A string-oriented energy storage collaborative scheduling method and system for distributed energy

Abstract

The application relates to the technical field of group string energy storage, and discloses a group string energy storage collaborative scheduling method and system for distributed energy, which is characterized in that, based on the characteristics of a group string energy storage system, a group string mirror body is constructed for each energy storage group string unit in a virtual mapping space, and the running state data of each energy storage group string unit is synchronized, a power difference vector is obtained by comparing a global scheduling demand field with the overall state of the virtual mapping space, a collaborative factor is then generated, virtual power adjustment is performed through the collaborative factor, and after the adjustment, the energy storage group string units are subjected to fine and differentiated collaborative scheduling based on the generated target scheduling instruction, so that the efficiency of each energy storage group string unit can be maximized, the overall service life is prolonged, and the power fluctuation caused by the distributed energy can be more accurately balanced.

Description

Technical Field

[0001] This application relates to the field of distributed energy technology, specifically to a string energy storage collaborative scheduling method and system for distributed energy. Background Technology

[0002] With the large-scale integration of distributed energy sources such as photovoltaics and wind power, the volatility and uncertainty of the power grid have significantly increased. String energy storage systems, by decomposing a large-capacity energy storage system into multiple strings of independently controllable energy storage units, provide flexible regulation capabilities. However, existing scheduling methods mostly focus on the overall charging and discharging of the energy storage power station, lacking refined modeling and coordinated control of each string of internal units. New energy power generation is affected by weather and geographical location, exhibiting intermittent output characteristics, while the load side exhibits fluctuating characteristics due to user behavior and electricity consumption periods. The combination of these factors leads to an imbalance between power grid supply and demand. Furthermore, the significant differences in the state of charge and health of each energy storage unit, coupled with fluctuating electricity price signals, make it difficult to achieve rapid and adaptive power allocation, thus failing to maximize the overall operating efficiency of the energy storage system. Summary of the Invention

[0003] The purpose of this application is to provide a string energy storage collaborative scheduling method for distributed energy resources to address the shortcomings of the prior art.

[0004] To achieve the above objectives, this application provides a string energy storage coordinated scheduling method for distributed energy resources, comprising the following steps:

[0005] Acquire distributed energy output data, load demand data, and grid electricity price signals to construct a global dispatch demand field;

[0006] Establish a virtual mapping space for the string energy storage system, and construct a string mirror body for each energy storage string unit in the virtual mapping space;

[0007] Real-time acquisition of operating status data of each energy storage string unit, and synchronization of the operating status data to the corresponding string mirror in the virtual mapping space;

[0008] Based on the global scheduling demand field and the operating status data, compare them with the overall status of the virtual mapping space to obtain a power difference vector;

[0009] Based on the power difference vector, multiple coordination factors are generated in the virtual mapping space, and the coordination factors are assigned to the corresponding string mirror bodies;

[0010] Each of the aforementioned string mirror bodies adjusts its virtual power according to the allocated coordination factor and performs coordinated balancing based on the conflict resolution mechanism to generate a target scheduling instruction;

[0011] The target scheduling command is sent to the corresponding energy storage string unit through the communication link to complete the collaborative scheduling task.

[0012] In a preferred embodiment, the step of establishing the virtual mapping space of the string energy storage system includes:

[0013] Create a virtual mapping space corresponding to the string energy storage system;

[0014] The virtual mapping space is divided into multiple cooperative domains, wherein each cooperative domain contains a fixed number of string mirror bodies;

[0015] Configure an attribute set for the string mirror body, wherein the attribute set includes rated capacity, current state of charge, charge / discharge efficiency, health status, and adjustment weight.

[0016] In a preferred embodiment, the step of comparing the overall state of the global scheduling demand field with that of the virtual mapping space to obtain the power difference vector includes:

[0017] The difference between the total demand power of the global scheduling demand field and the total adjustable power of the string mirror body in the virtual mapping space is calculated as the total power difference value.

[0018] Calculate the matching degree between the power change trend of the global scheduling demand field and the overall response speed of the virtual mapping space to obtain the power dynamic difference vector;

[0019] The power difference value and the power difference vector are fused to obtain the power difference vector.

