Cooperative scheduling method and system for distributed energy storage system

Abstract

The invention provides a cooperative scheduling method and system for a distributed energy storage system, and belongs to the technical field of energy storage systems, and the method specifically comprises the steps: taking an adaptive type of energy storage adjustment of a power distribution area of an energy storage adjustment combination as a basis, and when determining that a cooperative scheduling processing strategy in the energy storage adjustment combination needs to be optimized, carrying out optimization processing on the cooperative scheduling processing strategy; according to the method, the cooperative adjustment processing strategy of the preset adaptive type of power distribution transformer area is determined according to the coincidence condition of the power distribution transformer area in the energy storage adjustment combination and the energy storage adjustment demand time period of the specific type of power distribution transformer area, and the reliability degree of energy storage adjustment is improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system technology, and in particular relates to a collaborative scheduling method and system for distributed energy storage systems. Background Technology

[0002] To improve the operational reliability of the distribution network, power grid companies have set up multiple energy storage systems in different distribution substations within the distribution network to meet the energy storage regulation needs of the distribution network. In the invention patent application CN202510618362.5, "A Multi-Objective Distributed Energy Storage Multi-Objective Scheduling Optimization Method at the Substation Level," an upper-level optimization model is used to determine the access location and quantity of distributed energy storage. Based on the access location and quantity of distributed energy storage determined by the upper-level optimization model, the lower-level optimization model is solved to obtain the optimal output value of energy storage in each time period. This reduces the operating cost of the distribution network, increases the distributed power absorption rate, increases the voltage qualification rate, and reduces the network loss of the distribution network.

[0003] In the coordinated scheduling of distributed energy storage systems, existing technical solutions neglect to classify energy storage systems into the same energy storage system regulation combination based on the line loss of energy storage regulation between different distribution substations. Specifically, using a single distribution substation for energy storage regulation cannot guarantee the reliability of energy storage regulation in the distribution substation. Therefore, if energy storage systems cannot be classified into the same energy storage system regulation combination for coordinated scheduling and control, there may be insufficient remaining energy storage capacity in the distribution substation during the energy storage regulation demand period, making it impossible to respond to energy storage regulation demand in a timely and effective manner.

[0004] To address the aforementioned technical problems, this application provides a collaborative scheduling method and system for distributed energy storage systems. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a cooperative scheduling method for distributed energy storage systems, which includes: S1 determines the adaptation type of energy storage regulation of the distribution station based on the energy storage regulation data of the energy storage device of the distribution station, and determines the energy storage device of the distribution station to construct the energy storage regulation combination of the distribution station based on the adaptation type of the distribution station and the line loss data of the regulation processing of the energy storage system between the distribution stations. Based on the adaptation type of energy storage regulation of the distribution area of ​​the energy storage regulation combination, S2 determines the collaborative scheduling processing strategy of the distribution area of ​​the energy storage regulation combination when it is determined that the collaborative regulation processing strategy of the distribution area of ​​the energy storage regulation combination needs to be optimized, taking into account the overlap of the energy storage regulation demand time period of the distribution area of ​​the energy storage regulation combination with that of a specific type of distribution area.

[0006] The beneficial effects of this invention are as follows: Based on the matching type of distribution substations and the line loss data of the energy storage system regulation between distribution substations, the energy storage devices for the distribution substations to construct the energy storage regulation combination are determined. Thus, the energy storage devices for the distribution substations to construct the energy storage regulation combination are determined from the energy storage regulation requirements caused by the matching type of the distribution substations. Furthermore, by combining the line loss data, the technical problem of excessive line loss during the energy storage regulation process is avoided.

[0007] By determining the pre-set coordinated regulation strategy for distribution substations of the energy storage regulation combination and those of specific types, based on the overlap of their energy storage regulation demand periods, this approach ensures that the technical problem of poor reliability in energy storage regulation arises when the energy storage regulation demand periods of distribution substations with extremely high regulation frequency overlap excessively with those of distribution substations in the energy storage regulation combination, leading to frequent regulation processing in distribution substations with lower regulation frequency. Furthermore, by optimizing and controlling the pre-set coordinated regulation strategy for distribution substations of the appropriate type, the reliability of energy storage regulation is further improved.

[0008] Furthermore, the energy storage regulation data includes the distribution data of the energy storage devices in the distribution area during historical energy storage regulation periods.

[0009] Furthermore, the method for determining the adaptation type of the energy storage regulation of the distribution radio station area is as follows: Based on the energy storage regulation data of the energy storage devices in the distribution substation, the distribution data of the energy storage regulation time period in the distribution substation is determined; Based on the distribution data, the dates on which the distribution area has an energy storage regulation period are determined, and these dates are used as the energy storage regulation dates; Based on the distribution data of the energy storage regulation dates, the adaptation type of energy storage regulation for the distribution radio area is determined.

[0010] Furthermore, the method for determining the pre-defined collaborative adjustment processing strategy for the distribution radio area of ​​the adaptation type is as follows: Based on the overlap between the energy storage regulation demand periods of the distribution substations in the energy storage regulation combination and those of a specific type of distribution substation, the distribution substations in the energy storage regulation combination that simultaneously belong to the energy storage regulation demand periods of the specific type of distribution substation are identified, and these are taken as the overlapping distribution substations in the energy storage regulation demand periods. Based on the overlapping distribution area data of the energy storage regulation demand period of the specific type of distribution area, determine the energy storage regulation demand period data that overlaps with different distribution areas in the most recent preset time period. Based on the energy storage regulation demand time period data of a specific type of distribution station in the energy storage regulation combination, as well as the energy storage regulation demand time period data that overlaps with different distribution stations in the most recent preset time period, a pre-set coordinated regulation processing strategy for the distribution stations of the preset adaptation type in the energy storage regulation combination is determined.

[0011] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described cooperative scheduling method for a distributed energy storage system when running the computer program.

[0012] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0014] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0015] Figure 1 This is a flowchart of a collaborative scheduling method for distributed energy storage systems; Figure 2 This is a flowchart illustrating the method for determining the adaptation type of energy storage regulation in a distribution substation. Figure 3 This is a flowchart illustrating the method for determining the energy storage devices in a distribution substation that forms a combined energy storage and regulation system. Detailed Implementation

[0016] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0017] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc. Example

[0018] To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a collaborative scheduling method for distributed energy storage systems is provided, specifically including: S1 determines the adaptation type of energy storage regulation of the distribution station based on the energy storage regulation data of the energy storage device of the distribution station, and determines the energy storage device of the distribution station to construct the energy storage regulation combination of the distribution station based on the adaptation type of the distribution station and the line loss data of the regulation processing of the energy storage system between the distribution stations. Based on the adaptation type of energy storage regulation of the distribution area of ​​the energy storage regulation combination, S2 determines the collaborative scheduling processing strategy of the distribution area of ​​the energy storage regulation combination when it is determined that the collaborative regulation processing strategy of the distribution area of ​​the energy storage regulation combination needs to be optimized, taking into account the overlap of the energy storage regulation demand time period of the distribution area of ​​the energy storage regulation combination with that of a specific type of distribution area.

