coordination control system for a DC microgrid energy storage system, the system comprising: The monitoring module is used to monitor the real-time electrical status of multiple distributed energy storage units under their respective charging and discharging power. The real-time electrical status includes at least bus voltage, output current and state of charge. The processing module is used to detect the deviation between the current state of charge of each energy storage unit and the rated operating range, and then extract the cumulative voltage deviation and power regulation frequency of each energy storage unit based on the deviation and historical operating data to determine the coordination control priority of each energy storage unit. The processing module is also used to determine the voltage consistency error based on the difference between the real-time bus voltage and the neighboring unit, and to determine the current sharing error based on the difference between the real-time output current and the ideal current sharing reference value obtained by distributed iteration. The execution module is used to calculate the droop control reference voltage compensation amount of each energy storage unit through distributed iteration based on the voltage consistency error, current sharing error and the coordination control priority of each energy storage unit when the bus voltage is detected to deviate from the rated operating range, and to correct the reference value of the droop controller until the bus voltage is restored to the rated allowable range and the output current of each energy storage unit is evenly distributed according to the capacity ratio.

[0015] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the distributed coordination control method of the DC microgrid energy storage system described above.

[0016] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described distributed coordinated control method for a DC microgrid energy storage system.

[0017] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application integrates state of charge deviation and historical operating data, introduces nonlinear cross-excitation calculation based on remaining cycle life to determine the regulation demand index, and then dynamically allocates the responsibility for voltage recovery and current sharing according to priority. After the dual errors corrected by communication quality and operating mode are fused according to weight, the droop control reference value is iteratively corrected until the dual objectives of voltage and current sharing converge simultaneously, and finally the voltage and current coordinated stable control of the DC microgrid energy storage unit is achieved. First, the coupling strength coefficient is determined based on the remaining cycle life. The normalized results of the accumulated voltage deviation and power regulation frequency are cross-excited, causing the regulation demand index to grow superlinearly when both indicators are simultaneously high. This highlights the urgency of regulation for the doubly degraded unit and avoids excessive stress on the aging unit. Second, a dual-input fuzzy inference system is constructed using the deviation and regulation demand index. The output regulation membership priority is ranked to obtain the coordinated control priority, providing a sequential basis for responsibility allocation. Then, in the voltage consistency error calculation, linear extrapolation prediction values ​​are used to replace communication interruption neighbors, and the deviation is weighted by confidence level based on signal quality. In the current averaging error calculation, the gain coefficient is dynamically adjusted according to grid-connected or islanded modes to improve robustness under non-ideal communication and changing operating scenarios. Finally, the voltage recovery and current averaging weights are linearly mapped based on priority. The two types of errors are weighted and fused, and the droop compensation is obtained through rate and amplitude constraints. The droop controller reference voltage is successively corrected until the bus voltage recovers to the rated range and the output current is accurately averaged according to the capacity ratio. In summary, this application achieves decoupled convergence of voltage recovery and current equalization through a synergistic mechanism of priority-driven dynamic weight allocation and error confidence correction, significantly improving the operational stability of DC microgrids under conditions of multiple disturbances, non-ideal communication, and equipment aging differences. Attached Figure Description

[0018] Figure 1 This is an exemplary flowchart of a distributed coordinated control method for a DC microgrid energy storage system according to some embodiments of this application; Figure 2 These are schematic diagrams of monitoring scenarios shown in some embodiments of this application; Figure 3 This is a flowchart illustrating the process of determining coordination control priority according to some embodiments of this application; Figure 4 This is a schematic flowchart illustrating the determination of voltage consistency error according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a distributed coordinated control system for a DC microgrid energy storage system according to some embodiments of this application; Figure 6 This is a schematic diagram of the structure of a computer device for implementing a distributed coordinated control method for a DC microgrid energy storage system according to some embodiments of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] refer to Figure 1 The figure is an exemplary flowchart of a distributed coordinated control method for a DC microgrid energy storage system according to some embodiments of this application. The distributed coordinated control method for the DC microgrid energy storage system mainly includes the following steps: In step 101, the real-time electrical status of multiple distributed energy storage units under their respective charging and discharging power is monitored. The real-time electrical status includes at least the bus voltage, output current, and state of charge.

[0021] In some embodiments, reference Figure 2 As shown, for each distributed energy storage unit in the DC microgrid, its local controller is connected in parallel to the positive and negative terminals of the DC bus at the unit's output via a voltage Hall sensor to collect the analog bus voltage signal in real time. This signal is filtered and scaled by a signal conditioning circuit before being input to a 16-bit analog-to-digital converter to be converted into a digital quantity at a sampling frequency of 10kHz. The digital quantity is then filtered by a moving average to obtain the current bus voltage value. Simultaneously, a current sensor based on the fluxgate principle is connected in series in the output circuit of the energy storage unit to acquire the instantaneous value of the output current at the same sampling frequency. The voltage and current sampling timestamps are then used for synchronization. The state of charge (SOC) is obtained by the battery management system through an ampere-hour integration method combined with a lookup table of terminal voltage. The ampere-hour integration term is accumulated using the current sampling value. Every second, the deviation is corrected by the open-circuit voltage. Finally, the corrected SOC is reported to the local controller via the controller's local area network bus. The local controller stores the collected bus voltage, output current, and state of charge in a time series format into a circular buffer. Each data record is accompanied by a timestamp accurate to milliseconds. This monitoring process is executed continuously within each sampling control cycle, which is set to 100 milliseconds by default. This is just an example to ensure that the real-time electrical status information does not lag behind the dynamic changes of the system.