[0020] In a preferred embodiment, the step of calculating the matching degree between the power change trend of the global scheduling demand field and the overall response speed of the virtual mapping space to obtain the power dynamic difference vector includes:

[0021] Obtain the original power demand curve of the global scheduling demand field;

[0022] The power change rate of the original power demand curve within multiple key time windows is obtained to obtain multiple power change gradients;

[0023] Frequency domain analysis was performed on the original power demand curve to identify multiple dynamic genes in the original power demand curve;

[0024] A dynamic response model including dynamic characteristics is constructed for each of the aforementioned string mirror bodies, and all the aforementioned dynamic response models are aggregated to obtain a full-space dynamic model;

[0025] Based on the full-space dynamic model, a parallel synergistic analysis is performed on the dynamic characteristics of all the string mirror bodies to obtain the overall equivalent response bandwidth of the full-space dynamic model in the frequency domain;

[0026] A multidimensional matching degree analysis is performed based on multiple power change gradients, multiple dynamic genes, multiple dynamic response models, and the overall equivalent response bandwidth to generate a multidimensional power dynamic difference vector.

[0027] In a preferred embodiment, after the step of performing frequency domain analysis on the original power demand curve to identify multiple dynamic genes in the original power demand curve, the method includes:

[0028] Based on multiple power change gradients and multiple dynamic genes, the global scheduling demand field is dynamically divided into different spatiotemporal demand clusters.

[0029] In a preferred embodiment, the step of generating multiple cooperative factors within the virtual mapping space based on the power difference vector includes:

[0030] Obtain the scheduling target;

[0031] The total power adjustment amount, adjustment direction, and control parameters are obtained based on the power difference vector.

[0032] Parallel collaborative allocation information is generated based on the scheduling objective, the total power adjustment amount, and the adjustment direction;

[0033] Based on the collaborative allocation information, calculate the theoretical power allocation value for each of the string mirrors in the virtual mapping space;

[0034] The theoretical power allocation value, the adjustment direction, and the control parameters are encapsulated to obtain the coordination factor for each of the string mirrors.

[0035] In a preferred embodiment, the steps of adjusting virtual power for each of the string mirror bodies according to the allocated coordination factor, and performing cooperative balancing based on a conflict resolution mechanism to generate a target scheduling instruction include:

[0036] The coordination factor is analyzed to obtain the adjustment direction, theoretical power allocation value and control parameters, wherein the control parameters include priority information and time constraint information;

[0037] Each of the aforementioned string mirror bodies performs parallel virtual power adjustments based on the adjustment direction and the theoretical power allocation value, and updates its own virtual power state;

[0038] Within the virtual mapping space, it is detected whether the virtual power adjustment of each group of mirror bodies is coordinated and balanced.

[0039] If the coordination is unbalanced, the power is readjusted and redistributed among the conflicting string mirrors according to the priority information;

[0040] The process involves iteratively performing parallel virtual power adjustments on each of the string mirror bodies according to the adjustment direction and the theoretical power allocation value until the process becomes balanced. If the coordination is unbalanced, the process involves readjusting and redistributing power among the conflicting string mirror bodies according to the priority information.

[0041] Obtain the final virtual power state of each of the string mirror bodies after iteration, and use the final virtual power state as the target scheduling instruction.

[0042] This application also provides a string energy storage collaborative scheduling system for distributed energy, including multiple modules, which are used to implement the string energy storage collaborative scheduling method for distributed energy as described in any of the above claims.

[0043] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0044] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0045] The beneficial effects of this application are as follows:

[0046] Based on the characteristics of string energy storage systems, this application constructs a string mirror for each energy storage string unit in a virtual mapping space and synchronizes the operating status data of each energy storage string unit. By comparing the global scheduling demand field with the overall state of the virtual mapping space, a power difference vector is obtained, and a coordination factor is generated. Virtual power is adjusted through the coordination factor. After the adjustment is completed, the energy storage string units are finely and differentially coordinated and scheduled based on the generated target scheduling command. This maximizes the efficiency of each energy storage string unit, extends the overall lifespan, and more accurately balances the power fluctuations brought by distributed energy. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0048] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0049] Figure 2 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] Please see Figure 1-2 As shown, this application provides a string energy storage collaborative scheduling method for distributed energy resources, including the following steps:

[0052] S1. Acquire distributed energy output data, load demand data, and grid electricity price signals to construct a global dispatch demand field;