[0019] Furthermore, the energy storage regulation data includes the distribution data of the energy storage devices in the distribution area during historical energy storage regulation periods.

[0020] Specifically, such as Figure 2 As shown, the method for determining the adaptation type of the energy storage regulation in the distribution substation is as follows: Based on the energy storage regulation data of the energy storage devices in the distribution substation, the distribution data of the energy storage regulation time period in the distribution substation is determined; Based on the distribution data, the dates on which the distribution area has an energy storage regulation period are determined, and these dates are used as the energy storage regulation dates; Based on the distribution data of the energy storage regulation dates, the adaptation type of energy storage regulation for the distribution radio area is determined.

[0021] Specifically, based on the distribution data of the energy storage regulation dates, the adaptation type of the energy storage regulation for the distribution area is determined, including: When the proportion of the number of energy storage adjustment dates within the most recent preset time period is greater than the preset proportion threshold, the adaptation type of the energy storage adjustment of the distribution area is determined to be the adaptation deviation type. When the proportion of the number of energy storage adjustment dates in the most recent preset time period is not greater than a preset proportion threshold, and if the proportion of the number of the number of energy storage adjustment dates in the most recent preset time period is within a preset range, then the energy storage adjustment adaptation type of the distribution radio station is determined to be a general adaptation type. If the percentage of energy storage adjustment dates within the most recent preset time period is not within a preset range, then the energy storage adjustment adaptation type of the distribution area is determined to be a reliable adaptation type.

[0022] In one possible specific embodiment, when the proportion of energy storage adjustment dates in the most recent month is greater than 0.4, the energy storage adjustment adaptation type of the distribution station is determined to be the adaptation deviation type; when the proportion of energy storage adjustment dates in the most recent month is between 0.1 and 0.4, the energy storage adjustment adaptation type of the distribution station is determined to be the general adaptation type; otherwise, it belongs to the reliable adaptation type.

[0023] This embodiment aims to provide a quantitative evaluation method for determining the degree of matching or "adaptability" between the operating mode of the energy storage system and the actual needs of a distribution substation equipped with energy storage devices. Its core logic is: by statistically analyzing the start-up and adjustment frequency of energy storage over historical periods, the abstract "adaptability" is transformed into a specific "adjustment daily percentage," and based on this percentage, adaptability is divided into three levels: "deviation," "average," and "reliable," thereby providing a basis for decision-making regarding energy storage system operation optimization, capacity planning, or control strategy adjustment.

[0024] Step 1: Data Preparation and Core Indicator Extraction Keyword explanation: Energy storage regulation data: refers to the historical operation log of the energy storage device, including the start time, end time, regulation power, and regulation direction (charge / discharge) of each charge / discharge regulation.

[0025] Energy storage regulation period: The time segment from the above data in which the energy storage device actually performs charging or discharging power regulation.

[0026] Energy storage regulation date: refers to the calendar day in historical records where at least one energy storage regulation has occurred. A date is counted as long as there has been at least one regulation.

[0027] Percentage of Dates: The percentage of days in a given statistical period (e.g., the most recent month) that are used for energy storage regulation.

[0028] Focus on activity rather than details: This assessment focuses on whether energy storage is "needed," i.e., whether it is utilized. Adjustment details (such as charging / discharging rates and power output) are issues for subsequent optimization, while "utilization" forms the basis for evaluating the rationality of the current configuration.

[0029] Using "day" as the statistical unit: Aggregating continuous regulation behavior to the "day" dimension can effectively smooth out the interference from frequent start-stop cycles within a single day, highlighting its long-term dispatch pattern. This reflects the macroscopic matching relationship between the load / power characteristics of the distribution area and the energy storage response.

[0030] Significance: It extracts massive amounts of time-series operational data into a statistical indicator with clear business implications—the adjustment frequency (call rate)—laying the foundation for hierarchical evaluation.

[0031] Example: Analyze the operating data of an energy storage device in a distribution substation over the past 30 days (preset time period). Statistics show that the energy storage device was activated and adjusted at least once on 15 of these 30 days. Therefore, the number of "energy storage adjustment dates" is 15 days, and the "percentage of dates" is 15 / 30 = 0.5 (50%).

[0032] Step Two: Three-level classification based on frequency adaptation: Hierarchical logic and threshold settings: The tiered system is based on a core principle: the ideal frequency of energy storage deployment should be within a reasonable range. Deployment that is too frequent or too infrequent indicates a compatibility issue.

[0033] Preset threshold: High frequency threshold: 0.4 (40%). Exceeding this value means the calls are very frequent.

[0034] Reasonable range: [0.1, 0.4] (i.e., 10% to 40%). Within this range, the call frequency is considered moderate.

[0035] Low frequency threshold: <0.1 (10%). Values ​​below this indicate infrequent calls.

[0036] Hierarchical rules and their business implications: Adaptation deviation type Condition: The percentage of dates is greater than 0.4. Business Interpretation and Significance: The energy storage device was called up on more than 40% of the days, indicating that the distribution area has a very high dependence on energy storage or very frequent demand.

[0037] General compatibility type: Condition: 0.1 ≤ percentage of dates ≤ 0.4 Business Interpretation and Significance: The frequency of energy storage usage is within a reasonable range.

[0038] Reliable adapter type: Condition: The percentage of dates is less than 0.1%. Example (continued): The percentage of dates = 0.5 > 0.4.

[0039] Tiered decision: The adaptation type of energy storage regulation in this distribution area is determined to be "adaptation deviation type".

[0040] This embodiment transforms the complex system matching problem into a clearly defined, calculable, assessable, and actionable gradation through a cleverly defined statistical indicator, making it an effective management tool for promoting the large-scale and refined application of energy storage.

[0041] Specifically, such as Figure 3 As shown, the method for determining the energy storage device in the distribution substation of the energy storage regulation combination is as follows: Based on the line loss data of the energy storage system regulation and processing between other distribution substations and the distribution substation, the line loss rate between the other distribution substations and the distribution substation is determined. Based on the line loss rate, other distribution stations with line loss rates less than a preset threshold are identified and used as basic adaptation stations. Based on the adaptation type of different adaptation zones and the adaptation type of the distribution zone, determine whether the energy storage device of the adaptation zone is an energy storage device for constructing the energy storage regulation combination of the distribution zone.

