Cooperative control method and system for energy storage devices in power distribution area

Abstract

The invention discloses a cooperative control method and system for energy storage devices in a power distribution area, and the method comprises the steps: deploying an edge control unit at an energy storage matrix node of a target power distribution area, and enabling the edge control unit to comprise a lower-layer state perception function and a lower-layer action execution function; acquiring operation state data of the energy storage device by adopting a lower-layer state sensing function, and uploading the operation state data to a regional cooperative control platform through a communication link; the regional cooperative control platform comprises an upper-layer matrix cooperative optimization function; the upper-layer matrix collaborative optimization function performs power collaborative calculation and operation mode scheduling decision based on the operation state data, generates an optimal control instruction of each energy storage node and issues the optimal control instruction to the lower-layer action execution function; and the lower-layer action execution function implements corresponding operation on each energy storage device based on the optimal control instruction, monitors response effect data of each energy storage device to the optimal control instruction and uploads the response effect data to the upper-layer matrix collaborative optimization function. The control effect of the energy storage device in the power distribution area is improved.

Description

Technical Field

[0001] This invention belongs to the field of power system distribution technology, and particularly relates to a collaborative control method and system for energy storage devices in power distribution substations. Background Technology

[0002] Under current technological advancements, control strategies for energy storage devices in distribution substations generally suffer from limitations. These limitations manifest primarily in a relatively simplistic control approach, a lack of sufficient flexibility, and an inability to cope with complex and ever-changing operating conditions. Traditional control methods often rely on fixed parameter settings and simple logical judgments. This approach cannot dynamically adjust and optimize based on real-time operating data of the energy storage device, thus limiting the full realization of its performance. Furthermore, this control strategy also faces challenges in achieving coordinated power control across multiple nodes and rational scheduling of operating modes.

[0003] On the other hand, the efficiency and quality of data transmission and processing are also important factors affecting the control effect of energy storage devices. In practical applications, the timeliness and accuracy of data transmission are often difficult to guarantee, and data processing capabilities are also limited. These problems make it difficult for the upper-level control platform to obtain accurate operating status information, thus affecting the generation and issuance of control commands. When the system malfunctions or its operating status changes, existing control methods often struggle to respond and adjust quickly, which may adversely affect the stability and reliability of the system.

[0004] Therefore, in order to improve the control effect of energy storage devices in distribution substations, it is necessary to develop more intelligent and flexible control strategies and improve the efficiency and quality of data transmission and processing. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a collaborative control method and system for energy storage devices in distribution substations, which can solve the problems of single control strategies, insufficient flexibility, and poor data transmission processing efficiency and quality in energy storage devices in distribution substations.

[0006] The present invention adopts the following technical solution.

[0007] In a first aspect, the present invention discloses a collaborative control method for energy storage devices in a distribution substation, the method comprising the following steps: Step 1: Deploy an edge control unit at the energy storage matrix node of the target distribution area. The edge control unit includes a lower-level state perception function and a lower-level action execution function. Step 2: The lower-level state sensing function is used to collect the operating status data of the energy storage device, and the operating status data is uploaded to the regional collaborative control platform through a preset communication link; the regional collaborative control platform includes an upper-level matrix collaborative optimization function; Step 3: The upper-layer matrix collaborative optimization function performs multi-node power collaborative calculation and operation mode scheduling decision based on the operation status data, generates the optimal control command for each energy storage node, and sends it to the lower-layer action execution function in the edge control unit; Step 4: The lower-level action execution function performs corresponding operations on each energy storage device based on the optimal control command, monitors the response effect data of each energy storage device to the optimal control command, and uploads it to the upper-level matrix collaborative optimization function; the upper-level matrix collaborative optimization function adjusts the collaborative control strategy based on the response effect data.

[0008] More preferably, The energy storage matrix nodes include H-bridge cascaded energy storage units, modular multilevel converter energy storage units, DC-DC cascaded energy storage sub-modules, or photovoltaic energy storage hybrid units.

[0009] More preferably, The operating status data includes individual battery voltage, battery pack state of charge, charging and discharging current, inverter output power, bus voltage, circulating current, and equipment health status indicators.

[0010] More preferably, In step 3, the upper-level matrix collaborative optimization function specifically includes: The optimization sub-objectives are constructed, including minimizing distribution area network losses, minimizing voltage fluctuations, minimizing MMC circulating currents, minimizing the peak-valley difference of distribution area loads, and maximizing the grid frequency inertia support capability. Assign weight coefficients to each sub-objective with a sum of 1, and then linearly weight and fuse each sub-objective into a multi-objective optimization model based on its corresponding weight coefficients. Under the constraints of grid frequency deviation limit, bus voltage fluctuation range, energy storage state of charge safety range, converter capacity limit and load power supply priority, the multi-objective optimization model is solved to obtain the optimal active power and optimal reactive power of each energy storage node and map them to the optimal control command of each energy storage node.

[0011] More preferably, The process involves linearly weighting and fusing each sub-objective based on its corresponding weight coefficient into a multi-objective optimization model. The weight coefficients for each sub-objective can be dynamically adjusted based on the current system's operating scenario. Specific adjustment methods include: Before the start of each control cycle, the regional collaborative control platform reads the current operating status data and external dispatch instructions of the distribution sub-area to determine its operating mode. If it is in grid-connected operation mode, the weights of the sub-objectives of minimizing the peak-valley difference of the sub-area load and minimizing the sub-objective of the distribution sub-area network loss are increased; if it is in islanded operation mode, the weights of the sub-objectives of minimizing voltage fluctuation and maximizing the sub-objective of grid frequency inertia support capability are increased. Under the established operating mode, the weight allocation is further refined based on the load period type divided by the time of day. During peak electricity consumption periods, the weight of the sub-target of minimizing the peak-valley difference of the distribution area load is set to the highest, and the weight of the sub-target of minimizing voltage fluctuation is increased. During off-peak electricity consumption periods, the weight of the sub-target of minimizing the distribution area network loss is increased, and the weight of the sub-target of minimizing the peak-valley difference of the distribution area load is decreased.

