A method and system for optimizing configuration of energy storage system considering power consumption data

Abstract

The application provides a kind of energy storage system optimization configuration method and system considering power consumption data, belongs to optimization configuration technical field, specifically includes: based on the configuration target substation's constituent data and peak power consumption period data, the determination of the energy storage system of the configuration target substation in the energy storage adjustment control strategy of other distribution substation, based on the idle condition of the energy storage system of the configuration target substation in the energy storage adjustment demand period of the energy storage system in other distribution substation, determine the configuration target of the energy storage system in other distribution substation, reduce the configuration cost of energy storage system.

Description

Technical Field

[0001] This invention belongs to the field of optimization configuration technology, and in particular relates to an optimization configuration method and system for energy storage systems that takes into account electricity consumption data. Background Technology

[0002] To achieve optimal configuration of energy storage systems in power distribution networks, invention patent application CN202510821819.2, "A Power Grid Voltage Compensation Method and System Considering New Energy Fluctuations," solves a first and a second optimal configuration model based on reactive power compensation to generate a power grid voltage compensation configuration scheme. This scheme, combining reactive power compensation devices, series capacitors, and energy storage devices, can improve regional power grid voltage quality while promptly regulating power line voltage drops. However, it suffers from the following technical problems: When configuring energy storage systems, the overlap of peak electricity demand periods varies among different distribution substations. Therefore, once an energy storage system is configured, its flexibility in regulating and responding to other distribution substations differs. Thus, determining the configuration requirements for energy storage systems in other distribution substations, based on the configuration of energy storage systems in distribution substations with less overlap in peak electricity demand periods, and thereby improving the flexibility of energy storage system regulation and response, has become an urgent technical problem to be solved.

[0003] To address the aforementioned technical problems, this application provides a method and system for optimizing the configuration of an energy storage system that takes into account electricity consumption data. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for optimizing the configuration of an energy storage system that takes into account electricity consumption data, including: S1 uses the analysis results of electricity consumption data to determine the overlapping data of peak electricity consumption periods in different distribution substations. Based on the overlapping data, it determines the distribution substations that need to be configured with energy storage systems and uses them as the target substations for configuration. According to the overlap between the peak electricity consumption periods of the target substations and other distribution substations, it determines that energy storage system configuration processing needs to be carried out in other distribution substations, and then proceeds to the next step. S2 determines the energy storage regulation and control strategy of the energy storage system in other distribution substations based on the configuration data of the target substation and the peak electricity consumption period data. Based on the idle status of the energy storage system in the target substation during the energy storage regulation demand period of the energy storage system in other distribution substations, the configuration target of the energy storage system in the other distribution substations is determined.

[0005] The beneficial effects of this invention are as follows: Based on overlapping data, distribution substations that require energy storage system configuration processing are identified. This allows for the screening of distribution substations with low overlap in peak power consumption periods. By performing energy storage system configuration processing in these distribution substations, the reliability of energy storage system regulation is further improved.

[0006] Based on the idle status of the energy storage system in the target distribution area during the energy storage regulation demand periods of other distribution areas, the configuration target of the energy storage system in the other distribution areas is determined. Taking into account the idle status of the energy storage system in the target distribution area during the energy storage regulation demand periods of other distribution areas, this avoids the technical problem of high configuration costs caused by directly setting up the energy storage system when the energy storage regulation demand periods of other distribution areas cannot be met due to limitations of the energy storage regulation control strategy. Thus, while meeting the energy storage regulation demand, the reliability of the energy storage system regulation is further improved.

[0007] Furthermore, the distribution area is divided according to the distribution area of ​​the distribution transformer, specifically the distribution area of ​​the distribution transformer is divided into a distribution area.

[0008] Furthermore, the peak electricity consumption period is determined based on the unit time period of the distribution substation within the target electricity consumption range, that is, the electricity consumption at different times within the unit time period is within the target electricity consumption range.

[0009] Furthermore, the overlapping data of peak power consumption periods in the distribution substation includes the number of times the peak power consumption periods of the distribution substation overlap with those of other distribution substations.

[0010] Furthermore, the method for determining the energy storage regulation and control strategy of the energy storage system in the target distribution area in other distribution areas is as follows: Based on the configuration data of the target station area, determine the proportion of the target station area in all stations and use it as the configuration composition proportion. Based on the peak electricity consumption data of the configured target distribution area, the duration of the peak electricity consumption period of the configured target distribution area on different dates is determined; Based on the duration of peak electricity consumption periods and the proportion of configuration components in the target distribution area on different dates, the energy storage regulation and control strategy of the energy storage system in the target distribution area is determined in other distribution areas.

[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 method for optimizing the configuration of an energy storage system taking into account electricity consumption data 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 method for optimizing the configuration of an energy storage system that takes into account electricity consumption data. Figure 2 This is a flowchart illustrating the method for determining the distribution station area that requires configuration processing of the energy storage system. Figure 3 This is a flowchart illustrating the process of setting up an energy storage system in other distribution substations. 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.

[0018] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a method for optimizing the configuration of an energy storage system that takes into account electricity consumption data is provided, specifically including: S1 uses the analysis results of electricity consumption data to determine the overlapping data of peak electricity consumption periods in different distribution substations. Based on the overlapping data, it determines the distribution substations that need to be configured with energy storage systems and uses them as the target substations for configuration. According to the overlap between the peak electricity consumption periods of the target substations and other distribution substations, it determines that energy storage system configuration processing needs to be carried out in other distribution substations, and then proceeds to the next step. Furthermore, the distribution area is divided according to the distribution area of ​​the distribution transformer, specifically the distribution area of ​​the distribution transformer is divided into a distribution area.

[0019] Furthermore, the peak electricity consumption period is determined based on the unit time period of the distribution substation within the target electricity consumption range, that is, the electricity consumption at different times within the unit time period is within the target electricity consumption range.

[0020] It should be noted that the target power consumption range is determined based on the maximum power consumption of the distribution station in history. Specifically, the power consumption range constructed with 0 and the maximum power consumption is divided into multiple power consumption ranges at preset intervals. In one possible embodiment, the preset interval can be 10% of the maximum power consumption, where the target power consumption range is the power consumption range with the largest endpoint value.

[0021] Furthermore, the overlapping data of peak power consumption periods in the distribution substation includes the number of times the peak power consumption periods of the distribution substation overlap with those of other distribution substations.

[0022] Specifically, such as Figure 2 As shown, the method for determining the distribution station area that requires configuration processing of the energy storage system is as follows: Based on the overlapping data of peak power consumption periods in the distribution transformer area, determine the number of overlapping peak power consumption periods between the distribution transformer area and other distribution transformer areas; The overlap factor is determined based on the proportion of the number of peak power consumption periods of the distribution substation overlapping with other distribution substations in the peak power consumption periods of the distribution substation. Based on the overlap factor, it is determined whether the distribution station area is one that requires configuration processing of an energy storage system.

