Network construction type energy storage cooperative control system adaptive to energy storage system

Abstract

The invention discloses a network construction type energy storage cooperative control system adaptive to an energy storage system, and relates to the field of energy storage cooperative control, and the system comprises the steps: a data collection module obtains the operation parameters, environment parameters and access point impedance characteristics of an energy storage unit; the data processing module calculates a power margin and an impedance change trend; the path division module divides paths and marks states based on the change trend and historical scores; the path screening module screens and schedules response paths according to state labels; the path response module generates a power distribution instruction and issues and executes the instruction; the path adjustment module dynamically adjusts the path weight and the adjustment rate according to the system response deviation; the system response reliability and the resource utilization rate can be enhanced, and stable and efficient response control of the energy storage system is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of energy storage collaborative control, in particular to a network-constructing energy storage collaborative control system adapted to an energy storage system. BACKGROUND

[0002] As a key technology for new generation power system stability support, network-constructing energy storage can actively participate in voltage, frequency regulation and power distribution of the power grid through control strategy, and is an important support means to ensure stable operation of the power grid with high proportion of new energy access. Unlike traditional network-following energy storage systems, network-constructing energy storage systems do not rely on voltage and frequency signals of the power grid, but can independently build local voltage sources and guide other power sources to operate synchronously.

[0003] However, with the expansion of energy storage systems in capacity, quantity and distribution, the complexity of the control architecture has also increased significantly, and traditional centralized or simple distributed control methods cannot meet the multi-dimensional collaborative requirements of system stability, rapid response and disturbance resistance.

[0004] Under complex operating conditions, the electrical connection path between the energy storage unit and the power grid may have stability deterioration due to changes in impedance characteristics, and existing methods cannot identify the state of different paths and dynamically adjust their participation levels in time. At the same time, the power output capability of the energy storage unit varies greatly under different state of charge, temperature environment and operating conditions, and the traditional power scheduling method does not fully consider the influence of multi-dimensional parameters on the performance of the energy storage, and cannot reflect the actual available power margin. In addition, the current energy storage scheduling strategy usually relies on static path selection or simple rules based on power level, lacks a comprehensive evaluation mechanism combining path impedance change trend, historical stability and other multi-dimensional parameters, resulting in unreasonable response path selection and affecting system response quality. The control system often lacks effective feedback mechanisms based on real-time response deviation, response delay and other parameters, and cannot adaptively optimize the state label and power regulation rate of the path according to the current response effect, affecting the response efficiency and stability of the next cycle.

[0005] In view of the above problems, the prior art needs to be improved. SUMMARY

[0006] In view of the deficiencies in the prior art, the present application provides a network-constructing energy storage collaborative control system adapted to an energy storage system.

[0007] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0008] In a first aspect, the present application discloses a network-constructing energy storage collaborative control system adapted to an energy storage system, comprising:

[0009] The data acquisition module acquires operating parameters and environmental parameters of each energy storage unit, and acquires impedance amplitude and phase characteristics of the energy storage access point.

[0010] The data processing module calculates the normalized power margin of each energy storage unit according to the operating parameters, and calculates the impedance amplitude change rate and the phase characteristic change trend according to the impedance amplitude and the phase characteristic;

[0011] The path division module dynamically divides each response path in the energy storage system into a stable path, a transition path and a degraded path based on the impedance amplitude change rate, the phase characteristic change trend and the pre-stored historical stability score, and generates a corresponding state label for each response path based on a preset path division rule;

[0012] The path screening module screens the response paths based on the state labels, preferentially schedules the stable paths, activates the transition paths to participate in the response in a preset proportion when the total power margin of the stable paths is lower than a demand threshold, and excludes the real-time scheduling authority of the degraded paths;

[0013] The path response module generates a power distribution instruction based on the normalized power margin of each response path and the weighting coefficient corresponding to the state label, and issues the power distribution instruction to the corresponding energy storage unit for execution;

[0014] The path adjustment module obtains voltage recovery deviation, frequency recovery deviation, response delay and power deviation parameters after each power distribution instruction is executed; the current response effectiveness is judged according to the voltage recovery deviation and the frequency recovery deviation; if any parameter exceeds a preset stable interval, the participation proportion of the degraded path and the power deviation parameter are analyzed, and the state label weight and the power regulation rate limit value of the corresponding path are dynamically adjusted for path screening and power distribution in the next response period.

[0015] In a second aspect, the application discloses a network-constructed energy storage collaborative control method suitable for an energy storage system, comprising the following steps:

[0016] Obtain the operating parameters and environmental parameters of each energy storage unit, and obtain the impedance amplitude and phase characteristic of the energy storage access point;

[0017] Calculate the normalized power margin of each energy storage unit according to the operating parameters, and calculate the impedance amplitude change rate and the phase characteristic change trend according to the impedance amplitude and the phase characteristic;

[0018] Based on the impedance amplitude change rate, the phase characteristic change trend and the pre-stored historical stability score, each response path in the energy storage system is dynamically divided into a stable path, a transition path and a degraded path based on a preset path division rule, and a corresponding state label is generated for each response path;

[0019] Filter the response path based on the state label, preferentially schedule the stable path, when the total power margin of the stable path is lower than the demand threshold, activate the transition path to participate in response according to a preset proportion, and exclude the real-time scheduling authority of the degraded path;

[0020] Generate a power allocation instruction based on the normalized power margin of each response path and the weighting coefficient corresponding to the state label, and issue the power allocation instruction to the corresponding energy storage unit for execution;

[0021] After each power allocation instruction is executed, the voltage recovery deviation, frequency recovery deviation, response delay and power deviation parameters of the system response are obtained;

[0022] According to the voltage recovery deviation and the frequency recovery deviation, the current response effectiveness is judged: if any parameter exceeds the preset stable interval, the participation proportion and power deviation parameter of the degraded path are analyzed, and the state label weight and power regulation rate limit value of the corresponding path are dynamically adjusted for path screening and power allocation in the next response period.