[0012] More preferably, In step 3, the optimal control command for each energy storage node is obtained by mapping the optimal active power and optimal reactive power of each energy storage node obtained by solving the multi-objective optimization model. Specifically, this includes: First, identify the type of energy storage matrix node to which each energy storage node belongs; then, based on the power-command mapping rule library preset for each energy storage matrix node type, convert the optimal active power value into a charging and discharging power setpoint or a virtual synchronous generator control parameter, and convert the optimal reactive power value into a reactive power support command or an MMC circulating current suppression coefficient; finally, combine the current operating mode of the distribution substation and the equipment health status indicators to generate a fault submodule bypass command or an off-grid / grid-connected switching strategy.

[0013] More preferably, In step 4, the response performance data includes actual power tracking error, voltage support capability, fault switching time, circulating current suppression effect, and system stability indicators.

[0014] Secondly, the present invention discloses a collaborative control system for energy storage devices in distribution substations based on the aforementioned method, comprising a configuration module, a transmission module, a control module, and an execution module; The configuration module deploys an edge control unit at the energy storage matrix node of the target distribution area. The edge control unit includes a lower-level state perception function and a lower-level action execution function. The transmission module uses a lower-level state sensing function to collect the operating status data of the energy storage device and uploads the operating status data to the regional collaborative control platform through a preset communication link; the regional collaborative control platform includes an upper-level matrix collaborative optimization function. The control module, with its upper-layer matrix collaborative optimization function, performs multi-node power collaborative calculation and operation mode scheduling decision based on the operating status data, generates the optimal control command for each energy storage node, and sends it to the lower-layer action execution function in the edge control unit. The execution module, with its lower-level action execution function, performs corresponding operations on each energy storage device based on the optimal control command, monitors the response effect data of each energy storage device to the optimal control command, and uploads it to the upper-level matrix collaborative optimization function; the upper-level matrix collaborative optimization function adjusts the collaborative control strategy based on the response effect data.

[0015] Thirdly, the present invention provides an electronic device, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of the first aspects of the present invention.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects of the present invention.

[0017] The beneficial effects of this invention are compared with those of the prior art: This innovative approach employs a hierarchical control architecture, integrating lower-level state perception and action execution functions into the edge control unit, while the upper-level matrix collaborative optimization function operates on the regional collaborative control platform. This architecture effectively improves the timeliness and accuracy of data processing, enabling the system to respond more quickly to changes in the operating status of energy storage devices. Through local state monitoring and rapid response control by the edge control unit, and multi-node spatiotemporal collaborative scheduling and active support control of energy storage resources based on the AEMS system and rolling time-domain control principle implemented by the regional collaborative control platform, the performance of energy storage devices can be fully utilized, achieving collaborative control of multi-node power and rational scheduling of operating modes. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the collaborative control method for energy storage devices in distribution substations according to the present invention. Figure 2 This is a schematic diagram of the internal structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0020] like Figure 1 As shown, this invention discloses a collaborative control method for energy storage devices in a distribution substation, the method comprising the following steps: Step 1: Deploy an edge control unit at the energy storage matrix node of the target distribution area. The edge control unit includes a lower-level state perception function and a lower-level action execution function. The energy storage matrix nodes include H-bridge cascaded energy storage units, modular multilevel converter energy storage units, DC-DC cascaded energy storage sub-modules, or photovoltaic energy storage hybrid units.

[0021] Step 2: The lower-level state sensing function is used to collect the operating status data of the energy storage device, and the operating status data is uploaded to the regional collaborative control platform through a preset communication link; the regional collaborative control platform includes an upper-level matrix collaborative optimization function; The operating status data includes individual battery voltage, battery pack state of charge, charging and discharging current, inverter output power, bus voltage, circulating current, and equipment health status indicators.

[0022] Step 3: The upper-layer matrix collaborative optimization function performs multi-node power collaborative calculation and operation mode scheduling decision based on the operation status data, generates the optimal control command for each energy storage node, and sends it to the lower-layer action execution function in the edge control unit; The upper-level matrix collaborative optimization function specifically includes: The optimization sub-objectives are constructed, including minimizing distribution area network losses, minimizing voltage fluctuations, minimizing MMC circulating currents, minimizing the peak-valley difference of distribution area loads, and maximizing the grid frequency inertia support capability. Assign weight coefficients to each sub-objective with a sum of 1, and then linearly weight and fuse each sub-objective into a multi-objective optimization model based on its corresponding weight coefficients. Under the constraints of grid frequency deviation limit, bus voltage fluctuation range, energy storage state of charge safety range, converter capacity limit and load power supply priority, the multi-objective optimization model is solved to obtain the optimal active power and optimal reactive power of each energy storage node and map them to the optimal control command of each energy storage node.

[0023] The process involves linearly weighting and fusing each sub-objective based on its corresponding weight coefficient into a multi-objective optimization model. The weight coefficients for each sub-objective can be dynamically adjusted based on the current system's operating scenario. Specific adjustment methods include: Before the start of each control cycle, the regional collaborative control platform reads the current operating status data and external dispatch instructions of the distribution sub-area to determine its operating mode. If it is in grid-connected operation mode, the weights of the sub-objectives of minimizing the peak-valley difference of the sub-area load and minimizing the sub-objective of the distribution sub-area network loss are increased; if it is in islanded operation mode, the weights of the sub-objectives of minimizing voltage fluctuation and maximizing the sub-objective of grid frequency inertia support capability are increased. Under the established operating mode, the weight allocation is further refined based on the load period type divided by the time of day. During peak electricity consumption periods, the weight of the sub-target of minimizing the peak-valley difference of the distribution area load is set to the highest, and the weight of the sub-target of minimizing voltage fluctuation is increased. During off-peak electricity consumption periods, the weight of the sub-target of minimizing the distribution area network loss is increased, and the weight of the sub-target of minimizing the peak-valley difference of the distribution area load is decreased.

