Terminal power grid distributed energy storage coordination method and system based on multi-objective optimization

Abstract

The application is suitable for the field of power grid energy storage, and provides a terminal power grid distributed energy storage coordination method and system based on multi-objective optimization, which comprises the following steps: real-time monitoring of the electrical operating state of the terminal power grid station area, when it is detected that the station area meets the preset island operation judgment condition, starting the multi-objective optimization coordination strategy; based on the multi-objective optimization coordination strategy, obtaining the operating data of each distributed energy storage unit and the power demand and power supply priority level of the key load, and establishing a multi-objective optimization function; solving the multi-objective optimization function, dynamically allocating the active power output quota of each energy storage unit during island operation; when the SOC of any energy storage unit decreases to the preset early warning threshold, triggering the over-discharge early warning coordination adjustment. The multi-objective optimization framework adopted in the application takes into account the energy storage life loss and voltage support effect, and realizes the balance among power supply reliability, energy storage economy and power quality in the whole process of island operation.

Description

Technical Field

[0001] This invention relates to the field of grid energy storage, specifically to a collaborative method and system for distributed energy storage in the end-point grid based on multi-objective optimization. Background Technology

[0002] The end-point distribution networks in remote mountainous and forested areas of central and western China are generally characterized by large power supply radii, aging and missing line parameters, and a surge in distributed photovoltaic penetration. When a transformer substation is disconnected from the upstream grid due to faults such as lightning strikes, tree obstructions, or equipment aging, it often becomes isolated for several hours. During this isolation period, critical public services such as communication base stations and emergency lighting within the substation must rely on multiple distributed energy storage systems to maintain power supply. How to optimize and coordinate the operating strategies of each energy storage unit through multi-objective optimization under constraints such as limited energy storage capacity, large differences in initial state of charge (SOC), and varying electrical distances has become a key issue in improving the reliability of end-point power grid supply.

[0003] Existing control methods for energy storage systems operating in islanded mode mainly include master-slave control, droop control, and power allocation strategies based on SOC balancing. These methods typically aim to maintain voltage stability or balance the SOC of each energy storage unit, achieving good results in scenarios with clear microgrid structures and similar energy storage capacities. However, the islanded scenario of the end-grid has significant unique characteristics. The initial SOC of each energy storage unit varies greatly (for example, a farmer's energy storage may already be at a low SOC due to daytime self-use), and the electrical distance between each energy storage unit and the critical load differs, resulting in significant differences in the voltage support effect of the same power output. If the pursuit of SOC balancing continues, energy storage units close to the load with low initial SOC will discharge too quickly to the over-discharge protection threshold and be forcibly disconnected. The remaining energy storage capacity is insufficient to support the load independently, ultimately leading to a complete power outage of the critical load even when the system still has remaining energy storage capacity.

[0004] The aforementioned problem is essentially a multi-objective optimization problem: objective one is to maximize the total power supply duration of critical loads; objective two is to minimize the overall lifetime loss of the energy storage system (related to discharge depth and cycle count); and objective three is to minimize the voltage drop depth during islanded operation. These three objectives are in a non-cooperative game relationship. That is, excessively pursuing longer power supply duration may exacerbate deep discharge of energy storage, thus accelerating lifetime loss, while excessively protecting energy storage lifetime may prematurely cut off power to critical loads. Existing methods lack the ability to jointly optimize these multiple objectives in such specific scenarios. Therefore, a multi-objective optimization-based distributed energy storage collaborative method and system for end-grid systems is proposed to address the aforementioned problems. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for coordinated distributed energy storage in the end grid based on multi-objective optimization, so as to solve the problems existing in the above-mentioned background technology.

[0006] This invention is implemented as follows: a multi-objective optimization-based distributed energy storage coordination method for end-grid networks, the method comprising the following steps: Real-time monitoring of the electrical operation status of the terminal power grid distribution area; when the distribution area is detected to meet the preset islanding operation judgment conditions, a multi-objective optimization and coordination strategy is initiated. The judgment conditions are set according to voltage, frequency and communication interruption time. Based on a multi-objective optimization and collaborative strategy, the operation data of each distributed energy storage unit and the power demand and power supply priority of critical loads are obtained, and a multi-objective optimization function is established with the optimization objectives of maximizing the total power supply duration of critical loads, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude. Solve the multi-objective optimization function to dynamically allocate the active power output quota of each energy storage unit during islanded operation, so that each energy storage unit tends to reach the over-discharge protection threshold at the same time during the discharge process. When the SOC of any energy storage unit drops to the preset warning threshold, the over-discharge warning coordinated adjustment is triggered, the power quota of each energy storage unit is redistributed and non-critical loads are selectively reduced, so that all energy storage units tend to reach the over-discharge protection threshold at the same time.

[0007] As a further aspect of the present invention: the step of real-time monitoring of the electrical operation status of the terminal power grid distribution area, and initiating a multi-objective optimization and collaborative strategy when the distribution area meets the preset islanding operation judgment conditions, specifically includes: The effective value and frequency of the three-phase voltage on the low-voltage side of the terminal power grid area are continuously collected. When the effective value of any phase voltage exceeds the preset voltage range or the frequency deviation exceeds the preset frequency range, and this state continues for more than the preset over-limit duration threshold, a first-level islanding suspected marker is generated. The monitoring station checks the communication link status between the monitoring area and the upper-level power grid. When the communication interruption time exceeds the preset interruption delay threshold and there is a suspected first-level islanding marker, the active disturbance detection strategy is triggered. Based on the active disturbance detection strategy, the voltage amplitude change and voltage phase change at the injection point are obtained by injecting a preset short-time pulse width and small amplitude reactive current disturbance signal into the low-voltage side of the distribution area through any distributed energy storage inverter in standby state within the distribution area. Based on this, the equivalent impedance modulus of the distribution area at the current moment is calculated. When the magnitude of the equivalent impedance relative to the reference impedance in the grid-connected state exceeds the preset allowable range of impedance change, or when the voltage phase change exceeds the preset allowable range of phase offset, the transformer area is determined to have entered islanded operation state, and a multi-objective optimization and collaborative strategy is initiated.

[0008] As a further aspect of the present invention: the step of establishing a multi-objective optimization function with the optimization objectives of maximizing the total power supply duration of critical loads, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude specifically includes: Obtain the current SOC value, SOC over-discharge protection threshold, rated capacity, maximum discharge power of each distributed energy storage unit, as well as the power demand and power supply priority level of each critical load; The remaining dischargeable energy of each energy storage unit is obtained by multiplying the difference between the current SOC value and the SOC over-discharge protection threshold by the rated capacity. Divide the sum of the remaining dischargeable energy of all energy storage units by the total power demand of all critical loads to construct the first sub-objective function with the goal of maximizing the total power supply duration of critical loads; Based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current moment, a second sub-objective function is constructed with the goal of minimizing the SOC synchronous depletion deviation of each energy storage unit. Based on the cumulative deviation between the voltage amplitude and the rated voltage at each critical load connection point during islanded operation, a third sub-objective function is constructed with the goal of minimizing the voltage drop amplitude. The first, second, and third sub-objective functions are then combined into a multi-objective optimization function using a multi-objective evolutionary algorithm.