[0023] Compared with the prior art, the beneficial effects of the present application are:

[0024] 1. By introducing impedance amplitude change rate, phase characteristic change trend and historical stability score and other multi-dimensional parameters, a dynamic path division mechanism is established, which can divide the energy storage path into stable, transition and degraded paths in real time, effectively improving the accuracy and timeliness of path state identification, and significantly outperforming the existing methods based on static threshold or single parameter division;

[0025] 2. The power allocation is based on the normalized power margin index combined with the weighting coefficient, fully considering the state of charge, temperature factor and operation load rate of the energy storage unit, dynamically evaluating the actual available power resources, avoiding overload or invalid scheduling, and enhancing the reliability and resource utilization rate of the system response;

[0026] 3. The response deviation and delay feedback mechanism is introduced, the path state weight and power regulation rate limit value are dynamically adjusted based on the regulation efficiency factor, a closed-loop control loop of path evaluation and scheduling is constructed, which can quickly optimize the response strategy when the system runs fluctuates, and effectively suppress the influence of performance degradation path;

[0027] 4. Support running in complex scenes such as multiple energy storage units collaborative access, uneven distribution of multiple paths, frequent power grid disturbance, etc., combined with path priority scheduling and transition path activation mechanism, ensure that the response task is preferentially undertaken by the stable path, the transition path assists as needed, avoid the interference of degraded path to the system, and improve the overall stability and response speed. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to make the technical solutions of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description only represent some of the embodiments of the present application, and other drawings can be obtained by those of ordinary skill in the art without any creative effort on the basis of these drawings.

[0029] Figure 1 It is a whole block diagram of the system of the embodiment one of the present application.

[0030] Figure 2 It is a whole block diagram of the method of the embodiment two of the present application.

[0031] Figure 3 It is a flow chart of the method of the embodiment two of the present application. DETAILED DESCRIPTION

[0032] The technical solutions of the present application will be described clearly and completely below in combination with the embodiments. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those of ordinary skill in the art without any creative effort belong to the protection scope of the present application.

[0033] Summary of the application: In the prior art, the continuous increase of the proportion of new energy power generation leads to the decrease of the inertia support and stability of the power system, and the network-type energy storage as a key support technology needs to independently build a voltage source and coordinate the operation of multiple units. The traditional control method has deficiencies in path stability judgment, power margin evaluation and dynamic adjustment, and is difficult to cope with impedance changes and multi-dimensional parameter influences, resulting in unreasonable response path selection and insufficient system stability.

[0034] In order to solve the above problems, the inventors found that the prior art failed to effectively combine real-time impedance changes with historical stability data for dynamic classification of paths, and the power distribution did not consider the influence of environmental parameters on the availability of energy storage units. By analyzing the correlation between system response delay and power deviation, a path division mechanism based on impedance change trend and historical score is proposed, and a dynamic weighting coefficient is introduced to realize adaptive allocation of power margin, combined with feedback adjustment of path state weight to form a closed-loop control system.

[0035] Embodiment one:

[0036] As Figure 1As shown, the network configuration type energy storage collaborative control system suitable for the energy storage system comprises: a data acquisition module: obtaining the operating parameters and environmental parameters of each energy storage unit, and simultaneously obtaining the impedance amplitude and phase characteristics of the energy storage access point; a data processing module: calculating the normalized power margin of each energy storage unit according to the operating parameters, and calculating the impedance amplitude change rate and phase characteristic change trend according to the impedance amplitude and phase characteristics; a path division module: based on the impedance amplitude change rate, the phase characteristic change trend and the pre-stored historical stability score, the response paths in the energy storage system are dynamically divided into stable paths, transition paths and degraded paths based on the preset path division rules, and a corresponding state label is generated for each response path; a path screening module: screening the response paths based on the state label, preferentially scheduling the stable paths, and when the total power margin of the stable paths is lower than the demand threshold, activating the transition paths to participate in the response according to a preset proportion, and excluding the real-time scheduling authority of the degraded paths; a path response module: generating a power distribution instruction based on the normalized power margin of each response path and the corresponding weighted coefficient of the state label, and issuing the power distribution instruction to the corresponding energy storage unit for execution; a path adjustment module: after each power distribution instruction is executed, obtaining the voltage recovery deviation, frequency recovery deviation, response delay and power deviation parameters of the system response; judging the current response effectiveness according to the voltage recovery deviation and the frequency recovery deviation: if any parameter exceeds the preset stable interval, analyzing the participation proportion of the degraded path and the power deviation parameter, dynamically adjusting the state label weight and power adjustment rate limit value of the corresponding path, which is used for path screening and power distribution in the next response period.

[0037] Among them, the operating parameters include charging and discharging power, state of charge and single temperature, which are used to reflect the real-time output capability of the energy storage unit. The environmental parameters include temperature and cooling state, which are used to evaluate the influence of external conditions on the performance of the equipment. The impedance amplitude change rate is calculated by the amplitude difference and time length ratio of adjacent time windows, which can represent the stability change rate of the electrical path. The phase characteristic change trend is judged by the continuous phase angle change direction, which is used to identify the convergence or divergence state of the path. The historical stability score is calculated by weighting the response deviation rate and the failure times, which reflects the historical reliability level of the path. The state label includes three types of stable, transition and degradation, which is used to identify the real-time availability level of the path. The power distribution instruction is dynamically adjusted based on the weighted coefficient, which ensures that the stable path carries the power demand preferentially. The voltage recovery deviation and the frequency recovery deviation are used to verify the response effectiveness, and the response delay and the power deviation parameter are used to evaluate the execution quality.

[0038] Specifically, the data acquisition module collects parameters such as the charging and discharging power and temperature of the energy storage unit in real time, and synchronously monitors the impedance amplitude and phase of the access point. The data processing module linearly weights the operating parameters to obtain a normalized power margin, which integrates the state of charge, temperature difference and power load rate, and can accurately evaluate the available capacity of the energy storage unit. At the same time, the impedance amplitude change rate is calculated through time series analysis to judge the convergence of the phase trend. The path division module combines the above parameters with historical scores, and divides the electrical path into three categories according to the preset threshold rule, and triggers the re-division when the ambient temperature is abnormal. The path screening module preferentially schedules the stable path, activates the transition path in proportion to the difference when the margin is insufficient, and prohibits the deterioration path from participating in real-time response. The path response module adjusts the power distribution weight of each path through a weighting coefficient to ensure that the system demand matches the available capacity. The path adjustment module analyzes the voltage frequency deviation after each instruction execution, and if it exceeds the threshold, the weight coefficient of the deterioration path is reduced and its adjustment rate is limited, forming a closed-loop optimization mechanism.

[0039] Compared with the prior art, the traditional method adopts fixed path selection rules and cannot adapt to the dynamic change of impedance, while the present scheme realizes accurate classification of path state through real-time impedance analysis and fusion of historical data. The existing power distribution does not consider the influence of temperature on battery performance, and the present scheme effectively integrates multi-dimensional parameters through normalized power margin calculation. The conventional system lacks an execution feedback optimization mechanism, and the present scheme dynamically adjusts the weight and adjustment rate based on response deviation, enhancing the adaptive ability of the system.