[0023] It is understandable that when the overlap factor is less than the preset overlap factor threshold, the energy storage system is set up in the distribution area. Since the overlap with the peak power consumption period of other distribution areas is low, the energy storage regulation process of other distribution areas can be realized at the same time. Therefore, the distribution area is determined to be the distribution area that needs to be configured with an energy storage system.

[0024] In one possible specific embodiment, when the overlap factor is less than 0.2, the distribution station area is determined to be a distribution station area that needs to undergo energy storage system configuration processing.

[0025] This embodiment aims to solve the planning and site selection problem of distributed energy storage systems (DESS) in distribution networks: how to intelligently select the most suitable distribution substations for priority deployment of energy storage systems among numerous substations to maximize investment benefits. The core logic of this method is to identify "isolated peak" substations whose peak electricity consumption periods are significantly offset from most surrounding substations. Deploying energy storage in such substations can not only meet their own peak shaving and valley filling needs, but also release electricity during their off-peak periods (which happen to be the peak periods of other substations) to support adjacent substations, thereby leveraging the synergistic regulation value of "one storage, multiple uses" and significantly improving the cross-substation sharing utilization rate and return on investment of energy storage assets.

[0026] Step 1: Data Foundations and Key Definitions: Keyword explanation: Distribution area: A continuous geographical area powered by a single distribution transformer, which is the basic unit for distribution management and planning.

[0027] Peak electricity consumption period: refers to the time period during which the electricity load of a distribution area remains within the "target electricity consumption range".

[0028] Target electricity consumption range: Defined as follows: Take the maximum value P_max of the historical electricity consumption data for this distribution area.

[0029] Establish the range [0, P_max], and divide it into 10 equal intervals with each interval being 10% of P_max (i.e., 0.1 * P_max).

[0030] Among them, interval 10: [0.9 * P_max, P_max] is the "target power consumption interval". This interval represents the highest load operating state of the transformer area.

[0031] Number of overlapping peak electricity consumption periods: For a target transformer area A, count the number of other transformer areas that are also in their own peak electricity consumption period during each "peak electricity consumption period", and sum or average all peak periods to obtain the "number of overlapping periods".

[0032] Standardized peak value definition: Peak values ​​are defined using relative values ​​(percentage ranges) rather than absolute values, making it comparable between different capacities and different types of transformer substations.

[0033] Focusing on peak load: Only focusing on the top 10% of the load range, capturing the "peak" moment when the pressure on the power grid and equipment is greatest and the adjustment is most needed.

[0034] Quantitative time synchronization: "overlapping quantity" directly measures the degree of overlap between the peak electricity consumption of this transformer area and the peak consumption of surrounding areas in time.

[0035] Significance: It establishes a clear, calculable, and comparable data foundation for subsequent analysis.

[0036] Step 2: Calculate the peak synchronicity index (overlap factor): Calculation: Overlap factor = Total number of peak periods overlapping between transformer A and all other transformers / (Number of peak periods for transformer A * Total number of other transformers). A simplified version can be: Overlap factor = (Average number of other transformers overlapping with A) / (Total number of other transformers), with a value range of [0,1].

[0037] This factor reflects the probability that the peak electricity consumption of target transformer area A is synchronized with the peak electricity consumption of the entire surrounding area.

[0038] The overlap factor is close to 1, meaning that as long as A is experiencing peak electricity usage, almost all other transformer areas are also experiencing peak usage. Everyone's electricity consumption behavior is highly consistent.

[0039] The overlap factor is close to 0, which means that during peak hours for transformer A, most other transformer areas are in off-peak conditions. Transformer A's electricity consumption pattern is a unique "peak-off" pattern.

[0040] Significance: It condenses complex time series comparisons into a core indicator that represents "peak-shifting potential" or "degree of isolation".

[0041] Step 3: Site selection decision based on peak-shifting potential: Decision rule: If the overlap factor is less than the preset overlap factor threshold (e.g., 0.2), then the distribution area is determined to be a distribution area that needs to be configured with an energy storage system.

[0042] Logic and In-Depth Analysis: A low overlap factor (<0.2) means that this distribution area has less than a 20% probability of being at its peak load at the same time as other distribution areas. Its load curve has a very weak correlation with the overall regional load curve, exhibiting significant "anti-peak" or "independent peak" characteristics.

[0043] The significant advantages of deploying energy storage in such transformer substations: Self-peak shaving and valley filling: Energy storage can discharge during the peak hours of the distribution area to reduce the transformer load; and charge during the off-peak hours.

[0044] The value of cross-regional and cross-time-period energy transfer (core advantage): Since the peak hours of one distribution area coincide with the off-peak hours of others, the charging period for its energy storage can be scheduled during the peak hours of other distribution areas in the same region (when the overall grid electricity price is high or under pressure). In this way, the energy storage effectively absorbs the surplus energy or grid pressure from other distribution areas during their peak hours and discharges it during its own peak hours (when others are at their off-peak). This achieves cross-regional and cross-time-period energy transfer, amplifying the smoothing effect of energy storage on the overall grid load curve.

[0045] Improve asset utilization: In addition to serving the local distribution area, the energy storage's charging and discharging behavior naturally complements the regional demand, making it a natural "virtual shared energy storage." It can indirectly support neighboring areas without complex coordination protocols, and its asset utilization and use value far exceed those of distribution areas with highly synchronized loads.

[0046] Why set a threshold of 0.2? This is an empirical critical value, meaning that the load pattern of this distribution area is significantly different from the mainstream pattern in the region (the difference exceeds 80%). Below this threshold, the cross-regional synergistic gains brought by energy storage will be very significant, and the investment will have high added value.

[0047] Examples and scenario demonstrations: Suppose that a residential area (transformer R) and a commercial park (transformer C) are powered by a single upstream substation.

[0048] District R (residential area): High electricity consumption during the evening peak (18:00-22:00), with a high overlap factor (consistent with the main peak of the region).

[0049] Transformer Area C (Commercial Area): Daytime peak hours (9:00-17:00), extremely low nighttime load. The overlap factor between it and other transformer areas, including R, is only 0.15 (<0.2).

[0050] Site selection decision: The system determines that area C is a priority candidate area for energy storage configuration.

[0051] Operational value is reflected in the fact that energy storage can be charged from the grid during peak commercial hours (which may be due to high solar power generation or grid parity electricity).

[0052] During the evening rush hour, the energy storage discharges to transformer substation C (although C's own demand is low, this reduces the power supply from the upstream substation to C, leaving the power for R), which is indirectly equivalent to supporting transformer substation R during the evening rush hour.

