A method, apparatus, equipment and medium for reliability assessment of battery energy storage power stations

Abstract

This application discloses a method, apparatus, equipment, and medium for reliability assessment of a battery energy storage power station, relating to the field of battery management technology. The method includes: dividing the performance state of the energy storage battery compartment into four performance states based on the State of Health (SOH) of the battery compartment, and determining the fuzzy probability of the battery compartment in each performance state using fuzzy mathematics theory; dividing the energy storage power station into a four-level model; constructing a general generating function for the energy storage battery compartment based on the fuzzy probability of the battery compartment in each performance state and the SOH corresponding to each performance state; constructing a multi-state hybrid accuracy model for the energy storage power station based on the general generating function of the battery compartment and the topology of the energy storage power station; and conducting a reliability assessment of the energy storage power station based on the multi-state hybrid accuracy model and the reliability index system of the energy storage power station, obtaining the reliability assessment result of the energy storage power station, and realizing the reliability assessment of the energy storage power station.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a method, apparatus, equipment and medium for reliability assessment of battery energy storage power stations. Background Technology

[0002] With the continuous transformation of the global energy structure, the proportion of clean and renewable energy in total energy consumption is also steadily increasing. Wind and solar energy, due to their inexhaustible and environmentally friendly nature, have received widespread attention and application globally. However, these energy sources are constrained by various factors such as geography and climate, exhibiting intermittency and instability, posing challenges to energy supply and impacting the stable operation of the power grid. Therefore, energy storage technology has emerged, capable of storing these unstable renewable energy sources and releasing them when needed, thereby addressing the issues of continuity and stability in energy supply.

[0003] Energy storage devices can store excess electrical energy and release it when needed to balance power supply and demand, improving the stability and reliability of the power grid and ensuring the normal operation of the power system. However, as the scale of energy storage systems expands, stability and reliability issues after their integration into the power grid gradually emerge. Especially in the event of system failures or anomalies, the integration of large-scale energy storage power stations may negatively impact grid stability. Therefore, reliability assessment of energy storage power stations is crucial. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment and medium for reliability assessment of battery energy storage power stations, which can realize refined modeling of battery energy storage power stations and reliability assessment of battery energy storage power stations.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides a reliability assessment method for battery energy storage power stations, including the following steps.

[0007] Based on the State of Health (SOH) of the energy storage battery compartment, the performance status of the energy storage battery compartment is divided into four performance states: excellent, degraded, alarm, and failure.

[0008] Based on the four performance states of the energy storage battery compartment, the fuzzy probability of the energy storage battery compartment under each performance state is determined using fuzzy mathematics theory.

[0009] The energy storage power station is divided into a four-level model; the four-level model includes energy storage battery compartment - energy storage subsystem - energy storage hub - energy storage power station; the energy storage power station includes multiple energy storage hubs; the energy storage hubs include multiple energy storage subsystems; the energy storage subsystems include multiple energy storage battery compartments.

[0010] Based on the fuzzy probabilities of the energy storage battery compartment under various performance states and the SOH corresponding to each performance state, a general generating function for the energy storage battery compartment is constructed.

[0011] Based on the general generating function of the energy storage battery compartment and the topology of the energy storage power station, a multi-state hybrid accuracy model of the energy storage power station is constructed. The multi-state hybrid accuracy model of the energy storage power station includes the general generating function of the energy storage battery compartment, the general generating function of the energy storage subsystem, the general generating function of the energy storage hub line, and the general generating function of the energy storage power station.

[0012] Based on the multi-state hybrid accuracy model and reliability index system of energy storage power station, the reliability of energy storage power station is evaluated, and the reliability evaluation results of energy storage power station are obtained; the reliability index system of energy storage power station includes dynamic index, static index and importance index.

[0013] Secondly, this application provides a battery energy storage power station reliability assessment device, which includes the following modules.

[0014] The performance status classification module is used to classify the performance status of the energy storage battery compartment into four performance statuses based on the SOH of the energy storage battery compartment; the four performance statuses include excellent, degradation, alarm and failure.

[0015] The fuzzy probability determination module is used to determine the fuzzy probability of the energy storage battery compartment under each of its four performance states using fuzzy mathematics theory.

[0016] The four-level model partitioning module is used to divide the energy storage power station into a four-level model. The four-level model includes an energy storage battery compartment, an energy storage subsystem, an energy storage hub, and an energy storage power station. The energy storage power station includes multiple energy storage hubs. The energy storage hub includes multiple energy storage subsystems. The energy storage subsystem includes multiple energy storage battery compartments.

[0017] The general generating function construction module for the energy storage battery compartment is used to: construct a general generating function for the energy storage battery compartment based on the fuzzy probability of the energy storage battery compartment under each performance state and the SOH corresponding to each performance state.

[0018] The module for constructing a multi-state hybrid accuracy model for energy storage power stations is used to: construct a multi-state hybrid accuracy model for energy storage power stations based on the general generation function of the energy storage battery compartment and the topology of the energy storage power station; the multi-state hybrid accuracy model for energy storage power stations includes the general generation function of the energy storage battery compartment, the general generation function of the energy storage subsystem, the general generation function of the energy storage hub line, and the general generation function of the energy storage power station.

[0019] The energy storage power station reliability assessment module is used to: conduct reliability assessment of the energy storage power station based on the multi-state hybrid accuracy model and the reliability index system of the energy storage power station, and obtain the reliability assessment result of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indexes, static indexes and importance indexes.

[0020] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described battery energy storage power station reliability assessment method.

[0021] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described battery energy storage power station reliability assessment method.