[0042] It is understood that the line loss rate between the other distribution substations and the distribution substation is determined based on the average line loss rate of the energy storage devices in the other distribution substations during the energy storage regulation process of the distribution substation.

[0043] It should be noted that the adapted station area refers to other distribution stations with a line loss rate within a preset range. In a possible specific embodiment, when the line loss rate is less than 5%, the other distribution stations are determined to be adapted stations.

[0044] Specifically, based on the adaptation type of different adaptation zones and the adaptation type of the distribution zone, it is determined whether the energy storage device of the adaptation zone is an energy storage device for constructing the energy storage regulation combination of the distribution zone, specifically including: When the adaptation type of the distribution station is the adaptation deviation type, then all the energy storage devices of the adaptation stations are used as energy storage devices to construct the energy storage regulation combination of the distribution station.

[0045] When the adaptation type of the distribution station is not an adaptation deviation type, energy storage devices in the adaptation stations with a line loss rate less than a preset line loss rate threshold (e.g., energy storage devices in adaptation stations with a line loss rate less than 3%) are all used as energy storage devices to construct the energy storage regulation combination of the distribution station. Furthermore, based on the adaptation type of the adaptation stations with a line loss rate less than the preset line loss rate threshold, it is determined whether energy storage devices in other distribution stations with a line loss rate within a preset range are energy storage devices to construct the energy storage regulation combination of the distribution station.

[0046] It is understood that, based on the adaptation type of the adaptation area with a line loss rate less than a preset line loss rate threshold with respect to the distribution area, it is determined whether the energy storage device of other distribution areas with line loss rates within a preset range is an energy storage device for constructing the energy storage regulation combination of the distribution area. Specifically, this includes: When there is a distribution station with a reliable adaptation type among the adaptation stations with a line loss rate less than a preset line loss rate threshold, the energy storage devices of other distribution stations with line loss rates within the preset range are not energy storage devices for constructing the energy storage regulation combination of the distribution station. If there is no distribution station with a reliable adaptation type among the adaptation stations with a line loss rate less than a preset line loss rate threshold, then the energy storage devices of other distribution stations with a reliable adaptation type and a line loss rate within a preset range will be used as the energy storage devices for constructing the energy storage regulation combination of the distribution station.

[0047] This embodiment aims to address a key issue in achieving cross-regional energy storage resource sharing and coordinated regulation in distribution networks: how to intelligently select available energy storage resources from neighboring regions for a specific "demand area" to construct an efficient and economical "virtual energy storage regulation combination." This method employs a dual filtering logic: first, it screens technically feasible candidate resources based on the physical characteristics of the power grid (line loss rate); second, it optimizes the selection based on operational economics and resource status (fitness type), prioritizing the use of idle or inefficient resources while protecting high-efficiency operating resources.

[0048] Step 1: Preliminary screening based on power grid physical constraints (basic compatible distribution areas): Keyword explanation: Line loss rate: refers to the percentage of power loss on the tie line when energy storage devices from other distribution areas transmit (discharge support) or receive (charge consumption) power to this "demand distribution area". The calculation uses the average line loss rate during historical coordinated regulation processes to reflect typical transmission efficiency.

[0049] Preset threshold (e.g., 5%): This is a technically feasible boundary. If the line loss rate exceeds this value, it means that the power transmission efficiency is too low (the loss is too great), and the economics of cross-regional regulation will become extremely poor, or even unprofitable. Therefore, it is technically excluded.

[0050] The topology and impedance of the power grid are hard physical constraints. This step ensures that the candidate energy storage units being considered are electrically close enough to the demand distribution areas that power transmission is efficient and feasible. This is the physical basis for achieving cross-distribution area collaboration.

[0051] Significance: It can quickly identify a set of "neighbors" that are likely to provide technical support from a large number of neighboring stations, thus significantly narrowing and optimizing the search scope.

[0052] Example: The demand area is A. Calculate the average line loss rate when historically supporting neighboring areas B, C, D, and E, which are 3.2%, 6.1%, 2.8%, and 4.9%, respectively. Assume a technical feasibility threshold of 5%. Then, among areas B (3.2%), C (6.1%), D (2.8%), and E (4.9%), areas B, D, and E are selected as "basic compatible areas" (C is excluded due to excessive line loss).

[0053] Step Two: Refined Selection Based on Urgency of Need and Resource Status: Decision-making logic and rules: Scenario 1: The demand area is in the "adaptation deviation type" (high demand, high urgency): Condition: Adaptation type of demand area (A) == Adaptation deviation type Decision: Incorporate all energy storage devices in the "basic adaptation area" into the regulation combination.

[0054] Logic and Significance: When the energy storage capacity of a demand area is already "overworked" (frequently called up), it indicates a large adjustment gap and an extremely urgent need. At this point, all available nearby support resources should be utilized to the maximum extent possible to address the immediate crisis. To meet urgent power balancing or voltage support needs, considerations for resource efficiency can be temporarily relaxed to the technically feasible boundary (5% line loss). This is an emergency dispatch mode that prioritizes "safety and stability."

[0055] Example (continued, assuming A is of the adaptation bias type): then regardless of the state of B, D, and E, their energy storage is all incorporated into the adjustment combination that serves A.

[0056] Scenario 2: The demand area is in the "General / Reliable Adaptation Type" (routine or preventative demand): Condition: The adaptation type of the required transformer area (A) is either a general adaptation type or a reliable adaptation type. Decision-making process: Raise the efficiency threshold (preset line loss rate threshold, such as 3%): In the technically feasible "basic adaptation area", further filter out the "high-efficiency neighbors" with the highest transmission efficiency (line loss <3%). These are the high-quality resources that are prioritized for use.

[0057] Analyze the state of "efficient neighbors" (adaptation type): If a "Reliable Adaptability Type" transformer is found in the "High-Efficiency Neighbors" list, it indicates the existence of a "high-quality idle resource" that is very close, has extremely high transmission efficiency, and whose own energy storage has been idle for a long time. This is already the ideal supporter. To avoid disrupting the originally good operating status of other transformers, only these idle resources should be used, and the scope of use should not be expanded.