[0040] Through the above technical scheme, the present application effectively solves the problem of multi-path dynamic stability judgment, realizes accurate division of path state through impedance change trend and historical score. The power margin evaluation method is optimized, and the capacity calculation accuracy is improved by integrating environmental parameters and operating state. A closed-loop feedback mechanism is established, and the path weight is dynamically optimized according to the execution effect to ensure stable operation of the system under varying conditions. The fine scheduling of energy storage resources is realized, which ensures the response speed while reducing the negative impact of the deterioration path on system stability.

[0041] The present application further proposes that the operating parameters include the charging and discharging power, state of charge and cell temperature of each energy storage unit; the environmental parameters include the ambient temperature and the running state of the cooling system; and the calculation method of the normalized power margin is to linearly weight and sum the ratio of the state of charge to the rated state of charge, the difference between the cell temperature and the ambient temperature, and the ratio of the current charging and discharging power to the rated power, to obtain the normalized power margin.

[0042] The charge-discharge power refers to the actual active power or absorbed power output by the energy storage unit at present, which can be obtained by collecting current and voltage data through a real-time monitoring module of the energy storage converter and calculating, and is used to represent the instantaneous power output capability of the energy storage unit. The state of charge refers to the percentage of the remaining capacity of the energy storage unit to the rated total capacity, which can be estimated by using a voltage integration method or a Kalman filtering algorithm, and is used to reflect the available energy reserve of the energy storage unit. The cell temperature refers to the real-time temperature of the internal battery cell of the energy storage unit, which can be collected by a temperature sensor embedded on the surface of the battery cell, and is used to evaluate the limitation of the battery thermal state on power output. The ambient temperature refers to the external ambient temperature of the energy storage system, which can be measured by a temperature sensor deployed outside the energy storage cabinet, and is used to reflect the influence of the heat dissipation condition on system operation. The cooling system running state refers to the start-stop state and working parameters of devices such as cooling fans and liquid cooling pumps, which can be obtained through the state signal of the device controller, and is used to determine whether the system heat dissipation capacity meets the demand.

[0043] Specifically, the ratio of the state of charge to the rated state of charge is used to quantify the remaining energy margin of the energy storage unit, the difference between the cell temperature and the ambient temperature is used to represent the temperature rise effect of the battery during operation, and the ratio of the current charge-discharge power to the rated power is used to reflect the current load rate of the energy storage unit. By linearly weighting and summing these three parameters, the actual available power margin of the energy storage unit in the three dimensions of energy reserve, thermal management limitation and load capacity can be comprehensively evaluated. For example, the weight coefficient of the state of charge can be set to 0.5, the weight coefficient of the temperature difference can be set to 0.3, and the weight coefficient of the power ratio can be set to 0.2, and finally a normalized value in the range of 0 to 1 is obtained, and the higher the value, the greater the power output potential of the energy storage unit.

[0044] Compared with the prior art, the traditional method usually only schedules power based on a single parameter of the state of charge or the rated power, and cannot reflect the attenuation effect of temperature change on battery performance and the dynamic limitation of actual load rate on power output. However, the present scheme introduces multi-dimensional parameters of temperature difference and power ratio, and uses a linear weighting mechanism, so that the calculation result of the normalized power margin is closer to the actual available power of the energy storage unit under complex working conditions, avoiding scheduling errors caused by ignoring thermal effects or overload risks.

[0045] Through the above technical scheme, the power output potential of the energy storage unit under different operating conditions can be dynamically quantified, providing accurate data basis for subsequent path screening and power allocation, solving the power margin evaluation deviation problem caused by single parameter dimension of the traditional method, and thereby improving the reliability and response accuracy of system scheduling.

[0046] The application further proposes a calculation method of impedance amplitude change rate in a network-structured energy storage collaborative control system adapted to an energy storage system, which is to obtain an impedance amplitude sequence in a continuous time window, calculate a ratio of an impedance amplitude difference value of adjacent time windows to a time window length as the impedance amplitude change rate; a historical stability score is obtained by weighted calculation of a response deviation rate and a failure number of a corresponding response path in a preset period; the response deviation rate is determined by a cumulative difference value of an actual power response and an expected power, and the failure number is a number of times that the path fails to reach a stable state after participating in a response; the calculation method of the historical stability score is to multiply the response deviation rate and the failure number by corresponding weight coefficients respectively, sum them, and then normalize the sum with a total response number in the preset period to obtain the historical stability score.

[0047] The impedance amplitude change rate refers to a rate of impedance amplitude change in adjacent time windows, which can be realized by collecting impedance amplitude data sequences in different time windows and calculating a ratio of an amplitude difference value of adjacent time windows to a length of the time window, for example, the length of the time window can be 1 second or 5 seconds. The historical stability score refers to a quantitative indicator reflecting the historical operating state of the response path, which can be realized by statistically calculating the response deviation rate and the failure number of the path in a preset period, and then normalizing the weighted sum of the two by a weight coefficient. The response deviation rate refers to the cumulative deviation degree of the actual output power of the path and the expected power, which can be determined by calculating the integral or cumulative sum of the difference in time. The failure number refers to the number of times that the path fails to reach a preset stable state after participating in a response, for example, the voltage or frequency fails to recover to the allowable range after power regulation. The normalization processing is used to eliminate the influence of different response numbers on the score results, for example, the weighted sum is divided by the total response number to obtain the score result per unit number.

[0048] Specifically, in the calculation process of the impedance amplitude change rate, the continuous time window can be divided by a fixed length or a dynamic adjustment strategy, for example, impedance amplitude data is collected every fixed length, and the difference value of data of adjacent two time windows is calculated. In the generation process of the historical stability score, the weight coefficients of the response deviation rate and the failure number can be dynamically adjusted according to the system stability requirement, for example, when the system has a higher requirement on response accuracy, the weight coefficient of the response deviation rate is increased. The calculation of the response deviation rate can be combined with the product of the power difference absolute value and time for accumulation to reflect the continuous influence of the deviation. The statistics of the failure number can be based on whether the voltage recovery deviation or the frequency recovery deviation exceeds the threshold for determination. The historical stability score after normalization processing can more objectively reflect the average stability level of the path under different response numbers, avoiding the problem that the high-frequency path has a high score due to the large cumulative number.