[0009] As a further aspect of the present invention: the step of constructing a second sub-objective function with the goal of minimizing the SOC synchronization depletion deviation of each energy storage unit based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current moment specifically includes: For any energy storage unit, the total discharge time required for it to discharge from its current SOC value to the SOC over-discharge protection threshold is defined as its inherent lifespan, and this inherent lifespan is normalized and mapped to a complete virtual clock cycle, wherein the virtual clock pointer starts from 0 degrees and ends at 360 degrees. The current angle position of the virtual clock pointer is determined based on the ratio of the discharged amount of the energy storage unit to its remaining dischargeable energy at the current moment. The larger the proportion of the discharged amount, the closer the pointer angle is to 360 degrees. The current angle of the virtual clock pointer of each energy storage unit is calculated by iterating through the data to construct a sequence of virtual clock pointer angles. The sum of squared circumferential distances between all angles in the sequence is then calculated, and the result is defined as the synchronization depletion deviation index. The synchronization depletion deviation index is used as the second sub-objective function. The smaller the synchronization depletion deviation index, the more synchronized the depletion times of each energy storage unit are.

[0010] As a further aspect of the present invention: the step of solving the multi-objective optimization function and dynamically allocating the active power output quota of each energy storage unit during islanded operation specifically includes: The islanding operation time is discretized into continuous scheduling cycles. At the beginning of each scheduling cycle, each energy storage unit exchanges its remaining discharge time estimate with neighboring energy storage units through the local communication network. Each energy storage unit calculates the deviation between its own remaining discharge time and the average of the remaining discharge time estimates of all neighboring units based on the remaining discharge time estimates received from neighboring units. The deviation is multiplied by a preset consistency gain coefficient and then superimposed on the power quota benchmark value of the current scheduling cycle to obtain the adjusted active power output quota of each energy storage unit in this scheduling cycle. The consistency gain coefficient is a positive number and is used to control the convergence speed to make the remaining discharge time more consistent. Each energy storage unit discharges according to the adjusted active power output quota and updates its own SOC value at the end of the current scheduling cycle, thereby recalculating the estimated remaining discharge time at the start of the next scheduling cycle.

[0011] As a further aspect of the present invention: the step of triggering over-discharge early warning and coordinating adjustment, reallocating the power quota of each energy storage unit and selectively reducing non-critical loads specifically includes: The SOC value of each energy storage unit is monitored in real time. When the SOC of any energy storage unit drops to the preset warning threshold, the moment is recorded as the warning trigger moment. The process of dynamically allocating active power output quotas is terminated, and the remaining dischargeable energy of each energy storage unit from the current SOC value to the over-discharge protection threshold is obtained at the time of warning triggering. The power supply priority of each critical load is reduced step by step until the total power demand of all critical loads does not exceed the sum of the rated discharge power of each energy storage unit. Based on the proportion of the remaining dischargeable energy of each energy storage unit to the total remaining dischargeable energy of all energy storage units, the active power quota that each energy storage unit should undertake after the warning triggering time is calculated, so that the SOC decrease rate of each energy storage unit during the discharge process from the preset warning threshold to the over-discharge protection threshold is consistent.

[0012] Another object of the present invention is to provide a distributed energy storage collaborative system for end-grid grids based on multi-objective optimization, the system comprising: The status detection module is used to monitor the electrical operation status of the terminal power grid area in real time. When the area is detected to meet the preset islanding operation judgment conditions, a multi-objective optimization and coordination strategy is activated. The judgment conditions are set according to voltage, frequency and communication interruption time. The function construction module is used to obtain the operating data of each distributed energy storage unit and the power demand and power supply priority level of the critical load based on the multi-objective optimization collaborative strategy, and to establish a multi-objective optimization function with the optimization objectives of maximizing the total power supply duration of the critical load, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude. The power optimization module is used to solve the multi-objective optimization function and dynamically allocate the active power output quota of each energy storage unit during islanded operation, so that each energy storage unit tends to reach the over-discharge protection threshold at the same time during the discharge process. The early warning linkage module is used to trigger over-discharge early warning coordinated adjustment when the SOC of any energy storage unit drops to a preset early warning threshold. This reallocates the power quota of each energy storage unit and selectively reduces non-critical loads, so that all energy storage units tend to reach the over-discharge protection threshold at the same time.

[0013] As a further aspect of the present invention: the state detection module includes: The over-limit marking unit is used to continuously collect the effective value and frequency of the three-phase voltage on the low-voltage side of the terminal power grid area. When the effective value of any phase voltage exceeds the preset voltage range or the frequency deviation exceeds the preset frequency range, and the state continues to exceed the preset over-limit duration threshold, a first-level islanding suspected mark is generated. The disturbance triggering unit is used to monitor the communication link status between the distribution area and the upper-level power grid. When the communication interruption time exceeds the preset interruption delay threshold and there is a suspected first-level islanding marker, the active disturbance detection strategy is triggered. The impedance calculation unit is used to obtain the voltage amplitude change and voltage phase change at the injection point by injecting a preset short-time pulse width and small amplitude reactive current disturbance signal into the low-voltage side of the distribution area through any distributed energy storage inverter in standby state within the distribution area, based on the active disturbance detection strategy, and thereby calculate the equivalent impedance modulus of the distribution area at the current moment. The islanding confirmation unit is used to determine that the transformer area has entered the islanding operation state and to start a multi-objective optimization and coordination strategy when the change of the equivalent impedance magnitude relative to the grid-connected reference impedance magnitude exceeds the preset impedance change allowable range, or the voltage phase change exceeds the preset phase offset allowable range.

[0014] As a further aspect of the present invention: the function construction module includes: The parameter acquisition unit is used to acquire the current SOC value, SOC over-discharge protection threshold, rated capacity, maximum discharge power, power demand and power supply priority level of each distributed energy storage unit. The energy estimation unit is used to multiply the difference between the current SOC value and the SOC over-discharge protection threshold by the rated capacity to obtain the remaining dischargeable energy of each energy storage unit. The duration modeling unit is used to divide the sum of the remaining dischargeable energy of all energy storage units by the total power demand of all critical loads, and construct the first sub-objective function with the goal of maximizing the total power supply duration of critical loads; The synchronous modeling unit is used to construct a second sub-objective function with the goal of minimizing the SOC synchronous depletion deviation of each energy storage unit, based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current time. The voltage modeling unit is used to construct a third sub-objective function with the goal of minimizing the voltage drop amplitude based on the cumulative deviation between the voltage amplitude and the rated voltage at each critical load connection point during islanded operation. The first, second, and third sub-objective functions are then combined into a multi-objective optimization function using a multi-objective evolutionary algorithm.