[0058] If no "Reliable Adaptability Type" transformer substations are found in the "High-Efficiency Neighbors" list, it indicates that the nearby high-efficiency resources are also in reasonable use. In this case, to meet demand, the resource pool needs to be expanded. Substations with line loss rates between 3% and 5% (feasible but slightly less efficient) that are in the "Reliable Adaptability Type" (i.e., idle resources) should be added. This reflects the economic principle of "first utilizing idle resources, then utilizing high-efficiency resources in use."

[0059] Logic and Significance: In non-emergency situations, dispatching should prioritize overall economic efficiency and friendliness towards friendly resource providers. By analyzing the status of "efficient neighbors," it's possible to determine if better local solutions exist. This rule encourages the utilization of idle resources and aims to minimize impact on well-functioning transformer substations.

[0060] Example (continued, assuming A is a general fit type): Step 1: Select high-efficiency neighbors with line loss <3% from B (3.2%), D (2.8%), and E (4.9%): only D (2.8%).

[0061] Step 2: Check the adaptation type of the efficient neighbor D. Assume D is a general adaptation type (non-idle).

[0062] Decision: Since there are no "Reliable Adaptation Type" units among the efficient neighbors, additional resources are needed. Therefore, among the remaining transformer areas (B and E), we search for transformer areas with a line loss rate between 3% and 5% (within the preset range) that are of the "Reliable Adaptation Type". Assume that B is of the general adaptation type and E is of the reliable adaptation type.

[0063] The final combination is: efficient neighbor D (priority use) + idle resource E (supplementary use). Area B is not used this time because it is already in reasonable use and its line loss is not optimal.

[0064] Summary and Value of Implementation Examples: Achieving unified decision-making for "grid connectivity" and "resource availability": Integrating information from two dimensions—grid physical constraints (line losses) and energy storage operation status (adaptation type)—within a single decision framework, ensuring that the selected energy storage combination is both "deliverable" and "well-utilized".

[0065] A tiered response mechanism was established, distinguishing between "emergency supply guarantee mode" and "economic optimization mode," enabling cross-regional support strategies to flexibly adapt to different power grid operating conditions while balancing safety and economy.

[0066] Guiding the efficient allocation of resources: Through rule design (such as prioritizing the use of idle resources and protecting high-efficiency operating resources), the energy storage resources are guided to flow flexibly from "low-utilization" distribution areas to "high-demand" distribution areas at the entire distribution network level, thereby improving the overall utilization efficiency and value of energy storage assets across the entire network.

[0067] Supporting distributed energy storage aggregation and operation: This method provides clear and automated resource aggregation and call rules for virtual power plant (VPP) operators or distribution system operators (DSO), and is a key technological foundation for realizing large-scale collaborative and interactive operation of massive distributed energy storage.

[0068] This embodiment demonstrates a multi-objective trade-off intelligent decision-making method for practical engineering applications. It transforms cross-regional energy storage collaboration from a theoretical concept into a practical solution with clear execution rules and optimization guidance, which is of great significance for building a flexible, efficient, and economical modern power distribution network.

[0069] Specifically, the coordinated scheduling strategy in the energy storage regulation combination needs to be optimized, including: Based on the distribution station data of the energy storage regulation combination, the number of distribution stations in the energy storage regulation combination is determined; Based on the adaptation type of energy storage regulation of the distribution station area in the energy storage regulation combination, determine the distribution station area with reliable adaptation type and the distribution station area with adaptation deviation type. Based on the composition data of the number of distribution stations, the distribution stations with reliable adaptation type, and the distribution stations with adaptation deviation type in the energy storage regulation combination, it is determined whether the collaborative scheduling processing strategy in the energy storage regulation combination needs to be optimized.

[0070] It is understandable that when the energy storage regulation adaptation type of the distribution substation is the reliable adaptation type, since the energy storage regulation demand of the distribution substation is not high, it is determined that the collaborative scheduling processing strategy in the energy storage regulation combination does not need to be optimized.

[0071] Additionally, it is understandable that when the energy storage regulation adaptation type of the distribution substation is not a reliable adaptation type, and when the number of distribution substations does not meet the requirements, for example, less than 5, then due to the small number of distribution substations, the regulation reliability is not high when the distribution substation needs to use the energy storage regulation combination for energy storage regulation processing. Therefore, the collaborative scheduling processing strategy in the energy storage regulation combination needs to be optimized.

[0072] Additionally, it can be understood that when the number of distribution substations meets the requirements, the number of distribution substations of the reliable adaptation type is obtained. When the number of distribution substations of the reliable adaptation type meets the requirements, for example, not less than 3, the number of distribution substations of the reliable adaptation type meets the requirements. Therefore, when the distribution substation needs to use the energy storage regulation combination for energy storage regulation processing, its regulation reliability is high. Therefore, it is determined that the collaborative scheduling processing strategy in the energy storage regulation combination does not need to be optimized.

[0073] Furthermore, when the number of distribution stations of the reliable adaptation type does not meet the requirements, it is also necessary to determine the number of distribution stations of the adaptation deviation type. When the matching between the distribution stations of the adaptation deviation type and the distribution stations of the reliable adaptation type does not meet the requirements, since the number of distribution stations of the adaptation deviation type is large and the number of distribution stations of the reliable adaptation type is small, it is determined that the collaborative scheduling processing strategy in the energy storage regulation combination needs to be optimized.

[0074] In one possible specific embodiment, when the difference between the distribution station of the adaptation deviation type and the distribution station of the reliable adaptation type is not less than 2, it is determined that the collaborative scheduling processing strategy in the energy storage regulation combination needs to be optimized.

[0075] Additionally, it can be understood that when the matching of the distribution station of the adaptation deviation type and the distribution station of the reliable adaptation type meets the requirements, the weight values ​​of different distribution stations are determined based on the adaptation type of energy storage regulation of the distribution stations in the energy storage regulation combination. When the sum of the weight values ​​of different distribution stations is less than the preset weight threshold, it is determined that the collaborative scheduling processing strategy in the energy storage regulation combination needs to be optimized.

[0076] Furthermore, when the sum of the weight values ​​of different distribution radio zones is not less than a preset weight threshold, it is determined that the collaborative scheduling processing strategy in the energy storage regulation combination needs to be optimized.

[0077] It should be noted that the weight threshold is determined based on the adaptation type of the distribution radio area. The weight threshold of the distribution radio area with the adaptation type of adaptation deviation is greater than the weight threshold of the distribution radio area with the adaptation type of general adaptation. In a possible specific embodiment, the weight values ​​of the general adaptation type, the adaptation deviation type, and the reliable adaptation type are 0.1, 0.2, and 0.3, respectively, and the weight thresholds are 1 and 0.9, respectively.