[0053] As mentioned above, in order to coordinate string energy storage systems, it is first necessary to construct a global scheduling demand field that can comprehensively reflect the needs of the external environment. The global scheduling demand field is a multi-dimensional, time-varying virtual data space. Specifically, real-time and predicted output data of distributed energy sources such as photovoltaic and wind power can be obtained through data interfaces. Output data refers to real-time and predicted information about the power generation and operating status of each distributed energy source, such as the actual power generation value of the distributed energy source at the current instant, the power generation curve predicted for the next 15 minutes to several hours, and the operating status information of the distributed energy equipment itself. Load demand data of modules such as parks and buildings can be obtained, and time-of-use electricity price or real-time electricity price signals from the power grid can be received. By aligning and fusing these multi-source heterogeneous data in time and space, a global scheduling demand field that is continuous in the time dimension and quantified in the power dimension can be formed.

[0054] S2. Establish a virtual mapping space for the string energy storage system, and construct a string mirror body for each energy storage string unit in the virtual mapping space;

[0055] As mentioned above, both centralized and string energy storage systems are architectural technologies for energy storage systems, essentially large-scale energy storage power stations. The difference lies in their structure: centralized systems consist of a single large energy storage unit capable of directly storing various types of electrical energy, making scheduling relatively complex. String energy storage systems, on the other hand, are structurally divided into multiple small, distributed, and independently controllable string units. Each string unit can independently store or extract electrical energy and has its own management program. To achieve refined scheduling of distributed energy resources, this application adopts a string energy storage system and constructs a fully corresponding digital twin environment, namely a virtual mapping space, for it. This space is deployed on a cloud control platform or edge computing nodes. Within this space, a one-to-one string mirror image is created for each string unit, and each string mirror image is an independent data entity, thereby enabling the centralized one-to-one mapping of distributed string units to the virtual mapping space.

[0056] S3. Collect the operating status data of each energy storage string unit in real time, and synchronize the operating status data to the corresponding string mirror body in the virtual mapping space;

[0057] As described above, the monitoring unit installed on the energy storage string unit can collect real-time operating status data such as voltage, current, temperature, state of charge, and battery health status. These operating status data can be transmitted to the virtual mapping space via a communication network and updated accordingly on the attributes of the string mirror body, thereby ensuring the consistency between the string mirror body and the energy storage string unit data, and providing a data foundation for subsequent dynamic matching and virtual calculation.

[0058] S4. Based on the global scheduling demand field and the operating status data, compare the overall status with the virtual mapping space to obtain the power difference vector;

[0059] As described above, the total demand power of the global scheduling demand field can be compared with the instantaneous total adjustable power capacity of all string mirrors in the virtual mapping space (obtained based on operating status data) to obtain the total power difference. Then, the power change status of the global scheduling demand field can be analyzed to determine whether it matches the average response rate of the string mirrors in the virtual mapping space, thus obtaining the power dynamic difference. The power difference and the power dynamic difference are weighted and fused to obtain the power difference vector. Through this power difference vector, the demand between distributed energy and string energy storage systems can be quantified, which facilitates subsequent adaptive power adjustments based on the demand.

[0060] S5. Based on the power difference vector, generate multiple coordination factors in the virtual mapping space, and assign the coordination factors to the corresponding string mirror bodies;

[0061] As described above, the coordination factor is a virtual instruction packet that drives the adjustment of each string mirror body. Based on the power difference vector, a multi-objective optimization algorithm is used to calculate a theoretical power allocation value for each string mirror body, and the theoretical power allocation value is encapsulated with other coordination control parameters into a coordination factor, which is then assigned to the corresponding string mirror body.

[0062] S6. Each of the aforementioned string mirror bodies adjusts its virtual power according to the allocated coordination factor, and performs coordinated balancing based on the conflict resolution mechanism to generate a target scheduling instruction.