[0053] It realizes the spatial and temporal energy transfer of "commercial energy storage and residential electricity consumption", which greatly improves the utilization efficiency of energy storage and grid equipment.

[0054] Summary and Value of Implementation Examples: Revolutionizing traditional site selection concepts: Traditional energy storage site selection is mostly based on the peak-valley difference or voltage issues within the transformer substation itself. This method introduces a "regional collaborative perspective," prioritizing locations that maximize cross-substation benefits, representing a more advanced "grid-based" planning concept.

[0055] Data-driven, fair and efficient: Site selection is conducted through objective load data analysis, avoiding subjective assumptions and enabling limited energy storage investment to be precisely targeted at key nodes that can improve the overall network operating efficiency.

[0056] Laying the physical foundation for virtual power plants (VPPs): Energy storage selected and configured using this method naturally possesses the physical attributes to participate in regional collaborative optimization, making it easier to aggregate and form efficient VPPs to participate in the grid peak shaving, frequency regulation and other ancillary service markets.

[0057] Adapting to the demands of new power systems: In distribution networks with a high proportion of distributed photovoltaic (PV) grid integration, load curves are becoming increasingly complex. This method can accurately identify distribution areas that are naturally complementary to PV power generation curves or regional load curves. Configuring energy storage can effectively improve the local absorption capacity of PV power and the carrying capacity of the distribution network.

[0058] It should be noted that the other distribution radio areas mentioned refer to distribution radio areas other than the target distribution radio area.

[0059] Specifically, such as Figure 3 As shown, it has been determined that energy storage system setup needs to be implemented in other distribution substations, specifically including: Based on the overlap between the peak power consumption period of the target distribution area and other distribution areas, determine the number of overlaps between the peak power consumption periods of other distribution areas and the peak power consumption period of the target distribution area. Based on the number of overlaps, the overlap coefficient between the peak power consumption period of the other distribution transformer areas and the target distribution transformer area is determined. Based on the data of other distribution substations where the overlap coefficient does not meet the requirements and the target distribution substation, it is determined whether energy storage system needs to be installed in other distribution substations.

[0060] It should be noted that the other distribution radio areas whose overlap coefficient does not meet the requirements are other distribution radio areas whose overlap coefficient is within the preset range. In one possible embodiment, it is other distribution radio areas whose overlap coefficient is greater than 0.1.

[0061] Specifically, based on the data of other distribution substations where the overlap coefficient does not meet the requirements and the target distribution substation, it is determined whether energy storage system setup is needed in other distribution substations. This includes: If the number of other distribution substations that do not meet the overlap coefficient requirement is greater than the number of target distribution substations, then it is determined that energy storage system setup needs to be performed in other distribution substations.

[0062] This embodiment aims to solve the problem of coordinated configuration in distributed energy storage planning: after a "seed area" (target area) for priority energy storage configuration has been identified, how to determine whether a second or third energy storage unit needs to be added in its neighboring areas to form a "storage cluster" with a stronger synergistic effect. The core logic of this method is to detect whether there are one or more areas whose peak electricity consumption highly overlaps with the peak consumption of the "seed area" (high synchronicity). If there are enough such "highly synchronized areas", it indicates that there is a widespread and uniform peak pressure in the region, and a single energy storage unit may not be sufficient to cope with it or the optimization effect may be limited. Therefore, secondary energy storage configuration is needed in the group of "highly synchronized areas" to form a large-scale coordinated regulation capability.

[0063] Step 1: Identify the group of seed cells that are highly synchronized with the seed cell: Keyword explanation: Target storage area: The storage area selected in the previous embodiments and planned to be the first to be equipped with energy storage (denoted as storage area A). Its characteristic is that the peak period is staggered from the overall area (overlap factor < 0.2).

[0064] Overlap coefficient: For each other transformer area (denoted as transformer area X), calculate the degree of overlap between its peak power consumption period and the peak power consumption period of the target transformer area A. The calculation formula can be: Overlap coefficient = (Number of overlapping peak power consumption periods between transformer area X and transformer area A) / (Total number of peak power consumption periods in transformer area A). The value of this coefficient ranges from [0,1].

[0065] Overlap coefficient not meeting requirements: In this context, it specifically refers to an excessively high overlap coefficient, meaning that the peak electricity consumption of transformer area X is highly synchronized with the peak consumption of transformer area A. A high threshold is preset, for example, an overlap coefficient > 0.1. This means that whenever transformer area A is at its peak, there is a greater than 10% probability that transformer area X will also be at its peak at the same time.

[0066] The goal is to identify power distribution areas whose electricity usage patterns are almost identical to those of the "seed distribution area." Although the "seed distribution area" itself is not directly related to the overall regional peak demand, it may still have a "small circle" whose members share peak demand heights with each other.

[0067] Significance: Identifying potential "demand alliances" or "pressure communities." These clusters of transformer substations face peak challenges that are highly consistent in timing.

[0068] Step Two: Secondary Allocation Decision Based on the Scale of the "Pressure Community": Decision rule: If the number of distribution substations with an overlap coefficient > 0.1 > the number of target distribution substations, then it is determined that the energy storage system needs to be set up in other distribution substations.

[0069] Typically, the "number of target zones" is 1 (i.e., one initial seed). Therefore, the rule simplifies to: if there are at least 2 zones with a peak overlap coefficient > 0.1 with seed zone A, then secondary configuration is initiated.

[0070] Logic and In-Depth Analysis: Limitations of single-unit energy storage: Assuming energy storage is only deployed in transformer substation A, when A and its highly synchronized "allies" (such as B and C) simultaneously enter peak periods, this energy storage can only address the needs of A itself, and is powerless to alleviate the peak demand from B and C. The peak demands of B and C will still be superimposed on the upstream power grid, forming a concentrated high-power surge.

[0071] The necessity of large-scale collaboration: If there are multiple (≥2) highly synchronous distribution areas, it means that there is a synchronous peak load group with a wider range and larger total power in this local area. In order to effectively smooth out the overall peak-valley difference of this cluster, more collaboratively controllable energy storage resources need to be deployed within the cluster to form an "energy storage cluster".

[0072] Advantages of clustered configuration: Enhanced regulation capability: Multiple energy storage units have a larger total power and capacity, enabling them to cope with stronger peak impacts.

[0073] Improved reliability: Multiple devices serve as backups for each other, and a single point of failure does not affect the overall functionality.

[0074] Optimized operation strategy: The cluster can coordinate internally to perform more refined power allocation and charge / discharge timing optimization, achieving better overall economic efficiency and grid support (such as providing frequency regulation services) than single-point energy storage.