[0022] In step 102, the deviation of the current state of charge of each energy storage unit from the rated operating range is detected, and then the cumulative voltage deviation and power regulation frequency of each energy storage unit are extracted based on the deviation and historical operating data to determine the coordination control priority of each energy storage unit.

[0023] In some embodiments, detecting the deviation of the current state of charge of each energy storage unit from its rated operating range can be achieved using the following steps: For each energy storage unit, obtain the upper and lower limits of the preset rated state of charge for that energy storage unit, and compare the current state of charge with the upper and lower limits. If the current state of charge is within the rated operating range defined by the upper and lower limits, the deviation is set to zero. If the current state of charge is higher than the upper limit, the difference between the current state of charge and the upper limit is calculated, and the ratio of the difference to the width of the rated operating range is used as the deviation. If the current state of charge is lower than the lower limit, the difference between the lower limit and the current state of charge is calculated, and the ratio of this difference to the width of the rated operating range is used as the deviation.

[0024] It should be noted that the upper and lower limits of the rated state of charge of the energy storage unit in this application can be preset in the following ways: the upper limit of lithium iron phosphate batteries can be set to 90% and the lower limit to 20%, and the upper limit of ternary lithium batteries can be set to 95% and the lower limit to 25%, according to the battery type of the energy storage unit and the charge and discharge working window recommended by the manufacturer; or a set of fixed upper and lower limits can be calibrated offline and written into the local controller parameter table according to the specific requirements of the microgrid system for reserve capacity and overcharge and over-protection.

[0025] In practice, the controller first obtains the current state of charge (SOC) from the battery management system of each energy storage unit via the controller area network bus. This SOC is estimated in real time by the battery management system using the well-known ampere-hour integration method combined with the open-circuit voltage correction method. Simultaneously, the controller reads the pre-stored upper and lower limits of the rated SOC from its local non-volatile memory. The controller then compares the current SOC with the upper and lower limits, respectively. This is a standard threshold judgment operation in digital control systems: if the current SOC is greater than or equal to the lower limit and less than or equal to the upper limit, meaning the current SOC is within the rated operating range defined by the lower and upper limits, the controller follows the well-known limit-crossing logic. The deviation is directly assigned a value of 0, indicating that the current state is within the rated range. If the current state of charge is greater than the upper limit, the controller first calculates the difference between the current state of charge and the upper limit, and then calculates the difference between the upper limit and the lower limit as the rated operating range width. The quotient obtained by dividing the former by the latter is then used as the deviation. This calculation is essentially a normalization of the excess amount relative to the range width, which is a linear mapping method commonly used in the field of signal processing. If the current state of charge is less than the lower limit, the controller calculates the difference between the lower limit and the current state of charge, and then divides the difference by the rated operating range width as the deviation. The deviation is a dimensionless parameter used to characterize the degree to which the current state of charge of the energy storage unit deviates from its rated operating range.

[0026] In some embodiments, reference Figure 3 As shown in the figure, this is a flowchart illustrating the process of determining the coordination control priority according to some embodiments of this application. In this embodiment, the coordination control priority of each energy storage unit is determined by extracting the cumulative voltage deviation and power regulation frequency of each energy storage unit based on the deviation and historical operating data, which can be achieved through the following steps: In step 1021, for each energy storage unit, the historical operating data of that energy storage unit is obtained; In step 1022, based on historical operating data, the cumulative deviation of the bus voltage from the rated value is counted as the voltage deviation accumulation, and the number of times the output power regulation action occurs is counted as the power regulation frequency. In step 1023, the regulation demand index of the energy storage unit is determined based on the accumulated voltage deviation and the power regulation frequency. In step 1024, based on the fuzzy reasoning mechanism, the deviation degree corresponding to the energy storage unit and the regulation demand index are used as inputs, and fuzzy reasoning is performed through a preset fuzzy rule base to obtain the regulation membership priority of the energy storage unit. In step 1025, the coordination control priority of each energy storage unit is determined by sorting them according to the control priority of each energy storage unit.

[0027] It should be noted that the voltage deviation accumulation in this application is a statistical measure used to quantify the total cumulative degree of deviation of the bus voltage from the rated value during the historical operation of the energy storage unit; the power regulation frequency is a count statistic used to reflect the frequency of output power regulation actions of the energy storage unit during the historical operation; the regulation demand index is a comprehensive indicator used to quantify the urgency of coordinated regulation of the energy storage unit under the combined effects of voltage quality degradation, regulation fatigue, and lifespan decay; the regulation affiliation priority is a quantitative affiliation used to characterize the priority level of regulation that the energy storage unit should obtain in the current state; and the coordinated control priority is an indicator used to rank the responsibility order of each energy storage unit in voltage recovery and current sharing coordinated regulation.

[0028] In practice, the historical operating data of the energy storage unit can be obtained in the following way: The controller reads the pre-stored operating records for a continuous period of time from the local circular buffer. The operating records include the bus voltage value, output current value and droop control reference voltage compensation value at each sampling time. These operating records are organized into time series data as historical operating data.

[0029] In specific implementation, based on historical operating data, the cumulative deviation of the bus voltage from the rated value is counted as the voltage deviation accumulation, and the number of output power adjustment actions is counted as the power adjustment frequency. This can be achieved in the following way: Within the time window of the historical operating data, the controller calculates the difference between the bus voltage value and the rated voltage value at each sampling moment, and accumulates the absolute value of the difference into a cumulative variable. After traversing all sampling points within the time window, the final value of the cumulative variable is the voltage deviation accumulation. At the same time, within the same time window, the controller detects every change in the droop control reference voltage compensation. Whenever the change in the compensation exceeds a preset dead zone threshold, it is counted as a power adjustment action. The total number of such actions occurring within the time window is counted as the power adjustment frequency and stored in local memory.