[0015] As a further aspect of the present invention: the power optimization module includes: The periodic synchronization unit is used to discretize the islanding operation time into continuous scheduling cycles. At the beginning of each scheduling cycle, each energy storage unit exchanges its remaining discharge time estimate with the adjacent energy storage unit through the local communication network. The deviation calculation unit is used by each energy storage unit to calculate the deviation between its own remaining discharge time and the average of the remaining discharge time estimates of all neighboring units, based on the remaining discharge time estimates received from neighboring units. The quota correction unit is used to multiply the deviation by a preset consistency gain coefficient and then add it to the power quota benchmark value of the current scheduling cycle to obtain the adjusted active power output quota of each energy storage unit in the current scheduling cycle. The consistency gain coefficient is a positive number and is used to control the convergence speed to make the remaining discharge time more consistent. The periodic update unit is used for each energy storage unit to discharge according to the adjusted active power output quota, and to update its own SOC value at the end of the current scheduling cycle, thereby recalculating the estimated remaining discharge time at the start of the next scheduling cycle.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a multi-objective optimization function with the objectives of maximizing the total power supply duration of critical loads, minimizing the synchronous depletion deviation of the State of Charge (SOC) of each energy storage unit, and minimizing the voltage drop amplitude. In islanded grid scenarios with large initial SOC differences and inconsistent electrical distances, this function can actively guide the discharge process of each energy storage unit to converge synchronously towards reaching the over-discharge protection threshold. This overcomes the technical defect of traditional methods that cause premature system collapse due to premature over-discharge of low-SOC energy storage near the load. When the SOC of any energy storage unit drops to a preset warning threshold, an over-discharge warning and coordinated adjustment is triggered. By reallocating power quotas and selectively reducing non-critical loads, the SOC decline rate of each energy storage unit from the warning threshold to the over-discharge protection threshold is kept consistent. Thus, without the need for high-precision line parameters and complex online optimization, this invention achieves the ultimate utilization of energy storage resources and coordinated optimization of voltage quality. The multi-objective optimization framework adopted in this invention takes into account both energy storage life loss and voltage support effect, and achieves a balance between power supply reliability, energy storage economy and power quality throughout the entire island operation process. It is particularly suitable for remote mountainous and forest end distribution networks with large power supply radius, weak communication conditions and dispersed energy storage configuration. Attached Figure Description

[0017] Figure 1 This is a flowchart of a distributed energy storage collaborative method for end-grid grids based on multi-objective optimization.

[0018] Figure 2 This is a flowchart illustrating the initiation of a multi-objective optimization collaborative strategy in a distributed energy storage collaborative method for end-grid grids based on multi-objective optimization.

[0019] Figure 3 This is a flowchart illustrating the establishment of a multi-objective optimization function in a multi-objective optimization-based distributed energy storage collaborative method for end-grid power grids.

[0020] Figure 4 This is a flowchart illustrating the construction of the second sub-objective function in a multi-objective optimization-based distributed energy storage collaborative method for end-grid grids.

[0021] Figure 5 This is a flowchart illustrating the dynamic allocation of active power output quotas in a multi-objective optimization-based distributed energy storage collaborative method for end-grid grids.

[0022] Figure 6 This is a flowchart illustrating the coordinated adjustment for triggering over-discharge early warning in a multi-objective optimization-based distributed energy storage coordination method for end-grid grids.

[0023] Figure 7 This is a schematic diagram of a distributed energy storage collaborative system for the end-grid based on multi-objective optimization.

[0024] Figure 8This is a schematic diagram of the state detection module in a multi-objective optimization-based distributed energy storage collaborative system for end-grid power grids.

[0025] Figure 9 This is a schematic diagram of the function construction module in a multi-objective optimization-based distributed energy storage collaborative system for end-grid power grids.

[0026] Figure 10 This is a schematic diagram of the power optimization module in a multi-objective optimization-based distributed energy storage collaborative system for end-grid power grids. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0028] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0029] like Figure 1 As shown, this embodiment of the invention provides a method for coordinated distributed energy storage in the end-point power grid based on multi-objective optimization. The method includes the following steps: S100 monitors the electrical operation status of the terminal power grid area (i.e., the power supply area covered by the distribution transformer) in real time. When the area is detected to meet the preset islanding operation judgment conditions, a multi-objective optimization and coordination strategy is started. The judgment conditions are set according to voltage, frequency and communication interruption time. S200, based on a multi-objective optimization and collaborative strategy, obtains the operating data of each distributed energy storage unit and the power demand and power supply priority level of critical loads, and establishes a multi-objective optimization function with the optimization objectives of maximizing the total power supply duration of critical loads, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude. S300 solves a multi-objective optimization function to dynamically allocate the active power output quota of each energy storage unit during islanded operation, so that each energy storage unit tends to reach the over-discharge protection threshold simultaneously during the discharge process. S400 When the SOC of any energy storage unit drops to the preset warning threshold, it triggers the over-discharge warning coordinated adjustment, redistributes the power quota of each energy storage unit and selectively reduces non-critical loads, so that all energy storage units tend to reach the over-discharge protection threshold at the same time.

[0030] It should be noted that the over-discharge protection threshold (e.g., SOC = 10%) must be less than the preset warning threshold (e.g., SOC = 25%). The over-discharge warning and coordinated adjustment are triggered when the SOC first drops from 100% to 25%. During this process, the active power output quota is dynamically allocated. When the SOC drops from 25% to 10%, the power quota of each energy storage unit is redistributed and non-critical loads are selectively reduced.

[0031] In this embodiment of the invention, a multi-objective optimization function is constructed with the optimization objectives of maximizing the total power supply duration of critical loads, minimizing the synchronous depletion deviation of the SOC of each energy storage unit, and minimizing the voltage drop amplitude. In the end-grid island scenario with large differences in initial SOC and inconsistent electrical distances, the invention can actively guide the discharge process of each energy storage unit to converge towards synchronously reaching the over-discharge protection threshold. This overcomes the technical defect of traditional methods that cause premature system collapse due to premature over-discharge of low-SOC energy storage near the load. When the SOC of any energy storage unit drops to a preset warning threshold, an over-discharge warning and coordinated adjustment is triggered. By reallocating power quotas and selectively reducing non-critical loads, the SOC drop rate of each energy storage unit from the warning threshold to the over-discharge protection threshold is kept consistent. Thus, without the need for high-precision line parameters and complex online optimization, the invention achieves the ultimate utilization of energy storage resources and the coordinated optimization of voltage quality.