[0022] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, apparatus, equipment, and medium for reliability assessment of a battery energy storage power station. Based on the State of Harmony (SOH) of the energy storage battery compartment, the performance state of the energy storage battery compartment is divided into four performance states: excellent, degradation, alarm, and failure. Based on the four performance states of the energy storage battery compartment, fuzzy mathematics theory is used to determine the fuzzy probability of the energy storage battery compartment in each performance state. The energy storage power station is divided into a four-level model: the four-level model includes an energy storage battery compartment, an energy storage subsystem, an energy storage aggregation line, and an energy storage power station. The energy storage power station includes multiple energy storage aggregation lines; the energy storage aggregation lines include multiple energy storage subsystems; and the energy storage subsystems include multiple energy storage battery compartments. Based on the fuzzy probabilities of the energy storage battery compartment under various performance states and the State of Health (SOH) corresponding to each performance state, a general generating function for the energy storage battery compartment is constructed. Based on the general generating function of the energy storage battery compartment and the topology of the energy storage power station, a multi-state hybrid accuracy model of the energy storage power station is constructed. This multi-state hybrid accuracy model includes the general generating functions of the energy storage battery compartment, the energy storage subsystem, the energy storage convergence line, and the energy storage power station itself. Based on the multi-state hybrid accuracy model and the reliability index system of the energy storage power station, a reliability assessment of the energy storage power station is performed, yielding the reliability assessment results. This achieves refined modeling and reliability assessment of the battery energy storage power station. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is an application environment diagram of a battery energy storage power station reliability assessment method according to one embodiment of this application.

[0025] Figure 2 This is a flowchart illustrating a battery energy storage power station reliability assessment method provided in one embodiment of this application.

[0026] Figure 3 This is a schematic diagram of a multi-state hybrid accuracy model of an energy storage power station provided in an embodiment of this application.

[0027] Figure 4 This is a schematic diagram illustrating the specific implementation process of a battery energy storage power station reliability assessment method provided in one embodiment of this application.

[0028] Figure 5 This is a schematic diagram of the functional modules of a battery energy storage power station reliability assessment device provided in an embodiment of this application.

[0029] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] The battery energy storage power station reliability assessment method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send the State of Health (SOH) of the energy storage battery compartment to server 104. After receiving the energy storage power station parameters, server 104, based on the SOH of the energy storage battery compartment, divides the performance state of the energy storage battery compartment into four performance states and uses fuzzy mathematics theory to determine the fuzzy probability of the energy storage battery compartment in each performance state, dividing the energy storage power station into a four-level model. Based on the fuzzy probability of the energy storage battery compartment in each performance state and the corresponding SOH, a general generating function for the energy storage battery compartment is constructed. Based on the general generating function of the energy storage battery compartment and the topology of the energy storage power station, a multi-state hybrid precision model of the energy storage power station is constructed. Based on the multi-state hybrid precision model of the energy storage power station and the reliability index system of the energy storage power station, a reliability assessment of the energy storage power station is performed, obtaining the reliability assessment result of the energy storage power station. Server 104 can feed back the obtained reliability assessment results for the energy storage power station to terminal 102. In addition, in some embodiments, the battery energy storage power station reliability assessment method can also be implemented by server 104 or terminal 102 alone. For example, terminal 102 can directly perform the energy storage power station assessment, or server 104 can obtain the video to be processed from the data storage system and perform the energy storage power station assessment.

[0033] The terminal 102 can be, but is not limited to, various desktop computers and laptops. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0034] In one exemplary embodiment, such as Figure 2 , Figure 3 and Figure 4 As shown, a reliability assessment method for a battery energy storage power station is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 206.

[0035] Step 201: Based on the SOH of the energy storage battery compartment, the performance status of the energy storage battery compartment is divided into four performance statuses; the four performance statuses include excellent, degraded, alarm, and failure.

[0036] Step 202: Based on the four performance states of the energy storage battery compartment, use fuzzy mathematics theory to determine the fuzzy probability of the energy storage battery compartment under each performance state.

[0037] Step 203: Divide the energy storage power station into a four-level model; the four-level model includes energy storage battery compartment - energy storage subsystem - energy storage hub - energy storage power station; the energy storage power station includes multiple energy storage hubs; the energy storage hubs include multiple energy storage subsystems; the energy storage subsystems include multiple energy storage battery compartments.

[0038] Step 204: Construct a general generating function for the energy storage battery compartment based on the fuzzy probabilities of the battery compartment under each performance state and the SOH corresponding to each performance state.

[0039] Step 205: Based on the general generating function of the energy storage battery compartment and the topology of the energy storage power station, construct a multi-state hybrid accuracy model of the energy storage power station; the multi-state hybrid accuracy model of the energy storage power station includes the general generating function of the energy storage battery compartment, the general generating function of the energy storage subsystem, the general generating function of the energy storage convergence line, and the general generating function of the energy storage power station.

[0040] Step 206: Based on the multi-state hybrid accuracy model of the energy storage power station and the reliability index system of the energy storage power station, conduct a reliability assessment of the energy storage power station to obtain the reliability assessment results of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indexes, static indexes and importance indexes.

[0041] Steps 201 to 206 above are implemented as follows: First, the State of Health (SOH) of the energy storage battery compartment is defined, and a capacity decay model for the SOH of the energy storage battery compartment is established based on this. Then, based on the SOH value of the energy storage battery compartment, four performance states are divided into "excellent," "degraded," "alarm," and "failure." Combining the operating conditions of the battery compartment and the operating environment temperature, the mean and standard deviation of the battery compartment performance level distribution at each moment are calculated, and the fuzzy probability of the battery compartment being in different performance states is obtained. Second, based on the typical topology of a large-scale energy storage power station, and based on the fuzzy multi-state model of the energy storage battery compartment considering performance decay, a multi-state hybrid accuracy model of the energy storage power station (energy storage battery compartment-energy storage subsystem-energy storage collection line-energy storage power station) is extended using the Universal Generation Function (UGF) method. Finally, a reliability index system for the energy storage power station is constructed from three aspects: static, dynamic, and importance. Then, the Monte Carlo simulation method is used to simulate the energy storage power station for a specified number of operating scenarios. Combining the reliability index calculation results and their probabilities under each operating scenario, the reliability assessment results of the energy storage power station are obtained.