[0049] Compared with the prior art, the existing method generally only makes path stability judgment based on the current impedance amplitude or single historical data, without considering the relevance of the dynamic rate of impedance change and the historical operation state. The scheme can more accurately reflect the real-time stability and historical reliability difference of the path by introducing the impedance amplitude change rate to quantify the dynamic characteristics of the impedance, and combining the comprehensive evaluation of the response deviation rate and the failure times. At the same time, the influence of the response times on the score is eliminated through normalization processing, so that the paths of different participating frequencies are comparable, and the problem of distorted score of high-frequency paths in the traditional method is solved.

[0050] Through the above technical scheme, the application can dynamically capture the change trend of the impedance amplitude, and establish a multi-dimensional evaluation model combined with historical operation data, thereby improving the accuracy and timeliness of path stability judgment. Through the weighted calculation of the response deviation rate and the failure times, the high-risk paths with long-term power deviation or frequent failure can be effectively identified, and they are avoided from participating in critical response tasks. The scoring system after normalization processing provides a fair evaluation benchmark for paths of different response frequencies, ensures the scientificity of path screening decision, and finally realizes the stability and response efficiency improvement of the coordinated control of the energy storage system.

[0051] The application further proposes that the response path is an electrical interaction path between each energy storage unit and a grid access point in the energy storage system; the division rule of the response path is that the stable path is determined to meet all the following conditions: the impedance amplitude change rate is less than or equal to the stable path impedance threshold value; the phase feature change trend is convergence; the historical stability score is greater than or equal to the stable path score threshold value; the transition path is determined to meet any of the following conditions: the stable path impedance threshold value is less than the impedance amplitude change rate is less than or equal to the critical path impedance threshold value and the phase feature change trend is fluctuation; the critical path score threshold value is less than the historical stability score is less than the stable path score threshold value; the deterioration path is determined to meet any of the following conditions: the impedance amplitude change rate is greater than the critical path impedance threshold value; the phase feature change trend is divergence; the historical stability score is less than or equal to the critical path score threshold value; and the division is immediately re-divided when it is detected that the environmental temperature exceeds the temperature threshold value or the cooling system alarms.

[0052] The impedance amplitude change rate refers to a quantitative index of impedance amplitude change speed in adjacent time windows, and can be realized by calculating the difference ratio of adjacent time windows after continuous data collection by the impedance measuring device, and is used to reflect the short-term dynamic characteristics of the path impedance. The phase feature change trend refers to the trend direction of the impedance phase angle changing with time, and the convergence, fluctuation or divergence state can be judged by using the moving average algorithm of the phase angle sequence, and is used to evaluate the phase stability of the path. The historical stability score refers to a comprehensive evaluation index based on the path historical response deviation and failure times, and can be obtained by weighting and normalizing the response deviation rate and failure times, and is used to quantify the long-term reliability of the path. The temperature threshold refers to a critical temperature value for triggering the re-evaluation of the path state, and can be set according to the heat dissipation capacity of the energy storage unit and the safe operating temperature range of the equipment, and is used to forcibly update the path classification when the environment is abnormal.

[0053] Specifically, the response path establishes a dynamic classification mechanism through the impedance change trend of the electrical interaction path and the historical stability data. The stable path needs to meet the constraint conditions of smooth impedance change, phase convergence and score reaching the standard, to ensure its reliable power output capability. The transition path is activated when the impedance fluctuates or the score is in the critical interval, as a supplementary standby for the stable path. The degraded path is isolated when the impedance diverges, the score is too low or the phase is abnormal, to avoid affecting the system stability. When the environmental temperature exceeds the preset safety threshold or the cooling system is abnormal, the path state is immediately re-divided to eliminate the risk of performance degradation of the path caused by temperature factors. For example, when the cooling system alarms, the heat dissipation capacity of the energy storage unit may decrease, which may cause sudden changes in impedance characteristics. At this time, re-dividing the path can quickly exclude potential fault paths caused by temperature.

[0054] Compared with the prior art, the traditional path division method only sets fixed classification thresholds based on static impedance parameters or single historical data, and cannot adapt to dynamic impedance changes and environmental disturbances. The present scheme establishes a dynamic path classification rule by combining the multi-dimensional criteria of impedance change rate, phase trend and score data, which can track the path state changes in real time and actively isolate the degraded path. At the same time, the temperature threshold triggering mechanism is introduced to forcibly update the path classification when the environment is abnormal, to avoid the decline of system stability caused by heat dissipation failure.

[0055] Through the above technical solutions, the technical problems of single path classification rule, inability to adapt to dynamic impedance changes and environmental disturbances in the prior art are solved. Through the combination of dynamic criteria and temperature triggering mechanism, real-time state evaluation and classification optimization of the response path are realized, the accuracy and environmental adaptability of path screening are improved, and the stable power output capability of the energy storage system under complex operating conditions is ensured.

[0056] The application further proposes a process of weighted sum of normalized power margin of each response path, including: for the screened stable paths and activated transition paths, calculating the weighted power margin of each path, the weighting coefficient including a first correction factor of the stable path and a second correction factor of the transition path, and the second correction factor being smaller than the first correction factor. The weighted power margin of the stable path is obtained by multiplying the normalized power margin by the first correction factor, and the weighted power margin of the transition path is obtained by multiplying the normalized power margin by the second correction factor. The first correction factor and the second correction factor are preset dynamic coefficients, the values of which are dynamically adjusted according to the current system impedance amplitude change rate: when the impedance amplitude change rate increases, the value of the second correction factor is reduced, and the value of the first correction factor is increased. The weighted power margins of all paths in the stable paths and the transition paths are summed to obtain the total weighted power margin of the current response path.

[0057] The weighting coefficient is a parameter for adjusting the priority proportion of different response paths in power distribution, which can be specifically implemented by using a preset dynamic coefficient, for example, dynamically adjusting the sizes of the first correction factor and the second correction factor according to the impedance amplitude change rate. The first correction factor is a priority adjustment parameter of the stable path, which can be specifically implemented by using a dynamic coefficient with a value range of 0.8-1, for example, when the system impedance change rate increases, the first correction factor is increased to enhance the power distribution weight of the stable path. The second correction factor is a priority adjustment parameter of the transition path, which can be specifically implemented by using a dynamic coefficient with a value range of 0.3-0.6, for example, when the system impedance change rate increases, the second correction factor is reduced to reduce the power distribution weight of the transition path. The total weighted power margin is the total sum of the available power of all participating response paths after weighting, which can be specifically implemented by accumulating the weighted power margins of the paths, for example, adding the weighted power margin of the stable path and the weighted power margin of the transition path as the total power capacity currently schedulable by the system.