[0032] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of real-time monitoring of the electrical operation status of the terminal power grid distribution area, and initiating a multi-objective optimization and collaborative strategy when the distribution area meets the preset islanding operation judgment conditions, specifically includes: S101: Continuously collect the effective value and frequency of the three-phase voltage on the low-voltage side of the terminal power grid area. When the effective value of any phase voltage exceeds the preset voltage range or the frequency deviation exceeds the preset frequency range, and the state continues to exceed the preset over-limit duration threshold, generate a first-level islanding suspected marker. S102, monitors the communication link status between the monitoring area and the upper-level power grid. When the communication interruption time exceeds the preset interruption delay threshold and there is a suspected first-level islanding marker, the active disturbance detection strategy is triggered. S103, based on the active disturbance detection strategy, injects a preset short-time pulse width and small amplitude reactive current disturbance signal into the low-voltage side of the distribution area by any distributed energy storage inverter in standby state within the distribution area, thereby obtaining the voltage amplitude change and voltage phase change at the injection point, and calculating the equivalent impedance modulus of the distribution area at the current moment. S104. When the change in the equivalent impedance magnitude relative to the grid-connected reference impedance magnitude exceeds the preset impedance change allowable range, or the voltage phase change exceeds the preset phase offset allowable range, the transformer area is determined to enter the islanded operation state, and a multi-objective optimization and coordination strategy is initiated.

[0033] In this embodiment of the invention, the transformer substation intelligent terminal (TTU) can continuously collect the effective values ​​and frequencies of the three-phase voltage on the low-voltage side at a sampling rate of 64 points per cycle. When the effective value of any phase voltage is lower than 85% or higher than 110% of the rated value (preset voltage range), or the frequency deviation exceeds ±0.5Hz (preset frequency range), and this over-limit state lasts for more than 100ms (preset over-limit duration threshold), a first-level islanding suspected marker is generated. This is a passive islanding detection method, based on the voltage / frequency over-limit protection specified in the IEEE 1547 standard; the TTU continuously monitors the communication link status between the transformer substation and the upstream power grid. This communication link can use a 4G / 5G wireless public network or a fiber optic private network, with a heartbeat message period of 1 second. When a communication interruption lasts for more than 3 seconds (a preset interruption delay threshold) and a suspected first-level islanding marker already exists, an active disturbance detection strategy is triggered; otherwise, passive islanding detection is maintained, meaning that active disturbance is only initiated when both "abnormal electrical quantity" and "communication interruption" conditions are met simultaneously, thus avoiding false triggering caused by accidental packet loss or instantaneous voltage fluctuations. In the active disturbance detection strategy, a disturbance source is randomly selected from the distributed energy storage inverters currently in standby mode. This inverter injects a reactive current disturbance signal into the low-voltage side of the distribution area, lasting 20ms (a preset short pulse width) with an amplitude of 5% of the inverter's rated capacity (a preset small amplitude). Reactive current is chosen instead of active current because reactive injection does not cause additional energy consumption of the energy storage battery and has a small impact on the voltage amplitude of the end grid, meeting the power quality requirements of the distribution area. During the disturbance signal injection, the TTU synchronously collects the voltage response amplitude and voltage response phase at the injection point (i.e., the inverter's grid connection point). The voltage amplitude change and voltage phase change are calculated before and during the injection process, respectively. It should be noted that the injected reactive current amplitude is a known control variable. The equivalent impedance magnitude is equal to the ratio of the voltage amplitude change to the reactive current amplitude. Traditional impedance measurement requires offline injection of a sweep frequency signal or reliance on line parameter modeling, but the above method utilizes an existing energy storage inverter to perform online measurement during operation, requiring no additional hardware. Finally, the calculated equivalent impedance magnitude is compared with the grid-connected state reference impedance magnitude pre-stored in the TTU. The reference impedance magnitude is obtained by averaging multiple measurements using the same active disturbance process during normal grid-connected operation of the distribution area, representing the characteristic value of the grid-side equivalent impedance. If the change exceeds 30% (preset impedance change allowable range), or the voltage phase change exceeds 15 degrees (preset phase offset allowable range), the distribution area is determined to have entered islanded operation, and a multi-objective optimization and collaborative strategy is immediately initiated. In this process, two independent criteria, impedance magnitude change and phase offset, are introduced simultaneously, which effectively avoids misjudgment or omission of a single criterion under special loads (such as asynchronous motors and constant current source loads).

[0034] like Figure 3 As shown in the preferred embodiment of the present invention, the step of establishing a multi-objective optimization function with the optimization objectives of maximizing the total power supply duration of critical loads, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude specifically includes: S201, obtain the current SOC value, SOC over-discharge protection threshold, rated capacity, maximum discharge power of each distributed energy storage unit, as well as the power demand and power supply priority level of each critical load; S202, multiply the difference between the current SOC value and the SOC over-discharge protection threshold by the rated capacity to obtain the remaining dischargeable energy of each energy storage unit; S203, divide the sum of the remaining dischargeable energy of all energy storage units by the total power demand of all critical loads to construct the first sub-objective function with the goal of maximizing the total power supply duration of critical loads; S204, based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current time, a second sub-objective function is constructed with the goal of minimizing the SOC synchronous depletion deviation of each energy storage unit; S205. Based on the cumulative deviation between the voltage amplitude and the rated voltage of each critical load connection point during islanded operation, a third sub-objective function is constructed with the goal of minimizing the voltage drop amplitude. The first, second, and third sub-objective functions are then combined into a multi-objective optimization function using a multi-objective evolutionary algorithm.

[0035] In this embodiment of the invention, the current SOC value, SOC over-discharge protection threshold (e.g., 10%), rated capacity, and maximum discharge power are first obtained from the local controller of each distributed energy storage unit. Simultaneously, the power demand and power supply priority level of each critical load (such as a health center or communication base station) are read from the area load management database. Based on this, the remaining dischargeable energy of each energy storage unit is calculated (current SOC minus the over-discharge protection threshold multiplied by the rated capacity). The sum of the remaining dischargeable energy of all energy storage units is divided by the total power demand of all critical loads to obtain the theoretical maximum power supply duration. However, the actual duration is limited by whether the sum of the maximum discharge power of each energy storage unit can meet the total load demand. This forms the first sub-objective function. This provides a clear energy upper limit baseline for subsequent multi-objective optimization, enabling the system to intuitively assess whether the current energy storage resources can support the expected power supply time. The second sub-objective function quantifies the SOC synchronization depletion deviation; the third sub-objective function is based on real-time voltage acquisition at each critical load access point during islanded operation, accumulating the absolute value of the voltage amplitude deviation from the rated voltage at each moment to characterize the voltage drop amplitude. This avoids sacrificing voltage quality for the sake of simply pursuing power supply duration (e.g., injecting all energy storage power into remote loads, leading to excessively high near-end voltage). Finally, the above three sub-objective functions are combined into a unified multi-objective optimization function using a multi-objective evolutionary algorithm (such as NSGA-II). The Pareto front is iteratively solved in each scheduling cycle, thus simultaneously considering power supply duration (energy efficiency), energy storage lifetime (avoiding excessive discharge of some energy storage), and voltage quality in a single optimization.