[0042] An energy storage battery compartment is an integrated system that uses electrochemical methods to store electrical energy within multiple battery cells, which are then connected together in a specific series or parallel configuration. State of Health (SOH) is a crucial indicator for measuring the health and performance of an energy storage battery compartment. From a capacity perspective, the SOH can be defined as the ratio of the current rated capacity to the initial rated capacity. Monitoring SOH allows for understanding the gradual degradation or sudden performance decline of the battery compartment, providing an effective method for reflecting its performance and health status. Initially, if the battery compartment's rated capacity and current usable capacity are the same, the SOH is at 100%. If, during continuous use, the SOH drops to a specific range, the energy storage battery compartment is considered to be in a faulty state.

[0043]

[0044] Among them, C ag This is the maximum charging capacity of the energy storage battery compartment after aging; C ne This is the maximum charging capacity of the energy storage battery compartment when it is not in use; C re It represents the capacity decay of the energy storage battery compartment.

[0045] Based on the definition of SOH in the battery compartment, the key to obtaining the SOH value of the battery compartment is to calculate the capacity decay of the battery compartment. By interpolating and fitting the experimental data, the expression for the final capacity decay can be obtained as follows (2).

[0046]

[0047] Where, ΔC dy The capacity loss rate, i.e., the capacity loss C. dy With maximum charging capacity C ne The ratio; H is the pre-exponential factor; K is the charge / discharge rate; S is the gas constant; T is the absolute temperature; IA h It is the total amount of current flowing through.

[0048] Therefore, when using the above model to calculate capacity decay, the SOH of the battery compartment under given operating conditions can be calculated using the following equation.

[0049]

[0050] Wherein, SoH(0) is the SOH of the energy storage battery compartment when it is brand new and unused; C ne This refers to the capacity of the energy storage battery compartment when it is brand new and unused, in kWh; Pr b (δ) is the power of the energy storage battery compartment under the operating conditions of the b-th charge-discharge cycle; M bM represents the equivalent number of charge-discharge cycles before the energy storage battery compartment reaches its end-of-life (EOL) under the b-th operating condition. b The following equation can be used to calculate it.

[0051]

[0052] In the formula, K b U represents the charge / discharge rate under the b-th operating condition. O It is the open-circuit voltage of the energy storage battery compartment, IA hi (K i ) is the total current flowing under the b-th operating condition.

[0053] Combining the above equations, we can obtain the derivative of SOH with respect to time under the b-th operating condition, expressed as follows.

[0054]

[0055] Under certain operating conditions, capacity decline is an irreversible process. Considering that energy storage battery compartments will operate differently under different operating conditions, the SOH calculation formula can be rewritten as follows.

[0056]

[0057] Where SoH(0) is the initial capacity of the energy storage battery compartment, D is the total number of operating states, and t b It is the duration under the b-th operating condition.

[0058] Calculating the capacity decay of the battery compartment by measuring its State of Health (SOH) does not provide a comprehensive and detailed description of the changes in reliability during the performance degradation process. To more accurately assess the changes in reliability of battery storage during its aging process, the performance status of the battery compartment is divided into four performance states based on its SOH: excellent, degraded, alarm, and failure. This reflects the process from the initial moment to the final complete capacity decay of the battery compartment.

[0059] For an energy storage battery compartment 'a', based on the decay change of its State of Harm (SOH), the performance state of the battery compartment is divided into four states: excellent, degraded, alarming, and failed. The SOH values ​​for these four performance states are: excellent (80%-100%), degraded (60%-80%), alarming (20%-60%), and failed (0%-20%). G1, G2, G3, and G4 represent different battery compartment states. For each distinguished state, it corresponds to a certain SOH state range of the energy storage battery compartment, which can be expressed as follows.

[0060] G b =[SoH high SoH low (7).

[0061] Among them, SOH high SOH low This represents the upper and lower bounds of the SOH level corresponding to the b-th state of the energy storage battery compartment. The value of b is 1, 2, 3, or 4, representing the four performance states of the energy storage battery compartment: excellent, degraded, alarming, and failed, respectively.

[0062] To calculate the reliability of the battery compartment module, according to the central limit theorem, for a large number of battery compartments, it is assumed that the state of equilibrium (SOH) at any given time follows N(μ,σ). 2 If the battery compartment follows a normal distribution (except when the SOH of a brand new battery compartment is 1), then the battery compartment corresponds to different probability distributions in different states. Therefore, the probability set of battery compartment a in each performance state can be expressed as follows.

[0063]

[0064] After the initial time, based on the normal distribution, the probability of the battery compartment in different states (i.e., different SOH intervals) can be calculated using the following formula.

[0065] Pr b =y(G low-b )-y(G high-b (9).

[0066] Where y is the cumulative probability distribution function (CDF) of the normal distribution, which can be expressed as the following formula.

[0067]

[0068] Where μ is the mean of the distribution, σ is the standard deviation, and the relationship between σ and μ is:

[0069]

[0070] In the formula, G low-b For state G b The lower bound of the SOH level interval corresponding to the time, G high-b In state G b The upper bound of the corresponding SOH level range. The upper and lower bounds of the SOH level range are the maximum and minimum values ​​of SOH when the energy storage battery compartment is in one of the four states: good, degraded, alarm, or failed.

[0071] The probability of a large number of battery compartments in different SOH intervals can be calculated using the CDF of a normal distribution. However, since the domain of the CDF function of the normal distribution is [-∞, +∞], and the SOH level of the battery compartment is [0, 1], in order to ensure that the sum of the probabilities of the battery compartments in the state interval is 1, it is first necessary to normalize the CDF of the normal distribution. The normalization adjustment can be expressed as the following formula.

[0072]

[0073] The battery compartment is then in zone G. b =[SoH low-b SoH high-b The probability of ] can be calculated using the following formula.

[0074] Pr b =Pr(SoH high-b -SoH low-b )=Y'(SoH high-b )-Y'(SoH low-b (13).