[0058] Specifically, in the power allocation process, first, the stable paths and the transition paths that need to be activated are screened according to the path division result. For the stable paths, a higher first correction factor is used for power margin weighting, for example, when the impedance amplitude change rate is at a lower level, the first correction factor can be set to 0.9; for the transition paths, a lower second correction factor is used, for example, the second correction factor is set to 0.5 under the same conditions. When it is detected that the system impedance amplitude change rate increases, for example, when the impedance fluctuates due to disturbance at the grid access point, the control module will automatically reduce the second correction factor to 0.4, and at the same time, the first correction factor will be raised to 0.95. By dynamically adjusting the correction factor, the power output proportion of the stable path is preferentially guaranteed, and the participation degree of the transition path is limited. Finally, the weighted power margins of all paths are added up to form the total power capacity that can be dispatched by the system at present, providing a calculation benchmark for subsequent power allocation.

[0059] Compared with the prior art, the traditional method uses a fixed weight coefficient for power allocation of the path, which cannot adjust the priority according to real-time impedance changes, and is prone to cause excessive power allocation to the transition path in a high disturbance scenario, thereby causing stability problems. The present scheme automatically reduces the weight of the transition path when the impedance fluctuation intensifies, while strengthening the power contribution of the stable path, effectively avoiding the risk of power overload of the unstable path.

[0060] Through the above technical scheme, the present application realizes dynamic optimization of the power allocation priority of different response paths, and ensures that the participation weights of the stable path and the transition path are adjusted in time when the system impedance characteristics change. The method can improve the rationality of power allocation in a high disturbance scenario, avoid response delay or stability degradation caused by excessive power proportion of the transition path, and at the same time, through dynamic calculation of the total weighted power margin, accurate basis is provided for matching of power demand and supply capacity.

[0061] The present application further proposes that the process of power allocation includes determining a power allocation proportion factor according to the ratio of the system demand power to the total weighted power margin, specifically, if the demand power is less than or equal to the total weighted power margin, the proportion factor is equal to the demand power divided by the total weighted power margin; if the demand power is greater than the total weighted power margin, the proportion factor is equal to 1 and a system alarm is triggered; for each path participating in the response, the stable path allocation power is equal to the weighted power margin of the path multiplied by the proportion factor, and the transition path allocation power is equal to the weighted power margin of the path multiplied by the proportion factor and then multiplied by a preset proportion; the activation rule of the preset proportion is to allocate the power margin that needs to be activated in the transition path according to the difference between the total power margin of the stable path and the demand threshold in proportion, and the activation proportion is positively correlated with the difference.

[0062] The power allocation ratio factor refers to a dynamic matching coefficient based on the current available power resources of the system and the actual demand, and can be specifically realized by a real-time data acquisition and proportional operation module. By comparing the real-time relationship between the demand power and the system weighted power margin, the power allocation reference value is dynamically adjusted. The preset ratio refers to a dynamic adjustment coefficient of the transition path participating in power allocation, and can be specifically realized by a piecewise function or a lookup table method. According to the stable path margin gap, the intervention degree of the transition path is automatically adjusted. The activation rule refers to the trigger condition and proportional control mechanism of the transition path power call, and can be specifically realized by a difference driving algorithm. By calculating the dynamic deviation of the stable path margin and the demand threshold, the corresponding transition path activation instruction is generated.

[0063] Specifically, after the system receives a power demand instruction, the total weighted power margin of all available paths is first calculated, which is composed of the weighted power margin of the stable path and the weighted margin of the activated transition path. By comparing the demand power with the total margin in real time, an executable proportional factor parameter is formed. When the system is in normal working condition, only the stable path needs to be called to meet the demand, and at this time the transition path remains in standby state. When it is detected that the stable path margin is insufficient, the transition path activation mechanism is automatically started, and the contribution degree of the transition path is allocated according to the actual gap value. For example, when the stable path margin gap reaches 30% of the total demand, 50% of the available margin in the transition path can be activated to participate in response. This hierarchical calling mechanism not only ensures the stability of system response, but also realizes the effective utilization of standby resources.

[0064] Compared with the prior art, the traditional power allocation method mostly adopts a fixed proportion or priority static allocation mode, which cannot dynamically adapt to the change of system impedance characteristics and the fluctuation of resource margin. For example, the prior art may only allocate according to the rated power of the energy storage unit, without considering the influence of the actual operating environment on the power output. The present scheme can dynamically match the relationship between system demand and available resources by introducing a dynamic proportional factor and a transition path activation mechanism, and intelligently allocate transition path resources on the premise of ensuring the stable operation of the core path, thereby effectively avoiding the system risks caused by the overload of a single path.

[0065] Through the above technical scheme, the present application effectively solves the problem of insufficient dynamic resource matching precision in the power allocation process of the energy storage system, and realizes the accurate correspondence between system demand and the actual capacity of the energy storage unit. Through the hierarchical calling mechanism, the transition path resources are reasonably utilized while ensuring the stability of the main path, which not only avoids the system risks caused by the participation of degraded paths, but also improves the overall power response capability. When the system faces a sudden power demand, the present scheme can quickly identify the available resource gap and automatically trigger the transition path supplement mechanism, thereby significantly improving the robustness and response efficiency of the system in complex working conditions.

[0066] The application further proposes a dynamic adjustment method of state label weight: if the participation ratio of the degradation path exceeds the preset ratio threshold, the weight coefficient of the historical stability score of the path is reduced, and the time window length of the corresponding preset period is shortened.

[0067] The participation ratio of the degradation path refers to the ratio of the number of paths determined as degradation paths to the number of all response paths, which can be realized by using the ratio of the number of scheduled times to the total number of scheduled times in a statistical period, and is used to measure the influence degree of the degradation path on the system. The preset ratio threshold refers to the upper limit of the participation of the degradation path, which can be realized by using the maximum degradation path ratio in the historical running data when the system is stably running, and is used to trigger the weight adjustment mechanism. The weight coefficient refers to the calculation weight of the historical stability score in path state evaluation, which can be dynamically adjusted by using a sliding window algorithm, and is used to weaken the influence of the degradation path on the evaluation result of the system. The time window length refers to the statistical period for calculating the historical stability score, which can be adjusted by using a variable time interval mechanism, and is used to accelerate the evaluation update frequency of the degradation path.