[0036] like Figure 4 As shown, in a preferred embodiment of the present invention, the step of constructing a second sub-objective function with the goal of minimizing the SOC synchronization depletion deviation of each energy storage unit based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current time specifically includes: S2041, For any energy storage unit, the total discharge time required for it to discharge from the current SOC value to the SOC over-discharge protection threshold is defined as the inherent life cycle, and the inherent life cycle is normalized and mapped to a complete virtual clock cycle, wherein the virtual clock pointer starts from 0 degrees and ends at 360 degrees. S2042, the current angle position of the virtual clock pointer is determined according to the ratio of the discharged amount of the energy storage unit to its remaining dischargeable energy at the current moment. The larger the proportion of the discharged amount, the closer the pointer angle is to 360 degrees. S2043, iterate through and calculate the current angle of the virtual clock pointer of each energy storage unit to construct a virtual clock pointer angle sequence, and calculate the sum of squared circumferential distances between all angles in the sequence. Define the calculation result as the synchronization depletion deviation index. S2044, the synchronization depletion deviation index is used as the second sub-objective function. The smaller the synchronization depletion deviation index, the more synchronized the depletion time of each energy storage unit is.

[0037] In this embodiment of the invention, taking an isolated terminal distribution area scenario as an example, assuming there are three distributed energy storage units with current State of Charge (SOC) of 60%, 40%, and 20% respectively, over-discharge protection thresholds of 10% for each, and rated capacities of 50kWh, 30kWh, and 20kWh respectively, their remaining dischargeable energy is 25kWh, 9kWh, and 2kWh respectively. Traditional methods directly calculate the variance of the remaining discharge time. Since the power allocated to each energy storage unit is itself a decision variable, the variance calculation depends on the unknown power allocation result, leading to a highly coupled optimization problem that is prone to getting trapped in local optima. This embodiment employs a virtual clock pointer method to circumvent this limitation. First, the total discharge time required for each energy storage unit to discharge from its current SOC to the over-discharge protection threshold is defined as its inherent lifespan and normalized and mapped to a virtual clock cycle ranging from 0° to 360°. 0° corresponds to the starting point (before discharge), and 360° corresponds to the endpoint of reaching the over-discharge protection threshold. Then, based on the proportion of the energy storage unit's current discharge to its remaining dischargeable energy (the discharged amount refers to the energy actually consumed from the start of islanded operation to the current moment, while the remaining dischargeable energy is the calculated 25, 9, and 2 kWh respectively), the current angle of its virtual clock pointer is determined. For example, if an energy storage unit has discharged 50% of its remaining dischargeable energy, the pointer points to 180°. For the three energy storage units mentioned above, assuming the current discharge percentages are 30%, 60%, and 80% respectively, the pointer angles are 108°, 216°, and 288° respectively. Next, a pointer angle sequence [108°, 216°, 288°] is constructed by traversing all energy storage units, and the circumferential distance between each pair of angles in this sequence is calculated (considering circular boundaries, for example, the distance between 358° and 2° is 4°, not 356°). The sum of the squares of all circumferential distances is then used as the synchronization depletion deviation index. In this example, the sum of the squares of the circumferential distances is approximately (108-216)² + (216-288)² + (288-108)², which becomes a specific value after circumferential correction. The smaller this index is, the more concentrated the pointer angles are, meaning that the depletion times of each energy storage unit are more synchronized. Finally, this index is directly used as the value of the second sub-objective function and introduced into the multi-objective optimization process. In summary, the circumferential distance of the virtual clock pointer is essentially a nonlinear measure of the consistency of remaining discharge time, which can significantly reduce the nonconvexity of the optimization problem and improve the convergence speed of multi-objective evolutionary algorithms (such as NSGA-II). It also eliminates the dimensional effects of differences in capacity, maximum discharge power, and initial SOC between different energy storage units, allowing the goal of synchronous depletion to be uniformly quantified. Another advantage of this method is that it does not require prior knowledge of the line impedance between each energy storage unit and the critical load, relying only on locally measurable SOC and discharged quantity, making it very suitable for real-world environments where end-grid parameters are lacking.

[0038] like Figure 5As shown, in a preferred embodiment of the present invention, the step of solving the multi-objective optimization function and dynamically allocating the active power output quota of each energy storage unit during islanded operation specifically includes: S301 discretizes the islanding operation time into continuous scheduling cycles. At the beginning of each scheduling cycle, each energy storage unit exchanges its remaining discharge time estimate with its neighboring energy storage units through the local communication network. S302, Each energy storage unit calculates the deviation between its own remaining discharge time and the average of the remaining discharge time estimates of all neighboring units based on the received remaining discharge time estimates of neighboring units; S303, the deviation is multiplied by a preset consistency gain coefficient and then superimposed on the power quota benchmark value of the current scheduling cycle to obtain the adjusted active power output quota of each energy storage unit in this scheduling cycle. The consistency gain coefficient is a positive number and is used to control the convergence speed to make the remaining discharge time more consistent. S304, each energy storage unit discharges according to the adjusted active power output quota, and updates its own SOC value at the end of the current scheduling cycle, thereby recalculating the estimated remaining discharge time at the start of the next scheduling cycle.

[0039] In this embodiment of the invention, the method for dynamically allocating the active power output quota of each energy storage unit is particularly suitable for scenarios where the communication conditions of the end-grid are limited and it is difficult to deploy a centralized optimizer. The islanding operation time is discretized into continuous scheduling cycles (which can be set according to the response speed of the energy storage units). At the beginning of each scheduling cycle, each energy storage unit exchanges its remaining discharge time estimate (calculated based on the current SOC, remaining dischargeable energy, and the currently allocated power quota) only with its neighboring units through a local communication network (such as RS485 or LoRa wireless). After receiving the data from its neighbor, each energy storage unit calculates the deviation between its own estimate and the average value of its neighbor, multiplies this deviation by a preset consistency gain coefficient, and adds it to the power quota benchmark value for the current cycle to obtain the adjusted active power output quota for this cycle. Subsequently, each energy storage unit discharges according to this quota, updates its SOC at the end of the cycle, recalculates the estimate for the next cycle, and iterates in this way until convergence. The above method is a distributed proportional control, which has the advantages of not requiring a central controller and only requiring local communication, and has a certain robustness to communication delays and packet loss.