[0063] As described above, each string mirror body performs virtual power adjustment according to the allocated coordination factor, i.e., the simulation is performed within the virtual mapping space. After receiving the coordination factor, each string mirror body first performs power adjustment at the virtual level. Subsequently, the virtual mapping space initiates conflict detection to check whether any string mirror body exceeds its safe operating boundary after adjustment (e.g., exceeding the state of charge limit or power limit), or whether the total power within the coordination domain is unbalanced. If a conflicting voxel (the string mirror body in conflict) is detected, a conflict resolution mechanism is immediately initiated. This conflict resolution mechanism includes power weighted reallocation, priority pruning, and inter-domain mutual assistance mechanisms to achieve coordinated power balance in the virtual space and generate corresponding target scheduling instructions.

[0064] S7. The target scheduling instruction is sent to the corresponding energy storage string unit through the communication link to complete the collaborative scheduling task.

[0065] As described above, the target scheduling instructions generated in the virtual mapping space are accurately sent to each energy storage string unit through a secure communication link. The energy storage string unit executes the target scheduling instructions to complete the actual charging and discharging operations, thereby completing the collaborative scheduling task. Furthermore, the string energy storage system continuously monitors the execution results of the energy storage string units and feeds them back to step S3, thereby forming a closed-loop control, achieving fast and adaptive power allocation, maximizing the overall operating efficiency of the energy storage system, and possessing strong adaptive capabilities.

[0066] In one embodiment, step S2 of establishing the virtual mapping space of the string energy storage system includes:

[0067] S21. Create a virtual mapping space corresponding to the string energy storage system;

[0068] S22. Divide the virtual mapping space into multiple cooperative domains, wherein each cooperative domain contains a fixed number of string mirror bodies;

[0069] S23. Configure an attribute set for the string mirror body, wherein the attribute set includes rated capacity, current state of charge, charge / discharge efficiency, health status, and adjustment weight.

[0070] As described in steps S21-S23 above, each of the string mirror bodies is configured with an attribute set, which includes, but is not limited to: mirror body identifier, rated capacity, current state of charge, charge and discharge efficiency, health status, maximum charge and discharge power limit, and a dynamically calculated adjustment weight coefficient. This adjustment weight coefficient is used to perform differentiated resource allocation based on the real-time status of the string energy storage system. This transforms the original string energy storage system into a computable and analyzable digital model, with each string mirror representing an energy storage string unit. This allows for precise perception of the operating status of each smallest unit during subsequent power scheduling, avoiding the low operating efficiency caused by treating the energy storage system as a whole and using average value scheduling in existing technologies. Multiple string mirrors can divide the virtual mapping space into multiple cooperative domains based on their electrical connections or geographical locations for regionalized cooperative computation. Furthermore, after the cooperative domains are divided, when a new energy storage string unit needs to be added, only the string mirror needs to be added to the corresponding cooperative domain. If a local fault occurs in a cooperative domain, the fault can be effectively isolated to prevent it from affecting the entire virtual mapping space. Multiple cooperative domains are then integrated into a single virtual mapping space, which effectively reduces the computational complexity of the scheduling algorithm while achieving centralized optimization control.

[0071] In one embodiment, step S4, which compares the global scheduling demand field and the operating status data with the overall state of the virtual mapping space to obtain the power difference vector, includes:

[0072] S41. Calculate the difference between the total demand power of the global scheduling demand field and the total adjustable power of the string mirror body in the virtual mapping space, and use it as the total power difference value.

[0073] S42. Calculate the matching degree between the power change trend of the global scheduling demand field and the overall response speed of the virtual mapping space to obtain the power dynamic difference vector;

[0074] S43. The total power difference value and the power difference vector are fused to obtain the power difference vector.

[0075] As described in steps S41-S43 above, firstly, the total power demand value within the current scheduling cycle is extracted from the global scheduling demand field. This value is obtained based on the net power demand scalar of distributed energy output data, load demand data, and grid electricity price signals. Then, the real-time adjustable power of all string mirror bodies is queried in parallel, and the real-time adjustable power is summed to obtain the total adjustable power. The difference between the two is calculated to obtain the total power difference value. If the total power difference value is positive, it indicates that discharging is required; if it is negative, it indicates that charging is required. Therefore, the charging / discharging direction can be determined based on the total power difference value. By calculating the power change trend of the global scheduling demand field and the matching degree of the overall response speed of the virtual mapping space, the calculation method... The formula includes, but is not limited to, frequency domain matching degree analysis, gradient capability compliance test analysis, and response timing deviation. This can transform scheduling requirements from static capacity matching to dynamic capability adaptation, thereby predicting whether instability will occur due to insufficient response during dynamic adjustment. After generating the power dynamic difference vector, it is normalized with the total power difference value and fused as two components to obtain the power difference vector. This power difference vector contains information on the total power difference and power dynamic difference. The power difference vector can be classified into levels, and a power difference vector with level labels can be output, which is convenient for subsequent collaborative scheduling to understand the scale and urgency of the target task in advance.