[0030] In specific implementation, based on the fuzzy inference mechanism, using the deviation degree corresponding to the energy storage unit and the regulation demand index as inputs, fuzzy inference is performed through a preset fuzzy rule base to obtain the regulation membership priority of the energy storage unit. This can be achieved in the following way: A dual-input single-output fuzzy inference system is pre-built inside the controller, where the input variables are the deviation degree and the regulation demand index, and the output variable is the regulation membership priority. The controller divides the deviation degree into three fuzzy sets, named low, medium, and high, respectively, and the corresponding membership function adopts a triangular or trapezoidal function. The values ​​for low are 0 to 0.3, medium are 0.2 to 0.7, and high are 0.6 to 1.0. The adjustment demand index is also divided into three fuzzy sets: low, medium, and high, with the same value range as the deviation. The output variable adjustment membership priority is divided into five fuzzy sets, named extremely low, low, medium, high, and extremely high, with values ​​ranging from 0 to 1. Extremely low corresponds to 0 to 0.2, low to 0.1 to 0.35, medium to 0.3 to 0.7, high to 0.65 to 0.9, and extremely high to 0.8 to 1.0. The fuzzy rule base contains nine rules, as follows: If the deviation is low and the adjustment demand index is low, the output is extremely low; if the deviation is low and the adjustment demand index is medium, the output is low; if the deviation is low and the adjustment demand index is high, the output is medium; if the deviation is medium and the adjustment demand index is low, the output is low; if the deviation is medium and the adjustment demand index is medium, the output is medium; if the deviation is medium and the adjustment demand index is high, the output is high; if the deviation is high and the adjustment demand index is low, the output is medium; if the deviation is high and the adjustment demand index is medium, the output is high; if the deviation is high and the adjustment demand index is medium, the output is high; if the deviation is high and the adjustment demand index is medium, the output is high; if the deviation is high and the adjustment demand index is medium, the output is high. If the exponent is high, the output is extremely high. During inference, the controller first fuzzifies the two precise input values ​​using the membership function mentioned above to obtain the membership degree of each input to each fuzzy set. Then, for each rule, the minimum value of the two input membership degrees is taken as the trigger strength of the rule, and the output fuzzy set of the rule is truncated to this trigger strength. After that, the truncated output fuzzy sets of all rules are synthesized through a union operation to form the total fuzzy output region. Finally, the centroid method can be used to defuzzify the fuzzy output region to obtain the precise output value, which is the value used to adjust the membership priority.

[0031] In specific implementation, the coordination control priority of each energy storage unit is determined by sorting according to its control priority. This can be achieved in the following way: The controller collects the control priority of all energy storage units into an array, with each unit corresponding to a value. The controller uses a known numerical sorting algorithm, such as bubble sort or quick sort, to arrange the values ​​in the array in descending order and records the energy storage unit number corresponding to each value. After sorting, the coordination control priority of the energy storage unit ranked first is determined to be the highest, the second highest, and so on, with the coordination control priority of the energy storage unit ranked last being determined to be the lowest. The controller stores each energy storage unit and its corresponding coordination control priority in a local priority mapping table in the form of key-value pairs, which is called when dynamically allocating the adjustment weights of voltage recovery tasks and current sharing tasks.

[0032] Preferably, in some embodiments, determining the regulation demand index of the energy storage unit based on the accumulated voltage deviation and power regulation frequency can be achieved using the following steps: The accumulated voltage deviation and the power adjustment frequency are normalized to obtain the first intermediate value and the second intermediate value. The coupling strength coefficient is determined based on the remaining cycle life of the energy storage unit. The shorter the remaining cycle life, the larger the coupling strength coefficient, and the value of this coefficient is between 0 and 1. Using the coupling strength coefficient as a nonlinear fusion factor, cross-excitation calculations are performed on the first intermediate quantity and the second intermediate quantity to obtain the regulation demand index of the energy storage unit.

[0033] The coupling strength coefficient in this application is an adjustment factor used to quantify the intensity of cross-excitation fusion between the remaining cycle life of the energy storage unit and the cumulative voltage deviation and power regulation frequency.

[0034] In specific implementation, the voltage deviation accumulation and the power regulation frequency are normalized to obtain the first intermediate quantity and the second intermediate quantity, which can be achieved in the following way: The controller obtains the maximum and minimum values ​​of the voltage deviation accumulation of the energy storage unit in the past year or the entire service cycle from historical operating data, and uses the difference between the maximum and minimum values ​​as the normalized reference range of the voltage deviation; the controller subtracts the minimum value of the voltage deviation accumulation from the currently obtained voltage deviation accumulation, and then divides it by the difference between the maximum and minimum values ​​of the voltage deviation accumulation, and uses the quotient as the first intermediate quantity; if the maximum and minimum values ​​of the voltage deviation accumulation are equal, the first intermediate quantity is directly set to 0.5; similarly, the controller obtains the maximum and minimum values ​​of the power regulation frequency of the energy storage unit within the same time window, subtracts the minimum value of the power regulation frequency from the current power regulation frequency, and then divides it by the difference between the maximum and minimum values ​​of the power regulation frequency, and uses the quotient as the second intermediate quantity; if the maximum and minimum values ​​of the power regulation frequency are equal, the second intermediate quantity is directly set to 0.5.