[0040] like Figure 6 As shown, in a preferred embodiment of the present invention, the step of triggering over-discharge early warning and coordinating adjustment, reallocating the power quota of each energy storage unit and selectively reducing non-critical loads specifically includes: S401 monitors the SOC value of each energy storage unit in real time. When the SOC of any energy storage unit drops to the preset warning threshold, the moment is recorded as the warning trigger moment. S402, terminate the process of dynamically allocating active power output quotas, and obtain the remaining dischargeable energy of each energy storage unit from the current SOC value to the over-discharge protection threshold at the time of warning triggering; S403, reduce power supply level by level according to the power supply priority of each critical load until the total power demand of all critical loads does not exceed the sum of the rated discharge power of each energy storage unit; S404, based on the proportion of the remaining dischargeable energy of each energy storage unit to the total remaining dischargeable energy of all energy storage units, calculate the active power quota that each energy storage unit should undertake after the warning triggering time, so that the SOC decrease rate of each energy storage unit during the discharge process from the preset warning threshold to the over-discharge protection threshold remains consistent.

[0041] In this embodiment of the invention, the State of Charge (SOC) of each energy storage unit is monitored in real time at a second-level cycle. When the SOC of any energy storage unit drops to a preset warning threshold (e.g., 25%, far higher than the over-discharge protection threshold of 10%), this moment is immediately recorded as the warning trigger moment. This relatively high warning threshold retains a SOC buffer of approximately 15%, which is sufficient to complete subsequent load reduction and power redistribution without immediately facing the risk of over-discharge. After the warning is triggered, the ongoing dynamic allocation process is immediately terminated. This is because continuing iteration when the remaining energy is limited and close to depletion can easily cause oscillations or divergence. The dynamic allocation strategy is switched to a static proportional allocation strategy to ensure control stability. Then, the remaining dischargeable energy of each energy storage unit from its current SOC to the over-discharge protection threshold at the warning trigger moment is obtained. Non-critical loads are gradually reduced from low to high according to the power supply priority level of each critical load until the total power demand of all critical loads does not exceed the sum of the rated discharge power of each energy storage unit. Compared with a one-time full cut-off, more non-critical loads can be retained for power supply while ensuring power matching, thus improving the overall power consumption experience for users. Finally, based on the proportion of the remaining dischargeable energy of each energy storage unit to the total remaining dischargeable energy of all energy storage units, the active power quota that each energy storage unit should bear after the warning trigger time is directly calculated (i.e., the total power of the remaining critical load is allocated according to the proportion of remaining energy). Each energy storage unit discharges simultaneously according to this fixed quota until all energy storage units tend to reach the over-discharge protection threshold simultaneously. Before the warning, a complex consistency iteration fine synchronization approximation is used. After the warning, it is switched to a simple proportional allocation. This is because the previous iteration has made the remaining discharge time tend to be consistent, and the final proportional allocation is sufficient to ensure the synchronization of the SOC decline rate, while avoiding the control instability that may be caused by the final iteration.

[0042] like Figure 7 As shown, this embodiment of the invention also provides a distributed energy storage collaborative system for the end-grid based on multi-objective optimization, the system comprising: The status detection module 100 is used to monitor the electrical operation status of the terminal power grid area in real time. When the area is detected to meet the preset islanding operation judgment conditions, a multi-objective optimization and coordination strategy is started. The judgment conditions are set according to voltage, frequency and communication interruption time. The function construction module 200 is used to obtain the operating data of each distributed energy storage unit and the power demand and power supply priority level of the critical load based on the multi-objective optimization collaborative strategy, and to establish a multi-objective optimization function with the optimization objectives of maximizing the total power supply duration of the critical load, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude. The power optimization module 300 is used to solve the multi-objective optimization function and dynamically allocate the active power output quota of each energy storage unit during islanded operation, so that each energy storage unit tends to reach the over-discharge protection threshold at the same time during the discharge process. The early warning linkage module 400 is used to trigger over-discharge early warning coordinated adjustment when the SOC of any energy storage unit drops to a preset early warning threshold. This reallocates the power quota of each energy storage unit and selectively reduces non-critical loads, so that all energy storage units tend to reach the over-discharge protection threshold simultaneously.

[0043] like Figure 8 As shown, in a preferred embodiment of the present invention, the state detection module 100 includes: The over-limit marking unit 101 is used to continuously collect the effective value and frequency of the three-phase voltage on the low-voltage side of the terminal power grid area. When the effective value of any phase voltage exceeds the preset voltage range or the frequency deviation exceeds the preset frequency range, and the state continues to exceed the preset over-limit duration threshold, a first-level islanding suspected mark is generated. The disturbance triggering unit 102 is used to monitor the communication link status between the distribution area and the upper-level power grid. When the communication interruption time exceeds the preset interruption delay threshold and there is a suspected first-level island marker, the active disturbance detection strategy is triggered. Impedance calculation unit 103 is used to obtain the voltage amplitude change and voltage phase change at the injection point by injecting a preset short-time pulse width and small amplitude reactive current disturbance signal into the low-voltage side of the distribution area through any distributed energy storage inverter in standby state within the distribution area, based on an active disturbance detection strategy, and thereby calculate the equivalent impedance modulus of the distribution area at the current moment. The islanding confirmation unit 104 is used to determine that the transformer area has entered the islanding operation state and to start the multi-objective optimization and cooperation strategy when the change of the equivalent impedance magnitude relative to the grid-connected state reference impedance magnitude exceeds the preset impedance change allowable range, or the voltage phase change exceeds the preset phase offset allowable range.

[0044] like Figure 9As shown, in a preferred embodiment of the present invention, the function construction module 200 includes: The parameter acquisition unit 201 is used to acquire the current SOC value, SOC over-discharge protection threshold, rated capacity, maximum discharge power, power demand and power supply priority level of each distributed energy storage unit. The energy estimation unit 202 is used to multiply the difference between the current SOC value and the SOC over-discharge protection threshold by the rated capacity to obtain the remaining dischargeable energy of each energy storage unit. The duration modeling unit 203 is used to divide the sum of the remaining dischargeable energy of all energy storage units by the total power demand of all critical loads to construct the first sub-objective function with the goal of maximizing the total power supply duration of critical loads. Synchronous modeling unit 204 is used to construct a second sub-objective function with the goal of minimizing the SOC synchronous depletion deviation of each energy storage unit, based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current time. The voltage modeling unit 205 is used to construct a third sub-objective function with the goal of minimizing the voltage drop amplitude based on the cumulative deviation between the voltage amplitude and the rated voltage at each key load connection point during islanded operation. The first, second, and third sub-objective functions are then combined into a multi-objective optimization function using a multi-objective evolutionary algorithm.