[0076] In one embodiment, step S42, which calculates the matching degree between the power change trend of the global scheduling demand field and the overall response speed of the virtual mapping space to obtain the power dynamic difference vector, includes:

[0077] S421. Obtain the original power demand curve of the global scheduling demand field;

[0078] S422. Obtain the power change rate of the original power demand curve within multiple key time windows to obtain multiple power change gradients;

[0079] S423. Perform frequency domain analysis on the original power demand curve to identify multiple dynamic genes in the original power demand curve;

[0080] S424. Construct a dynamic response model including dynamic characteristics for each of the said string mirror bodies, and aggregate all the said dynamic response models to obtain a full-space dynamic model;

[0081] S425. Perform parallel synergistic analysis on the dynamic characteristics of all the string mirror bodies according to the full-space dynamic model to obtain the overall equivalent response bandwidth of the full-space dynamic model in the frequency domain.

[0082] S426. Based on multiple power change gradients, multiple dynamic genes, multiple dynamic response models, and the overall equivalent response bandwidth, perform multi-dimensional matching degree analysis to generate a multi-dimensional power dynamic difference vector.

[0083] As described in steps S421-S426 above, by obtaining the original power demand curve, we can understand the sum of all fluctuation information that needs to be addressed. By taking the first derivative of the original power demand curve, we can obtain its power change rate. By selecting the power change rate within a key time window, such as the next 1 minute, 5 minutes, 15 minutes, etc., we can divide the demand into multiple power change rates and obtain multiple power change gradients. These gradients can reflect the steepness of the original power demand curve.

[0084] By using Fast Fourier Transform or Wavelet Analysis, dynamic genes in the original power demand curve can be identified. Dynamic genes refer to the main frequency components in the power demand fluctuations of the original power demand curve. For example, it can be identified whether the high-frequency fluctuations are caused by the passing of photovoltaic clouds or the low-frequency trends are caused by changes in weather systems. A dynamic response model including dynamic characteristics is constructed for each string mirror body. The dynamic characteristics include, but are not limited to, response delay, power ramp rate, and state dependence. The power ramp rate refers to the maximum rate at which power can increase or decrease per unit time, and the state dependence refers to the relationship between its response capability and the current state of charge and temperature (e.g., stronger discharge capability at a high state of charge).

[0085] By treating all the aforementioned dynamic response models as a single full-space dynamic model and comprehensively analyzing the response models of all string mirrors, the overall equivalent response bandwidth of the full-space dynamic model in response to different frequency fluctuations is calculated. This overall equivalent response bandwidth reveals the string energy storage system's ability to keep up with multiple frequency fluctuations. Specifically, comparing multiple dynamic parameters with the overall equivalent response bandwidth identifies frequency fluctuations that the full-space dynamic model struggles to effectively track. Furthermore, comparing multiple power change gradients with the maximum collective power ramp rate provided by the full-space dynamic model calculates the necessary steps to keep up with the steepest power fluctuations. For steep climbing requirements, the full-space dynamic model needs to reserve dynamic power margin. The response delays of multiple dynamic response models are compared with the response timing of the global scheduling demand field to obtain the response timing deviation. The matching results of the above dimensions are integrated into a power dynamic difference vector. This power dynamic difference vector includes, but is not limited to, high-frequency mismatch (quantification of the fluctuation energy of the non-tracking frequency band), gradient risk value (representing the risk level under the worst climbing conditions), response timing deviation (the average time delay of the predicted overall response action relative to the ideal curve), and dynamic margin requirement (the average time delay of the predicted overall response action relative to the ideal curve).

[0086] By analyzing the dynamic characteristics of the global scheduling demand field and the dynamic capabilities of the virtual mapping space through multi-dimensional matching, the power dynamic difference vector can contain matching information in multiple dimensions, which facilitates subsequent power dynamic balancing based on the power dynamic difference vector and ensures the transient stability of the string energy storage system.