[0075] "Quantity greater than 1" is a simple yet effective triggering condition. It indicates that the synchronization with the seed area is not an accidental, isolated area, but a statistically significant "group phenomenon." Allocating energy storage to this group has clear scale effects and room for synergistic optimization.

[0076] Examples and scenario demonstrations: Scenario: A power supply area containing transformer substations A (business park), B (data center), C (technology office building), D (residential area 1), and E (residential area 2).

[0077] Analysis results: Transformer A was selected as the target transformer area (seed) due to its unique daytime peak.

[0078] Calculate the peak overlap coefficients of B, C, D, E and A: B=0.15, C=0.18, D=0.05, E=0.10.

[0079] Identifying highly synchronized transformer areas: The overlap coefficients of B and C with A are both greater than 0.1. Therefore, transformer areas B and C are the only two that do not meet the overlap coefficient requirement.

[0080] Decision: Number of highly synchronized distribution areas (2) > Number of target distribution areas (1). Therefore, the system determines that energy storage system needs to be configured in other distribution areas (specifically B and C).

[0081] Significance of the plan: Energy storage should not be configured only at point A, but rather a distributed energy storage system should be planned across the commercial load cluster of A, B, and C. These three energy storage systems can operate collaboratively to cope with daytime peak loads on weekdays, achieving deep optimization of the overall load curve for the region.

[0082] Achieving "from point to surface" in energy storage planning: This method expands the planning perspective from the selection of a single optimal point to the identification of distribution area clusters with similar load characteristics, promoting the clustering and networking of energy storage, which is highly consistent with the future development direction of the energy internet.

[0083] Mitigating the risk of underinvestment: It avoids situations where the deployment of single-point energy storage fails to address regional peak load issues, resulting in poor investment outcomes. It ensures that energy storage investment is matched to the actual load concentration pattern.

[0084] Providing a blueprint for building microgrids or virtual power plants: The identified "highly synchronized distribution network clusters" and their accompanying "energy storage clusters" actually constitute the ideal core of a potential microgrid or virtual power plant. They share consistent operating objectives and collaboratively modifiable resources, facilitating more advanced aggregated control and market participation.

[0085] Improving the accuracy and foresight of distribution network planning: This method enables distribution network planners to make strategic deployments of energy storage resources in areas with similar load characteristics based on data, rather than passively responding to problems in individual transformer substations, thereby improving the resilience, economy and greenness of the distribution network from a higher dimension.

[0086] This method is a key link in the scientific planning chain of energy storage, ensuring that the success of the first batch of energy storage demonstration projects can naturally lead to and guide subsequent large-scale, coordinated expansion investments, forming a virtuous cycle of planning.

[0087] S2 determines the energy storage regulation and control strategy of the energy storage system in other distribution substations based on the configuration data of the target substation and the peak electricity consumption period data. Based on the idle status of the energy storage system in the target substation during the energy storage regulation demand period of the energy storage system in other distribution substations, the configuration target of the energy storage system in the other distribution substations is determined.

[0088] Specifically, the method for determining the energy storage regulation and control strategy of the energy storage system in the target distribution area in other distribution areas is as follows: Based on the peak power consumption period data of the configured target distribution area, the duration of the peak power consumption period of the configured target distribution area on different dates is determined. Based on the average duration of the peak power consumption period on different dates, the configured target distribution areas that do not require response processing are determined. If the average duration of peak electricity consumption periods in the target distribution area on different dates is greater than a preset duration threshold, for example, greater than 2 hours, then in order to ensure the reliability of the energy storage system's regulation processing in the target distribution area, it is determined that the energy storage system in the target distribution area will not respond when there is an energy storage regulation demand in other distribution areas, i.e., it belongs to the target distribution area that does not respond.

[0089] Based on the constituent data in the configured target area, determine the proportion of the configured target areas (excluding those that do not respond) in all areas, and use this proportion as the available adjustable area proportion. Based on the duration of peak electricity consumption periods and the proportion of available adjustable distribution areas in the target distribution area on different dates, the energy storage regulation and control strategy of the energy storage system in the target distribution area in other distribution areas is determined.

[0090] It should be noted that if the proportion of available regulation areas does not meet the requirements, i.e., the number is small, for example, less than 0.2, then the energy storage regulation control strategy of the energy storage system in the target area is determined to be that all energy storage systems in the target area, except those that do not respond, will respond when there is an energy storage regulation demand in other distribution areas.

[0091] Additionally, it is understandable that if the proportion of the target distribution area that does not respond to any of the configurations meets the requirements, then it is necessary to determine the duration of the peak electricity consumption period of the target distribution area on different dates. If the duration of the peak electricity consumption period of the target distribution area on different dates is less than the first duration threshold, for example, 1 hour, then it is determined that the energy storage system of the target distribution area will respond to the energy storage regulation control strategy in other distribution areas when there is an energy storage regulation demand in other distribution areas.

[0092] Furthermore, if the duration of peak electricity consumption periods in the target distribution area on different dates is not less than the first duration threshold, then the energy storage regulation control strategy of the energy storage system in the target distribution area in other distribution areas is determined to be that when the capacity of the energy storage system in the target distribution area is within the target capacity range, the energy storage regulation control strategy of the energy storage system in the target distribution area in other distribution areas is determined to be that it responds whenever there is an energy storage regulation demand in other distribution areas.

[0093] Another understandable point is that when the target capacity range is not met, the response will occur when there is energy storage regulation demand in other distribution substations where the overlap factor does not meet the requirements.

[0094] This embodiment aims to address a key issue in the operation of target transformer substations with configured energy storage: how to formulate control strategies for their energy storage systems to respond to external substation support requests. The core logic of this method is to balance "self-preservation priority" with "global mutual assistance" based on the target substation's own load pressure (peak duration) and the availability of similar resources within the region, generating tiered and condition-based cross-regional support strategies. Its goal is to maximize the collaborative and shared value of energy storage assets within the region while ensuring the reliability of its own power supply.

[0095] Step 1: Identify the transformer areas with "self-preservation priority" (those that cannot access resources): Keyword explanation: Target transformer areas that do not receive a response: These are transformer areas with extremely high peak load pressure. The criterion is that the average daily duration of their historical peak electricity consumption period is greater than a preset duration threshold (e.g., 2 hours).

[0096] Why do this? A long peak duration (>2 hours) means the transformer substation operates at high load for extended periods each day, making it extremely reliant on its own energy storage. This requires frequent and deep participation of its storage in local peak shaving. Responding to external requests during this time could easily deplete its own energy storage, leaving it unable to guarantee local power supply during subsequent peak hours and posing a risk. Therefore, the energy storage in such substations should be designated as regional "strategic reserves," used solely for self-protection and not for routine inter-regional support.

[0097] Significance: To set a safety red line for resource allocation and ensure that the power supply reliability of core nodes is not eroded by coordinated scheduling.

