A frequency self-healing method for active power distribution network considering multiple energy storage modes

Abstract

The application discloses a kind of active power distribution network frequency self-healing method considering containing multiple energy storage mode, the method includes the following steps: step S1: establish based on Markov chain multi-mode energy storage model, respectively determine the remaining frequency modulation capacity of multiple mode energy storage, and the power output uncertainty of multi-mode energy storage model is quantitatively analyzed;Step S2: construct the safety evaluation index system suitable for containing distributed energy active power distribution network, the frequency modulation capacity of multi-mode energy storage model is evaluated, and according to its frequency modulation capacity evaluation result, generate first frequency sequence;Step S3: in first frequency sequence, introduce energy storage participation frequency modulation willingness strategy, generate second frequency sequence;Step S4: introduce the frequency modulation incentive factor considering frequency modulation mileage, sort frequency modulation mileage, and set frequency modulation incentive factor according to frequency modulation mileage sorting, and with second frequency sequence again superimposed, generate third frequency sequence.

Description

Technical Field

[0001] This invention relates to the field of active distribution network security assessment technology, and in particular to an active distribution network frequency self-healing method that considers multiple energy storage modes. Background Technology

[0002] With the acceleration of the global energy transition, the penetration rate of distributed energy sources, such as photovoltaic power generation, wind power generation, and energy storage systems, in distribution networks is constantly increasing. These distributed energy sources have advantages such as being clean, efficient, and flexible, effectively alleviating the pressure on traditional energy sources and improving energy utilization efficiency. However, the integration of distributed energy sources also brings new challenges to the safe operation of distribution networks. Due to the intermittent and uncertain output power of distributed energy sources, and the more complex topology and operation mode of distribution networks, traditional distribution network safety assessment methods are no longer suitable for the new operating environment.

[0003] The new power distribution network includes lithium-ion battery energy storage, flow battery energy storage, flywheel energy storage, compressed air energy storage, and supercapacitor energy storage. Different types of energy storage have different power densities and charging / discharging capabilities, and are located in different positions in the power network. They are distributed on the user side, as well as in distribution substations and switching stations. In addition, some energy storage is directly used as shared energy storage for commercial dispatch.

[0004] This invention discloses a frequency self-healing method for active distribution networks considering multiple energy storage modes. It addresses the problem that with the large-scale integration of multi-mode energy storage, the operating characteristics of distribution networks have changed significantly, making traditional frequency regulation methods insufficient to meet practical needs. Summary of the Invention

[0005] In view of the above-mentioned prior art, the present invention provides an active distribution network frequency self-healing method and system that considers multiple energy storage modes, mainly to solve the technical problems existing in the background art.

[0006] To achieve the above objectives, the technical solution of this invention is implemented as follows:

[0007] This invention discloses a frequency self-healing method for an active distribution network considering multiple energy storage modes. The method includes the following steps: Step S1: Establish a Markov chain-based multi-mode energy storage model, determine the remaining frequency regulation capacity of multiple energy storage modes respectively, and perform quantitative analysis on the power output uncertainty of the multi-mode energy storage model.

[0008] Step S2: Construct a safety evaluation index system suitable for active distribution networks with distributed energy resources, evaluate the frequency regulation capability of the multi-mode energy storage model, and generate the first frequency regulation order based on the evaluation results of its frequency regulation capability; Step S3: In the first frequency regulation order, introduce the energy storage participation frequency regulation willingness strategy to generate the second frequency regulation order.

[0009] Step S4: Introduce a frequency modulation excitation factor that takes into account the frequency modulation mileage, sort the frequency modulation mileage, set the frequency modulation excitation factor according to the frequency modulation mileage sorting, and superimpose it with the second frequency modulation order to generate the third frequency modulation order.

[0010] As a preferred embodiment of the present invention, step S1: For a multi-mode energy storage system, its remaining energy storage capacity is represented by a stationary distribution, which is obtained by solving the following system of equations: in, Represents the remaining energy storage capacity of distributed energy storage The initial distribution, Indicates the remaining energy storage capacity of small-capacity centralized energy storage The initial distribution, Indicates the remaining energy storage capacity of large-capacity centralized energy storage The initial distribution, Represents the remaining energy storage capacity of distributed energy storage a stable distribution Indicates the remaining energy storage capacity of small-capacity centralized energy storage a stable distribution Indicates the remaining energy storage capacity of large-capacity centralized energy storage a stable distribution This represents the state transition matrix of an active power distribution network.