[0024] The optimal control commands for each energy storage node are obtained by mapping the optimal active power and optimal reactive power of each energy storage node obtained from solving the multi-objective optimization model. Specifically, this includes: First, identify the type of energy storage matrix node to which each energy storage node belongs; then, based on the power-command mapping rule library preset for each energy storage matrix node type, convert the optimal active power value into a charging and discharging power setpoint or a virtual synchronous generator control parameter, and convert the optimal reactive power value into a reactive power support command or an MMC circulating current suppression coefficient; finally, combine the current operating mode of the distribution substation and the equipment health status indicators to generate a fault submodule bypass command or an off-grid / grid-connected switching strategy.

[0025] Step 4: The lower-level action execution function performs corresponding operations on each energy storage device based on the optimal control command, monitors the response effect data of each energy storage device to the optimal control command, and uploads it to the upper-level matrix collaborative optimization function; the upper-level matrix collaborative optimization function adjusts the collaborative control strategy based on the response effect data.

[0026] The response performance data includes actual power tracking error, voltage support capability, fault switching time, circulating current suppression effect, and system stability indicators.

[0027] Example 1: Existing technologies suffer from several problems, such as limited control strategies for energy storage devices in distribution substations, poor flexibility, and difficulty in handling complex operating conditions. Traditional control relies on fixed parameters and simple logic, making it impossible to dynamically adjust and optimize based on real-time operating status, thus limiting the performance of energy storage devices and presenting challenges in multi-node power coordination control and operating mode scheduling.

[0028] This invention provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to achieve coordinated control of the energy storage device in a distribution area with reference to embodiments. Reference Figure 1 This invention provides a method for coordinated control of energy storage devices in a distribution substation, comprising: Step 1: Deploy an edge control unit at the energy storage matrix node of the target distribution area. The edge control unit includes a lower-level state perception function and a lower-level action execution function. It should be noted that, in order to achieve coordinated control of energy storage devices within a distribution network, edge control units need to be strategically deployed at each energy storage matrix node in the target distribution network. These edge control units act like intelligent "sentinels" distributed at various key locations. The lower-level state perception functions are like the "sentinels'" keen eyes, capable of periodically collecting comprehensive and detailed operational status data of the local energy storage modules. The lower-level action execution functions are like the "sentinels'" flexible hands, undertaking important tasks such as implementing charge and discharge control, converter regulation, and fault bypass operations locally.

[0029] In the embodiments of the present invention, each edge control unit includes a lower-level state perception function and a lower-level action execution function.

[0030] In this embodiment of the invention, the lower-level state perception function is used to periodically collect the operating status data of the local energy storage module; In some specific implementations, the lower-level state sensing function can accurately and stably acquire various key data according to a preset acquisition frequency. For example, in each acquisition cycle, it can acquire the voltage of a single battery cell. This data can intuitively reflect the battery's operating state, determine whether the battery is within the normal voltage range, and avoid affecting the battery's lifespan or even causing safety issues due to abnormal voltage. Battery pack state of charge data helps to accurately grasp the remaining capacity of the battery pack, providing a basis for rationally arranging charging and discharging strategies. Charging and discharging current data allows the invention to clearly define the charging and discharging rate of the energy storage module, preventing overcharging and over-discharging. Inverter output power data is crucial for evaluating the inverter's efficiency and performance; bus voltage data reflects the voltage stability of the entire system; circulating current data helps to identify potential circulating current problems in the system and take timely measures to suppress them; and equipment health status indicators allow the invention to quickly identify potential equipment malfunctions.

[0031] In this embodiment of the invention, the lower-level state awareness function includes: A fixed sampling period is preset, and the sampling frequency of the lower-level state sensing function is set according to the period; Traverse each energy storage matrix node and acquire operational status data by type within each acquisition cycle; The system filters and identifies outliers in the running status data, and adds timestamps and node codes to the processed data. Operating status data includes individual cell voltage, battery pack state of charge, charging and discharging current, inverter output power, bus voltage, circulating current, and equipment health status indicators.

[0032] Specifically, a fixed sampling period is preset. Based on this, the sampling frequency of the lower-level state perception function is set. This cycle should satisfy the Nyquist sampling theorem to ensure accurate capture of key dynamic processes in the energy storage system (such as power fluctuations and voltage sags). The typical value range is [value range missing]. .

[0033] Furthermore, in each sampling period Inside, the system traverses all The energy storage matrix node collects the data of the first energy storage matrix node. Nodes Multiple types of operational state data constitute a state vector:

[0034] in, Indicates the voltage of a single cell (which can be an average value or an extreme value); The state of charge of the battery pack can be estimated using the ampere-hour integration method or the extended Kalman filter (EKF):

[0035] in, For charging and discharging efficiency, Rated capacity; Indicates the current charging / discharging current; This indicates the active and reactive power output of the inverter. Indicates the local bus voltage; This refers to the circulating current in a modular multilevel structure (especially applicable to MMC type energy storage units). Indicates the health status of the equipment (0 indicates normal, 1 indicates a fault or warning).

[0036] Furthermore, to improve data quality, the raw acquired data is filtered. Taking single-unit voltage as an example, a first-order low-pass digital filter is used; Furthermore, adopt Criteria are used to identify outliers. For a certain type of data sequence... Calculate the mean within its sliding window. with standard deviation If the current sampled value satisfies If the value is not found, it is considered an outlier and is either removed or compensated by interpolation.

[0037] Timestamps and node encodings are appended to the processed valid data packets to add metadata information, forming standardized upload data frames. :

[0038] in, Represents a UTC timestamp, accurate to milliseconds; This indicates a unique node code, using a hierarchical coding rule (e.g., area code, cluster code, unit code). This represents the cleanliness state vector after filtering and verification. This data frame is uploaded to the regional collaborative control platform via a pre-defined communication link for multi-objective optimization calculations of the upper-level matrix collaborative optimization function.