[0068] Specifically, during the system operation, when the participation ratio of the degradation path exceeds the preset threshold, the weight coefficient of the historical stability score of the path is reduced to reduce the evaluation influence of the path in subsequent path division. At the same time, the preset period time window length of the corresponding path is shortened, so that the historical stability score can reflect the latest state change of the path faster. For example, when a path is determined as a degradation path in three consecutive scheduling periods and the participation ratio exceeds 15%, the weight coefficient of the historical stability score of the path is adjusted from 0.6 to 0.4, and the statistical period is shortened from 1 hour to 30 minutes. Therefore, the score calculation of the path in the next period path division will depend more on real-time state data than on historical cumulative data.

[0069] Compared with the prior art, the traditional method usually uses fixed weight coefficient and statistical period, which cannot be dynamically adjusted according to the degradation degree of the path, resulting in a lag in the response of the system to stability degradation. The present scheme reduces the evaluation weight of the degradation path before the influence of the degradation path is expanded, and strengthens the weight of real-time data by shortening the time window, which significantly improves the timeliness of path state evaluation.

[0070] Through the above technical scheme, the application effectively suppresses the negative influence of the degradation path on the system cooperative control, prevents the misjudgment risk caused by the inertia of historical data, and realizes rapid tracking of the path state by shortening the evaluation period, thereby ensuring the response accuracy and stability of the system in a dynamic changing environment.

[0071] The application further proposes an adjustment mode of the power adjustment rate limit value, which is dynamically limiting the upper limit of the power adjustment rate of the corresponding path in the next response period according to the ratio of the power deviation parameter and the response delay.

[0072] The power deviation parameter refers to the difference between the actual power response and the expected power, which can be calculated by the difference between the real-time collected power sensor data and the target power instruction, and is used to represent the accuracy of power adjustment. The response delay refers to the time consumption from the issuance of the power distribution instruction to the actual execution of the energy storage unit, which can be calculated by the difference between the time stamp recording the issuance time and the time when the power feedback reaches the steady state, and is used to reflect the influence of response speed on system dynamic performance. The adjustment efficiency factor refers to the ratio of the absolute value of the power deviation parameter to the response delay, which can be obtained by dividing the absolute value of the power deviation by the response delay time, and is used to quantify the adjustment deviation degree in unit time. The adjustment rule refers to dynamically adjusting the power adjustment rate limit value according to the size of the adjustment efficiency factor, which can be compared with the preset threshold value, and the limit value is adjusted in proportion according to the comparison result, which is used to balance the adjustment speed and stability. The upper limit and lower limit of the power adjustment rate limit value correspond to the maximum allowed adjustment rate of the energy storage unit hardware and the minimum adjustment rate to maintain the effectiveness of power distribution respectively, which can be set according to the rated parameters of the energy storage converter and the system stability requirements, and is used to prevent equipment overload or adjustment failure.

[0073] Specifically, at the end of each response period, the adjustment efficiency factor is obtained by calculating the ratio of the absolute value of the power deviation parameter to the response delay. When the adjustment efficiency factor exceeds the first preset threshold value, it indicates that there is an imbalance between speed and accuracy in the current adjustment process, and the power adjustment rate limit value of the corresponding path is reduced by the first preset proportion, for example, by 15%-20% of the original value, to slow down the adjustment speed and reduce the deviation risk. When the adjustment efficiency factor is lower than the second preset threshold value and the corresponding path has no failure record in the preset period, it indicates that the path has the condition to improve the adjustment speed, and the limit value is increased by the second preset proportion, for example, by 10%-15% of the original value. The adjustment range of the limit value is constrained between the maximum rate allowed by the hardware and the minimum rate to maintain the effectiveness of the instruction, ensuring the safety of the equipment and the effectiveness of the control. The adjusted limit value is associated with the path state label, and in the power distribution instruction generation process of the next response period, the dynamic optimization is realized by limiting the power change rate not to exceed the new limit value.

[0074] Compared with the prior art, the traditional method usually adopts a fixed power regulation rate limit value, which cannot be dynamically adjusted according to the actual operating state, and is prone to cause power fluctuations caused by rapid regulation or response delays caused by slow regulation. The scheme can adaptively optimize the regulation speed according to real-time deviation and delay data by introducing a regulation efficiency factor and a dynamic limit value adjustment mechanism, thereby improving response efficiency while ensuring regulation accuracy.

[0075] Through the above technical scheme, the application effectively solves the problem that the power regulation rate and the system dynamic performance are difficult to match, can automatically adjust the power change rate of the energy storage unit under different operating conditions, avoids power overshoot caused by too fast regulation speed or response lag caused by too slow regulation speed, and prevents hardware overload through limit value constraint, thereby improving the stability and control reliability of the system in different response stages.

[0076] The application further provides a method for dynamically limiting the upper limit of the power regulation rate of a network-constructed energy storage collaborative control system adapted to an energy storage system, including: calculating a regulation efficiency factor according to a power deviation parameter and a response delay, the regulation efficiency factor being a ratio of the absolute value of the power deviation parameter to the response delay; establishing an adjustment rule of the regulation efficiency factor and the power regulation rate limit value: when the regulation efficiency factor is greater than a first preset threshold, the power regulation rate limit value of the corresponding path is reduced by a first preset proportion; when the regulation efficiency factor is less than a second preset threshold and the historical stability score of the corresponding path has no failure record in a preset period, the power regulation rate limit value is increased by a second preset proportion; setting an upper limit value and a lower limit value of the power regulation rate limit value, the upper limit value being a maximum allowable regulation rate of the energy storage unit hardware, and the lower limit value being a minimum regulation rate for maintaining the effectiveness of the power distribution instruction; associating the adjusted power regulation rate limit value with the state tag of the corresponding path, and limiting the power change rate of the instruction to be not more than the limit value when generating the power distribution instruction in the next response period.

[0077] The adjustment efficiency factor is a quantitative index reflecting the relationship between power adjustment effect and response speed, which can be realized by the ratio of power deviation absolute value to response delay, and is used to evaluate whether the current path power adjustment reaches the expected target within a reasonable time range. The adjustment rule is a judgment logic for comparing the adjustment efficiency factor with the preset threshold interval, which can be realized by a hierarchical threshold triggering method. When the adjustment efficiency is too high or too low, different limit value adjustment strategies are correspondingly adopted, so as to balance the adjustment speed and stability. The upper limit value and the lower limit value are the physical constraint boundaries of the power adjustment rate of the energy storage unit, which can be realized by the maximum allowable rate in the device technical parameters and the minimum necessary rate in the system control requirements, to prevent system failure caused by limit value setting beyond the hardware capability or below the control accuracy requirement. The state label association is to bind the adjusted limit value with the path state information, which can be realized by a data mapping table or a dynamic parameter configuration module, to ensure that the latest limit value parameter can be directly called when generating the next period power distribution instruction.