[0075] Based on step 201 above, the SOH values ​​corresponding to different performance state intervals of the energy storage battery compartment can be obtained. However, due to the lack of sufficient and accurate battery reliability data, its performance distribution information is difficult to obtain and describe accurately. Therefore, the membership of the battery to each performance state at a certain moment is not mutually exclusive. In order to describe the fuzzy phenomenon that the battery compartment is in a state that is both one and the other and difficult to distinguish precisely, based on fuzzy mathematics theory, fuzzy numbers can be used to represent the mean and standard deviation of the battery compartment performance level distribution, respectively.

[0076] The probability distribution of the state performance of the energy storage battery compartment at a certain moment is represented by fuzzy numbers (fuzzy sets). Taking time t as an example, the probability that energy storage battery compartment a belongs to state k at time t is... We use fuzzy numbers instead, meaning the probability at this point is a fuzzy set. The value of k is 1, 2, 3, or 4, representing four performance states of the energy storage battery compartment: excellent, degraded, alarming, and failed, respectively. Different membership functions are used to represent and operate on fuzzy numbers. Considering that the constructed probabilities are all in the real number domain, the corresponding membership functions can also be called fuzzy distributions. Fuzzy numbers with a triangular distribution... For example, the degree to which each element in a fuzzy set belongs to a fuzzy number is represented by its membership function. as well as For fuzzy numbers The degree of membership is the lowest, and they can be respectively... The lower and upper limits of the possible values. For fuzzy numbers The membership degree should ideally be 1, indicating that it is the most likely value for the fuzzy number.

[0077] Therefore, the probability distribution of the four performance states of the energy storage battery compartment—excellent, degraded, alarmed, and failed—can be reconstructed into a power set composed of fuzzy numbers, which can be expressed by the following formula.

[0078]

[0079] Correspondingly, for any energy storage battery compartment a, the probability of it being in each performance state is... The probability power set that constitutes it can be represented by the following formula.

[0080]

[0081] For a fuzzy number (fuzzy set), the set of elements with membership degrees greater than λ is called the λ-cut of the fuzzy number. Then, the fuzzy probability that the energy storage battery compartment a belongs to state k at time t is... Its corresponding λ-cut set can be represented as

[0082]

[0083] Here, Pr refers to all elements that have a membership relationship with the fuzzy set. For the corresponding membership function, when the membership function adopts a normal fuzzy distribution, It can be expressed as the following formula.

[0084]

[0085] Here, μ is the mean, which is the element with the strongest membership degree.

[0086] If the mean and variance of the distribution are represented using fuzzy numbers, then... It can be obtained using the following formula.

[0087]

[0088] Where Y is the cumulative probability density function of a normal distribution. express The λ-cut set; express The λ-cut set.

[0089] According to the decomposition theorem, fuzzy numbers can be represented by the classical set of the corresponding cuts. It can be expressed as the following formula.

[0090]

[0091] Based on the calculation methods described above, a fuzzy multi-state model of the energy storage battery compartment considering SOH performance degradation can be obtained. According to the typical topology of a large-scale energy storage power station, a four-level refined model is constructed, from the energy storage battery compartment to the energy storage subsystem, to the energy storage collection line, and finally to the energy storage power station. Using a general generating function method, the state distribution of the energy storage power station is derived from the state distribution of the energy storage battery compartment.

[0092] In another exemplary embodiment of this application, the general generation function of the energy storage battery compartment in step 204 can be expressed as the following formula.

[0093]

[0094] Among them, u a (m) represents the general generating function value of the a-th energy storage battery compartment, where m is the power coefficient. This represents the probability that the a-th energy storage battery compartment corresponds to the b-th performance state (i.e., the fuzzy probability obtained in step 202). This represents the State of Harm (SOH) performance level corresponding to the b-th performance state of the a-th energy storage battery compartment. For the energy storage battery compartment model with four performance states, the value of b is 1, 2, 3, or 4.

[0095] Since large-scale energy storage power stations are composed of various energy storage subsystems connected in parallel, a corresponding UGF operator operation rule reflecting the series-parallel relationship needs to be designed. For a parallel connection between two energy storage battery modules, based on the characteristics of the parallel structure, the reliability of the entire structure should be determined by the energy storage battery module with the best performance. However, considering that the removal of a faulty parallel battery module will affect the output power of the entire parallel structure, it is equivalent to a sudden capacity decay in the battery module. Therefore, for a parallel structure, determining the capacity decay solely based on the battery module with the best performance contradicts the original multi-state concept and is not reasonable in reflecting reliability. Therefore, a new weighted operation rule is formulated based on the general generating function operator. The different states of each parallel structure of the energy storage power station are ultimately obtained by weighted combination of each part according to the following operation rule. The specific operation of the general generating function operator is shown in the following equation.

[0096]

[0097] Where Φ is the general generating function operator, Let be a function of random variables in the series structure pattern, as shown in the following equation.

[0098]

[0099] In the formula, Let be a function of random variables in a parallel structure, specifically in the form shown below.

[0100]

[0101] The overall energy storage power station first consists of multiple battery compartments connected in parallel, then connected in series with a PCS (Power Conversion System) and a step-up transformer to form an energy storage subsystem. These subsystems are then connected in parallel (there are 5 or 6 subsystems), and finally connected in parallel via a busbar to form the complete energy storage power station. Since the PCS and step-up transformer have relatively small impacts on the energy storage subsystems, this embodiment does not consider them. Therefore, the general generating function of the energy storage subsystem can be expressed as follows.

[0102]

[0103] Among them, u system u(m) represents the general generating function value of the energy storage subsystem, u1(m) represents the general generating function value of the first energy storage battery compartment in the energy storage subsystem, and u2(m) represents the general generating function value of the second energy storage battery compartment in the energy storage subsystem. a (m) represents the general generating function value of the a-th energy storage battery compartment in the energy storage subsystem, u L (m) represents the general generation function value of the Lth energy storage battery compartment in the energy storage subsystem. This is the SOH performance level of the a-th energy storage battery compartment. Let be the probability corresponding to the a-th energy storage battery compartment in the b-th performance state.

[0104] An energy storage convergence line is formed by connecting multiple energy storage subsystems in parallel. The general generating function of each energy storage convergence line can be expressed as the following formula.