[0087] The steps that enable the full-space dynamic model to provide the maximum collective power ramp rate include:

[0088] Determine the instantaneous available ramp rate for each group of mirror images;

[0089] The instantaneous available gradient rates are aggregated to obtain the ideal gradient rate;

[0090] The ideal ramp rate is corrected by obtaining the collaborative efficiency factor, and the maximum collective power ramp rate is obtained.

[0091] In one embodiment, after step S423 of performing frequency domain analysis on the original power demand curve to identify multiple dynamic genes in the original power demand curve, the method includes:

[0092] S4231. Based on multiple power change gradients and multiple dynamic genes, the global scheduling demand field is dynamically divided into different spatiotemporal demand clusters. These clusters include, but are not limited to, steep climbing clusters (requiring fast, high-power response), high-frequency oscillation clusters (requiring sensitive, low-power frequent throughput), and smooth transition clusters (requiring stable, continuous power support). By dynamically dividing the global scheduling demand field, when performing multi-dimensional matching analysis of the dynamic characteristics of the global scheduling demand field and the dynamic capabilities of the virtual mapping space, the global scheduling demand field can be efficiently identified based on different spatiotemporal demand clusters, enabling targeted matching. For example, when performing frequency domain matching analysis, special attention is paid to whether the dynamic genes related to the high-frequency oscillation cluster exceed the bandwidth; when performing gradient capability compliance testing, the power gradient corresponding to the steep climbing cluster is monitored to ensure it is within the overall climbing capability, thereby improving the accuracy of multi-dimensional matching.

[0093] In one embodiment, step S5, which generates multiple cooperative factors within the virtual mapping space based on the power difference vector, includes:

[0094] S51. Obtain the scheduling target;

[0095] S52. Obtain the total power adjustment amount, adjustment direction and control parameters based on the power difference vector;

[0096] S53. Generate parallel collaborative allocation information based on the scheduling target, the total power adjustment amount, and the adjustment direction;

[0097] S54. Calculate the theoretical power allocation value for each of the string mirrors in the virtual mapping space based on the cooperative allocation information;

[0098] S55. The theoretical power allocation value, the adjustment direction, and the control parameters are encapsulated to obtain the coordination factor of each string mirror body.

[0099] As described in steps S51-S55 above, the scheduling objective refers to the goal that the global scheduling demand site wants to achieve. For example, to minimize operating costs, the scheduling objective is to prioritize discharging when the electricity price is high and prioritizing charging when the electricity price is low. Or, to delay the overall aging of the energy storage system and extend its service life, the scheduling objective is to minimize lifespan loss or achieve a balanced health state. Alternatively, it could be to maximize the overall energy conversion of the energy storage system, in which case the corresponding scheduling objective is to minimize losses, etc. Parallel collaborative allocation information is generated based on the scheduling objective and the total power adjustment amount, adjustment direction, and control parameters corresponding to the power difference vector. This allows for the generation of corresponding collaborative allocation information based on a clear scheduling objective, enabling the scheduling process to be carried out in an optimal manner. Then, the theoretical power allocation value of each string mirror body is calculated based on the collaborative allocation information. The collaborative allocation information can be dynamic weight information, which can assign corresponding dynamic weights to each string mirror body according to its attribute set, thereby ensuring that each string mirror body is allocated the most suitable theoretical power allocation value, improving resource utilization efficiency, and balancing the aging rate among strings. The above information is encapsulated to obtain the coordination factor of each string mirror body. This coordination factor contains all the information for power coordination scheduling, providing accurate input for subsequent virtual adjustment and conflict resolution, and solving the problem of reduced control accuracy caused by information simplification and omission in traditional systems.

[0100] Even better, the spatiotemporal demand cluster can be applied to the step of constructing the cooperating factor. Specifically, when the current cluster is in a high-frequency oscillation, the cooperating allocation information will tend to select those string mirrors with low response latency and high adjustment accuracy, and generate high-priority, short-time-constrained cooperating factors for them. Conversely, for a smooth transition cluster, string mirrors with large capacity and low cost may be selected, and more relaxed time constraints may be adopted.