[0035] In practice, the coupling strength coefficient based on the remaining cycle life of the energy storage unit can be determined in the following way: The number of cycles and the rated cycle life of the energy storage unit are read from the battery management system via the controller local area network bus. The number of cycles is the total number of complete charge-discharge cycles accumulated by the battery management system since the energy storage unit was put into use. The rated cycle life is the number of cycles that the energy storage unit manufacturer provides to maintain normal performance under nominal operating conditions. The controller calculates the ratio of the number of cycles to the rated cycle life to obtain the cycle life consumption rate. Then, this consumption rate is subtracted from 1 to obtain the remaining cycle life ratio. The controller then calculates 1 minus the remaining cycle life ratio, and the difference is used as the coupling strength coefficient. If the remaining cycle life ratio is 0, meaning the number of cycles has reached or exceeded the rated cycle life, the coupling strength coefficient is directly set to 1. The shorter the remaining cycle life, the closer the coupling strength coefficient is to 1.

[0036] Preferably, in some embodiments, the regulation demand index of the energy storage unit is obtained by using the coupling strength coefficient as a nonlinear fusion factor and performing cross-excitation calculations on the first intermediate quantity and the second intermediate quantity, which can be achieved by the following steps: Multiply the first intermediate quantity by the second intermediate quantity to obtain the cross product term; Using the coupling strength coefficient as the exponent, the cross-product term is transformed by a power function to obtain the nonlinear excitation term; The linear combination of the first intermediate quantity and the second intermediate quantity is superimposed on the nonlinear excitation term to generate the regulation demand index of the energy storage unit, so that when both the first intermediate quantity and the second intermediate quantity are at a high level, the regulation demand index exhibits superlinear growth.

[0037] In specific implementation, firstly, the first and second intermediate quantities, which have already been normalized, are read from memory. These two intermediate quantities represent the normalized results of the accumulated voltage deviation and the power regulation frequency, respectively. Then, a known multiplication instruction is called to multiply the first and second intermediate quantities, and the resulting product is used as a cross-product term. This cross-product term reflects the degree of coupling between the two intermediate quantities, which are both at their peak values. Next, the coupling strength coefficient, calculated based on the remaining cycle life of the energy storage unit, is obtained. This coefficient is between 0 and 1. A known power function calculation method is used, with the cross-product term as the base and the coupling strength coefficient as the exponent, to perform a power operation, i.e., the coupling strength coefficient of the cross-product term is raised to the power of the cross-product term. The result is used as a nonlinear excitation term. This transformation has a compression effect on the cross-product term when the coupling strength coefficient is less than 1. Finally, the first and second intermediate quantities are summed with equal weights to obtain a linear combination term. The linear combination term is then added to the nonlinear excitation term, and the sum is used as the regulation demand index of the energy storage unit. Since the first and second intermediate quantities are both close to 1 and the coupling strength coefficient is large, the nonlinear excitation term will exhibit the characteristic of superlinear growth, thus making the adjustment demand index significantly higher than the simple linear combination value under the same conditions, achieving a superlinear growth effect.

[0038] In step 103, the voltage consistency error is determined based on the difference between the real-time bus voltage and the neighboring unit, and the current sharing error is determined based on the difference between the real-time output current and the ideal current sharing reference value obtained by distributed iteration.

[0039] In some embodiments, reference Figure 4 As shown in the figure, this is a schematic flowchart illustrating the determination of voltage consistency error according to some embodiments of this application. In this embodiment, the determination of voltage consistency error based on the difference between the real-time bus voltage and the neighboring unit can be achieved by the following steps: In step 1031, the current real-time bus voltage values ​​of this energy storage unit and all neighboring units are collected; In step 1032, the validity of the neighbor voltages is determined, and the neighbor voltages that are interrupted or have not been updated due to timeout are replaced with predicted estimates based on historical data to obtain a set of valid neighbor voltages. In step 1033, the deviation between the voltage of this energy storage unit and the arithmetic mean of the effective neighbor voltage set is calculated to obtain the voltage consistency error.

[0040] The effective neighbor voltage set in this application is an array used to store the real-time bus voltage values ​​or predicted estimates of neighboring units that are considered reliable after validity judgment; the voltage consistency error is used to measure the error value of voltage consistency between this energy storage unit and neighboring energy storage units.

[0041] In practice, the local controller of the energy storage unit periodically broadcasts voltage data requests to all neighboring units via the local area network bus at a period of 100 milliseconds, and receives the real-time bus voltage values ​​returned by each neighboring unit within 20 milliseconds. Simultaneously, it reads the real-time bus voltage value of its own unit collected by the local voltage sensor. A preset timeout window of 200 milliseconds is used. If voltage data from a neighboring unit is not received within the timeout window, it is determined that the neighboring unit's communication has been interrupted or the data has timed out. At this time, the two most recently successfully received voltage values ​​and their corresponding timestamps from the local cache are read, and the voltage change slope is calculated by subtracting the previous voltage value from the later voltage value and dividing by the two sampling timestamps. The time interval is calculated by adding the most recent voltage value to the difference between the current time and the most recent timestamp, multiplied by the slope, and using the result as the voltage prediction estimate for the current time to replace missing data. For neighboring units with normal communication, their returned real-time bus voltage values ​​are used directly to obtain an effective neighbor voltage set containing the voltage of this unit and the effective voltage values ​​of all neighboring units. The voltage values ​​of all neighboring units are extracted from this set and their arithmetic mean is calculated. Specifically, the voltage values ​​of all neighboring units are summed and then divided by the total number of neighboring units. The difference between the real-time bus voltage of this unit and the arithmetic mean is then calculated and the absolute value is taken. This absolute value is used as the voltage consistency error.