[0105]

[0106] Among them, u line (m) represents the universal generating function value of the energy storage collection line, u system,1 (m) represents the universal generating function value of the first energy storage subsystem in the energy storage convergence line, u system,2 (m) represents the universal generating function value of the second energy storage subsystem in the energy storage convergence line, u system,a-1 (m) represents the universal generating function value of the (a-1)th energy storage subsystem in the energy storage pooling line, u system,a (m) represents the general generating function value of the a-th energy storage subsystem in the energy storage pool line, u system,K-1 (m) represents the universal generating function value of the (K-1)th energy storage subsystem in the energy storage pool, u system,K (m) is the general generating function value of the Kth energy storage subsystem in the energy storage pool line, where K represents the number of energy storage subsystems in an energy storage pool line, and can be 10 or 12. This represents the State of Health (SOH) performance level corresponding to the b-th performance state of the energy storage power station. This represents the probability corresponding to the energy storage aggregation line in the b-th performance state.

[0107] An energy storage power station is formed by multiple energy storage collection lines connected in parallel. The general generation function of the energy storage power station is shown in formula (26).

[0108]

[0109] Among them, u station (m) represents the general generating function value for the energy storage power station, u line,1 (m) is the universal generating function value of the first energy storage collection line, u line,2 (m) represents the universal generating function value of the second energy storage collection line, u line,a (m) represents the universal generating function value of the a-th energy storage collection line, u line,I-1 (m) represents the universal generating function value of the (I-1)th energy storage collection line, u line,I (m) represents the general generating function value of the i-th energy storage hub, where i represents the number of energy storage hubs in the energy storage power station. This represents the State of Health (SOH) performance level corresponding to the b-th performance state of the energy storage power station. Let be the probability corresponding to the energy storage power station in the b-th performance state.

[0110] Based on the above steps, a mixed-precision modeling method for different levels under different states of a large-scale energy storage power station was developed, completing a refined model of the large-scale energy storage power station. Next, a reliability assessment of the energy storage power station is conducted, firstly requiring the definition of reliability indicators. For large-scale energy storage power stations, indicators are proposed from three aspects: dynamic indicators, static indicators, and importance indicators. Static state indicators aim to reflect the basic state and state margin of the energy storage power station; dynamic change indicators aim to characterize the changes in the state of the energy storage power station and its adaptability to changes; importance indicators reflect the connection and influence between the internal battery compartment and the overall structure of the energy storage power station, and can be used to explore weak points in the power station.

[0111] 1) In terms of static indicators, the main indicators include: energy storage battery compartment reliability, energy storage battery compartment capacity expectation, and energy storage battery compartment capacity margin.

[0112] Energy storage battery compartment energy storage reliability R BAT A certain SOH (State of Health) threshold is set. When the SOH value of the battery compartment is less than the threshold, the battery compartment is considered to have failed. The sum of the probabilities that the SOH value is greater than the threshold is the reliability of the battery compartment.

[0113]

[0114] Where α is the set threshold for the SOH state, F BAT Let SoH be the cumulative probability density function of the state distribution. b Pr represents the SOH value of performance state b. b The probability of performance state b.

[0115] Expected capacity of energy storage battery compartment E BAT : Set a certain SOH state threshold. The expected value of the SOH state of the battery compartment when the SOH state is greater than the threshold is the expected health state of the battery compartment.

[0116]

[0117] Energy storage battery compartment capacity margin C BAT Battery compartment capacity margin is defined as the difference between the current expected capacity of the battery compartment and the capacity value (failure threshold) corresponding to the SOH threshold.

[0118] C BAT =C ini ×(E BAT {Ψ(SoH)}-α) (29).

[0119] Among them, C ini E represents the initial state capacity of the battery. BAT {Ψ(SoH)} represents the expected state of health of the battery, i.e., the expected capacity of the energy storage battery compartment.

[0120] 2) Dynamic indicators focus on describing the state of battery energy storage from the perspective of the rate of change of various performance state parameters of the battery. These mainly include: reliability degradation rate, average state of equilibrium (SOH) degradation rate of the power station, expected capacity degradation rate of the power station, and average capacity degradation rate of the power station.

[0121] Reliability degradation rate I dR The reliability degradation rate is defined as the rate of decrease in the reliability of the energy storage battery compartment per unit time, and is used to describe how fast the reliability of the battery compartment changes.

[0122]

[0123] Where, ΔR BAT Δt represents the total decrease in reliability during the simulation, and Δt represents the total time.

[0124] SOH decay rate I dSoH SOH decay rate is defined as the change in SOH (Sodium Hydrochloric Oxide) of the energy storage battery compartment per unit time. It is used to describe the rate at which the health status of the battery compartment deteriorates.

[0125]

[0126] Wherein, ΔSoH is the total decrease in SOH during the simulation.

[0127] Average SOH decay rate of power plant I adSoH : The average SOH decay rate of all energy storage battery compartments in the energy storage power station under each state.

[0128]

[0129] Where S is the number of states of all battery compartments, Δt k Let ΔSoH be the duration of the k-th state. k This represents the decrease in SOH at the k-th state.

[0130] Expected capacity decay rate I of power plant dC Expected capacity C of energy storage power station s Differential calculations over time reflect the rate at which the power plant's capacity decays.

[0131]

[0132] Average capacity decay rate of power plant I C The ratio of the decrease in energy storage power station capacity to time within the statistical period is used to reflect the average rate of capacity decay of the power station.

[0133]

[0134] Where, ΔC re This indicates the energy storage power station has reached its capacity threshold C. α Total capacity decay; T α This indicates the time it takes for the capacity of an energy storage power station to decay to its capacity threshold.

[0135] 3) Considering the differences between large-scale energy storage power stations and mechanical energy storage power stations such as pumped hydro storage and compressed air storage, battery energy storage power stations contain multiple energy storage battery compartments. To reflect the connection between the energy storage battery compartments and the overall energy storage power station, we analyze its weak points and define importance indicators, including the contribution of reliability changes, the contribution of expected capacity changes, and reliability sensitivity.