[0078] It should be noted that the specific type of distribution station is a distribution station with an adaptation type of adaptation deviation for energy storage regulation, and the preset adaptation type of distribution station is a distribution station with an adaptation type of reliable adaptation for energy storage regulation.

[0079] This embodiment aims to address a core issue in cross-regional energy storage collaborative scheduling: how to assess the robustness and reliability of the established collaborative scheduling strategy of an "energy storage regulation portfolio" and determine whether optimization is necessary. The core logic is to quantitatively assess the portfolio's potential to meet future regulation demands by analyzing the number and composition of member regions within the portfolio (especially the ratio and quantity of stable resources with "reliable adaptation" to high-demand resources with "adaptation deviation"), and the weighted overall "resource quality score." If the portfolio's structure has defects (such as insufficient resources, a lack of high-quality resources, or excessive demand pressure), then its strategy needs optimization.

[0080] Step 1: Evaluate the basic components of the adjustment portfolio: Keyword explanation: Reliable and adaptable distribution substations: These are substations with a very low self-storage energy utilization rate (<10%). They are high-quality, stable, and predictable "energy supply" or "regulation" resources that can respond to external demands at any time and serve as the "ballast" for coordinated combinations.

[0081] Distribution areas with adaptation deviation type: These are distribution areas with a very high self-storage call rate (>40%). They are high-demand, high-uncertainty "energy users" or "recipients of assistance", whose self-regulation capacity is saturated, and have a strong and frequent need for external support. They are the "demand initiators" of the combination.

[0082] Typical compatible distribution radio areas have a moderate call rate (10%-40%), belonging to the "intermediate force" with a certain degree of self-adjustment capability and the ability to provide some support.

[0083] Different types of members play vastly different roles in collaboration. Clearly defining their roles is fundamental to assessing the capabilities and risks of a collaborative effort.

[0084] Step Two: A Five-Level Decision Funnel Based on Member Composition Level 1: Exclusion of a single reliable resource (strategy already optimal): Condition: The adaptation type of all distribution radio areas within the adjustment combination is "reliable adaptation type".

[0085] Decision: The strategy does not need to be optimized.

[0086] Logic and Significance: The entire combination consists of idle and stable resources. The collaborative goal of this combination is usually to concentrate idle resources to participate in grid-level services (such as frequency regulation and demand response). Its scheduling strategy is relatively simple and reliable, without complex demand balancing pressure, so no optimization is required.

[0087] Level 2: Insufficient resource pool size (needs expansion): Condition: The total number of units within the combination is less than 5.

[0088] Decision: The strategy needs to be optimized.

[0089] Logic and significance: A small resource pool means: Insufficient regulation capacity margin: unable to cope with large or continuous regulation demands.

[0090] Low system reliability: The failure or exit of a few members can cause the entire system to fail.

[0091] Risk concentration: Over-reliance on individual transformer substations.

[0092] Level 3: Abundant high-quality resources (sound strategy): Condition: The number of reliable adaptation type substations within the combination is ≥ 3.

[0093] Decision: The strategy does not need to be optimized.

[0094] Logic and Significance: Having at least three stable and reliable "ballast" resources is sufficient to support the portfolio in meeting routine adjustment needs, and provides the portfolio with good robustness and flexibility. Even if the demand of individual "deviation" members surges, there are ample backup resources to cope.

[0095] Level 4: Supply and demand imbalance (high risk, requires restructuring): Triggering condition: The "no optimization required" condition of the first three levels is not met, that is, the combination size is ≥5, but the number of reliable resources is <3.

[0096] Analysis: Calculate the difference between the number of stations with adaptation deviation and the number of stations with reliable adaptation.

[0097] Decision condition: If the above difference is ≥ 2.

[0098] Decision: The strategy needs to be optimized.

[0099] Logic and Implications: This reveals a structural risk in the portfolio—there are at least two more "demand-side" (deviation type) suppliers than "stable-side" (reliable type) suppliers. This means: The demand pressure is enormous: there is a high probability that multiple high-demand areas will need support at the same time.

[0100] Supply capacity is tight: There is an insufficient quantity of stable resources, which may not be able to meet peak demand, leading to scheduling failures.

[0101] Level 5: Weighted Resource Quality Assessment (Refined Decision Making): Triggering conditions: The first four levels of decision-making are not met (i.e., size ≥ 5, reliable resources < 3, and the difference between deviation and reliability < 2).

[0102] Operation: Assign weight value: Assign a "resource quality" weight based on the member type.

[0103] Reliable fit type (weight=0.3): highest value.

[0104] Adaptation bias type (weight=0.2): High demand, but also a reflection of value (in paid services).

[0105] General fit type (weight=0.1): Average value.

[0106] Calculate the total weight sum: Add up the weights of all members in the combination.

[0107] Threshold decision: Scenario A (combination includes adaptation bias type): Use a higher weighting threshold of 1.0. This is because there are high-demand members, requiring higher overall resource quality.

[0108] Scenario B (combination does not contain fit bias type): Use a lower weighting threshold of 0.9. Demand is relatively mild.

[0109] decision making: If the total weights are greater than or equal to the corresponding threshold: the strategy does not need optimization. The weighted resource quality of the combination meets the requirements.

[0110] If the total weights are less than the corresponding threshold, the strategy needs optimization. The combined weighted resource quality is insufficient.

[0111] Logic and Significance: This is the most refined assessment. It considers the type and quantity of all members and reflects the relative value of different types of resources through weights. A low weighted score means that the overall "resource endowment" or "value density" of the portfolio is insufficient, and it may not be able to achieve the scheduling objectives economically or technically efficiently.

[0112] Example: A portfolio has 7 members: 2 reliable (0.3 * 2 = 0.6), 3 general (0.1 * 3 = 0.3), and 2 biased (0.2 * 2 = 0.4). The total weight sum is 1.3. Because it includes biased members, the threshold is 1.0. 1.3 > 1.0, so the strategy does not need optimization.

[0113] Summary and Value of Implementation Examples Achieving "health check" and "early warning" for scheduling strategies: This method can periodically perform automated health checks on established collaborative scheduling strategies, identify structural weaknesses (such as insufficient resources, structural imbalance, and low quality) in advance, and avoid failures in actual scheduling.

[0114] Hierarchical classification for efficient decision-making: By using a clear decision funnel (scale → quantity of high-quality resources → structural balance → weighted quality), the problem type can be quickly identified and different optimization directions can be pointed out (expansion, structural adjustment, member replacement), thus improving decision-making efficiency.

[0115] Quantitative assessment supports refined management: The introduction of weighted quality scores enables quantitative comparisons between different combinations, providing a scientific basis for operators to conduct multi-combination management, resource allocation, and performance evaluation.