[0042] In some embodiments, determining the current sharing error based on the difference between the real-time output current and the ideal current sharing reference value obtained through distributed iteration can be achieved using the following steps: Each energy storage unit iteratively exchanges rated capacity and real-time output current information with its neighboring units; A globally unified ideal current sharing reference value is obtained through distributed consensus computation convergence. This ideal current sharing reference value reflects the expected current allocated according to the capacity ratio. The initial current deviation is obtained by comparing the real-time output current of this unit with the ideal current sharing reference value. The gain coefficient of the initial current deviation is adjusted according to the system operating mode to obtain the current average error.

[0043] The current sharing error in this application is an indicator that measures the degree of deviation of the output current of the energy storage unit from the ideal value allocated according to the capacity ratio.

[0044] In practice, each energy storage unit's local controller exchanges its rated capacity and real-time output current with all neighboring units via a local area network bus. The rated capacity is a fixed value specified by the manufacturer, and the real-time output current is the instantaneous value collected by the local current sensor. First, the rated capacity of the unit is used as a weight, and the ratio of the unit's real-time output current to its rated capacity is used as the initial state variable. Then, a well-known average consensus iterative algorithm is employed: The state variable of the unit in the current iteration is set to the state variable of the previous iteration plus a step size coefficient of 0.5 multiplied by the sum of the differences between the state variables of all neighboring units and the state variable of the unit. The unit sends its state variable to all neighboring units and receives their state variables. This iterative calculation is repeated until the difference between the maximum and minimum values ​​of all energy storage unit state variables is less than 0.001. The converged state variable is then recorded as the global consensus variable. Further steps are taken to obtain the total real-time output current of all energy storage units. The sum can be provided either through distributed accumulation or centrally by the microgrid central controller. Then, the ideal current sharing reference value is calculated, which is equal to the global consistency variable multiplied by the rated capacity of this unit, reflecting the expected current allocated according to the capacity ratio. The real-time output current of this unit is read, and the difference between it and the ideal current sharing reference value is calculated and the absolute value is taken to obtain the initial current deviation. The operating mode flag provided by the microgrid central controller is further read. If the current mode is grid-connected, the gain coefficient is set to 0.8. If the current mode is islanded, the gain coefficient is set to 1.2. The initial current deviation is multiplied by the gain coefficient, and the product is used as the current sharing error.

[0045] In step 104, when the bus voltage is detected to deviate from the rated operating range, the droop control reference voltage compensation amount of each energy storage unit is calculated through distributed iteration based on the voltage consistency error, current sharing error and the coordination control priority of each energy storage unit, and the reference value of the droop controller is corrected until the bus voltage is restored to the rated allowable range and the output current of each energy storage unit is evenly distributed according to the capacity ratio.

[0046] It should be noted that when the bus voltage deviates from the rated operating range, it indicates that the DC microgrid is currently in a state of power supply and demand imbalance or voltage instability caused by output mismatch of distributed energy storage units, requiring immediate triggering of coordinated regulation. This application's scheme dynamically allocates voltage recovery and current sharing responsibilities based on priority, merging voltage consistency errors and current sharing errors according to the actual state of each unit. This allows for prioritizing the suppression of the maximum voltage deviation while maintaining current sharing constraints, avoiding the coupled oscillation problems of voltage rise disrupting current sharing convergence or current sharing regulation pulling down voltage in the traditional fixed-weight method. This simultaneously improves transient voltage recovery speed and steady-state current sharing accuracy.

[0047] In some embodiments, based on the voltage consistency error, current sharing error, and the coordinated control priority of each energy storage unit, the droop control reference voltage compensation amount of each energy storage unit is calculated through distributed iteration, and the reference value of the droop controller is corrected to achieve the restoration of the bus voltage to the rated value and the equal distribution of the output current of each energy storage unit according to the capacity ratio. This can be achieved by the following steps: Based on the coordination and control priority of each energy storage unit, the adjustment weight between voltage restoration tasks and current sharing tasks is dynamically allocated. Units with higher priority bear more responsibility for voltage restoration, while units with lower priority bear more responsibility for current sharing. The voltage consistency error and the current equalization error are fused according to the adjustment weight to generate a preliminary compensation amount for the droop control reference voltage. The initial compensation amount is subjected to rate constraints and amplitude limits to obtain the final compensation amount; The final compensation amount is superimposed on the reference voltage of the current droop controller to generate the corrected reference voltage, and the output of the droop controller is updated. Repeat the above compensation calculation and reference voltage correction operations until the bus voltage is restored to the rated value within the allowable range and the deviation of the output current of each unit from the ideal current sharing reference value is less than the preset tolerance.

[0048] The droop control reference voltage compensation amount in this application is used to superimpose on the current reference voltage of the droop controller to achieve bus voltage recovery and output current proportional to capacity-based coordinated regulation of voltage adjustment increment; the droop controller is a device used to generate pulse width modulation signal to drive the power converter based on the reference voltage and real-time output current to achieve energy storage unit output voltage stabilization and load power distribution regulation.