[0039] Step 2: The lower-level state sensing function is used to collect the operating status data of the energy storage device, and the operating status data is uploaded to the regional collaborative control platform through a preset communication link; the regional collaborative control platform includes an upper-level matrix collaborative optimization function; In this embodiment of the invention, the lower-level state perception function and the lower-level action execution function are integrated into the edge control unit for local state monitoring and fast response control. The upper-level matrix collaborative optimization function runs on the regional collaborative control platform. Based on the AEMS system and the rolling time domain control principle, it realizes the spatiotemporal collaborative scheduling and active support control of multi-node energy storage resources. The edge control unit and the regional collaborative control platform form a secure communication link through industrial Ethernet or wireless private network, supporting low-latency interaction of scheduling commands and status data.

[0040] It should be noted that this integrated communication architecture design fully leverages the respective advantages of the edge control unit and the regional collaborative control platform. The edge control unit, with its local status monitoring and rapid response control capabilities, can promptly capture real-time status changes of energy storage devices and distribution substations, and quickly take corresponding control actions to effectively respond to emergencies and local anomalies, ensuring the stability and reliability of the system. Meanwhile, the regional collaborative control platform, based on the AEMS system and rolling time-domain control principles, coordinates multi-node energy storage resources at a macro level, achieving spatiotemporal collaborative scheduling and active support control. This optimizes the energy allocation and utilization efficiency of the entire distribution substation, improving the system's economy and flexibility.

[0041] It should be noted that a secure communication link, constructed via industrial Ethernet or a private wireless network, enables efficient, stable, and low-latency interaction between the edge control unit and the regional collaborative control platform. Scheduling commands can be transmitted from the regional collaborative control platform to the edge control unit in a timely and accurate manner, ensuring the rapid execution of control strategies. Simultaneously, status data can be fed back to the regional collaborative control platform in real time, providing accurate data support for the calculation of the upper-level matrix collaborative optimization function, forming a closed-loop control system.

[0042] It is important to note that the security of this communication link is paramount. It employs advanced encryption technologies and security mechanisms to effectively prevent external network attacks and data leaks, ensuring the system's information security and operational safety. In practical applications, both industrial Ethernet and private wireless networks have undergone rigorous testing and verification to ensure stable operation even in complex electromagnetic environments and harsh weather conditions.

[0043] It should be noted that, to further improve the system's reliability and fault tolerance, the communication link also has redundancy backup and automatic switching functions. When the primary communication link fails, the system can automatically switch to the backup link to ensure the normal transmission of scheduling instructions and status data, avoiding control failures and system malfunctions caused by communication interruptions.

[0044] It should also be noted that this integrated control architecture and secure and reliable communication link provide a solid technical guarantee for the coordinated control of energy storage devices in distribution substations, which can effectively improve the operating efficiency and stability of distribution substations and promote the efficient use of energy and sustainable development.

[0045] It should be noted that uploading the operational status data is a crucial step in achieving coordinated control of energy storage devices. After receiving the data, the regional collaborative control platform can perform spatiotemporal coordinated scheduling and active support control of multi-node energy storage resources based on the AEMS system and rolling time-domain control principles. The operational status data acquired by the lower-level state perception function contains detailed information about each energy storage matrix node. The regional collaborative control platform can use this information to construct an accurate system model and analyze the operational status of energy storage devices throughout the entire distribution area.

[0046] Step 3: The upper-layer matrix collaborative optimization function performs multi-node power collaborative calculation and operation mode scheduling decision based on the operation status data, generates the optimal control command for each energy storage node, and sends it to the lower-layer action execution function in the edge control unit; In this embodiment of the invention, the regional collaborative control platform is used to deploy and run the upper-level matrix collaborative optimization function; The upper-level matrix collaborative optimization function performs multi-node power collaborative calculation and operation mode scheduling decision based on the operating status data, and generates the optimal control command for each energy storage node; In this embodiment of the invention, the upper-level matrix collaborative optimization function includes: Several optimization sub-objectives are preset in the upper-level matrix collaborative optimization function; Establish an objective function to characterize the optimization objective. Several optimization sub-objectives include minimizing distribution area network loss, minimizing voltage fluctuation, minimizing MMC circulating current, minimizing the peak-valley difference of the distribution area load, and maximizing the grid frequency inertia support capability. Establish a set of constraints, including grid frequency deviation limits, bus voltage fluctuation range, safe range of energy storage charge state, converter capacity limits, and load power supply priority. Based on the established objective function and constraint set, multi-objective collaborative optimization is performed using operational status data to generate optimal control commands for each energy storage node.

[0047] Specifically, based on the operational needs of the distribution substation and the requirements of the power grid's support functions, several key optimization objectives are set, including: Minimizing distribution network losses, minimizing voltage fluctuations, minimizing MMC circulating currents, minimizing peak-valley load differences in distribution areas, and maximizing the grid frequency inertia support capacity—these objectives together constitute a comprehensive performance index system aimed at improving the safety, economy, and power quality of the distribution system.

[0048] Furthermore, by establishing a multi-objective optimization function, the above optimization objectives are formalized into a multi-objective optimization problem, whose objective function can be expressed as:

[0049] in, , Each energy storage node at time [time] The active and reactive power command vectors.

[0050] The sub-objective functions are defined as follows: Minimize the distribution area network loss, and its corresponding objective function is... Specifically:

[0051] in, For the collection of branches in the power distribution network, Let be the line resistance of branch ij (the line connecting node i and node j) in the power distribution network. For the apparent power of the branch, Let be the bus voltage at node i at time k.

[0052] Minimize voltage fluctuations: The design uses the sum of squared voltage deviations as the evaluation index, and the specific objective function is as follows:

[0053] in, The rated voltage (e.g., 400V) is used to bring the voltage at each node close to the reference value.

[0054] Minimize MMC circulating current: For energy storage units employing a modular multilevel structure, a circulating current suppression objective is defined, and the specific objective function is as follows:

[0055] in, It is an MMC type energy storage node collection. This is a weighting coefficient that reflects the node's sensitivity to circulation. Let be the circulating current of the i-th MMC energy storage node at time k.