[0136] Contribution of reliability change w Rcon,k This indicator reflects the impact of changes in the performance of the kth energy storage battery compartment on the overall reliability of the power station.

[0137]

[0138] Among them, Γ(SoH|d SoH,k ) represents the performance increase of the k-th energy storage battery compartment by d. SoH,k The rear battery compartment assembly.

[0139] Expected capacity change contribution wEcon,k This indicator reflects the impact of changes in the performance of the k-th energy storage battery compartment on the expected overall power station capacity.

[0140]

[0141] Reliability and sensitivity d Rp,k This indicator reflects the impact of changes in the State of Harmonic Drive (SOH) of the energy storage battery compartment on the overall reliability of the energy storage power station. It is defined as the differential calculation of the SOH of the battery compartment on the overall reliability of the energy storage power station.

[0142]

[0143] In the formula, R s This indicates the overall reliability of the energy storage power station.

[0144] In another exemplary embodiment of this application, step 206 specifically includes: based on the multi-state hybrid accuracy model of the energy storage power station and the reliability index system of the energy storage power station, using the Monte Carlo simulation method to simulate the operation scenarios of the energy storage power station, and obtaining the calculation results and occurrence probabilities of the reliability index under each operation scenario; based on the calculation results and occurrence probabilities of the reliability index under each operation scenario, performing a reliability assessment of the energy storage power station, and obtaining the reliability assessment result of the energy storage power station.

[0145] After modeling the multi-state hybrid precision of the large-scale energy storage power station and constructing the reliability index system of the energy storage power station, the reliability of the battery energy storage system is evaluated by calculating the reliability using the Monte Carlo simulation method. First, a fuzzy multi-state model of the energy storage battery compartment considering performance degradation is established based on the reliability test data of the energy storage battery compartment in the energy storage power station. Combining the working conditions and working environment temperature of the energy storage battery compartment, the mean and standard deviation of the performance level distribution of the energy storage battery compartment at each moment are calculated to obtain the fuzzy probability of the energy storage battery compartment being in each performance state and quantify the degree of change in the health state of the energy storage battery compartment. On this basis, based on the general generating function, models of different levels under different states of the energy storage power station are constructed to obtain the probability of the energy storage power station being in different performance states at each moment. Finally, the reliability indexes of the energy storage power station in three aspects, namely dynamic indexes, static indexes and importance indexes, are calculated according to the reliability index system to complete the reliability evaluation of the energy storage power station. The specific descriptions of each step are as follows (1) to (5).

[0146] (1) Establish a reliability parameter database using actual reliability data to determine the performance distribution G of the energy storage battery compartment under normal operating conditions. a , wait.

[0147] (2) Set up a reliability assessment environment and determine parameters such as ambient temperature T, battery charge / discharge rate K, and typical topology in the energy storage power station.

[0148] (3) Assume that all energy storage battery compartments are in perfect working condition at the initial moment G. aM (1≤a≤4).

[0149] (4) Based on the fuzzy multi-state model of the energy storage battery compartment, a mixed-precision model of different levels under different states of the energy storage power station is completed. Then, the Monte Carlo simulation method is used to simulate the operation scenario of the energy storage power station a specified number of times. In each simulation, the performance distribution G of the power station at each time point is recorded. Tb , Reliability indicators are calculated based on the reliability index system of energy storage power stations.

[0150] (5) The reliability assessment results of the energy storage power station are obtained by combining the reliability index calculation results and the probability of occurrence under each operating scenario.

[0151] This application has the following beneficial effects.

[0152] (1) This application considers the SOH of the energy storage battery compartment and classifies the battery compartment into four performance states: "excellent-degraded-alarm-failure" based on the SOH value. Combining the working conditions of the battery compartment and the ambient temperature, the mean and standard deviation of the battery compartment performance level distribution at each time are calculated, thereby obtaining the fuzzy probability of the battery compartment under different performance states. This can accurately assess the performance and health status of the battery compartment and provide accurate reliability testing.

[0153] (2) This application adopts a general generating function method to extend the fuzzy multi-state model of the energy storage battery compartment that considers performance degradation, forming a multi-state, multi-level hybrid precision model of energy storage battery compartment-energy storage subsystem-energy storage collection line-energy storage power station, which can comprehensively reflect the probability of different levels under different states of the energy storage power station, and effectively improve the accuracy and reliability of the model.

[0154] (3) This application constructs a reliability index system for energy storage power stations from three aspects: static, dynamic, and importance. Monte Carlo simulation is used to simulate multiple operating scenarios for the energy storage power station. The reliability index calculation results and their probabilities under each operating scenario are combined to obtain the reliability assessment results of the energy storage power station. This allows for a more comprehensive reliability assessment through multiple simulations using indicators from different perspectives, enhancing the credibility and practicality of the assessment results.

[0155] This application also provides an application scenario in which the above-described battery energy storage power station reliability assessment method is applied. Specifically, the battery energy storage power station reliability assessment method provided in this embodiment can be applied in an energy storage power station assessment scenario. The energy storage power station assessment scenario includes an information acquisition stage and an energy storage power station assessment link; energy storage power station assessment parameters enter the energy storage power station assessment link from the information acquisition stage, and the corresponding reliability assessment results are obtained through human-machine collaboration. The battery energy storage power station reliability assessment method provided in this embodiment belongs to the energy storage power station assessment link. Specifically, in the energy storage power station evaluation process, based on the State of Health (SOH) of the energy storage battery compartment, the performance state of the battery compartment can be divided into four performance states. Fuzzy mathematics theory is used to determine the fuzzy probability of the battery compartment in each performance state, thus dividing the energy storage power station into a four-level model. Based on the fuzzy probability of the battery compartment in each performance state and the SOH corresponding to each performance state, a general generating function for the battery compartment is constructed. Based on the general generating function of the battery compartment and the topology of the energy storage power station, a multi-state hybrid accuracy model of the energy storage power station is constructed. Based on the multi-state hybrid accuracy model of the energy storage power station and the reliability index system of the energy storage power station, the reliability of the energy storage power station is evaluated, and the reliability evaluation result of the energy storage power station is obtained.