[0098] Step 2: Assess the richness of the regional collaborative resource pool: Keyword explanation: Available adjustment area ratio: refers to the proportion of the number of available energy storage areas in the region that are not "self-protection priority" to the total number of all target areas (i.e., energy storage areas already equipped).

[0099] If the available resources are very limited (less than 0.2%), it means that the vast majority of energy storage in the region is in a "self-preservation" state, and shareable resources are extremely scarce. In this case, every available resource is extremely valuable, and its utilization efficiency must be maximized to support the overall regulation needs of the region.

[0100] Significance: To assess the level of tension in regional coordination capabilities from a macro perspective and decide whether to adopt the "full support" model.

[0101] Step 3: Refined strategy generation based on two-layer criteria: Decision-making process and logic: Extremely scarce resources mode (unconditional full support); Triggering condition: Available adjustment area percentage < 0.2; Strategy: All available energy storage must respond to external demand.

[0102] Logic: This is a strategy employed under "wartime" or "resource scarcity" conditions. Due to the scarcity of mutually supportive resources, in order to maintain the region's most basic coordinated regulatory capacity, all available resources must be forced to be shared, even if this may cause certain risks or efficiency losses for the region itself. In this situation, the overall stability of the region takes precedence over the local optimization of individual energy storage systems.

[0103] Relatively abundant resources mode (with conditional refined support): Triggering condition: Available adjustment area percentage ≥ 0.2 Further decision-making: Further subdivide based on the load pressure characteristics of the transformer area itself.

[0104] A. Low-load pressure distribution area (“affluent households”); Feature: Peak daily duration is less than 1 hour.

[0105] Strategy: Respond unconditionally to all external support requests.

[0106] Logic: These types of transformer substations have very light loads and short peak periods, with their energy storage mostly idle. They are the "main force" and "high-quality shared resource" for regional collaboration, and their regulation potential should be fully utilized and prioritized for supporting external environments.

[0107] B. Medium-load pressure distribution area (“Balanced Household”); Feature: Peak daily duration ≥ 1 hour.

[0108] Refined strategy: Condition 1 (when in good condition): When the energy storage capacity is within the target capacity range (e.g., the "high-efficiency and flexible zone" with a state of charge (SOC) of 50%-80%), respond to all external requests.

[0109] Logic: At this point, the energy storage has both sufficient power and the capacity to absorb power, and is in its optimal working state. Participating in cross-regional regulation has the least impact on its own operation and the highest efficiency.

[0110] Condition 2 (when its own state is limited): When the energy storage capacity is not in the target range (such as when the SOC is too high or too low), it will only respond to requests from "highly synchronous stations with a coincidence factor > 0.1".

[0111] Logic: This is the brilliance of the strategy. When its own condition is poor, support actions need to be more selective. Prioritizing support for "highly synchronized areas" is because these areas have load curves highly similar to its own: Supporting your grid partners is helping yourself: their peak periods are also your peak periods. When your own energy storage is in poor condition (such as when you are about to run out of power), preparing in advance for your grid partners' peak periods (for example, charging them before their peak, which actually helps the grid to transfer load in advance) can indirectly alleviate the pressure of the upcoming shared peak periods that you will also face.

[0112] Establish a "priority mutual assistance circle": This is equivalent to forming a priority mutual assistance alliance based on load similarity within the region. When resources are limited, the stability within the alliance is prioritized, which is more strategic and efficient than aimless support.

[0113] Summary and Value of Implementation Examples: Achieving an intelligent balance between "self-interest" and "altruism": This strategy system perfectly balances the dual role of energy storage as a local asset and a shared asset. It not only sets up a "firewall" to ensure the security of local power supply (self-protection priority distribution areas), but also designs refined rules to maximize overall benefits under different resource adequacy levels and its own state.

[0114] Building a resilient collaborative network: The strategy is dynamically adjusted based on the degree (proportion) of regional resource scarcity, making the collaborative network resilient: refined operation when resources are abundant, and switching to a backup mode when resources are scarce. This enhances the resilience of the regional energy system to cope with different operating conditions.

[0115] Encouraging the emergence of "priority mutual assistance circles": By guiding energy storage to prioritize support for distribution transformer areas with similar loads, a "micro-ecosystem" or "community" based on data similarity is implicitly fostered within the distribution network. This mutual assistance within the community is more efficient and predictable, representing an important self-organizing form for future distributed smart grids.

[0116] Improved operational economy and safety: Through condition detection (capacity range) and selective response, inefficient or even harmful charging and discharging of energy storage under adverse conditions is avoided, protecting equipment lifespan. At the same time, the overall power supply quality of critical load groups is improved through priority mutual assistance mechanism.

[0117] This method upgrades cross-regional energy storage collaboration from a simple "request-response" model to an intelligent decision-making system with self-state awareness, regional resource assessment, and strategic priority judgment capabilities. It is the core control logic for achieving truly efficient, safe, and intelligent aggregation of distributed resources on the distribution network side.

[0118] Specifically, the method for determining the configuration targets of energy storage systems in the other distribution substations is as follows: Based on the energy storage regulation data of energy storage systems in other distribution areas during the energy storage regulation demand periods, the energy storage regulation demand periods that have not undergone energy storage regulation processing are determined. Based on the idle status of the energy storage system in the target area during the energy storage regulation demand period without energy storage regulation processing, determine the target area where the energy storage system is idle during different energy storage regulation demand periods without energy storage regulation processing. Based on the other distribution substations, during the energy storage regulation demand period when no energy storage regulation processing is performed, there are a number of target substations where the energy storage system is idle, and it is determined whether the other distribution substations are the target substations for the energy storage system.

[0119] It should be noted that if the average daily number of other distribution substations whose energy storage regulation demand during the period of energy storage regulation is not met, and the number of other distribution substations is within the preset range of the number of distribution substations, for example, less than 3, then the working mode of the energy storage system can be adjusted to further determine whether other distribution substations need to be configured with energy storage systems. Therefore, it is determined that the other distribution substations are not the configuration targets of the energy storage system.

[0120] Specifically, if the average number of other distribution substations during the energy storage regulation demand period that does not meet the requirements is not within the preset distribution substation number range, and the average number of target configuration substations where the energy storage system is idle during the energy storage regulation demand period that does not meet the requirements (e.g., not less than 1.5), then it is determined that the other distribution substations do not belong to the configuration targets of the energy storage system.

[0121] Furthermore, if the average number of target distribution areas where the energy storage system is idle during the energy storage regulation demand period without energy storage regulation processing meets the requirements, and if the average daily duration of the energy storage regulation demand period without energy storage regulation processing is less than a preset average daily duration threshold, for example, less than 20 minutes, then it is determined that the other distribution areas do not belong to the configuration targets of the energy storage system.