[0056] Minimize the peak-valley difference in transformer area load: Reduce the peak-valley difference by smoothing the load curve. The specific objective function is as follows:

[0057] in, The active power of the transformer substation interacting with the main network. To optimize the time domain (e.g., 1 hour).

[0058] Maximizing the grid's frequency inertial support capability: Simulating the synchronous machine's inertial response during frequency disturbances, with the objective function specifically as follows:

[0059]

[0060] in, Let be the virtual inertial power increment of the i-th energy storage unit at time k. Let be the virtual inertia coefficient of the i-th energy storage unit, which is dynamically allocated by the upper-level function based on the SOC level and system requirements. Represents the power grid frequency at time k; Let be the total virtual inertia of all energy storage nodes at time k.

[0061] To ensure the safe and stable operation of the system, the optimization process must satisfy the following physical and operational constraints: Grid frequency deviation limit:

[0062] in, The rated frequency of the power grid. This is the maximum permissible deviation value for the power grid frequency. It should be noted that, typically... Hz (GB / T15945 standard).

[0063] Bus voltage fluctuation range:

[0064] in, , These are the lower and upper threshold values ​​for the bus voltage, respectively. It should be noted that they are generally taken as... pu, pu Energy storage state of charge (SOC) safe range:

[0065] in, , These are the lower and upper threshold values ​​for the State of Charge (SOC) of energy storage, respectively. It should be noted that... The typical value is 20%. The typical value is 90% to prevent overcharging and over-discharging.

[0066] Converter capacity limitations:

[0067] in, , Let be the output active power and output reactive power of the converter at the i-th node at time k, respectively. For the first The rated capacity of each node converter.

[0068] Load power supply priority constraints: For critical load nodes Priority should be given to ensuring its voltage and power supply:

[0069] in, Let k be the actual operating voltage of the j-th critical load node at time k. This is the lower voltage threshold. Let be the actual active power of the j-th critical load node at time k. Let be the active power demand of the j-th critical load node at time k. The power supply guarantee factor for the j-th node (critical load is taken as...). ).

[0070] Furthermore, the multi-objective collaborative optimization solution and instruction generation integrate the above objective functions and constraints to construct a complete optimization model:

[0071] in, The weighting coefficients for each objective can be dynamically adjusted based on the operating mode (e.g., grid-connected / islanded), time period (peak / off-peak / valley), or scheduling instructions. This optimization problem can be solved using a rolling time-domain optimization framework, within each control cycle. Based on predicted load, photovoltaic output, and electricity price information, the future... The optimal control sequence for each step, and only the optimal instruction at the current moment is executed:

[0072] Ultimately, the upper-level matrix collaborative optimization function will generate the optimal control command. The lower-level action execution functions are sent to the corresponding energy storage nodes to achieve precise coordinated control. This represents the optimal active power vector at time step k. This represents the optimal reactive power vector at time step k.

[0073] The adjustment methods for the weight coefficients of each objective specifically include: First, before the start of each control cycle, the regional collaborative control platform reads the current operating status data and external dispatch instructions of the distribution substation to determine whether the current operating mode is grid-connected or islanded. If it is in grid-connected mode, the weights of the sub-objectives of minimizing the peak-valley load difference and minimizing the grid loss of the distribution substation are increased, because the energy storage device mainly participates in the economic dispatch of the grid side at this time. If it is in islanded mode, the weights of the sub-objectives of minimizing voltage fluctuation and maximizing the grid frequency inertia support capacity are significantly increased to ensure continuous power supply and frequency stability for important loads.

[0074] Furthermore, the regional collaborative control platform refines the weight allocation based on the load period types defined by the day's time. During peak electricity consumption periods, the weight of the sub-objective of minimizing the peak-valley load difference in the distribution area is set to the highest, while the weight of the sub-objective of minimizing voltage fluctuations is increased, and reactive power support is enhanced to alleviate line overload and bus voltage drop issues. During off-peak electricity consumption periods, minimizing distribution area network losses and state-of-charge (SOC) balance management become the dominant objectives. At this time, the weight of the sub-objective of minimizing the peak-valley load difference in the distribution area is reduced, while the weight corresponding to the SOC safety range constraint remains constant to prevent battery overcharging.

[0075] For example, in a certain distribution area, the peak electricity consumption period is from 10:00 AM to 12:00 PM. At this time, the upper-level matrix collaborative optimization function sets the peak shaving and valley filling target weight to 0.4, the voltage stability weight to 0.25, the minimum network loss weight to 0.15, the virtual inertia support weight to 0.1, and the circulating current suppression weight to 0.1. However, during the off-peak electricity consumption period from 2:00 AM to 4:00 AM, the peak shaving and valley filling weight decreases to 0.1, the minimum network loss weight increases to 0.35, the voltage stability weight remains at 0.2, and the remaining weights are adjusted accordingly.

[0076] When the regional collaborative control platform receives an emergency mode switching command from the dispatch center, such as requiring the energy storage system to enter the "frequency emergency support" state, the weight of the sub-objective maximizing the grid frequency inertial support capability will be forcibly increased to above 0.5, while the weights of other objectives will be proportionally reduced to ensure that energy storage resources respond to frequency disturbances first. For example, when the grid frequency suddenly drops to 49.6 Hz and lasts for more than 2 seconds, the dispatch center issues a frequency support command. The upper-level matrix collaborative optimization function immediately adjusts the weight configuration to make the virtual inertial support objective dominate, and simultaneously increases the constraint weight of the state-of-charge safe range to prevent battery over-discharge due to high-power discharge.

[0077] Among them, the operation mode refers to whether the energy storage system in the distribution area is currently connected to the main grid (grid-connected operation) or independently supplies power to local loads (islanding operation) without being connected to the main grid; the load time period characteristics refer to the peak, flat or low time periods of electricity consumption divided according to the historical load curve; the dispatch center instruction refers to the mandatory intervention command from the superior power dispatching agency on the operation strategy of the energy storage system; and the dynamic adjustment of weights refers to the mechanism in which the importance coefficients assigned to different optimization objectives in the multi-objective optimization function are updated in real time as the operating conditions change.