[0116] Promoting optimal resource allocation: This assessment method will continuously encourage operators to optimize the portfolio structure, such as proactively seeking and incorporating more "reliable and adaptable" resources and replacing low-value members, thereby guiding the energy storage resources of the distribution network towards a more efficient and stable collaborative state on a macro level.

[0117] This method upgrades collaborative scheduling from a static "build and use" model to an intelligent adaptive model of "continuous evaluation and dynamic optimization," which is a key link in ensuring the collaborative efficiency of distributed resources in the regional energy internet.

[0118] Furthermore, the method for determining the pre-defined collaborative adjustment processing strategy for the distribution radio area of ​​the adaptation type is as follows: Based on the overlap between the energy storage regulation demand periods of the distribution substations in the energy storage regulation combination and those of a specific type of distribution substation, the distribution substations in the energy storage regulation combination that simultaneously belong to the energy storage regulation demand periods of the specific type of distribution substation are identified, and these are taken as the overlapping distribution substations in the energy storage regulation demand periods. Based on the overlapping distribution area data during the energy storage regulation demand period in the most recent preset time period, the overlap correlation factor between the specific type of distribution area and the distribution area is determined; Based on the overlapping correlation factor, the coordinated regulation and processing strategy of the pre-set adaptive type distribution radio area in the energy storage regulation combination is determined.

[0119] Furthermore, when the proportion of a specific type of distribution station in the energy storage regulation combination does not meet the requirements, since there are a large number of specific type distribution stations, in order to ensure the reliability of the energy storage regulation of the distribution stations, the preset coordinated regulation processing strategy of the matching type distribution stations in the energy storage regulation combination is determined as the preset coordinated regulation strategy. When the average value of the overlap correlation factor between the specific type of distribution station and the distribution station in the energy storage regulation combination is greater than the preset correlation factor threshold, the coordinated regulation processing strategy of the preset matching type of distribution station in the energy storage regulation combination is determined to be the second preset coordinated regulation strategy.

[0120] It should also be noted that when the average value of the overlap correlation factor between the specific type of distribution station and the distribution station in the energy storage regulation combination is not greater than the preset correlation factor threshold, the coordinated regulation processing strategy of the preset matching type of distribution station in the energy storage regulation combination is determined to be the third preset coordinated regulation strategy.

[0121] This embodiment aims to address a core tactical problem in cross-regional energy storage collaborative scheduling: when there are high-demand members ("specific type" / adaptation deviation type substations) within the group, how to formulate differentiated collaborative scheduling response strategies for substations of the "pre-set adaptation type" (reliable / general adaptation type) that act as "stable energy suppliers" within the group. The core logic is: by analyzing the overlap (demand synchronicity) between the "high-demand users" and other members in historical demand adjustment periods, and combining this with the proportion of high-demand users within the group, to assess the "concentration pressure" and "resource competition risk" of overall demand. Based on this assessment, scheduling strategies with different response priorities and call methods are matched to the "stable energy suppliers" to achieve the optimal balance between meeting demand, ensuring reliability, and optimizing resource utilization.

[0122] Step 1: Quantify the risk of demand synchronicity and resource competition (overlapping correlation factors): Keyword explanation: Specific type of distribution area: refers to the "adaptation deviation type" area within this combination, that is, high demand and the party in need of assistance.

[0123] Energy storage regulation demand period: refers to the period in history when a specific type of transformer area actually needs external support due to insufficient self-regulation capacity.

[0124] Overlapping distribution area: During a certain demand period, other distribution areas within the same group also have their own adjustment needs (charging or discharging).

[0125] Overlapping correlation factor: For a given specific type of transformer area (e.g., A) and another transformer area within the same group (e.g., B), this factor is defined as the proportion of the number of time periods in the most recent statistical period where A has external support needs and B also has its own adjustment needs, relative to the total number of time periods of A's needs. This factor reflects the temporal synchronicity of the needs of the two transformer areas.

[0126] Average Overlap Factor: Calculates the average overlap factor between this specific type of transformer area and all other transformer areas within the portfolio. This value reflects the degree of synchronization between the demand of this high-demand user and the overall demand of the portfolio.

[0127] Identify the intensity of resource competition: A high average overlap factor (e.g., >0.7) means that when high-demand individuals need help, most other members in the group are also busy, resulting in intense resource competition and a shortage of available "idle" capacity.

[0128] Identify resource complementarity potential: A low average overlap factor (e.g., ≤0.3) means that the demand periods of high demanders are often the idle periods of other members, indicating strong resource complementarity and ample available capacity for scheduling.

[0129] Significance: It transforms the qualitative question of "whether the demands are conflicting" into the quantitative question of "conflict probability", providing key input for strategy selection.

[0130] Example: A specific type of network area A (adaptation bias type) requires support for 20 time periods in the past month. Calculate its overlap correlation factor with the other 5 network areas in the group: with B = 0.8, with C = 0.9, with D = 0.2, with E = 0.85, with F = 0.1. Therefore, the average overlap correlation factor = (0.8 + 0.9 + 0.2 + 0.85 + 0.1) / 5 = 0.57. Assume the preset correlation factor threshold is 0.5.

[0131] Step Two: Three-Level Strategy Decision-Making Framework Decision-making Dimension 1: Conservative Strategy Based on the Proportion of High-Demand Consumers (Pre-set Coordination Adjustment Strategy): Condition: The proportion of a specific type of transformer station does not meet the requirements (e.g., the proportion exceeds 40%, or the absolute number exceeds 2).

[0132] Strategy content: Preset coordinated adjustment strategy – adopting a “layered backup” invocation method: First tier: Prioritize the use of energy storage from other specific or generally compatible transformer substations within the same portfolio for support. Since these substations themselves also have some demand or are being used, utilizing them is similar to "resource reallocation."

[0133] Second tier: Energy storage of the preset adaptation type (reliable type) will only be called when the remaining capacity of the first tier resources is insufficient (e.g., <30%).

[0134] When there are many high-demand customers, the portfolio faces systemic and continuous high-pressure support needs. The core of this strategy is "protecting core, high-quality resources": Reliable energy storage should be used as a strategic reserve and last resort to prevent it from being frequently used and prematurely depleted or occupied.

[0135] Prioritizing the use of other non-core resources for "internal consumption" can effectively "filter out" a large number of routine demands, even if the efficiency is slightly lower, ensuring that there are still stable and reliable "trump card" resources available at truly critical moments (when all other resources are exhausted).

[0136] This reflects the robust approach of prioritizing the long-term reliability of the system under high-pressure conditions.