[0101] In one embodiment, step S6, in which each of the string mirror bodies performs virtual power adjustment according to the allocated coordination factor and performs cooperative balancing based on a conflict resolution mechanism to generate a target scheduling instruction, includes:

[0102] S61. Analyze the coordination factor to obtain the adjustment direction, theoretical power allocation value and control parameters, wherein the control parameters include priority information and time constraint information;

[0103] S62. Each of the string mirror bodies performs parallel virtual power adjustment according to the adjustment direction and the theoretical power allocation value, and updates its own virtual power state.

[0104] S63. Within the virtual mapping space, detect whether there are conflicting voxels during the parallel virtual power adjustment process of each group of mirror bodies.

[0105] S64. If there are conflicting voxels, the power is readjusted and allocated among the conflicting string mirrors according to the priority information.

[0106] S65. Iteratively execute the step of adjusting the virtual power of each of the string mirror bodies in parallel according to the adjustment direction and the theoretical power allocation value until there are conflicting voxels, and readjusting and allocating the power among the conflicting string mirror bodies according to the priority information, until there are no conflicting voxels in the process of parallel virtual power adjustment of each string mirror body.

[0107] S66. Obtain the final virtual power state of each of the string mirror bodies after iteration, and generate a target scheduling instruction based on the final virtual power state.

[0108] As described in steps S61-S66 above, the power coordination simulation of the energy storage system is completed through parallel virtual adjustment and conflict detection of the string mirror. Potential conflicts (such as overcharging, over-discharging, and power exceeding limits) that may exist in the energy storage system are discovered and exposed in advance in the virtual mapping space, avoiding the direct issuance of instructions with potential risks to the energy storage system and ensuring the safety of the energy storage system. When the adjustable power and available capacity of the energy storage system are insufficient to meet all demands at the same time, the core demand information can be intelligently and selectively guaranteed according to priority information, ensuring the realization of the overall scheduling goal of the energy storage system. By finding the optimal global equilibrium point through multiple iterations, the final virtual power state not only meets the ideal state of each energy storage string unit, but also coordinates the realization of the overall goal of the energy storage system, so that the generated target scheduling instructions can be directly applied to the energy storage system, realizing adaptive coordinated equilibrium scheduling of power and improving the reliability and robustness of the energy storage system operation.

[0109] Even better, the spatiotemporal demand clusters can be used as the priority basis for conflict arbitration. When the energy storage system resources are insufficient to meet all demands at the same time, the conflict resolution mechanism can prioritize the scheduling tasks corresponding to high-priority demand clusters. For example, it can ensure the execution of instructions for steep climbing clusters, while appropriately pruning tasks for smooth transition clusters.

[0110] This application also provides a string energy storage collaborative scheduling system for distributed energy resources, including the string energy storage collaborative scheduling method for distributed energy resources described in any of the above claims.

[0111] like Figure 2 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 2 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all the data required for the process of a string energy storage coordinated dispatch method for distributed energy resources. The network interface is used for communication with external terminals via a network connection. The computer program is executed by the processor to implement the string energy storage coordinated dispatch method for distributed energy resources.