[0078] After solving for the optimization objective and obtaining the active and reactive power commands for each energy storage node, these power values ​​need to be mapped into specific optimal control commands to drive the lower-level action execution functions to complete operations such as local charging and discharging, converter regulation, and operating mode switching. This mapping process is a key step in realizing the implementation of upper-level collaborative decisions. Only by accurately converting abstract power settings into executable device-level commands can the multi-node energy storage system be ensured to operate collaboratively according to the optimization intent.

[0079] Specifically, after completing multi-objective optimization calculations in each control cycle, the regional collaborative control platform obtains the optimal active power vector and optimal reactive power vector of each energy storage matrix node at the current moment. Then, based on the topology type and local equipment capabilities of each node, the power command is decomposed into the corresponding underlying control parameters.

[0080] Specifically, the system first identifies the type of energy storage matrix node to which each energy storage node belongs, including H-bridge cascaded energy storage units, modular multilevel converter energy storage units, DC-DC cascaded energy storage submodules, or photovoltaic energy storage hybrid units. Then, based on the node type, it matches a preset power-command mapping rule library to convert active power values ​​into charge and discharge power setpoints or virtual synchronous generator control parameters, and reactive power values ​​into reactive power support commands or MMC circulating current suppression coefficients. Finally, combining the current operating mode (grid-connected or islanded) and equipment health status indicators, it generates fault submodule bypass commands or off-grid / grid-connected switching strategies.

[0081] For example, in a certain distribution substation, there is a modular multilevel converter energy storage unit. Its optimization result is an active power command of +150 kW (discharge) and a reactive power command of +30 kVar (inductive support). The regional collaborative control platform first identifies that the node belongs to the MMC type structure, and then calls the corresponding mapping rules: mapping the +150 kW active power to the discharge current reference value of the DC-DC converter, and simultaneously setting the rotational inertia parameter of the virtual synchronous generator to 0.8 seconds; mapping the +30 kVar reactive power to the reactive current command of the inverter, and calculating the corresponding MMC circulating current suppression coefficient to 0.12; at the same time, since it is currently in grid-connected operation and the equipment health status is normal, the bypass command is not triggered, but the grid-connected operation strategy remains unchanged.

[0082] For example, another node is a DC-DC cascaded energy storage submodule, with an optimized active power of -80 kW (charging) and reactive power of 0 kVAR. After the platform identifies it as a DC-DC cascaded structure, it maps the -80 kW to a constant current charging command for the DC-DC converter, sets the charging current to 200 Amps, and disables the reactive power compensation function because the reactive power demand is zero, setting the circulating current suppression coefficient to the default value of 0.05. If the equipment health status indicator shows an abnormal voltage in a certain submodule, an additional faulty submodule bypass command is generated, instructing the edge control unit to disconnect the bypass switch of that submodule.

[0083] Finally, all generated optimal control commands are packaged into standardized control frames, including node codes, power setpoints, control parameters, operating mode flags, and fault handling flags. These frames are then sent to the lower-level action execution functions of the corresponding edge control units via industrial Ethernet. After parsing, these functions apply the commands to the DC-DC converter, inverter, and bypass switch, respectively, to achieve precise power regulation, fault isolation, and fault-tolerant operation.

[0084] Among them, the optimal control command refers to the set of device-level commands generated by the upper-level matrix collaborative optimization function to guide the edge control unit to perform specific operations. These commands include the energy storage module charging and discharging power setpoint, virtual synchronous generator control parameters, reactive power support commands, MMC circulating current suppression coefficient, fault submodule bypass commands, and off-grid / grid switching strategies. The power-command mapping rule base refers to the logical table pre-configured in the regional collaborative control platform that converts active / reactive power values ​​into specific control parameters according to different energy storage matrix node types. The device health status flag refers to the status identifier bit collected and uploaded by the lower-level status perception function, reflecting whether there is a fault or warning in the energy storage submodule.

[0085] In this embodiment of the invention, the optimal control command includes: Energy storage module charging and discharging power setting values, virtual synchronous generator control parameters, reactive power support commands, MMC circulating current suppression coefficient, fault submodule bypass commands, and off-grid / grid switching strategies; The energy storage matrix nodes include H-bridge cascaded energy storage units, modular multilevel converter energy storage units, DC-DC cascaded energy storage sub-modules, and photovoltaic energy storage hybrid units; The regional collaborative control platform supports switching between collaborative control strategies in both grid-connected and islanded operation modes.

[0086] It should be noted that the settings for these control commands, energy storage matrix nodes, and collaborative control modes are all designed to better achieve coordinated control of the energy storage device within the distribution substation. The energy storage module's charging and discharging power setting values ​​precisely control the charging and discharging process, ensuring the rational storage and release of electrical energy. Virtual synchronous generator control parameters help the energy storage device simulate the characteristics of a synchronous generator, enhancing system stability. Reactive power support commands effectively improve the power factor of the distribution substation, enhancing power quality. Precise setting of the MMC circulating current suppression coefficient reduces circulating current within the modular multilevel converter, improving its operating efficiency. The fault submodule bypass command allows for timely bypassing of the energy storage submodule in case of a fault, preventing disruption to the normal operation of the entire system. Off-grid / grid-connected switching strategies ensure a smooth transition of the energy storage device under different operating scenarios.

[0087] It should be noted that different types of energy storage matrix nodes play their unique roles in distribution substations. H-bridge cascaded energy storage units have the advantages of simple structure and easy expansion, flexibly adapting to energy storage needs of different scales. Modular multilevel converter energy storage units can achieve multilevel output, reducing the harmonic content of the output voltage. DC-DC cascaded energy storage submodules can precisely control the voltage and current of the energy storage battery, extending battery life. Photovoltaic-energy storage hybrid units combine photovoltaic power generation with energy storage, improving the utilization rate of renewable energy.