[0122] Additionally, it can be understood that if the average daily duration of the energy storage regulation demand period without energy storage regulation is not less than a preset average daily duration threshold, then the other distribution substations are determined to be configuration targets of the energy storage system.

[0123] This embodiment aims to address a deeper issue in energy storage planning: after a batch of "seed" energy storage areas (target areas) have been established and put into operation, how to accurately identify which areas in a region still "must" build their own energy storage and which can rely entirely on shared resources. The core logic is: by analyzing historical operational data, identify periods where there is regulation demand that cannot be effectively met by existing "seed" energy storage resources ("unmet demand periods"), and further explore the availability of "seed" energy storage during these periods. If there is a significant demand gap, and the gap is not due to busy "seed" resources, it indicates that the area has a unique or strong demand pattern that cannot be reliably met through a sharing mechanism, and therefore, self-built energy storage is necessary.

[0124] Step 1: Define and locate the "unmet regulatory needs"; Keyword explanation: Energy storage regulation demand period: refers to the period during which other substations (non-seed substations) need external energy storage for power support or absorption due to their own load or power fluctuations.

[0125] Energy storage regulation demand periods without energy storage regulation processing: These refer to the periods within the aforementioned demand periods where no cross-regional energy storage regulation actually occurs. This may be due to the control strategy not being triggered, or more fundamentally, all available "seed" energy storage failing to respond.

[0126] Idle status of the energy storage system in the target area: This refers to the "seed" energy storage being in a callable state during a specific period of time, neither serving the local area nor external systems.

[0127] Identifying points of failure in collaborative energy sharing: The goal is to pinpoint the moments when existing sharing mechanisms "fail." These moments provide crucial evidence for assessing the need for additional independent energy storage.

[0128] Distinguishing the reasons: Unmet demand could be due to unavailable "seed" energy storage (all are busy), or it could be due to strategic non-responsiveness (such as the self-preservation strategy in the previous example). Analyzing the "idle state" aims to identify periods where "resources are available but not used," which points to problems such as strategic conservatism or communication issues; while periods where "no idle resources are available" indicate insufficient total resources.

[0129] Significance: By working backward from operational results to identify planning deficiencies, decisions can be made based on actual "pain points" rather than theoretical predictions.

[0130] Step 2: Identify "must-build" transformer substations through a multi-level decision-making funnel; Overall analysis target: For each non-seed station area (i.e., "other distribution stations"), analyze its historical data.

[0131] Level 1: Demand scarcity filtering (sparse demand, no need to build it yourself); Condition: The number of transformer substations with an average daily number of more than 2 periods without energy storage regulation is less than 3.

[0132] Decision: These transformer substations are not targets for energy storage system configuration.

[0133] Logic and Significance: These types of distribution areas have almost no unmet regulation needs, or the needs are very sporadic. For sporadic needs, it is entirely possible to cover them by optimizing the control strategies of existing "seed" energy storage (such as adjusting response thresholds and enabling deep charge and discharge), without the need to initiate expensive new hardware. The small number (<3) also indicates that this is not a widespread problem.

[0134] Level 2: Resource availability check (If resources are available but not used, the problem lies in the strategy). Triggering condition: Passing the first level (i.e., the number of transformer substations with an average of more than 2 daily time periods without energy storage regulation is ≥3).

[0135] Analysis: During the energy storage regulation demand period without energy storage regulation processing, there is an average number of target substations where the energy storage system is idle, provided that the "average number of idle substations" is ≥ 1.5.

[0136] Decision: This area is not a target for energy storage system configuration.

[0137] Logic and Significance: Numerous contradictory situations exist where "idle resources exist but demand remains unmet." This strongly suggests that the root cause lies in operational strategies, communication, or market mechanisms, rather than insufficient physical resources. For example, the strategy might be overly conservative, prohibiting invocation, or there might be a lack of effective scheduling instructions. In such cases, optimizing software, strategies, and mechanisms should be prioritized over adding new hardware.

[0138] Level 3: Severity test of the demand gap (the gap is short-lived and tolerable): Triggering condition: Passing the second level (i.e., insufficient average idle resources, <1.5).

[0139] Analysis: Calculate the average cumulative daily duration of unmet adjustment needs.

[0140] Condition: If the average daily duration is less than 20 minutes.

[0141] Decision: This area is not a target for energy storage system configuration.

[0142] Logic and Significance: Although resources are relatively scarce and there is indeed unmet demand, the total daily shortfall time is very short (<20 minutes). From an engineering economics perspective, investing in energy storage for a shortfall of less than 20 minutes per day results in an extremely low return on investment. Shortfalls of this magnitude can be addressed more economically through demand-side management (such as fine-tuning air conditioning), activating backup diesel generators, or temporarily accommodating minor voltage deviations.

[0143] Final stage: Self-construction is confirmed (insufficient resources, significant gap). Triggering conditions: Passing the first three layers of filtering (i.e., there is significant unmet demand, insufficient idle resources, and a long gap period).

[0144] Condition: The average daily cumulative time spent not meeting the requirement is ≥ 20 minutes.

[0145] Decision: This area is a target for the configuration of an energy storage system.

[0146] Logic and Significance: This transformer substation simultaneously meets the following three stringent conditions: There is a persistent and significant need for adjustment (not sporadic).

[0147] Existing regional shared energy storage resources cannot meet the demand during critical periods (due to insufficient resources or they have already been occupied).

[0148] The duration of unmet needs has reached a level that cannot be ignored (≥20 minutes / day).

[0149] This constitutes a clear chain of evidence proving that relying solely on regional resource sharing cannot reliably guarantee the operational quality of the area, providing indisputable proof of the necessity for building its own energy storage.

[0150] Summary and Value of Implementation Examples This shift from "subjective judgment" to "data-driven argumentation" means that the decision to allocate energy storage to a particular transformer area no longer relies on guesswork based on experience, but is based on rigorous data analysis of historical cases of failed collaborative operations, making investment decisions more scientific and persuasive.

[0151] Avoid overinvestment and underinvestment: Over-investment: Through the first three levels of filtering, areas that can be resolved by "optimizing software strategies" or "tolerating minor gaps" are effectively eliminated, preventing unnecessary hardware investment.

[0152] Insufficient investment: By using the most stringent conditions, we can accurately identify those transformer substations that truly have "hard gaps" and ensure that key issues are resolved.

[0153] Driving a closed-loop iteration of operation and planning: The results analyzed by this method (such as the strategic issues revealed in the second level) can be immediately fed back to the operations department to optimize the control strategies of existing energy storage and improve sharing efficiency. This forms a closed loop of "planning-operation-evaluation-replanning".