[0011] As a preferred embodiment of the present invention, the safety evaluation indicators in the safety evaluation index system for active distribution networks containing distributed energy in step S2 specifically include: power balance of active distribution network, power balance of active distribution network, risk of distributed power source absorption, voltage safety risk of distribution network, and frequency safety risk of distribution network.

[0012] As a preferred embodiment of the present invention, step S2 specifically includes: performing normalization and dimensionless processing on the basis of the traditional entropy weight method, and using it to calculate the weight of each safety evaluation index; at the same time, solving the entropy value of each safety evaluation index through the characteristic coefficients of the safety evaluation index, as follows: in, Indicates the first The weight of each safety evaluation indicator, Indicates the first The entropy value corresponding to each security evaluation indicator This represents the total number of safety evaluation indicators.

[0013] As a preferred embodiment of the present invention, step S2 specifically includes: calculating the comprehensive evaluation value of each energy storage mode by assigning weights to each safety evaluation index; determining the weights of safety evaluation indices that are lower than or equal to the average level using the entropy method; setting the weights of comprehensive evaluation values ​​that are higher than the average level to 0; and obtaining the weight matrix. The overall evaluation value of the energy storage mode is: in, For the first The comprehensive evaluation value of each energy storage mode For the first assessment The entropy value of an energy storage mode. The total number of each energy storage mode is represented; the comprehensive evaluation value of each energy storage mode specifically represents the frequency regulation capability of the distributed energy storage Fs, small-capacity centralized energy storage Xs, and large-capacity centralized energy storage Ds generated, and the first frequency regulation order is generated according to the frequency regulation capability of each energy storage mode.

[0014] As a preferred embodiment of the present invention, the specific process of step S3 is as follows: Classify the energy storage frequency regulation willingness of user-side energy storage, distribution substation-side energy storage, switching station energy storage, and commercial shared energy storage, and set the user-side energy storage frequency regulation willingness factor as follows: The frequency regulation willingness factor for distribution radio station area-side energy storage is The frequency regulation willingness factor for energy storage in switching stations is The willingness factor for commercial shared energy storage frequency regulation is Set the rating as > = > The frequency modulation strategy incorporates various user preferences and generates a second frequency modulation sequence.

[0015] As a preferred embodiment of the present invention, step S4 specifically includes: setting the frequency regulation mileage according to the remaining energy storage capacity, setting the frequency regulation rate, sorting the frequency regulation capabilities of each generated energy storage mode according to the frequency regulation mileage, setting the frequency regulation excitation factor according to the frequency regulation mileage sorting, superimposing it with the second frequency regulation order, and then generating the third frequency regulation order.

[0016] The beneficial effects of this invention are as follows: Compared with the prior art, this invention provides a frequency self-healing method for active distribution networks considering multiple energy storage modes. With the large-scale integration of multi-mode energy storage, the operating characteristics of distribution networks have changed significantly, and traditional frequency regulation methods are no longer sufficient to meet actual needs. Firstly, uncertainty analysis is performed on distributed energy storage, small-capacity centralized energy storage, and large-capacity centralized energy storage based on Markov chains. Then, frequency regulation reliability analysis is conducted on active distribution networks with multiple energy storage modes by incorporating different uncertainties from various energy storage modes. Energy storage frequency regulation reliability coefficients are introduced for each mode, and the frequency regulation order is set according to the reliability coefficients. A frequency regulation intention classification strategy is proposed. By configuring different frequency regulation intentions and frequency regulation incentive factors, the frequency self-healing recovery capability of the local active distribution network is improved, providing strong technical support for the safe operation of the distribution network. At the same time, the frequency self-healing capability and economy of the active distribution network are improved. This method is more in line with actual operating conditions and is suitable for complex grid environments with a high proportion of renewable energy integration. Attached Figure Description