[0088] It should also be noted that the regional collaborative control platform supports switching between grid-connected and islanded operation modes, enabling energy storage devices to respond flexibly according to actual grid conditions. In grid-connected operation, the energy storage device can interact with the grid, participating in peak shaving and frequency regulation. In islanded operation, the energy storage device can independently supply power to loads, ensuring the continuous operation of critical loads. This mode-switching function significantly enhances the reliability and stability of power supply in distribution substations, providing strong support for the safe and efficient operation of the power system.

[0089] Step 4: The lower-level action execution function performs corresponding operations on each energy storage device based on the optimal control command, monitors the response effect data of each energy storage device to the optimal control command, and uploads it to the upper-level matrix collaborative optimization function; the upper-level matrix collaborative optimization function dynamically adjusts the collaborative control strategy based on the response effect data.

[0090] It should be noted that after receiving the optimal control command, the lower-level action execution function will carry out various operations precisely according to the command.

[0091] In this embodiment of the invention, the lower-level action execution function includes: Pre-set local control rules and parameter configurations, which are adapted to the optimal control commands issued by the upper-level matrix collaborative optimization function; Upon receiving the optimal control command, the lower-level action execution function performs precise control on the DC-DC converter, inverter, and bypass switch of the local energy storage module according to preset rules, thereby achieving power regulation, fault isolation, and fault-tolerant operation.

[0092] It should be noted that this precise control is based on a deep understanding and accurate grasp of the characteristics of each component of the local energy storage module. The control of the DC-DC converter can flexibly adjust the charging and discharging power of the energy storage module to meet energy demands under different operating conditions. Precise adjustment of the inverter ensures that the output power quality meets the requirements of the grid or load, guaranteeing a stable power supply. Furthermore, the proper operation of the bypass switch can quickly isolate the faulty part when a fault occurs in the energy storage module, preventing the fault from spreading and affecting the operation of the entire system, while simultaneously achieving fault-tolerant operation and maintaining the system's basic functions.

[0093] Furthermore, the lower-level action execution function also possesses real-time monitoring and feedback capabilities. During the execution of control operations, it continuously collects operating status information of various components of the local energy storage module, such as voltage, current, and temperature. Once an abnormality is detected, it immediately takes corresponding measures to adjust or issue an alarm to ensure the safe and reliable operation of the system. Moreover, this real-time monitoring data is also fed back to the upper-level matrix collaborative optimization function, providing a basis for its subsequent optimization decisions, forming a closed-loop control system, and further improving the efficiency and effectiveness of coordinated control of the entire energy storage device in the distribution area.

[0094] In this embodiment of the invention, the response effect data of the lower-level action execution function of each node to the optimal control command is monitored to obtain the actual power tracking error, voltage support capability, fault switching time, circulating current suppression effect and system stability index. The response data is fed back to the upper-level matrix collaborative optimization function to optimize the collaborative control strategy; When the response data of the system meets the preset safety threshold within multiple consecutive control cycles, or when a mode switching instruction is received from the dispatch center, it is determined that the conditions for the operation state transition are met, and the current control strategy is maintained or adjusted. Otherwise, the closed-loop control process of state awareness, collaborative decision-making, and local execution continues.

[0095] For example, when the system determines that the conditions for a transition to a new operating state are met, if the current control strategy is centralized control mode, the control strategy can be adjusted to distributed control mode based on the upper-level matrix collaborative optimization function and the feedback response data. In distributed control mode, the lower-level action execution functions of each node can make decisions and execute control operations autonomously to a certain extent, reducing reliance on upper-level centralized control and improving the system's flexibility and response speed.

[0096] If the current control strategy is in distributed control mode, it can be switched to centralized control mode when the operating state transition conditions are met. In centralized control mode, the upper-level matrix collaborative optimization function can integrate global information to more accurately coordinate and optimize the operation of the entire energy storage device in the distribution area, thereby enhancing the overall stability and controllability of the system.

[0097] During the continuous execution of the closed-loop control process involving state awareness, collaborative decision-making, and local execution, the lower-level action execution functions continuously collect operational data from each node. For example, for voltage data, the output voltage of each energy storage module is precisely measured, and its fluctuation range and trend are analyzed. For current data, the current magnitude of each line is recorded in detail over different time periods to promptly detect potential overload or short-circuit risks.

[0098] Meanwhile, the upper-level matrix collaborative optimization function dynamically adjusts the collaborative control strategy based on the continuously fed-back response data. For example, when a large actual power point tracking error is detected, the optimal control command is recalculated, and the inverter parameter settings are adjusted to improve the accuracy of power point tracking. When the voltage support capability is insufficient, the charging and discharging strategy of the energy storage module is optimized to enhance the voltage regulation capability.

[0099] It should be noted that the upper-level matrix collaborative optimization function will evaluate and improve the fault switching time and circulating current suppression effect based on real-time data. If the fault switching time is too long, it will analyze which link is experiencing a delay and optimize the logic and algorithm of the lower-level action execution function accordingly. If the circulating current suppression effect is poor, it will adjust the connection method and control parameters between energy storage modules to reduce the impact of circulating current on the system and ensure the stability and reliability of the system.

[0100] Example 2: This embodiment also provides a collaborative control system for energy storage devices in distribution substations based on the aforementioned method, including: The configuration module deploys an edge control unit at the energy storage matrix node of the target distribution area. The edge control unit includes a lower-level state perception function and a lower-level action execution function. The transmission module uses a lower-level state sensing function to collect the operating status data of the energy storage device and uploads the operating status data to the regional collaborative control platform through a preset communication link; the regional collaborative control platform includes an upper-level matrix collaborative optimization function. The control module, with its upper-layer matrix collaborative optimization function, performs multi-node power collaborative calculation and operation mode scheduling decision based on the operating status data, generates the optimal control command for each energy storage node, and sends it to the lower-layer action execution function in the edge control unit. The execution module, with its lower-level action execution function, performs corresponding operations on each energy storage device based on the optimal control command, monitors the response effect data of each energy storage device to the optimal control command, and uploads it to the upper-level matrix collaborative optimization function; the upper-level matrix collaborative optimization function dynamically adjusts the collaborative control strategy based on the response effect data.