[0049] In practice, the adjustment weights between voltage recovery and current sharing tasks are dynamically allocated based on the coordination control priority of each energy storage unit. Units with higher priority bear more voltage recovery responsibility, and units with lower priority bear more current sharing responsibility. This can be achieved as follows: First, obtain the coordination control priority ranking results of all energy storage units. Then, record the sequence number corresponding to the highest priority as 1 and the sequence number corresponding to the lowest priority as N, where N is the total number of energy storage units. For the i-th energy storage unit, the controller uses a linear mapping function to calculate its voltage recovery weight, specifically: the voltage recovery weight equals N minus... Add 1 to the priority number of the unit, then divide by N; the current sharing weight equals 1 minus the voltage recovery weight; according to this mapping method, for a unit with a priority number of 1, its voltage recovery weight equals N divided by N, which equals 1, and its current sharing weight equals 0, indicating that the unit fully assumes the responsibility for voltage recovery and does not assume the responsibility for current sharing; for a unit with a priority number of N, its voltage recovery weight equals 1 divided by N, and its current sharing weight equals 1 minus 1 divided by N, indicating that the unit assumes the responsibility for most of the current sharing; for units with intermediate priorities, the voltage recovery weight decreases linearly as the priority decreases, and the current sharing weight increases linearly accordingly.

[0050] In specific implementation, the voltage consistency error and the current averaging error are fused according to the adjustment weights to generate the initial compensation amount for the droop control reference voltage. This is achieved by reading the voltage recovery weight and current averaging weight of the current energy storage unit, and reading the voltage consistency error of that unit at the current moment. and current averaging error ; Obtain the preset current-to-voltage conversion coefficient This coefficient is pre-calibrated based on the droop slope of the energy storage unit's droop controller or the system's allowable voltage regulation range, with a typical value range of 0.01–0.1 V / A. Multiplying the current distribution error by the conversion factor yields a dimensionlessly uniform voltage equivalent error. Then, multiply the voltage recovery weight by the voltage consistency error to obtain the first product; multiply the current equalization weight by the voltage equivalent error to obtain the second product; add the first product and the second product together, and use the sum as the initial compensation amount for the droop control reference voltage; if the sum of the voltage recovery weight and the current equalization weight is 1, then the initial compensation amount is the weighted sum of the two voltage dimension errors.

[0051] In specific implementation, the initial compensation amount is subject to rate constraints and amplitude limits to obtain the final compensation amount in the following manner: The compensation amount from the previous moment is read, the current control cycle duration (e.g., 0.1 seconds) is read, and the preset maximum rate of change (e.g., 0.5 volts per second) is set. The difference between the current initial compensation amount and the compensation amount from the previous moment is calculated, and the absolute value of this difference is compared with the product of the maximum rate of change and the control cycle duration. If the absolute value of this difference is less than or equal to the product of the maximum rate of change and the control cycle duration, the current initial compensation amount is used as the compensation amount after rate constraints. If the absolute value of this difference is greater than the product of the maximum rate of change and the control cycle duration, restrictions are applied based on the sign of the difference: when the current initial compensation amount is greater than the compensation amount from the previous moment, the compensation amount after rate constraints is... The compensation amount equals the compensation amount at the previous moment plus the maximum rate of change multiplied by the control cycle duration. When the current initial compensation amount is less than the compensation amount at the previous moment, the compensation amount after rate constraint equals the compensation amount at the previous moment minus the maximum rate of change multiplied by the control cycle duration. Obtain the preset maximum amplitude, for example, ±2 volts, and compare the compensation amount after rate constraint with this maximum amplitude: if the compensation amount after rate constraint is greater than the maximum amplitude, then the final compensation amount is set to the maximum amplitude; if the compensation amount after rate constraint is less than the negative maximum amplitude, then the final compensation amount is set to the negative maximum amplitude; if the compensation amount after rate constraint is between the two, then the final compensation amount is set to the compensation amount after rate constraint itself, and the value obtained after the above rate constraint and amplitude limitation is used as the final compensation amount output.

[0052] In specific implementation, the final compensation amount is superimposed on the current reference voltage of the droop controller to generate a corrected reference voltage, and the output of the droop controller is updated in the following way: the current reference voltage value is read from the reference voltage register of the droop controller, the reference voltage value is added to the final compensation amount, and the result of the addition operation is written into the reference voltage register of the droop controller as the corrected reference voltage, thereby completing the update of the droop controller output; the droop controller calculates the duty cycle of the pulse width modulation signal driving the power converter according to the pre-stored droop control equation based on the updated reference voltage and real-time output current, and then adjusts the output voltage and current of the energy storage unit.

[0053] In practice, the above-mentioned compensation calculation and reference voltage correction operations are repeated until the bus voltage is restored to the rated value within the allowable range and the deviation of the output current of each unit from the ideal current sharing reference value is less than the preset tolerance. This means that after each compensation superposition and reference voltage update, the difference between the bus voltage and the rated voltage of all energy storage units, as well as the difference between the output current of each unit and the ideal current sharing reference value, are re-monitored. Iterative adjustment stops only when the voltage difference of all units is less than the preset voltage tolerance and the current difference of all units is less than the preset equal sharing tolerance; otherwise, the next round of compensation calculation and correction continues.