[0017] Figure 1 This application presents a flowchart of an active distribution network frequency self-healing method incorporating multiple energy storage modes. Figure 1 ;

[0018] Figure 2 This application presents a flowchart of an active distribution network frequency self-healing method incorporating multiple energy storage modes. Figure 2 ;

[0019] Figure 3 This application provides a schematic diagram illustrating the generation of the second frequency modulation sequence;

[0020] Figure 4 This is a schematic diagram illustrating the generation sequence of the third frequency modulation provided in this application. Detailed Implementation

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0022] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0023] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0024] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0025] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0026] Please refer to the attached document. Figure 1 and attached Figure 2 A frequency self-healing method for an active distribution network considering multiple energy storage modes, the method includes the following steps: Step S1: Establish a Markov chain-based multi-mode energy storage model, determine the remaining frequency regulation capacity of multiple energy storage modes respectively, and perform quantitative analysis on the power output uncertainty of the multi-mode energy storage model.

[0027] Step S2: Construct a safety evaluation index system suitable for active distribution networks with distributed energy resources, evaluate the frequency regulation capability of the multi-mode energy storage model, and generate the first frequency regulation order based on the evaluation results of its frequency regulation capability; Step S3: In the first frequency regulation order, introduce the energy storage participation frequency regulation willingness strategy to generate the second frequency regulation order.

[0028] Step S4: Introduce a frequency modulation excitation factor that takes into account the frequency modulation mileage, sort the frequency modulation mileage, set the frequency modulation excitation factor according to the frequency modulation mileage sorting, and superimpose it with the second frequency modulation order to generate the third frequency modulation order.

[0029] For example, this invention discloses a frequency self-healing method for active distribution networks considering multiple energy storage modes. With the large-scale integration of multi-mode energy storage, the operating characteristics of distribution networks have changed significantly, and traditional frequency regulation methods are no longer sufficient to meet practical needs. Firstly, uncertainty analysis is performed on distributed energy storage, small-capacity centralized energy storage, and large-capacity centralized energy storage based on Markov chains. Existing analyses of the frequency regulation capability of active distribution networks based on energy storage do not consider the different frequency regulation capabilities of various energy storage modes in the power network, and generate the first frequency regulation sequence based on reliable capacity, instantaneous power output rate, etc.

[0030] The strategy of incorporating energy storage into frequency regulation is introduced. The energy storage frequency regulation willingness is classified into user-side energy storage, distribution area-side energy storage, switching station energy storage, and commercial shared energy storage. User willingness is integrated into the frequency regulation strategy, resulting in a second frequency regulation order.

[0031] A frequency modulation excitation factor that takes into account the frequency modulation mileage is set up so that the initial frequency modulation order of the strategy that only considers frequency modulation capability and not frequency modulation intention is given priority, thus generating the third frequency modulation order.

[0032] By introducing different frequency regulation influencing factors in sequence, different frequency regulation sequences are generated, which can better utilize the energy storage resources in the local active power distribution network and improve the frequency self-healing capability of the local active power distribution network.

[0033] As a preferred embodiment of the present invention, step S1: For a multi-mode energy storage system, its remaining energy storage capacity is represented by a stationary distribution, which is obtained by solving the following system of equations: in, Represents the remaining energy storage capacity of distributed energy storage The initial distribution, Indicates the remaining energy storage capacity of small-capacity centralized energy storage The initial distribution, Indicates the remaining energy storage capacity of large-capacity centralized energy storage The initial distribution, Represents the remaining energy storage capacity of distributed energy storage a stable distribution Indicates the remaining energy storage capacity of small-capacity centralized energy storage a stable distribution Indicates the remaining energy storage capacity of large-capacity centralized energy storage a stable distribution This represents the state transition matrix of an active power distribution network, i.e., the state transition of distributed energy resources in an active power distribution network from state to state. to state The number of times and the residual state of frequency regulation power in active distribution networks The ratio of .

[0034] Specifically, the parameters are derived from actual power system operation data, covering remaining power information and status change information for various energy storage modes. To ensure data accuracy and usability, the raw data underwent preprocessing, including data cleaning, missing value imputation, and outlier handling.