[0101] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0102] Example 3: This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as follows. Figure 2 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for coordinating and controlling an energy storage device in a distribution area. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0103] Example 4: The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the embodiments.

[0104] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0105] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0106] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0107] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A power distribution district energy storage device cooperative control method, characterized in that, The method comprises the following steps: Step 1: deploying an edge control unit at a target power distribution substation energy storage matrix node, the edge control unit comprising a lower layer state perception function and a lower layer action execution function; Step 2: collecting the operating state data of the energy storage device using the lower layer state perception function, and uploading the operating state data to the regional collaborative control platform through a preset communication link; the regional collaborative control platform comprises an upper layer matrix collaborative optimization function; Step 3: the upper layer matrix collaborative optimization function performs multi-node power collaborative calculation and operation mode scheduling decision based on the operating state data, generates optimal control instructions for each energy storage node and issues the optimal control instructions to the lower layer action execution function in the edge control unit; Step 4: the lower layer action execution function implements corresponding operations on each energy storage device based on the optimal control instructions, monitors the response effect data of each energy storage device to the optimal control instructions and uploads the response effect data to the upper layer matrix collaborative optimization function; the upper layer matrix collaborative optimization function adjusts the collaborative control strategy based on the response effect data.

2. The power distribution substation energy storage device collaborative control method according to claim 1, wherein: The types of the energy storage matrix nodes include H-bridge cascaded energy storage units, modular multilevel converter energy storage units, DC / DC cascaded energy storage submodules or photovoltaic energy storage hybrid units.

3. The power distribution substation energy storage device collaborative control method according to claim 2, wherein: The operating state data includes single battery voltage, battery pack state of charge, charge and discharge current, inverter output power, bus voltage, circulating current and device health status flag.

4. The power distribution substation energy storage device collaborative control method according to claim 3, wherein: In step 3, the upper layer matrix collaborative optimization function specifically comprises: Constructing optimization sub-targets, the optimization sub-targets including minimizing power distribution substation network loss, minimizing voltage fluctuation, minimizing MMC circulating current, minimizing substation load peak-valley difference and maximizing grid frequency inertia support capability; Assigning weight coefficients with a total of 1 to each sub-target, and linearly weighting and fusing each sub-target based on the respective weight coefficient to form a multi-objective optimization model; Solving the multi-objective optimization model under each constraint condition including grid frequency deviation limit constraint, bus voltage fluctuation range constraint, energy storage state of charge safety interval constraint, converter capacity limitation constraint and load power supply priority constraint, to obtain the optimal active power and optimal reactive power of each energy storage node and map them to the optimal control instructions of each energy storage node.

5. The power distribution substation energy storage device collaborative control method according to claim 4, wherein: The linear weighting and fusion of each sub-target based on the respective weight coefficient to form a multi-objective optimization model, wherein the weight coefficients corresponding to each sub-target can be dynamically adjusted based on the current system operating scenario, and the adjustment method specifically includes: Before each control cycle, the regional collaborative control platform reads the running state data of the current distribution area and external dispatching instructions to determine the operation mode. If the operation mode is grid-connected operation mode, the weights of the sub-targets of minimizing the load peak-valley difference of the distribution area and minimizing the network loss of the distribution area are increased. If the operation mode is island operation mode, the weights of the sub-targets of minimizing the voltage fluctuation and maximizing the frequency inertia support capability of the power grid are increased. In the determined operation mode, the weight distribution is further refined according to the load period type divided according to the time of day. In the peak electricity consumption period, the weight of the sub-target of minimizing the load peak-valley difference of the distribution area is set to the highest, and the weight of the sub-target of minimizing the voltage fluctuation is increased. In the valley electricity consumption period, the weight of the sub-target of minimizing the network loss of the distribution area is increased, and the weight of the sub-target of minimizing the load peak-valley difference of the distribution area is decreased.

6. The energy storage device collaborative control method for a distribution area according to claim 4, characterized in that: In step 3, the optimal control instructions of each energy storage node are obtained by mapping the optimal active power and the optimal reactive power of each energy storage node obtained by solving the multi-objective optimization model, and specifically comprising: First, identify the energy storage matrix node type to which each energy storage node belongs; then, according to the pre-set power-instruction mapping rule library of each energy storage matrix node type, convert the optimal active power value into a charge-discharge power set value or a virtual synchronous generator control parameter, and convert the optimal reactive power value into a reactive power support instruction or an MMC circulating current suppression coefficient; finally, combined with the operation mode and the equipment health state flag of the current distribution area, generate a fault sub-module bypass instruction or an off-grid / grid-connected switching strategy.

7. The energy storage device collaborative control method for a distribution area according to claim 6, characterized in that: In step 4, the response effect data includes actual power tracking error, voltage support capability, fault switching time, circulating current suppression effect, and system stability indicators.

8. An energy storage device collaborative control system for a distribution area based on the method of any one of claims 1-7, comprising a configuration module, a transmission module, a control module, and an execution module, characterized in that: The configuration module deploys an edge control unit in the target distribution area energy storage matrix node, and the edge control unit includes a lower layer state perception function and a lower layer action execution function; The transmission module uses the lower layer state perception function to collect the running state data of the energy storage device, and uploads the running state data to the regional collaborative control platform through a pre-set communication link; the regional collaborative control platform includes an upper layer matrix collaborative optimization function; The control module performs multi-node power collaborative calculation and operation mode scheduling decision based on the running state data by the upper layer matrix collaborative optimization function, generates the optimal control instructions of each energy storage node, and delivers them to the lower layer action execution function in the edge control unit; The execution module implements corresponding operations on each energy storage device based on the optimal control instructions by the lower layer action execution function, monitors the response effect data of each energy storage device to the optimal control instructions, and uploads them to the upper layer matrix collaborative optimization function; the upper layer matrix collaborative optimization function adjusts the collaborative control strategy based on the response effect data.

9. An electronic device, comprising a processor and a storage medium; characterized in that: the storage medium is configured to store instructions; the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the steps of the method according to any one of claims 1-7.

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