[0112] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0113] An embodiment of this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described string energy storage collaborative scheduling methods for distributed energy resources.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0115] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0116] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for coordinated scheduling of string energy storage for distributed energy resources, characterized in that, Includes the following steps: Acquire distributed energy output data, load demand data, and grid electricity price signals to construct a global dispatch demand field; Establish a virtual mapping space for the string energy storage system, and construct a string mirror body for each energy storage string unit in the virtual mapping space; Real-time acquisition of operating status data of each energy storage string unit, and synchronization of the operating status data to the corresponding string mirror in the virtual mapping space; Based on the global scheduling demand field and the operating status data, compare them with the overall status of the virtual mapping space to obtain a power difference vector; Based on the power difference vector, multiple coordination factors are generated in the virtual mapping space, and the coordination factors are assigned to the corresponding string mirror bodies; Each of the aforementioned string mirror bodies adjusts its virtual power according to the allocated coordination factor and performs coordinated balancing based on the conflict resolution mechanism to generate a target scheduling instruction; The target scheduling command is sent to the corresponding energy storage string unit through the communication link to complete the coordinated scheduling task; The step of comparing the overall state of the global scheduling demand field with that of the virtual mapping space to obtain the power difference vector includes: The difference between the total demand power of the global scheduling demand field and the total adjustable power of the string mirror body in the virtual mapping space is calculated as the total power difference value. Calculate the matching degree between the power change trend of the global scheduling demand field and the overall response speed of the virtual mapping space to obtain the power dynamic difference vector; The power difference value and the power difference vector are fused to obtain the power difference vector; The step of calculating the matching degree between the power change trend of the global scheduling demand field and the overall response speed of the virtual mapping space to obtain the power dynamic difference vector includes: Obtain the original power demand curve of the global scheduling demand field; The power change rate of the original power demand curve within multiple key time windows is obtained to obtain multiple power change gradients; Frequency domain analysis was performed on the original power demand curve to identify multiple dynamic genes in the original power demand curve; A dynamic response model including dynamic characteristics is constructed for each of the aforementioned string mirror bodies, and all the aforementioned dynamic response models are aggregated to obtain a full-space dynamic model; Based on the full-space dynamic model, a parallel synergistic analysis is performed on the dynamic characteristics of all the string mirror bodies to obtain the overall equivalent response bandwidth of the full-space dynamic model in the frequency domain; A multidimensional matching degree analysis is performed based on multiple power change gradients, multiple dynamic genes, multiple dynamic response models, and the overall equivalent response bandwidth to generate a multidimensional power dynamic difference vector.

2. The string energy storage collaborative scheduling method for distributed energy resources according to claim 1, characterized in that: The step of establishing the virtual mapping space of the string energy storage system includes: Create a virtual mapping space corresponding to the string energy storage system; The virtual mapping space is divided into multiple cooperative domains, wherein each cooperative domain contains a fixed number of string mirror bodies; Configure an attribute set for the string mirror body, wherein the attribute set includes rated capacity, current state of charge, charge / discharge efficiency, health status, and adjustment weight.

3. The string energy storage collaborative scheduling method for distributed energy resources according to claim 1, characterized in that: Following the step of performing frequency domain analysis on the original power demand curve to identify multiple dynamic genes in the original power demand curve, the method includes: Based on multiple power change gradients and multiple dynamic genes, the global scheduling demand field is dynamically divided into different spatiotemporal demand clusters.

4. The string energy storage collaborative scheduling method for distributed energy resources according to claim 1, characterized in that: The step of generating multiple cooperative factors in the virtual mapping space based on the power difference vector includes: Obtain the scheduling target; The total power adjustment amount, adjustment direction, and control parameters are obtained based on the power difference vector. Parallel collaborative allocation information is generated based on the scheduling objective, the total power adjustment amount, and the adjustment direction; Based on the collaborative allocation information, calculate the theoretical power allocation value for each of the string mirrors in the virtual mapping space; The theoretical power allocation value, the adjustment direction, and the control parameters are encapsulated to obtain the coordination factor for each of the string mirrors.

5. The string energy storage collaborative scheduling method for distributed energy resources according to claim 1, characterized in that: The steps of adjusting virtual power for each of the aforementioned string mirror bodies according to the allocated coordination factor, and performing coordinated balancing based on a conflict resolution mechanism to generate a target scheduling instruction include: The coordination factor is analyzed to obtain the adjustment direction, theoretical power allocation value and control parameters, wherein the control parameters include priority information and time constraint information; Each of the aforementioned string mirror bodies performs parallel virtual power adjustments based on the adjustment direction and the theoretical power allocation value, and updates its own virtual power state; Within the virtual mapping space, it is detected whether there are conflicting voxels during the parallel virtual power adjustment process of each group of mirror images; If conflicting voxels exist, power is readjusted and redistributed among the conflicting string mirrors according to the priority information. The process iteratively executes the steps of adjusting the virtual power of each string mirror body in parallel according to the adjustment direction and the theoretical power allocation value until there are no conflicting voxels in the process of parallel virtual power adjustment of each string mirror body. If there are conflicting voxels, the power is readjusted and allocated among the conflicting string mirror bodies according to the priority information. Obtain the final virtual power state of each of the string mirror bodies after iteration, and generate a target scheduling instruction based on the final virtual power state.

6. A string energy storage collaborative dispatch system for distributed energy resources, characterized in that: It includes multiple modules, which are used to implement the string energy storage collaborative scheduling method for distributed energy as described in any one of claims 1-5.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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