[0154] Supporting accurate investment benefit analysis: For transformer areas that are determined to "require self-construction", indicators such as "average daily duration of unmet demand" can be directly converted into the basis for estimating the capacity and power demand of energy storage configuration, as well as the expected problem electricity / power value that can be solved after the investment is completed, providing key inputs for financial evaluation.

[0155] This approach represents the core thinking behind next-generation distribution network planning tools: dynamic, precise, and economical asset investment decisions based on feedback from actual system operation. It ensures that every penny invested in energy storage is used effectively to solve real problems that cannot be addressed through other, more economical means.

[0156] Example 2 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 method for optimizing the configuration of an energy storage system taking into account electricity consumption data when running the computer program.

[0157] Specifically, it has been determined that energy storage systems need to be installed in other distribution substations, including: Based on the overlap between the peak power consumption period of the target distribution area and other distribution areas, determine the number of overlaps between the peak power consumption periods of other distribution areas and the peak power consumption period of the target distribution area. It should be noted that if the data from other distribution substations does not meet the requirements in the above steps, since there are a large number of other distribution substations, it is necessary to configure the energy storage system in other distribution substations to ensure the reliability of the regulation and processing of the energy storage system in these other distribution substations.

[0158] Additionally, it is understood that if the data of the other distribution substations meet the requirements, it is necessary to further determine the ratio of the number of other distribution substations to the number of target substations, and use it as the adjustment matching ratio. When the adjustment matching ratio meets the requirements, the number of target substations is relatively large while the number of other distribution substations is relatively small, so it is determined that there is no need to perform energy storage system setup in the other distribution substations.

[0159] In this application, by first assessing the number of other distribution substations, and then, when there are a large number of other distribution substations, further optimization of the energy storage system configuration can be carried out, thereby improving the reliability of energy storage regulation in other distribution substations.

[0160] Based on the number of overlaps, the overlap coefficient between the peak power consumption period of the other distribution transformer areas and the target distribution transformer area is determined. It is understood that the overlap coefficient between the peak power consumption periods of the other distribution substations and the target substation is determined based on the proportion of the peak power consumption periods of the other distribution substations that also belong to the peak power consumption periods of the target substation.

[0161] It should be noted that the overlap coefficient ranges from 0 to 1. The larger the overlap coefficient, the worse the reliability of energy storage regulation in other distribution substations using the target substation. Therefore, it is necessary to set up energy storage systems in other distribution substations.

[0162] Understandably, if the average overlap coefficient between the peak power consumption periods of other distribution substations and the target substation does not meet the requirements, it indicates a high degree of overlap, thus necessitating the installation of energy storage systems in other distribution substations.

[0163] By utilizing the overlap coefficient between the peak power consumption periods of the other distribution substations and the target substation, as well as the data from the other distribution substations, it is determined whether energy storage system setup is required in the other distribution substations.

[0164] Specifically, by utilizing the overlap coefficient between the peak electricity consumption periods of the other distribution substations and the target substation, as well as data from the other distribution substations, it is determined whether energy storage system setup is required in the other distribution substations. This includes: Based on the average overlap coefficient between the peak power consumption periods of the other distribution transformer areas and the target distribution transformer area, the threshold for adjusting the matching ratio is determined; If the adjustment matching ratio is less than the adjustment matching ratio value, then it is determined that there is no need to set up an energy storage system in other distribution substations.

[0165] It should be noted that the threshold for adjusting the matching ratio is determined based on the average overlap coefficient between the peak power consumption periods of the other distribution substations and the target substation. The larger the average overlap coefficient between the peak power consumption periods of the other distribution substations and the target substation, the smaller the threshold for adjusting the matching ratio.

[0166] Example 3 Furthermore, the method for determining the energy storage regulation and control strategy of the energy storage system in the target distribution area in other distribution areas is as follows: Based on the configuration data of the target station area, determine the proportion of the target station area in all stations and use it as the configuration composition proportion. It should be noted that when the configuration composition ratio does not meet the requirements, since the number of target distribution areas is relatively small, in order to quickly identify the target distribution areas that need to be configured with energy storage systems, the energy storage regulation and control strategy for the energy storage systems of the target distribution areas in other distribution areas is determined as follows: all energy storage systems of the target distribution areas will respond when there is an energy storage regulation demand in other distribution areas, thereby ensuring that other distribution areas with energy storage system settings can be identified in a timely and effective manner.

[0167] Additionally, it can be understood that when the configuration composition ratio meets the requirements, the total number of target distribution areas is further determined. If the total number of target distribution areas is not within the preset range, i.e., the number is small, the energy storage regulation and control strategy for the energy storage system of the target distribution area in other distribution areas is determined to be that all energy storage systems of the target distribution areas respond when there is an energy storage regulation demand in other distribution areas, thereby ensuring that other distribution areas with energy storage systems can be identified in a timely and effective manner.

[0168] Based on the peak electricity consumption data of the configured target distribution area, the duration of the peak electricity consumption period of the configured target distribution area on different dates is determined; It should be noted that in the above steps, it is also necessary to determine the duration of the peak electricity consumption period of the target distribution area on different dates. If the average duration of the peak electricity consumption period of the target distribution area on different dates is greater than the preset duration threshold, in order to ensure the reliability of the regulation and processing of the energy storage system of the target distribution area, it is determined that the energy storage system of the target distribution area will not respond when there is an energy storage regulation demand in other distribution areas.

[0169] Based on the duration of peak electricity consumption periods and the proportion of configuration components in the target distribution area on different dates, the energy storage regulation and control strategy of the energy storage system in the target distribution area is determined in other distribution areas.

[0170] Specifically, if the proportion of the target distribution area that does not respond to any configuration does not meet the requirements (i.e., the number is small), then the energy storage regulation and control strategy of the energy storage system of the target distribution area in other distribution areas is determined to be that all energy storage systems of the target distribution area that does not respond to any configuration respond to any configuration respond to any energy storage regulation needs in other distribution areas.

[0171] Additionally, it is understandable that if the proportion of the target distribution area that does not respond to any of the configurations meets the requirements, then it is necessary to determine the duration of the peak electricity consumption period of the target distribution area on different dates. If the duration of the peak electricity consumption period of the target distribution area on different dates is less than the first duration threshold, then it is determined that the energy storage system of the target distribution area will respond to the energy storage regulation control strategy in other distribution areas when there is an energy storage regulation demand in other distribution areas.

[0172] Furthermore, if the duration of peak electricity consumption periods in the target distribution area on different dates is not less than the first duration threshold, then the energy storage regulation control strategy of the energy storage system in the target distribution area in other distribution areas is determined to be that when the capacity of the energy storage system in the target distribution area is within the target capacity range, the energy storage regulation control strategy of the energy storage system in the target distribution area in other distribution areas is determined to be that it responds whenever there is an energy storage regulation demand in other distribution areas.