[0035] For energy storage devices with multiple energy storage modes, the number of transitions between different states is counted and normalized to transition probabilities.

[0036] For example, suppose the state space of an energy storage device with multiple energy storage modes is: Based on historical data or observations, statistics are generated from each state. Transition to state The frequency of [the event / event]. Therefore, the state transition matrix of the active power distribution network [is determined by the event / event]. Indicates from state to state The number of times and the residual state of frequency regulation power in active distribution networks The ratio of .

[0037] For example, an active distribution network system containing multiple energy storage modes can be viewed as simultaneously including distributed energy storage, small-capacity centralized energy storage, and large-capacity centralized energy storage. To analyze the power output uncertainty of multi-mode energy storage, a model capable of describing its dynamic behavior is established.

[0038] The purpose of uncertainty analysis is to quantify the uncertainty of a system's state and assess its impact on system performance. Within the Markov chain framework, uncertainty can be described by the stationary distribution of the system. The stationary distribution of the system... Represents the remaining energy storage capacity of distributed energy storage Small-capacity centralized energy storage with remaining energy storage capacity Large-capacity centralized energy storage with remaining energy storage capacity Remaining energy storage capacity during long-term operation The probability of.

[0039] The uncertainty of an active power distribution network system with multiple energy storage modes was quantitatively analyzed using a Markov chain model. Specifically, we calculated the expected power demand and the variance of the power demand to assess the level of uncertainty. The power demand fluctuations of the system are mainly concentrated in certain specific periods, which is closely related to the state transition characteristics of multi-mode energy storage. For example, state transitions are more frequent during certain peak periods, resulting in larger fluctuations in the system's power demand and a higher degree of uncertainty.

[0040] As a preferred embodiment of the present invention, the safety evaluation indicators in the safety evaluation index system for active distribution networks containing distributed energy in step S2 specifically include: power balance of active distribution network, power balance of active distribution network, risk of distributed power source absorption, voltage safety risk of distribution network, and frequency safety risk of distribution network.

[0041] As a preferred embodiment of the present invention, step S2 specifically includes: performing normalization and dimensionless processing on the basis of the traditional entropy weight method to avoid the problem of excessively amplifying the weight difference when all entropy values ​​approach 1 in the traditional entropy weight method, and using it to calculate the weight of each safety evaluation index. At the same time, the entropy value of each safety evaluation index is solved by the characteristic coefficient of the safety evaluation index, that is, the weight of the i-th safety evaluation index is expressed by the entropy value method, as follows: in, Indicates the first The weight of each safety evaluation indicator, Indicates the first The entropy value corresponding to each security evaluation indicator This represents the total number of safety evaluation indicators.

[0042] As a preferred embodiment of the present invention, step S2 specifically includes: calculating the comprehensive evaluation value of each energy storage mode by assigning weights to each safety evaluation index; determining the weights of safety evaluation indices that are lower than or equal to the average level using the entropy method; setting the weights of comprehensive evaluation values ​​that are higher than the average level to 0; and obtaining the weight matrix. The overall evaluation value of the energy storage mode is: in, For the first The comprehensive evaluation value of each energy storage mode For the first assessment The entropy value of an energy storage mode. This represents the total number of all energy storage modes; the comprehensive evaluation value of each energy storage mode specifically represents the distributed energy storage generated. The frequency regulation capabilities of small-capacity centralized energy storage (Xs) and large-capacity centralized energy storage (Ds) are determined, and the first frequency regulation sequence is generated according to the frequency regulation capability of each energy storage mode.

[0043] For example, through comprehensive evaluation, the frequency regulation capabilities of distributed energy storage Fs, small-capacity centralized energy storage Xs, and large-capacity centralized energy storage Ds will be generated, that is, the comprehensive evaluation value of distributed energy storage 1 SFs1, the comprehensive evaluation value of distributed energy storage 2 SFs2, ..., distributed energy storage Comprehensive evaluation value SFsn; Comprehensive evaluation value SXs1 for small-capacity centralized energy storage No. 1, Comprehensive evaluation value SXs2 for small-capacity centralized energy storage No. 2, ..., Small-capacity centralized energy storage Overall rating value SXs ;

[0044] Comprehensive evaluation value SDs1 for large-capacity centralized energy storage No. 1, comprehensive evaluation value SDs2 for large-capacity centralized energy storage No. 2, ..., large-capacity centralized energy storage Overall Evaluation Value (SDs) The first frequency modulation sequence.