[0156] Based on the same inventive concept, this application also provides a battery energy storage power station reliability assessment device for implementing the aforementioned battery energy storage power station reliability assessment method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations in one or more battery energy storage power station reliability assessment device embodiments provided below can be found in the limitations of the battery energy storage power station reliability assessment method described above, and will not be repeated here.

[0157] In one exemplary embodiment, such as Figure 5 As shown, a battery energy storage power station reliability assessment device is provided, which includes the following modules.

[0158] The performance status classification module T1 is used to classify the performance status of the energy storage battery compartment into four performance statuses based on the SOH of the energy storage battery compartment; the four performance statuses include excellent, degradation, alarm and failure.

[0159] The fuzzy probability determination module T2 is used to determine the fuzzy probability of the energy storage battery compartment under each performance state based on the four performance states of the energy storage battery compartment using fuzzy mathematics theory.

[0160] The four-level model partitioning module T3 is used to: divide the energy storage power station into a four-level model; the four-level model includes energy storage battery compartment - energy storage subsystem - energy storage hub - energy storage power station; the energy storage power station includes multiple energy storage hubs; the energy storage hubs include multiple energy storage subsystems; the energy storage subsystems include multiple energy storage battery compartments.

[0161] The general generating function construction module T4 for the energy storage battery compartment is used to: construct a general generating function for the energy storage battery compartment based on the fuzzy probability of the energy storage battery compartment under each performance state and the SOH corresponding to each performance state.

[0162] The T5 module for constructing a multi-state hybrid accuracy model of an energy storage power station is used to: construct a multi-state hybrid accuracy model of an energy storage power station based on the general generation function of the energy storage battery compartment and the topology of the energy storage power station; the multi-state hybrid accuracy model of an energy storage power station includes the general generation function of the energy storage battery compartment, the general generation function of the energy storage subsystem, the general generation function of the energy storage hub line, and the general generation function of the energy storage power station.

[0163] The energy storage power station reliability assessment module T6 is used to: conduct reliability assessment of the energy storage power station based on the multi-state hybrid accuracy model and the reliability index system of the energy storage power station, and obtain the reliability assessment result of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indexes, static indexes and importance indexes.

[0164] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores evaluation data for energy storage power stations. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a reliability evaluation method for battery energy storage power stations.

[0165] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0166] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0167] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0169] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0170] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0171] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A reliability assessment method for battery energy storage power stations, characterized in that, The reliability assessment method for the battery energy storage power station includes: Based on the State of Health (SOH) of the energy storage battery compartment, the performance status of the energy storage battery compartment is divided into four performance states: excellent, degraded, alarm, and failure. Based on the four performance states of the energy storage battery compartment, the fuzzy probability of the energy storage battery compartment under each performance state is determined using fuzzy mathematics theory. The energy storage power station is divided into a four-level model; the four-level model includes energy storage battery compartment - energy storage subsystem - energy storage aggregation line - energy storage power station; the energy storage power station includes multiple energy storage aggregation lines; the energy storage aggregation line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments. Based on the fuzzy probabilities of the energy storage battery compartment under various performance states and the SOH corresponding to each performance state, a general generating function for the energy storage battery compartment is constructed. Based on the general generating function of the energy storage battery compartment and the topology of the energy storage power station, a multi-state hybrid accuracy model of the energy storage power station is constructed. This model includes the general generating functions of the energy storage battery compartment, the energy storage subsystem, the energy storage manifold, and the energy storage power station itself. The general generating function of the energy storage battery compartment is: Among them, u a (m) represents the general generating function value of the a-th energy storage battery compartment, where m is the power coefficient. Let represent the probability that the a-th energy storage battery compartment corresponds to the b-th performance state. This represents the SOH performance level corresponding to the b-th performance state of the a-th energy storage battery compartment; The general generating function of the energy storage subsystem can be expressed as: Among them, u system (m) represents the general generating function value of the energy storage subsystem, Φ represents the general generating function operator, u1(m) represents the general generating function value of the first energy storage battery compartment in the energy storage subsystem, u2(m) represents the general generating function value of the second energy storage battery compartment in the energy storage subsystem, u a (m) represents the general generating function value of the a-th energy storage battery compartment in the energy storage subsystem, u L (m) represents the general generation function value of the Lth energy storage battery compartment in the energy storage subsystem. This represents the State of Harm (SOH) performance level of the a-th energy storage battery compartment under the b-th performance state. Let be the probability corresponding to the a-th energy storage battery compartment in the b-th performance state; The general generating function for the energy storage collection line is: Among them, u line (m) represents the universal generating function value of the energy storage collection line, u system,1 (m) represents the universal generating function value of the first energy storage subsystem in the energy storage convergence line, u system,2 (m) represents the universal generating function value of the second energy storage subsystem in the energy storage convergence line, u system,a-1 (m) represents the universal generating function value of the (a-1)th energy storage subsystem in the energy storage pooling line, u system,a (m) represents the general generating function value of the a-th energy storage subsystem in the energy storage pool line, u system,K-1 (m) represents the universal generating function value of the (K-1)th energy storage subsystem in the energy storage pool, u system,K (m) represents the general generating function value of the Kth energy storage subsystem in an energy storage hub, where K represents the number of energy storage subsystems in an energy storage hub. This refers to the SOH level corresponding to the b-th performance state of the energy storage power station. Let be the probability corresponding to the energy storage aggregation line in the b-th performance state; The general generating function of the energy storage power station is: Among them, u station (m) represents the general generating function value for the energy storage power station, u line,1 (m) is the universal generating function value of the first energy storage collection line, u line,2 (m) represents the universal generating function value of the second energy storage collection line, u line,a (m) represents the universal generating function value of the m-th energy storage collection line, u line,I-1 (m) represents the universal generating function value of the (I-1)th energy storage collection line, u line,I (m) represents the general generating function value of the i-th energy storage hub, where i represents the number of energy storage hubs in the energy storage power station. This represents the State of Health (SOH) performance level corresponding to the b-th performance state of the energy storage power station. Let be the probability corresponding to the energy storage power station in the b-th performance state; Based on the multi-state hybrid accuracy model and reliability index system of energy storage power station, the reliability of energy storage power station is evaluated, and the reliability evaluation results of energy storage power station are obtained; the reliability index system of energy storage power station includes dynamic index, static index and importance index.