[0054] It should be noted that this application constructs a fuzzy priority inference model by integrating the state-of-charge deviation with the cumulative voltage deviation and power regulation frequency in historical operating data. It introduces a nonlinear cross-excitation operation based on remaining cycle life to generate a regulation demand index. Then, it dynamically allocates regulation weights for voltage recovery and current sharing tasks according to priority. Furthermore, it integrates the voltage consistency error corrected by communication quality and the current sharing error adjusted by operating mode according to weights to generate a droop compensation amount. After rate amplitude constraints, iteratively corrects the droop controller reference value until both voltage and current sharing objectives converge simultaneously. This scheme achieves priority-based active decoupling of voltage and current coupling conflicts in distributed energy storage control, avoiding the oscillation problems of voltage rise damaging current sharing accuracy or current sharing regulation lowering voltage in traditional methods. It also considers robustness under equipment aging equalization and non-ideal communication environments, thus significantly improving the dynamic stability, current sharing accuracy, and overall service life of the energy storage system in the DC microgrid.

[0055] On the other hand, in some embodiments, this application provides a distributed coordination control system for a DC microgrid energy storage system, with reference to... Figure 5 The figure is a schematic diagram of the structure of a distributed coordination control system for a DC microgrid energy storage system according to some embodiments of this application. The distributed coordination control system 500 of the DC microgrid energy storage system includes: a monitoring module 501, a processing module 502, and an execution module 503, which are described below: Monitoring module 501, in this application, is mainly used to monitor the real-time electrical status of multiple distributed energy storage units under their respective charging and discharging power. The real-time electrical status includes at least bus voltage, output current and state of charge. Processing module 502, in this application, is used to detect the deviation between the current state of charge of each energy storage unit and the rated operating range, and then extract the cumulative voltage deviation and power adjustment frequency of each energy storage unit based on the deviation and historical operating data, and determine the coordination control priority of each energy storage unit. In this application, the processing module 502 is also used to determine the voltage consistency error based on the difference between the real-time bus voltage and the neighboring unit, and to determine the current sharing error based on the difference between the real-time output current and the ideal current sharing reference value obtained by distributed iteration. The execution module 503 in this application is mainly used to calculate the droop control reference voltage compensation amount of each energy storage unit through distributed iteration based on the voltage consistency error, current sharing error and the coordination control priority of each energy storage unit when the bus voltage is detected to deviate from the rated operating range, and to correct the reference value of the droop controller until the bus voltage is restored to the rated allowable range and the output current of each energy storage unit is evenly distributed according to the capacity ratio.

[0056] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the distributed coordination control method of the DC microgrid energy storage system described above.

[0057] In some embodiments, reference Figure 6 The figure is a schematic diagram of the structure of a computer device for implementing a distributed coordinated control method for a DC microgrid energy storage system according to some embodiments of this application. The distributed coordinated control method for the DC microgrid energy storage system in the above embodiments can be implemented through... Figure 6 The computer device shown is used to implement this, and the computer device 600 includes at least one processor 601, a communication bus 602, a memory 603, and at least one communication interface 604.

[0058] The processor 601 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0059] The communication bus 602 can be used to transmit information between the aforementioned components.

[0060] The memory 603 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 603 may exist independently and be connected to the processor 601 via a communication bus 602. The memory 603 may also be integrated with the processor 601.

[0061] The memory 603 stores program code for executing the scheme of this application, and its execution is controlled by the processor 601. The processor 601 executes the program code stored in the memory 603. The program code may include one or more software modules. In the above embodiments, the distributed coordinated control method of the DC microgrid energy storage system can be implemented by the processor 601 and one or more software modules in the program code in the memory 603.

[0062] Communication interface 604 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0063] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0064] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0065] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described distributed coordinated control method for a DC microgrid energy storage system.

[0066] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0067] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A distributed coordinated control method for a DC microgrid energy storage system, characterized in that, include: The real-time electrical status of multiple distributed energy storage units under their respective charging and discharging power is monitored, and the real-time electrical status includes at least bus voltage, output current and state of charge. The deviation between the current state of charge of each energy storage unit and the rated operating range is detected. Then, based on the deviation and historical operating data, the cumulative voltage deviation and power regulation frequency of each energy storage unit are extracted to determine the coordination control priority of each energy storage unit. Specifically, determining the coordinated control priority of each energy storage unit by extracting the cumulative voltage deviation and power regulation frequency of each energy storage unit based on the deviation and historical operating data includes: For each energy storage unit, acquire the historical operating data of that energy storage unit; Based on historical operating data, the cumulative deviation of the bus voltage from the rated value is counted as the voltage deviation cumulative amount, and the number of output power regulation actions is counted as the power regulation frequency. Based on the accumulated voltage deviation and power regulation frequency, the regulation demand index of the energy storage unit is determined; Based on the fuzzy reasoning mechanism, the deviation degree corresponding to the energy storage unit and the regulation demand index are used as inputs. Fuzzy reasoning is performed through a preset fuzzy rule base to obtain the regulation membership priority of the energy storage unit. The coordination control priority of each energy storage unit is determined by sorting them according to the control priority of each unit. Specifically, determining the regulation demand index of the energy storage unit based on the accumulated voltage deviation and power regulation frequency includes: The accumulated voltage deviation and the power adjustment frequency are normalized to obtain the first intermediate value and the second intermediate value. The coupling strength coefficient is determined based on the remaining cycle life of the energy storage unit. The shorter the remaining cycle life, the larger the coupling strength coefficient, and the value of this coefficient is between 0 and 1. Using the coupling strength coefficient as a nonlinear fusion factor, cross-excitation calculations are performed on the first intermediate quantity and the second intermediate quantity to obtain the regulation demand index of the energy storage unit. Specifically, the regulation demand index of the energy storage unit is obtained by using the coupling strength coefficient as a nonlinear fusion factor and performing cross-excitation calculations on the first intermediate quantity and the second intermediate quantity. Multiply the first intermediate quantity by the second intermediate quantity to obtain the cross product term; Using the coupling strength coefficient as the exponent, the cross-product term is transformed by a power function to obtain the nonlinear excitation term; The linear combination of the first intermediate quantity and the second intermediate quantity is superimposed on the nonlinear excitation term to generate the regulation demand index of the energy storage unit, so that when both the first intermediate quantity and the second intermediate quantity are at a high level, the regulation demand index exhibits superlinear growth. The voltage consistency error is determined based on the difference between the real-time bus voltage and the neighboring unit, and the current sharing error is determined based on the difference between the real-time output current and the ideal current sharing reference value obtained by distributed iteration. When the bus voltage is detected to deviate from the rated operating range, the droop control reference voltage compensation amount of each energy storage unit is calculated through distributed iteration based on the voltage consistency error, current sharing error and the coordination control priority of each energy storage unit, and the reference value of the droop controller is corrected until the bus voltage is restored to the rated value allowable range and the output current of each energy storage unit is evenly distributed according to the capacity ratio. Specifically, based on the voltage consistency error, current sharing error, and the coordination control priority of each energy storage unit, the droop control reference voltage compensation amount of each energy storage unit is calculated through distributed iteration, and the reference value of the droop controller is corrected until the bus voltage recovers to the rated allowable range and the output current of each energy storage unit is evenly distributed according to the capacity ratio. Based on the coordination and control priority of each energy storage unit, the adjustment weight between voltage restoration tasks and current sharing tasks is dynamically allocated. Units with higher priority bear more responsibility for voltage restoration, while units with lower priority bear more responsibility for current sharing. The voltage consistency error and the current equalization error are fused according to the adjustment weight to generate a preliminary compensation amount for the droop control reference voltage. The initial compensation amount is subjected to rate constraints and amplitude limits to obtain the final compensation amount; The final compensation amount is superimposed on the reference voltage of the current droop controller to generate the corrected reference voltage, and the output of the droop controller is updated. Repeat the above compensation calculation and reference voltage correction operations until the bus voltage is restored to the rated value within the allowable range and the deviation of the output current of each unit from the ideal current sharing reference value is less than the preset tolerance.