[0137] Decision-making dimension two: Aggressive / equilibrium strategies based on high demand synchronicity: When the proportion of high-demand consumers "meets the requirements" (i.e., the proportion is not high, perhaps only 1-2), further decisions are made based on the "average overlap correlation factor": Condition A: High synchronization (average overlap factor > threshold, e.g., >0.5) Strategy details: The second preset coordinated adjustment strategy is "Immediate Full Response". Once a high-demand user makes a request, the energy storage of all preset adaptive type (reliable) transformer areas within the combination will be mobilized simultaneously for support.

[0138] High synchronicity means that the demand periods of high-demand users are also the periods when other members of the team are generally busy ("everyone is busy"). At this time, resources are scarce and competition is fierce. Any delays or staggered calls could lead to support failure (because the idle window is fleeting). Therefore, the most decisive and rapid strategy must be adopted, concentrating all high-quality and reliable resources to meet the demand in the shortest possible time with maximum power and capacity, ensuring the success rate of the support operation. This is a "powerful strike, quick resolution" strategy.

[0139] Condition B: Low synchronicity (average overlap factor ≤ threshold, e.g., ≤0.5): Strategy Content: The third preset coordinated adjustment strategy – “Optimal Individual Response”. When a high-demand user makes a request, the system selects the substation with the “largest remaining energy storage capacity” from the preset adaptation type (reliable type) substations for support.

[0140] Low synchronicity means that the demand periods of high-demand individuals often coincide with the "leisure time" of other members ("I'm busy, you're idle"). At this time, resources are abundant, and competition is low. The core strategy shifts to "economic efficiency and resource conservation": Choosing the largest capacity: This usually allows the demand to be met in a single operation, avoiding the simultaneous shallow charging and discharging of multiple energy storage units, which helps improve the utilization efficiency and cycle life of a single energy storage unit.

[0141] Rotational deployment: Due to ample resources, different reliable transformer areas can be rotated according to capacity status to achieve balanced resource utilization and avoid overuse of individual units. This is a "meticulous and sustainable" optimization strategy.

[0142] Summary and Value of Implementation Examples: Achieving a leap from "unified scheduling" to "contextualized scheduling": This method completely changes the "one-size-fits-all" scheduling instructions. Based on the pressure structure (proportion of high-demand users) and the time pattern of demand (overlap), it dynamically generates three refined strategies that best match the current situation (layered backup, full response, and optimal individual response).

[0143] Balancing multiple objectives: The preset strategy balances immediate needs with long-term reliability.

[0144] The second preset strategy prioritizes ensuring the success rate of support when resources are scarce.

[0145] The third preset strategy prioritizes economic efficiency and equipment lifespan when resources are plentiful.

[0146] Enhancing the intelligence level of the collaborative system: The automatic selection process of strategies simulates the experience judgment of human scheduling experts, enabling the energy storage aggregation system to have a basic level of "intelligence" to cope with complex situations, and greatly improving the efficiency and adaptability of collaborative regulation.

[0147] Laying the foundation for advanced applications: The strategies and triggering conditions generated by this method can form a knowledge base for training more advanced reinforcement learning models or as constraints for multi-objective optimization problems, which is a key component in building a new generation of intelligent distributed energy management systems (DERMS).

[0148] Through this embodiment, cross-regional energy storage collaboration has evolved from a simple "resource aggregation + unified control" to an "intelligent collaborative body" capable of sensing internal states, predicting conflicts, and making differentiated tactical arrangements, significantly improving the practical value and operational level of distributed energy storage in the distribution network. Example

[0149] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described cooperative scheduling method for a distributed energy storage system when running the computer program.

[0150] Furthermore, the method for determining the pre-defined collaborative adjustment processing strategy for the distribution radio area of ​​the adaptation type is as follows: Based on the overlap between the energy storage regulation demand periods of the distribution substations in the energy storage regulation combination and those of a specific type of distribution substation, the distribution substations in the energy storage regulation combination that simultaneously belong to the energy storage regulation demand periods of the specific type of distribution substation are identified, and these are taken as the overlapping distribution substations in the energy storage regulation demand periods. Based on the overlapping distribution area data of the energy storage regulation demand period of the specific type of distribution area, determine the energy storage regulation demand period data that overlaps with different distribution areas in the most recent preset time period. Based on the energy storage regulation demand time period data of a specific type of distribution station in the energy storage regulation combination, as well as the energy storage regulation demand time period data that overlaps with different distribution stations in the most recent preset time period, a pre-set coordinated regulation processing strategy for the distribution stations of the preset adaptation type in the energy storage regulation combination is determined.

[0151] Furthermore, when the proportion of a specific type of distribution station in the energy storage regulation combination does not meet the requirements, since there are a large number of specific type distribution stations, in order to ensure the reliability of the energy storage regulation of the distribution stations, the preset coordinated regulation processing strategy of the matching type distribution stations in the energy storage regulation combination is determined as the preset coordinated regulation strategy. Furthermore, when the proportion of a specific type of distribution substation in the energy storage regulation combination meets the requirements, it is determined that the energy storage regulation demand period of the specific type of distribution substation in the most recent preset time period does not meet the requirements, that is, when the specific type of distribution substation in the most recent preset time period has energy storage regulation demand periods on different dates, in order to ensure the reliability of the energy storage regulation of the distribution substation, the preset coordinated regulation processing strategy of the matching type of distribution substation in the energy storage regulation combination is determined to be the preset coordinated regulation strategy. In one possible specific embodiment, the most recent preset time period is the most recent month.

[0152] Additionally, it can be understood that when the energy storage regulation demand period of a specific type of distribution transformer area within the most recent preset time period is met, the overlap between the energy storage regulation demand period of the specific type of distribution transformer area within the most recent preset time period and the energy storage regulation period of the distribution transformer area in the energy storage regulation combination is determined. When the average number of overlapping distribution transformer areas in the energy storage regulation demand period of the specific type of distribution transformer area within the most recent preset time period is greater than a preset number threshold, in order to ensure the reliability of the energy storage regulation of the distribution transformer area, the preset coordinated regulation processing strategy of the distribution transformer area of ​​the appropriate type in the energy storage regulation combination is determined as the preset coordinated regulation strategy. Furthermore, when the average number of overlapping distribution substations in the energy storage regulation demand period of a specific type of distribution substation within the most recent preset time period is not greater than a preset number threshold, then the overlap correlation factor between the specific type of distribution substation and the distribution substation is determined based on the overlap between the energy storage regulation demand period of the specific type of distribution substation and different distribution substations within the most recent preset time period. When the average value of the overlap correlation factor between the specific type of distribution substation and the distribution substations in the energy storage regulation combination is greater than a preset correlation factor threshold, the pre-set coordinated regulation processing strategy for the distribution substations of the appropriate type in the energy storage regulation combination is determined as the second pre-set coordinated regulation strategy. In one possible specific embodiment, the overlap correlation factor is determined based on the proportion of the energy storage regulation demand period that overlaps with the specific type of distribution substation in the energy storage regulation demand period of the distribution substation, and the preset correlation factor threshold value is 0.4.