[0173] Another understandable point is that when the target capacity range is not met, the response will occur when there is energy storage regulation demand in other distribution substations where the overlap factor does not meet the requirements.

[0174] In one possible embodiment, the target capacity range is an energy storage capacity range with a high degree of adjustment flexibility. In another possible embodiment, the target capacity range is between 40% and 60% of the rated energy storage capacity.

[0175] Example 4 Furthermore, the method for determining the configuration targets of energy storage systems in the other distribution substations is as follows: Based on the energy storage regulation data of energy storage systems in other distribution areas during the energy storage regulation demand periods, the energy storage regulation demand periods that have not undergone energy storage regulation processing are determined. Specifically, the energy storage regulation demand period is the period during which the other distribution radio areas issue energy storage regulation demand commands, and the energy storage regulation demand period without energy storage regulation processing is the period during which energy storage systems lack energy storage regulation demand commands.

[0176] Based on the idle status of the energy storage system in the target area during the energy storage regulation demand period without energy storage regulation processing, determine the target area where the energy storage system is idle during different energy storage regulation demand periods without energy storage regulation processing. Based on the other distribution substations, during different energy storage regulation demand periods without energy storage regulation processing, the configuration target substation data of the energy storage system in an idle state are used to determine whether the other distribution substations are the configuration targets of the energy storage system.

[0177] It is understandable that if there are no periods of energy storage regulation demand in the other distribution substations that have not undergone energy storage regulation, then the other distribution substations are determined not to be the configuration target of the energy storage system.

[0178] Furthermore, if there are periods of energy storage regulation demand in other distribution substations that have not undergone energy storage regulation, and the average daily number of such periods meets the requirements, then it is determined that the other distribution substations are not part of the energy storage system's configuration target.

[0179] It should also be noted that if the average daily quantity of energy storage regulation demand during periods without energy storage regulation does not meet the requirements, then the number of other distribution substations whose average daily quantity during periods without energy storage regulation does not meet the requirements will be determined. If the number of other distribution substations whose average daily quantity during periods without energy storage regulation does not meet the requirements is within the preset range of the number of distribution substations, then this can be further determined by adjusting the working mode of the energy storage system, thereby determining whether other distribution substations need to be configured with energy storage systems. Therefore, it is determined that the other distribution substations are not the target for energy storage system configuration.

[0180] Specifically, if the average daily number of other distribution substations that do not meet the requirements during the energy storage regulation demand period is not within the preset distribution substation number range, and if the average daily number of other distribution substations that do not meet the requirements during the energy storage regulation demand period does not meet the requirements, then the average number of target distribution substations where the energy storage system is idle during the energy storage regulation demand period does not meet the requirements, then it is determined that the other distribution substations do not belong to the energy storage system's configuration target.

[0181] Furthermore, if the average number of target distribution areas where the energy storage system is idle during the energy storage regulation demand period without energy storage regulation processing meets the requirements, then the other distribution areas are determined to be the target distribution areas of the energy storage system.

[0182] 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.

[0183] 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.

[0184] 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 method for optimizing the configuration of an energy storage system that considers electricity consumption data, characterized in that, Specifically, it includes: Based on the analysis results of electricity consumption data, the overlapping data of peak electricity consumption periods in different distribution substations are determined. Based on the overlapping data, the distribution substations that need to be configured with energy storage systems are determined and used as the configuration target substations. According to the overlap between the peak electricity consumption periods of the configuration target substations and other distribution substations, if it is determined that energy storage system configuration processing needs to be carried out in other distribution substations, the next step is initiated. Based on the configuration data of the target distribution area and the peak electricity consumption period data, the energy storage regulation and control strategy of the energy storage system in the target distribution area in other distribution areas is determined. Based on the idle status of the energy storage system in the target distribution area during the energy storage regulation demand period of the energy storage system in other distribution areas, the configuration target of the energy storage system in the other distribution areas is determined.

2. The energy storage system optimization configuration method considering electricity consumption data as described in claim 1, characterized in that, The distribution substation is divided according to the distribution area of ​​the distribution transformer, specifically the distribution area of ​​the distribution transformer is divided into a distribution substation.

3. The energy storage system optimization configuration method considering electricity consumption data as described in claim 1, characterized in that, The peak electricity consumption period is determined based on the unit time period of the distribution substation within the target electricity consumption range.

4. The energy storage system optimization configuration method considering electricity consumption data as described in claim 1, characterized in that, The target power consumption range is determined based on the maximum power consumption of the distribution substation in history.

5. The energy storage system optimization configuration method considering electricity consumption data as described in claim 1, characterized in that, The method for determining the distribution station area that requires configuration processing of the energy storage system is as follows: Based on the overlapping data of peak power consumption periods in the distribution transformer area, determine the number of overlapping peak power consumption periods between the distribution transformer area and other distribution transformer areas; The overlap factor is determined based on the proportion of the peak power consumption periods of the distribution substation overlapping with those of other distribution substations within the peak power consumption periods of the distribution substation. Based on the overlap factor, it is determined whether the distribution station area is one that requires configuration processing of an energy storage system.

6. The energy storage system optimization configuration method considering electricity consumption data as described in claim 5, characterized in that, When the overlap factor is less than a preset overlap factor threshold, the energy storage system is set up in the distribution area.

7. The energy storage system optimization configuration method considering electricity consumption data as described in claim 1, characterized in that, The other distribution radio areas refer to distribution radio areas excluding the target distribution radio area.

8. The energy storage system optimization configuration method considering electricity consumption data as described in claim 1, characterized in that, The method for determining the configuration targets of energy storage systems in the other distribution substations is as follows: Based on the energy storage regulation data of energy storage systems in other distribution areas during the energy storage regulation demand periods, the energy storage regulation demand periods that have not undergone energy storage regulation processing are determined. Based on the idle status of the energy storage system in the target area during the energy storage regulation demand period without energy storage regulation processing, determine the target area where the energy storage system is idle during different energy storage regulation demand periods without energy storage regulation processing. Based on the other distribution substations, during different energy storage regulation demand periods without energy storage regulation processing, the configuration target substation data of the energy storage system in an idle state are used to determine whether the other distribution substations are the configuration targets of the energy storage system.

9. The energy storage system optimization configuration method considering electricity consumption data as described in claim 8, characterized in that, If there are no periods of energy storage regulation demand in the other distribution substations that have not undergone energy storage regulation, then it is determined that the other distribution substations are not the configuration targets of the energy storage system.

10. A computer system, comprising: A memory and 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 method for optimizing the configuration of an energy storage system taking into account electricity consumption data as described in any one of claims 1-9.

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