2. The battery energy storage power station reliability assessment method according to claim 1, characterized in that, Based on a multi-state hybrid accuracy model and a reliability index system for energy storage power stations, a reliability assessment is conducted, yielding the following results: Based on the multi-state hybrid accuracy model and reliability index system of energy storage power station, the Monte Carlo simulation method is used to simulate the operation scenario of energy storage power station, and the calculation results and occurrence probability of reliability index under each operation scenario are obtained. Based on the calculation results and occurrence probability of reliability indicators under various operating scenarios, the reliability of the energy storage power station is assessed, and the reliability assessment results of the energy storage power station are obtained.

3. The reliability assessment method for battery energy storage power stations according to claim 1, characterized in that, The dynamic indicators include reliability degradation rate, average SOH degradation rate of the power plant, expected capacity degradation rate of the power plant, and average capacity degradation rate of the power plant; the static indicators include energy storage battery compartment reliability, expected capacity of the energy storage battery compartment, and capacity margin of the energy storage battery compartment; the importance indicators include reliability change contribution, expected capacity change contribution, and reliability sensitivity.

4. A reliability assessment device for a battery energy storage power station, characterized in that, The battery energy storage power station reliability assessment device includes: The performance status classification module is used to classify the performance status of the energy storage battery compartment into four performance statuses based on the SOH of the energy storage battery compartment; the four performance statuses include excellent, degradation, alarm, and failure. The fuzzy probability determination module is used to: determine the fuzzy probability of the energy storage battery compartment under each of its four performance states using fuzzy mathematics theory. The four-level model partitioning module is used to: divide the energy storage power station into a four-level model; the four-level model includes energy storage battery compartment - energy storage subsystem - energy storage hub - energy storage power station; the energy storage power station includes multiple energy storage hubs; the energy storage hubs include multiple energy storage subsystems; the energy storage subsystems include multiple energy storage battery compartments; A general generating function construction module for the energy storage battery compartment is used to: construct a general generating function for the energy storage battery compartment based on the fuzzy probability of the energy storage battery compartment under each performance state and the SOH corresponding to each performance state. A multi-state hybrid accuracy model construction module for energy storage power stations is used to: construct a multi-state hybrid accuracy model of the energy storage power station based on the general generation function of the energy storage battery compartment and the topology of the energy storage power station; the multi-state hybrid accuracy model of the energy storage power station includes the general generation function of the energy storage battery compartment, the general generation function of the energy storage subsystem, the general generation function of the energy storage hub, and the general generation function of the energy storage power station; the general generation function of the energy storage battery compartment is: Among them, u a (m) represents the general generating function value of the a-th energy storage battery compartment, where m is the power coefficient. Let represent the probability that the a-th energy storage battery compartment corresponds to the b-th performance state. This represents the SOH performance level corresponding to the b-th performance state of the a-th energy storage battery compartment; The general generating function of the energy storage subsystem can be expressed as: Among them, u system (m) represents the general generating function value of the energy storage subsystem, Φ represents the general generating function operator, u1(m) represents the general generating function value of the first energy storage battery compartment in the energy storage subsystem, u2(m) represents the general generating function value of the second energy storage battery compartment in the energy storage subsystem, u a (m) represents the general generating function value of the a-th energy storage battery compartment in the energy storage subsystem, u L (m) represents the general generation function value of the Lth energy storage battery compartment in the energy storage subsystem. This represents the State of Harm (SOH) performance level of the a-th energy storage battery compartment under the b-th performance state. Let be the probability corresponding to the a-th energy storage battery compartment in the b-th performance state; The general generating function for the energy storage collection line is: Among them, u line (m) represents the universal generating function value of the energy storage collection line, u system,1 (m) represents the universal generating function value of the first energy storage subsystem in the energy storage convergence line, u system,2 (m) represents the universal generating function value of the second energy storage subsystem in the energy storage convergence line, u system,a-1 (m) represents the universal generating function value of the (a-1)th energy storage subsystem in the energy storage pooling line, u system,a (m) represents the general generating function value of the a-th energy storage subsystem in the energy storage pool line, u system,K-1 (m) represents the universal generating function value of the (K-1)th energy storage subsystem in the energy storage pool, u system,K (m) represents the general generating function value of the Kth energy storage subsystem in an energy storage hub, where K represents the number of energy storage subsystems in an energy storage hub. This refers to the SOH level corresponding to the b-th performance state of the energy storage power station. Let be the probability corresponding to the energy storage aggregation line in the b-th performance state; The general generating function of the energy storage power station is: Among them, u station (m) represents the general generating function value for the energy storage power station, u line,1 (m) is the universal generating function value of the first energy storage collection line, u line,2 (m) represents the universal generating function value of the second energy storage collection line, u line,a (m) represents the universal generating function value of the m-th energy storage collection line, u line,I-1 (m) represents the universal generating function value of the (I-1)th energy storage collection line, u line,I (m) represents the general generating function value of the i-th energy storage hub, where i represents the number of energy storage hubs in the energy storage power station. This represents the State of Health (SOH) performance level corresponding to the b-th performance state of the energy storage power station. Let be the probability corresponding to the energy storage power station in the b-th performance state; The energy storage power station reliability assessment module is used to: conduct reliability assessment of the energy storage power station based on the multi-state hybrid accuracy model and the reliability index system of the energy storage power station, and obtain the reliability assessment result of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indexes, static indexes and importance indexes.

5. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the battery energy storage power station reliability assessment method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the battery energy storage power station reliability assessment method as described in any one of claims 1-3.

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