[0045] As a preferred embodiment of the present invention, the specific process of step S3 is as follows: Classify the energy storage frequency regulation willingness of user-side energy storage, distribution substation-side energy storage, switching station energy storage, and commercial shared energy storage, and set the user-side energy storage frequency regulation willingness factor as follows: The frequency regulation willingness factor for distribution radio station area-side energy storage is The frequency regulation willingness factor for energy storage in switching stations is The willingness factor for commercial shared energy storage frequency regulation is Set the rating as > = > And by incorporating various user preference factors into the frequency modulation strategy, a result is generated. The second frequency modulation sequence is illustrated as follows: Figure 3 As shown.

[0046] Specifically, the frequency regulation willingness factor for energy storage on the distribution radio station side is: The frequency regulation willingness factor for energy storage in switching stations is Distribution area-side energy storage is typically installed in distribution area energy storage systems for dynamic capacity expansion, load mitigation, and smoothing of renewable energy generation output within the distribution area, thereby improving power quality and grid security. It is usually installed on the low-voltage side of the distribution area. Switchyard energy storage, on the other hand, is an energy storage system installed at switchyards to quickly restore power supply during grid faults, reducing outage time. Distribution area-side energy storage, unlike the other two types, is generally centrally dispatched by the grid operator to protect grid stability. The responsibility for its operation lies with the grid operator, hence the separate installation of distribution area-side energy storage systems. and The willingness to tune frequencies is equal.

[0047] Specifically, the frequency regulation willingness factor is derived by normalizing the frequency regulation economic cost, and its range is (0,1). Therefore, the user-side energy storage frequency regulation willingness factor is... The frequency regulation willingness factor for distribution radio station area-side energy storage is The frequency regulation willingness factor for energy storage in switching stations is The willingness factor for commercial shared energy storage frequency regulation is ;

[0048] Among them, the economic cost of frequency modulation = the cost of frequency modulation mileage × the cost of frequency modulation mileage + the cost of frequency modulation capacity × the cost of frequency modulation capacity.

[0049] As a preferred embodiment of the present invention, step S4 specifically includes: setting the frequency regulation mileage according to the remaining energy storage capacity, setting the frequency regulation rate, sorting the frequency regulation capabilities of each generated energy storage mode according to the frequency regulation mileage, setting the frequency regulation excitation factor according to the sorting result, superimposing it with the second frequency regulation order, and then generating the third frequency regulation order.

[0050] For example, the initial frequency regulation order, which prioritizes strategies that only consider frequency regulation capability and not frequency regulation intention, is prioritized to generate a third frequency regulation order. S represents the total frequency regulation mileage throughout the energy storage power station's lifecycle; the frequency regulation mileage is defined as: remaining energy storage capacity × frequency regulation rate × frequency regulation capability. Calculations are performed according to this formula, and the results are sorted from largest to smallest, with larger values ​​participating first.

[0051] Frequency regulation incentives mainly involve economic and policy measures to eliminate negative participation intentions of energy storage users. However, some frequency regulation intentions will not change due to incentives. Furthermore, the third frequency regulation comprehensively considers the modeling of capacity that transforms uncertainty into as certainty as possible, and takes into account the frequency regulation capability of the entire active power distribution network system. FM mileage is sorted, and FM excitation factor is set according to the FM mileage sorting. ,in, This indicates the frequency modulation excitation factor that ranks first. The meanings of the remaining frequency modulation excitation factors follow the same pattern, and this order is then superimposed on the second frequency modulation order to generate the third frequency modulation order. Since some energy storage modes do not change due to the presence of frequency modulation excitation factors, excitation limits need to be set for the frequency modulation excitation factors. Therefore, the third frequency modulation order is illustrated as follows: Figure 4 As shown.