[0078] Specifically, the method dynamically evaluates the execution effect of the current power adjustment instruction by calculating the adjustment efficiency factor in real time. If the adjustment efficiency factor exceeds the first preset threshold, it indicates that the power deviation is too large or the response delay is too low, and reducing the power adjustment rate limit value can avoid oscillation caused by too fast adjustment speed. If the adjustment efficiency factor is lower than the second preset threshold and the path historical stability is good, the limit value is appropriately increased to improve the response speed. By setting the upper and lower limit values, it is ensured that the power adjustment rate is always within the allowed range of the device, while maintaining the basic effectiveness of system control. The adjusted limit value is bound with the path through the state label, so that the optimized adjustment rate parameter can be automatically applied when the next period power distribution is performed.

[0079] In some embodiments, the first preset ratio can adopt a stepwise decreasing manner, for example, when the adjustment efficiency factor exceeds the threshold interval by 10%, the limit value is reduced by 5%; the second preset ratio can adopt a dynamic adjustment manner based on historical score, for example, different increase amplitudes are set according to the score level.

[0080] Compared with the prior art, the existing method usually adopts a fixed power adjustment rate limit value, which cannot be dynamically adjusted according to the real-time response effect, and is prone to cause mismatch between the adjustment speed and the system stability. The present scheme can adaptively optimize the limit value parameter by introducing the adjustment efficiency factor and the dynamic adjustment rule, to avoid system instability or response lag caused by too high or too low adjustment rate.

[0081] By the technical solution, the power adjustment rate limit can be dynamically optimized according to actual response effects, response efficiency is improved under the premise of maintaining system stability. By associating the limit value with the path state label, accurate matching of the control parameter and the path characteristic is realized, the problem of incompatibility between the adjustment rate and the path state caused by the fixed limit value in the traditional method is solved, and the execution accuracy of the power distribution instruction and the system dynamic performance are effectively improved.

[0082] Embodiment two:

[0083] As shown in Figures 2-3 The network construction type energy storage collaborative control method suitable for the energy storage system includes the following steps:

[0084] Obtain the operating parameters and environmental parameters of each energy storage unit, and obtain the impedance amplitude and phase characteristics of the energy storage access point;

[0085] Calculate the normalized power margin of each energy storage unit according to the operating parameters, and calculate the impedance amplitude change rate and phase characteristic change trend according to the impedance amplitude and phase characteristics;

[0086] Based on the impedance amplitude change rate, the phase characteristic change trend, and the pre-stored historical stability score, each response path in the energy storage system is dynamically divided into a stable path, a transition path, and a degraded path based on a preset path division rule, and a corresponding state label is generated for each response path;

[0087] Based on the state label, the response paths are screened, and the stable paths are preferentially dispatched. When the total power margin of the stable paths is lower than the demand threshold, the transition paths are activated to participate in the response according to a preset proportion, and the real-time dispatching authority of the degraded paths is excluded;

[0088] Based on the normalized power margin of each response path and the corresponding weighted coefficient of the state label, a power distribution instruction is generated, and the power distribution instruction is issued to the corresponding energy storage unit for execution;

[0089] After each power distribution instruction is executed, the voltage recovery deviation, the frequency recovery deviation, the response delay, and the power deviation parameters of the system response are obtained;

[0090] The current response effectiveness is judged according to the voltage recovery deviation and the frequency recovery deviation. If any parameter exceeds the preset stable interval, the participation proportion of the degraded path and the power deviation parameter are analyzed, the state label weight and the power adjustment rate limit of the corresponding path are dynamically adjusted, and the path screening and power distribution in the next response cycle are used.

[0091] The above merely illustrates and describes the structure of the present application, and those skilled in the art can make various modifications or supplements to the specific embodiments described or use similar ways to replace, as long as the modifications or supplements do not deviate from the structure of the present application or exceed the scope defined by the claims, and should be within the protection scope of the present application.

[0092] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0093] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details and limit the present application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A grid-forming energy storage collaborative control system adapted to an energy storage system, characterized in that , comprising: a data acquisition module configured to acquire operating parameters and environmental parameters of each energy storage unit, and to acquire impedance amplitude and phase characteristics of an energy storage access point; a data processing module configured to calculate normalized power margin of each energy storage unit according to the operating parameters, and to calculate impedance amplitude change rate and phase characteristic change trend according to the impedance amplitude and phase characteristics; a path division module configured to divide each response path in the energy storage system into a stable path, a transition path and a degraded path based on the impedance amplitude change rate, the phase characteristic change trend and a pre-stored historical stability score, and to generate a corresponding state label for each response path based on a preset path division rule; a path screening module configured to screen the response paths based on the state labels, to preferentially schedule the stable paths, and to activate the transition paths to participate in response in a preset proportion and to exclude the degraded paths from real-time scheduling authority when the total power margin of the stable paths is lower than a demand threshold; a path response module configured to generate a power distribution instruction according to the normalized power margin of each response path and a weighting coefficient corresponding to the state label, and to issue the power distribution instruction to the corresponding energy storage unit for execution; a path adjustment module configured to acquire voltage recovery deviation, frequency recovery deviation, response delay and power deviation parameters of system response after each power distribution instruction is executed, to determine current response effectiveness according to the voltage recovery deviation and the frequency recovery deviation, to analyze participation proportion and power deviation parameters of the degraded paths if any parameter exceeds a preset stable interval, and to dynamically adjust state label weight and power adjustment rate limit value of the corresponding path for path screening and power distribution in a next response period.

2. The networked energy storage collaborative control system adapted to an energy storage system of claim 1, wherein: The operating parameters include charging and discharging power, state of charge and single temperature of each energy storage unit, and the environmental parameters include environmental temperature and cooling system operating state. The normalized power margin is calculated by linearly weighting and summing a ratio of state of charge to rated state of charge, a difference between single temperature and environmental temperature, and a ratio of current charging and discharging power to rated power.

3. The network-structured energy storage collaborative control system adapted to an energy storage system according to claim 2, characterized in that: The impedance amplitude change rate is calculated by obtaining an impedance amplitude sequence in a continuous time window, calculating a ratio of impedance amplitude difference between adjacent time windows to time window length, and taking the ratio as the impedance amplitude change rate. The historical stability score is obtained by weighting and calculating response deviation rate and failure times of the corresponding response path in a preset period. The response deviation rate is determined by cumulative difference between actual power response and expected power, and the failure times are the number of times that the path fails to reach a stable state after participating in response. The historical stability score is calculated by multiplying the response deviation rate and the failure times by corresponding weight coefficients respectively, summing the products, and then normalizing the sum with the total number of responses in the preset period to obtain the historical stability score.