[0153] It should also be noted that when the average value of the overlap correlation factor between the specific type of distribution station and the distribution station in the energy storage regulation combination is not greater than the preset correlation factor threshold, the coordinated regulation processing strategy of the preset matching type of distribution station in the energy storage regulation combination is determined to be the third preset coordinated regulation strategy. It should be noted that the preset coordinated regulation strategy first utilizes the distribution stations other than the preset compatible distribution stations in the energy storage regulation combination to perform energy storage regulation processing on the distribution stations. When the energy storage regulation capacity of the distribution stations other than the preset compatible distribution stations in the energy storage regulation combination does not meet the requirements, for example, when the remaining energy storage capacity of the energy storage system is less than 30%, the energy storage system of the preset compatible distribution stations in the energy storage regulation combination is then used simultaneously to perform energy storage regulation processing on the distribution stations, thereby ensuring the reliability of energy storage regulation of the preset compatible distribution stations.

[0154] Furthermore, the second preset coordinated regulation strategy is to simultaneously use the energy storage system of the distribution station of the preset adaptive type in the energy storage regulation combination to perform energy storage regulation processing of the distribution station.

[0155] Furthermore, the third preset coordinated adjustment strategy is to use the energy storage system of the distribution station with the largest remaining energy storage capacity of the preset adapted type to perform the energy storage adjustment processing of the distribution station.

[0156] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0157] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0158] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A collaborative scheduling method for distributed energy storage systems, characterized in that, Specifically, it includes: Based on the energy storage regulation data of the energy storage devices in the distribution substation, the adaptation type of the energy storage regulation of the distribution substation is determined. Based on the adaptation type of the distribution substation and the line loss data of the regulation processing of the energy storage system between the distribution substations, the energy storage devices of the distribution substation that construct the energy storage regulation combination of the distribution substation are determined. Based on the adaptation type of energy storage regulation of the distribution substations in the energy storage regulation combination, when it is determined that the collaborative scheduling processing strategy in the energy storage regulation combination needs to be optimized, the collaborative regulation processing strategy of the distribution substations of the energy storage regulation combination with the energy storage regulation demand time period of the distribution substations of the energy storage regulation combination and the distribution substations of a specific type is determined to be a preset adaptation type of collaborative regulation processing strategy.

2. The collaborative scheduling method for distributed energy storage systems as described in claim 1, characterized in that, The energy storage regulation data includes the distribution data of the energy storage devices in the distribution area during historical energy storage regulation periods.

3. The collaborative scheduling method for distributed energy storage systems as described in claim 1, characterized in that, The method for determining the adaptation type of the energy storage regulation in the distribution substation is as follows: Based on the energy storage regulation data of the energy storage devices in the distribution substation, the distribution data of the energy storage regulation time period in the distribution substation is determined; Based on the distribution data, the dates on which the distribution area has an energy storage regulation period are determined, and these dates are used as the energy storage regulation dates; Based on the distribution data of the energy storage regulation dates, the adaptation type of energy storage regulation for the distribution radio area is determined.

4. The collaborative scheduling method for distributed energy storage systems as described in claim 3, characterized in that, Based on the distribution data of the energy storage regulation dates, the adaptation type of the energy storage regulation for the distribution area is determined, specifically including: When the proportion of the number of energy storage adjustment dates within the most recent preset time period is greater than the preset proportion threshold, the energy storage adjustment adaptation type of the distribution radio area is determined to be the adaptation deviation type.

5. The collaborative scheduling method for distributed energy storage systems as described in claim 1, characterized in that, The method for determining the energy storage device in the distribution substation area for constructing the energy storage regulation combination is as follows: Based on the line loss data of the energy storage system regulation and processing between other distribution substations and the distribution substation, the line loss rate between the other distribution substations and the distribution substation is determined. Based on the line loss rate, other distribution stations with line loss rates less than a preset threshold are identified and used as basic adaptation stations. Based on the adaptation type of different adaptation zones and the adaptation type of the distribution zone, determine whether the energy storage device of the adaptation zone is an energy storage device for constructing the energy storage regulation combination of the distribution zone.

6. The collaborative scheduling method for distributed energy storage systems as described in claim 5, characterized in that, The line loss rate between the other distribution substations and the aforementioned distribution substation is determined based on the average line loss rate of the energy storage devices in the other distribution substations during the energy storage regulation process of the aforementioned distribution substation.

7. The collaborative scheduling method for distributed energy storage systems as described in claim 6, characterized in that, The adapted distribution area refers to other distribution distribution areas whose line loss rate is within a preset range.

8. The collaborative scheduling method for distributed energy storage systems as described in claim 1, characterized in that, The method for determining the pre-defined collaborative adjustment processing strategy for the distribution radio area of ​​the adaptation type is as follows: Based on the overlap between the energy storage regulation demand periods of the distribution substations in the energy storage regulation combination and those of a specific type of distribution substation, the distribution substations in the energy storage regulation combination that simultaneously belong to the energy storage regulation demand periods of the specific type of distribution substation are identified, and these are taken as the overlapping distribution substations in the energy storage regulation demand periods. Based on the overlapping distribution area data of the energy storage regulation demand period of the specific type of distribution area, determine the energy storage regulation demand period data that overlaps with different distribution areas in the most recent preset time period. Based on the energy storage regulation demand time period data of a specific type of distribution station in the energy storage regulation combination, as well as the energy storage regulation demand time period data that overlaps with different distribution stations in the most recent preset time period, a pre-set coordinated regulation processing strategy for the distribution stations of the preset adaptation type in the energy storage regulation combination is determined.

9. The collaborative scheduling method for distributed energy storage systems as described in claim 8, characterized in that, When the proportion of a specific type of distribution station in the energy storage regulation combination does not meet the requirements, the pre-set collaborative regulation processing strategy of the distribution station of the appropriate type in the energy storage regulation combination is determined as the pre-set collaborative regulation strategy.

10. A computer system, comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a collaborative scheduling method for a distributed energy storage system as described in any one of claims 1-9.

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