2. The method as described in claim 1, characterized in that, The deviation of the current state of charge of each energy storage unit from its rated operating range is specifically detected, including: For each energy storage unit, obtain the upper and lower limits of the preset rated state of charge for that energy storage unit, and compare the current state of charge with the upper and lower limits. If the current state of charge is within the rated operating range defined by the upper and lower limits, the deviation is set to zero. If the current state of charge is higher than the upper limit, the difference between the current state of charge and the upper limit is calculated, and the ratio of the difference to the width of the rated operating range is used as the deviation. If the current state of charge is lower than the lower limit, the difference between the lower limit and the current state of charge is calculated, and the ratio of this difference to the width of the rated operating range is used as the deviation.

3. The method as described in claim 1, characterized in that, The voltage consistency error is determined based on the difference between the real-time bus voltage and that of neighboring units, specifically including: Collect the current real-time bus voltage values ​​of this energy storage unit and all neighboring units; The validity of neighbor voltages is determined, and neighbor voltages that are interrupted or have not been updated due to timeout are replaced with predicted estimates based on historical data to obtain a set of valid neighbor voltages. The voltage consistency error is obtained by calculating the deviation between the voltage of this energy storage unit and the arithmetic mean of the effective neighbor voltage set.

4. The method as described in claim 1, characterized in that, The current sharing error is determined based on the difference between the real-time output current and the ideal current sharing reference value obtained through distributed iteration. Specifically, it includes: Each energy storage unit iteratively exchanges rated capacity and real-time output current information with its neighboring units; A globally unified ideal current sharing reference value is obtained through distributed consensus computation convergence. This ideal current sharing reference value reflects the expected current allocated according to the capacity ratio. The initial current deviation is obtained by comparing the real-time output current of this unit with the ideal current sharing reference value. The gain coefficient of the initial current deviation is adjusted according to the system operating mode to obtain the current average error.

5. The method as described in claim 1, characterized in that, The bus voltage and output current of each energy storage unit are collected by voltage sensors and Hall current sensors, respectively.

6. The method as described in claim 1, characterized in that, The state of charge of each energy storage unit is obtained by using the ampere-hour integration method and combined with terminal voltage lookup table correction through the battery management system.

7. A distributed coordination control system for a DC microgrid energy storage system, wherein the distributed coordination control is performed using the method described in any one of claims 1 to 6, characterized in that, The system includes: The monitoring module is used to monitor the real-time electrical status of multiple distributed energy storage units under their respective charging and discharging power. The real-time electrical status includes at least bus voltage, output current and state of charge. The processing module is used to detect the deviation between the current state of charge of each energy storage unit and the rated operating range, and then extract the cumulative voltage deviation and power regulation frequency of each energy storage unit based on the deviation and historical operating data to determine the coordination control priority of each energy storage unit. The processing module is also used to determine the voltage consistency error based on the difference between the real-time bus voltage and the neighboring unit, and to determine the current sharing error based on the difference between the real-time output current and the ideal current sharing reference value obtained by distributed iteration. The execution module is used to calculate the droop control reference voltage compensation amount of each energy storage unit through distributed iteration based on the voltage consistency error, current sharing error and the coordination control priority of each energy storage unit when the bus voltage is detected to deviate from the rated operating range, and to correct the reference value of the droop controller until the bus voltage is restored to the rated allowable range and the output current of each energy storage unit is evenly distributed according to the capacity ratio.

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