4. The network-structured energy storage collaborative control system adapted to an energy storage system according to claim 3, characterized in that: The response path is an electrical interaction path between each energy storage unit and a grid access point in the energy storage system. The division rule of the response path is: The determination condition of the stable path is that all of the following conditions are met: Condition 1: the impedance amplitude change rate is less than or equal to the stable path impedance threshold value; Condition 2: the phase feature change trend is convergent; Condition 3: the historical stability score is greater than or equal to the stable path score threshold value; The determination condition of the transition path is that any of the following conditions is met: Condition 1: the stable path impedance threshold value is less than the impedance amplitude change rate and the phase feature change trend is fluctuant; Condition 2: the critical path score threshold value is less than the historical stability score and the historical stability score is less than the stable path score threshold value; The determination condition of the deterioration path is that any of the following conditions is met: Condition 1: the impedance amplitude change rate is greater than the critical path impedance threshold value; Condition 2: the phase feature change trend is divergent; Condition 3: the historical stability score is less than or equal to the critical path score threshold value; When it is detected that the ambient temperature exceeds the temperature threshold value or the cooling system is alarmed, the division is immediately re-divided.

5. The networked energy storage coordinated control system adapted to an energy storage system of claim 4, wherein: The process of weighted sum of the normalized power margin of each response path includes: For the stable path and the activated transition path after screening, the weighted power margin of each path is calculated: The weighted coefficient includes a first correction factor of the stable path and a second correction factor of the transition path, and the second correction factor is less than the first correction factor; The weighted power margin of the stable path = the normalized power margin × the first correction factor; The weighted power margin of the transition path = the normalized power margin × the second correction factor; The first correction factor and the second correction factor are preset dynamic coefficients, and the second correction factor < the first correction factor ≤ 1 is met; The values of the first correction factor and the second correction factor are dynamically adjusted according to the current system impedance amplitude change rate: when the impedance amplitude change rate increases, the value of the second correction factor is reduced, and the value of the first correction factor is increased; The weighted power margins of all paths in the stable path and the transition path are summed to obtain the total weighted power margin of the current response path.

6. The network-forming energy storage collaborative control system adapted to an energy storage system of claim 5, wherein: The process of power distribution includes: According to the ratio of the system demand power to the total weighted power margin, a power distribution proportion factor is determined. Specifically: If the demand power ≤ the total weighted power margin, then the proportion factor = the demand power / the total weighted power margin; if the demand power > the total weighted power margin, then the proportion factor = 1, and a system alarm is triggered; For each path participating in the response, the actual distribution power is generated according to the following rules: The stable path distribution power = the weighted power margin of the path × the proportion factor; The transition path distribution power = the weighted power margin of the path × the proportion factor × a preset proportion; The activation rule of the preset proportion is that the power margin of the transition path to be activated is proportionally distributed according to the difference between the total power margin of the stable path and the demand threshold value, and the activation proportion is positively correlated with the difference.

7. The network-forming energy storage collaborative control system adapted to an energy storage system according to claim 6, characterized in that: The way of dynamically adjusting the state label weight is that if the participation proportion of the deterioration path exceeds a preset proportion threshold value, the weight coefficient of the historical stability score of the path is reduced, and the time window length of the corresponding preset period is shortened. 8.The networked energy storage coordinated control system adapted to energy storage system of claim 7, wherein: The adjustment mode of the power adjustment rate limit value is that the upper limit of the power adjustment rate of the corresponding path in the next response period is dynamically limited according to the ratio of the power deviation parameter to the response delay. 9.The networked energy storage coordinated control system adapted to energy storage system of claim 8, wherein: The process of dynamically limiting the upper limit of the power adjustment rate of the corresponding path in the next response period comprises: calculating an adjustment performance factor according to the power deviation parameter and the response delay, the adjustment performance factor being the ratio of the absolute value of the power deviation parameter to the response delay; establishing an adjustment rule of the adjustment performance factor and the power adjustment rate limit value; when the adjustment performance factor is greater than a first preset threshold, reducing the power adjustment rate limit value of the corresponding path by a first preset proportion; when the adjustment performance factor is less than a second preset threshold and the historical stability score of the corresponding path has no failure record in a preset period, increasing the power adjustment rate limit value by a second preset proportion; setting an upper limit value and a lower limit value of the power adjustment rate limit value, the upper limit value being the maximum allowed adjustment rate of the energy storage unit hardware, and the lower limit value being the minimum adjustment rate for maintaining the effectiveness of the power distribution instruction; associating the adjusted power adjustment rate limit value with the state label of the corresponding path, and limiting the power change rate of the power distribution instruction to be less than the limit value when generating the power distribution instruction in the next response period.

10. The network-forming energy storage collaborative control system adapted to an energy storage system according to any one of claims 1-9, characterized in that, The working method of the network-forming energy storage collaborative control system adapted to the energy storage system comprises the following steps: acquiring operating parameters and environmental parameters of each energy storage unit, and acquiring impedance amplitude and phase characteristics of an energy storage access point; calculating a normalized power margin of each energy storage unit according to the operating parameters, and calculating an impedance amplitude change rate and a phase characteristic change trend according to the impedance amplitude and phase characteristics; based on the impedance amplitude change rate, the phase characteristic change trend, and a pre-stored historical stability score, dynamically dividing each response path in the energy storage system into a stable path, a transition path, and a degraded path based on a preset path division rule, and generating a corresponding state label for each response path; based on the state label, screening the response paths, preferentially scheduling the stable paths, activating the transition paths to participate in the response by a preset proportion when the total power margin of the stable paths is lower than a demand threshold, and excluding the real-time scheduling authority of the degraded paths; based on the normalized power margin of each response path and the weighting coefficient corresponding to the state label, generating a power distribution instruction, and issuing the power distribution instruction to the corresponding energy storage unit for execution; after each power distribution instruction is executed, acquiring a voltage recovery deviation, a frequency recovery deviation, a response delay, and a power deviation parameter of the system response; based on the voltage recovery deviation and the frequency recovery deviation, judging the current response effectiveness: if any parameter exceeds a preset stable interval, analyzing the participation proportion of the degraded paths and the power deviation parameter, and dynamically adjusting the state label weight and the power adjustment rate limit value of the corresponding path, which are used for path screening and power distribution in the next response period.

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