[0045] like Figure 10 As shown, in a preferred embodiment of the present invention, the power optimization module 300 includes: The periodic synchronization unit 301 is used to discretize the islanding operation time into continuous scheduling cycles. At the beginning of each scheduling cycle, each energy storage unit exchanges its remaining discharge time estimate with the adjacent energy storage unit through the local communication network. Deviation calculation unit 302 is used for each energy storage unit to calculate the deviation between its own remaining discharge time and the average of the remaining discharge time estimates of all neighboring units based on the received remaining discharge time estimates of neighboring units; The quota correction unit 303 is used to multiply the deviation by a preset consistency gain coefficient and then add it to the power quota benchmark value of the current scheduling cycle to obtain the adjusted active power output quota of each energy storage unit in the current scheduling cycle. The consistency gain coefficient is a positive number and is used to control the convergence speed to make the remaining discharge time more consistent. The periodic update unit 304 is used for each energy storage unit to discharge according to the adjusted active power output quota, and to update its own SOC value at the end of the current scheduling cycle, thereby recalculating the estimated remaining discharge time at the start of the next scheduling cycle.

[0046] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0047] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0048] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A distributed energy storage collaborative method for end-grid grids based on multi-objective optimization, characterized in that, The method includes the following steps: Real-time monitoring of the electrical operation status of the terminal power grid distribution area; when the distribution area is detected to meet the preset islanding operation judgment conditions, a multi-objective optimization and coordination strategy is initiated. The judgment conditions are set according to voltage, frequency and communication interruption time. Based on a multi-objective optimization and collaborative strategy, the operation data of each distributed energy storage unit and the power demand and power supply priority of critical loads are obtained, and a multi-objective optimization function is established with the optimization objectives of maximizing the total power supply duration of critical loads, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude. Solve the multi-objective optimization function to dynamically allocate the active power output quota of each energy storage unit during islanded operation, so that each energy storage unit tends to reach the over-discharge protection threshold at the same time during the discharge process. When the SOC of any energy storage unit drops to the preset warning threshold, the over-discharge warning coordinated adjustment is triggered, the power quota of each energy storage unit is redistributed and non-critical loads are selectively reduced, so that all energy storage units tend to reach the over-discharge protection threshold at the same time. The step of real-time monitoring of the electrical operation status of the terminal power grid distribution area, and initiating a multi-objective optimization and collaborative strategy when the distribution area meets the preset islanding operation judgment conditions, specifically includes: The effective value and frequency of the three-phase voltage on the low-voltage side of the terminal power grid area are continuously collected. When the effective value of any phase voltage exceeds the preset voltage range or the frequency deviation exceeds the preset frequency range, and this state continues for more than the preset over-limit duration threshold, a first-level islanding suspected marker is generated. The monitoring station checks the communication link status between the monitoring area and the upper-level power grid. When the communication interruption time exceeds the preset interruption delay threshold and there is a suspected first-level islanding marker, the active disturbance detection strategy is triggered. Based on the active disturbance detection strategy, the voltage amplitude change and voltage phase change at the injection point are obtained by injecting a preset short-time pulse width and small amplitude reactive current disturbance signal into the low-voltage side of the distribution area through any distributed energy storage inverter in standby state within the distribution area. Based on this, the equivalent impedance modulus of the distribution area at the current moment is calculated. When the magnitude of the equivalent impedance relative to the reference impedance in the grid-connected state exceeds the preset allowable range of impedance change, or the voltage phase change exceeds the preset allowable range of phase offset, the transformer area is determined to enter the islanded operation state, and a multi-objective optimization and coordination strategy is initiated. The steps for establishing a multi-objective optimization function with the objectives of maximizing the total power supply duration of critical loads, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude specifically include: Obtain the current SOC value, SOC over-discharge protection threshold, rated capacity, maximum discharge power of each distributed energy storage unit, as well as the power demand and power supply priority level of each critical load; The remaining dischargeable energy of each energy storage unit is obtained by multiplying the difference between the current SOC value and the SOC over-discharge protection threshold by the rated capacity. Divide the sum of the remaining dischargeable energy of all energy storage units by the total power demand of all critical loads to construct the first sub-objective function with the goal of maximizing the total power supply duration of critical loads; Based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current moment, a second sub-objective function is constructed with the goal of minimizing the SOC synchronous depletion deviation of each energy storage unit. Based on the cumulative deviation between the voltage amplitude and the rated voltage at each critical load connection point during islanded operation, a third sub-objective function is constructed with the goal of minimizing the voltage drop amplitude. The first, second, and third sub-objective functions are then combined into a multi-objective optimization function using a multi-objective evolutionary algorithm. The step of constructing a second sub-objective function, based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current moment, with the goal of minimizing the SOC synchronization depletion deviation of each energy storage unit, specifically includes: For any energy storage unit, the total discharge time required for it to discharge from its current SOC value to the SOC over-discharge protection threshold is defined as its inherent lifespan, and this inherent lifespan is normalized and mapped to a complete virtual clock cycle, wherein the virtual clock pointer starts from 0 degrees and ends at 360 degrees. The current angle position of the virtual clock pointer is determined based on the ratio of the discharged amount of the energy storage unit to its remaining dischargeable energy at the current moment. The larger the proportion of the discharged amount, the closer the pointer angle is to 360 degrees. The current angle of the virtual clock pointer of each energy storage unit is calculated by iterating through the data to construct a sequence of virtual clock pointer angles. The sum of squared circumferential distances between all angles in the sequence is then calculated, and the result is defined as the synchronization depletion deviation index. The synchronization depletion deviation index is used as the second sub-objective function. The smaller the synchronization depletion deviation index, the more synchronized the depletion times of each energy storage unit are.

2. The method for coordinated distributed energy storage in the end-point power grid based on multi-objective optimization according to claim 1, characterized in that, The steps of solving the multi-objective optimization function and dynamically allocating the active power output quota of each energy storage unit during islanded operation specifically include: The islanding operation time is discretized into continuous scheduling cycles. At the beginning of each scheduling cycle, each energy storage unit exchanges its remaining discharge time estimate with neighboring energy storage units through the local communication network. Each energy storage unit calculates the deviation between its own remaining discharge time and the average of the remaining discharge time estimates of all neighboring units based on the remaining discharge time estimates received from neighboring units. The deviation is multiplied by a preset consistency gain coefficient and then superimposed on the power quota benchmark value of the current scheduling cycle to obtain the adjusted active power output quota of each energy storage unit in this scheduling cycle. The consistency gain coefficient is a positive number and is used to control the convergence speed to make the remaining discharge time more consistent. Each energy storage unit discharges according to the adjusted active power output quota and updates its own SOC value at the end of the current scheduling cycle, thereby recalculating the estimated remaining discharge time at the start of the next scheduling cycle.