[0052] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A frequency self-healing method for active distribution networks considering multiple energy storage modes, characterized in that, The method includes the following steps: Step S1: Establish a multi-mode energy storage model based on Markov chains, determine the remaining frequency regulation capacity of various energy storage modes, and perform quantitative analysis on the power output uncertainty of the multi-mode energy storage model. Step S2: Construct a safety evaluation index system suitable for active distribution networks with distributed energy resources, calculate the comprehensive evaluation value of each energy storage mode by weighting each safety evaluation index, evaluate the frequency regulation capability of the multi-mode energy storage model, and generate the first frequency regulation order based on the evaluation results of its frequency regulation capability. Step S3: In the first frequency regulation sequence, introduce the energy storage participation frequency regulation willingness strategy to generate the second frequency regulation sequence; Specifically, this includes classifying the frequency regulation willingness of user-side energy storage, distribution substation-side energy storage, switchyard energy storage, and commercial shared energy storage, and setting a frequency regulation willingness factor for user-side energy storage. The frequency regulation willingness factor for distribution radio station area-side energy storage is The frequency regulation willingness factor for energy storage in switching stations is The willingness factor for commercial shared energy storage frequency regulation is Set the rating as > = > The second frequency modulation order is generated by incorporating various user preference factors into the frequency modulation strategy. Step S4: Calculate the frequency regulation mileage of each energy storage unit according to the formula: Frequency Regulation Mileage = Remaining Energy Storage Capacity × Frequency Regulation Rate × Frequency Regulation Capacity; Sort the frequency regulation mileages from largest to smallest, set the frequency regulation excitation factor according to the frequency regulation mileage sorting, and superimpose it with the second frequency regulation order to generate the third frequency regulation order.

2. The method for frequency self-healing of an active distribution network considering multiple energy storage modes according to claim 1, characterized in that, Step S1: For a multi-mode energy storage system, its remaining energy storage capacity is represented by a stationary distribution, which is obtained by solving the following system of equations: in, Represents the remaining energy storage capacity of distributed energy storage The initial distribution, Indicates the remaining energy storage capacity of small-capacity centralized energy storage The initial distribution, Indicates the remaining energy storage capacity of large-capacity centralized energy storage The initial distribution, Represents the remaining energy storage capacity of distributed energy storage a stable distribution Indicates the remaining energy storage capacity of small-capacity centralized energy storage a stable distribution Indicates the remaining energy storage capacity of large-capacity centralized energy storage a stable distribution This represents the state transition matrix of an active power distribution network.

3. The method for frequency self-healing of an active distribution network considering multiple energy storage modes according to claim 2, characterized in that, The safety evaluation indicators in step S2 that are used to construct a safety evaluation indicator system applicable to active distribution networks containing distributed energy resources specifically include: power balance of active distribution networks, power balance of active distribution networks, risk of distributed power source absorption, voltage safety risk of distribution networks, and frequency safety risk of distribution networks.

4. The frequency self-healing method for an active distribution network considering multiple energy storage modes according to claim 3, characterized in that, Step S2 specifically includes: performing normalization and dimensionless processing on the traditional entropy weight method, and using this normalization to calculate the weights of each safety evaluation index; simultaneously, solving for the entropy value of each safety evaluation index through its characteristic coefficients, as detailed below: in, Indicates the first The weight of each safety evaluation indicator, Indicates the first The entropy value corresponding to each security evaluation indicator This represents the total number of safety evaluation indicators.

5. The method for frequency self-healing of an active distribution network considering multiple energy storage modes according to claim 4, characterized in that, Step S2 specifically includes: determining the weights of safety evaluation indicators that are below or equal to the average level using the entropy method, setting the weights of comprehensive evaluation values ​​that are above the average level to 0, and obtaining the weight matrix. The overall evaluation value of the energy storage mode is: in, For the first The comprehensive evaluation value of each energy storage mode For the first assessment The entropy value of an energy storage mode. This represents the total number of safety evaluation indicators. Indicates the first The weights of each safety evaluation index; the comprehensive evaluation value of each energy storage mode specifically represents the frequency regulation capability of the distributed energy storage Fs, small-capacity centralized energy storage Xs, and large-capacity centralized energy storage Ds, and generates the first frequency regulation order according to the frequency regulation capability of each energy storage mode.

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