3. The method for coordinated distributed energy storage in the end-point power grid based on multi-objective optimization according to claim 1, characterized in that, The steps of triggering over-discharge early warning and coordinating adjustments, reallocating power quotas for each energy storage unit, and selectively reducing non-critical loads specifically include: The SOC value of each energy storage unit is monitored in real time. When the SOC of any energy storage unit drops to the preset warning threshold, the moment is recorded as the warning trigger moment. The process of dynamically allocating active power output quotas is terminated, and the remaining dischargeable energy of each energy storage unit from the current SOC value to the over-discharge protection threshold is obtained at the time of warning triggering. The power supply priority of each critical load is reduced step by step until the total power demand of all critical loads does not exceed the sum of the rated discharge power of each energy storage unit. Based on the proportion of the remaining dischargeable energy of each energy storage unit to the total remaining dischargeable energy of all energy storage units, the active power quota that each energy storage unit should undertake after the warning triggering time is calculated, so that the SOC decrease rate of each energy storage unit during the discharge process from the preset warning threshold to the over-discharge protection threshold is consistent.

4. A distributed energy storage collaborative system for end-grid grids based on multi-objective optimization, characterized in that: The system includes: The status detection module is used to monitor the electrical operation status of the terminal power grid area in real time. When the area is detected to meet the preset islanding operation judgment conditions, a multi-objective optimization and coordination strategy is activated. The judgment conditions are set according to voltage, frequency and communication interruption time. The function construction module is used to obtain the operating data of each distributed energy storage unit and the power demand and power supply priority level of the critical load based on the multi-objective optimization collaborative strategy, and to establish a multi-objective optimization function with the optimization objectives of maximizing the total power supply duration of the critical load, minimizing the SOC synchronous depletion deviation of each energy storage unit, and minimizing the voltage drop amplitude. The power optimization module is used to solve the multi-objective optimization function and dynamically allocate the active power output quota of each energy storage unit during islanded operation, so that each energy storage unit tends to reach the over-discharge protection threshold at the same time during the discharge process. The early warning linkage module is used to trigger over-discharge early warning coordinated adjustment when the SOC of any energy storage unit drops to a preset early warning threshold. This reallocates the power quota of each energy storage unit and selectively reduces non-critical loads, so that all energy storage units tend to reach the over-discharge protection threshold at the same time. The state detection module includes: The over-limit marking unit is used to continuously collect the effective value and frequency of the three-phase voltage on the low-voltage side of the terminal power grid area. When the effective value of any phase voltage exceeds the preset voltage range or the frequency deviation exceeds the preset frequency range, and the state continues to exceed the preset over-limit duration threshold, a first-level islanding suspected mark is generated. The disturbance triggering unit is used to monitor the communication link status between the distribution area and the upper-level power grid. When the communication interruption time exceeds the preset interruption delay threshold and there is a suspected first-level islanding marker, the active disturbance detection strategy is triggered. The impedance calculation unit is used to obtain the voltage amplitude change and voltage phase change at the injection point by injecting a preset short-time pulse width and small amplitude reactive current disturbance signal into the low-voltage side of the distribution area through any distributed energy storage inverter in standby state within the distribution area, based on the active disturbance detection strategy, and thereby calculate the equivalent impedance modulus of the distribution area at the current moment. The islanding confirmation unit is used to determine that the transformer area has entered the islanding operation state and to start a multi-objective optimization and coordination strategy when the change of the equivalent impedance magnitude relative to the grid-connected state reference impedance magnitude exceeds the preset impedance change allowable range, or the voltage phase change exceeds the preset phase offset allowable range. The function construction module includes: The parameter acquisition unit is used to acquire the current SOC value, SOC over-discharge protection threshold, rated capacity, maximum discharge power, power demand and power supply priority level of each distributed energy storage unit. The energy estimation unit is used to multiply the difference between the current SOC value and the SOC over-discharge protection threshold by the rated capacity to obtain the remaining dischargeable energy of each energy storage unit. The duration modeling unit is used to divide the sum of the remaining dischargeable energy of all energy storage units by the total power demand of all critical loads, and construct the first sub-objective function with the goal of maximizing the total power supply duration of critical loads; The synchronous modeling unit is used to construct a second sub-objective function with the goal of minimizing the SOC synchronous depletion deviation of each energy storage unit, based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current time. The voltage modeling unit is used to construct a third sub-objective function with the goal of minimizing the voltage drop amplitude based on the cumulative deviation between the voltage amplitude and the rated voltage at each key load connection point during islanded operation. The first, second, and third sub-objective functions are then combined into a multi-objective optimization function using a multi-objective evolutionary algorithm. Based on the dispersion of the remaining discharge time required for each energy storage unit to reach the SOC over-discharge protection threshold from the current moment, a second sub-objective function is constructed with the goal of minimizing the SOC synchronization depletion deviation of each energy storage unit. Specifically, this includes: For any energy storage unit, the total discharge time required for it to discharge from its current SOC value to the SOC over-discharge protection threshold is defined as its inherent lifespan, and this inherent lifespan is normalized and mapped to a complete virtual clock cycle, wherein the virtual clock pointer starts from 0 degrees and ends at 360 degrees. The current angle position of the virtual clock pointer is determined based on the ratio of the discharged amount of the energy storage unit to its remaining dischargeable energy at the current moment. The larger the proportion of the discharged amount, the closer the pointer angle is to 360 degrees. The current angle of the virtual clock pointer of each energy storage unit is calculated by iterating through the data to construct a sequence of virtual clock pointer angles. The sum of squared circumferential distances between all angles in the sequence is then calculated, and the result is defined as the synchronization depletion deviation index. The synchronization depletion deviation index is used as the second sub-objective function. The smaller the synchronization depletion deviation index, the more synchronized the depletion times of each energy storage unit are.

5. The distributed energy storage collaborative system for end-grid grids based on multi-objective optimization according to claim 4, characterized in that, The power optimization module includes: The periodic synchronization unit is used to discretize the islanding operation time into continuous scheduling cycles. At the beginning of each scheduling cycle, each energy storage unit exchanges its remaining discharge time estimate with the adjacent energy storage unit through the local communication network. The deviation calculation unit is used by each energy storage unit to calculate the deviation between its own remaining discharge time and the average of the remaining discharge time estimates of all neighboring units, based on the remaining discharge time estimates received from neighboring units. The quota correction unit is used to multiply the deviation by a preset consistency gain coefficient and then add it to the power quota benchmark value of the current scheduling cycle to obtain the adjusted active power output quota of each energy storage unit in the current scheduling cycle. The consistency gain coefficient is a positive number and is used to control the convergence speed to make the remaining discharge time more consistent. The periodic update unit is used for each energy storage unit to discharge according to the adjusted active power output quota, and to update its own SOC value at the end of the current scheduling cycle, thereby recalculating the estimated remaining discharge time at the start of the next scheduling cycle.

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