Battery energy storage power station reliability evaluation method, device, equipment and medium

Abstract

The invention discloses a battery energy storage power station reliability evaluation method and device, equipment and a medium, and relates to the technical field of battery management, and the method comprises the steps: dividing the performance state of an energy storage battery cabin into four performance states based on the SOH of the energy storage battery cabin, and determining the fuzzy probability of the energy storage battery cabin in each performance state through employing a fuzzy mathematical theory; dividing the energy storage power station into a four-level model; constructing a general generation function of the energy storage battery cabin according to the fuzzy probability of the energy storage battery cabin in each performance state and the SOH corresponding to each performance state; according to the general generation function of the energy storage battery cabin and the topological structure of the energy storage power station, constructing a multi-state mixing precision model of the energy storage power station; and based on the energy storage power station multi-state mixing precision model and the energy storage power station reliability index system, performing reliability evaluation on the energy storage power station to obtain a reliability evaluation result of the energy storage power station, and realizing reliability evaluation of the energy storage power station.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a reliability assessment method, device, equipment and medium for a battery energy storage power station. Background Art

[0002] With the continuous transformation of the global energy structure, the proportion of clean and renewable energy in total energy consumption is also increasing. Wind and solar energy have received widespread attention and application around the world due to their unlimited and environmentally friendly nature. However, these energy sources are restricted by multiple factors such as geography and climate, and are intermittent and unstable, which brings challenges to energy supply and has a certain impact on the stable operation of the power grid. Therefore, energy storage technology came into being, which can store these unstable renewable energy sources and release them when needed, thereby solving the problems of continuity and stability of energy supply.

[0003] Energy storage equipment can store excess electrical energy and release it when needed to balance the supply and demand of electricity, improve the stability and reliability of the power grid, and ensure the normal operation of the power system. However, as the scale of energy storage systems expands, the stability and reliability issues after they are connected to the grid are gradually emerging, especially in the case of system failures or abnormalities. The access of large-scale energy storage power stations may have a negative impact on the stability of the grid. Therefore, it is very important to conduct reliability assessments on energy storage power stations. Summary of the invention

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

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

[0006] In a first aspect, the present application provides a method for evaluating the reliability of a battery energy storage power station, comprising the following steps.

[0007] Based on the SOH of the energy storage battery compartment, the performance status of the energy storage battery compartment is divided into four performance states; the four performance states include excellent, degraded, warning and failure.

[0008] Based on the four performance states of the energy storage battery compartment, the fuzzy probability of the energy storage battery compartment in 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 collection line-energy storage power station; the energy storage power station includes multiple energy storage collection lines; the energy storage collection line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments.

[0010] According to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state, a universal generation function of the energy storage battery compartment is constructed.

[0011] According to the universal generation function of the energy storage battery compartment and the topological structure of the energy storage power station, a multi-state mixed precision model of the energy storage power station is constructed; the multi-state mixed precision model of the energy storage power station includes the universal generation function of the energy storage battery compartment, the universal generation function of the energy storage subsystem, the universal generation function of the energy storage collection line and the universal generation function of the energy storage power station.

[0012] Based on the multi-state mixed precision 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 to obtain the reliability evaluation result of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indicators, static indicators and importance indicators.

[0013] In a second aspect, the present application provides a battery energy storage power station reliability assessment device, comprising 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 states based on the SOH of the energy storage battery compartment; the four performance states include excellent, degraded, warning and failure.

[0015] The fuzzy probability determination module is used to: determine the fuzzy probability of the energy storage battery compartment in each performance state based on the four performance states of the energy storage battery compartment using fuzzy mathematics theory.

[0016] The four-level model division 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 collection line-energy storage power station; the energy storage power station includes multiple energy storage collection lines; the energy storage collection line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments.

[0017] The universal generation function construction module of the energy storage battery compartment is used to: construct the universal generation function of the energy storage battery compartment according to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state.

[0018] The multi-state mixed precision model construction module of the energy storage power station is used to: construct a multi-state mixed precision model of the energy storage power station according to the universal generation function of the energy storage battery compartment and the topological structure of the energy storage power station; the multi-state mixed precision model of the energy storage power station includes the universal generation function of the energy storage battery compartment, the universal generation function of the energy storage subsystem, the universal generation function of the energy storage collection line and the universal generation function of the energy storage power station.

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

[0020] In a third aspect, the present 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-mentioned battery energy storage power station reliability assessment method.

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

[0022] According to the specific embodiments provided by the present application, the present application discloses the following technical effects: the present application provides a reliability assessment method, device, equipment and medium for a battery energy storage power station. Based on the SOH of the energy storage battery compartment, the performance state of the energy storage battery compartment is divided into four performance states; the four performance states include excellent, degraded, warning and failure; based on the four performance states of the energy storage battery compartment, the fuzzy probability of the energy storage battery compartment in 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 an energy storage battery compartment-energy storage subsystem-energy storage collection line-energy storage power station; the energy storage power station includes multiple energy storage collection lines; the energy storage collection line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments; According to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state, a universal generating function of the energy storage battery compartment is constructed; according to the universal generating function of the energy storage battery compartment and the topological structure of the energy storage power station, a multi-state mixed precision model of the energy storage power station is constructed; the multi-state mixed precision model of the energy storage power station includes the universal generating function of the energy storage battery compartment, the universal generating function of the energy storage subsystem, the universal generating function of the energy storage collection line and the universal generating function of the energy storage power station; based on the multi-state mixed precision 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, so as to realize the refined modeling of the battery energy storage power station and the reliability evaluation of the battery energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0025] Figure 2 A flowchart of a method for evaluating the reliability of a battery energy storage power station provided in one embodiment of the present application.

[0026] Figure 3 A schematic diagram of a multi-state mixed-precision model of an energy storage power station provided in one embodiment of the present application.

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

[0028] Figure 5 A schematic diagram of functional modules of a battery energy storage power station reliability assessment device provided in one embodiment of the present application.

[0029] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] The reliability assessment method of the battery energy storage power station provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the SOH of the energy storage battery compartment to the server 104. After the server 104 receives the parameters of the energy storage power station, the server 104 divides the performance state of the energy storage battery compartment into four performance states based on the SOH of the energy storage battery compartment, and uses fuzzy mathematics theory to determine the fuzzy probability of the energy storage battery compartment in each performance state, and divides the energy storage power station into a four-level model. According to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state, a general generation function of the energy storage battery compartment is constructed. According to the general generation function of the energy storage battery compartment and the topological structure of the energy storage power station, a multi-state mixed precision model of the energy storage power station is constructed. Based on the multi-state mixed precision 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. The server 104 can feed back the obtained reliability assessment result for the energy storage power station to the terminal 102. In addition, in some embodiments, the reliability assessment method of the battery energy storage power station can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform the energy storage power station assessment, or the 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 may be, but is not limited to, various desktop computers and laptop computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0034] In an exemplary embodiment, Figure 2 , Figure 3 and Figure 4 As shown, a method for evaluating the reliability of a battery energy storage power station is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include 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 states; the four performance states include excellent, degraded, warning and failure.

[0036] Step 202: Based on the four performance states of the energy storage battery compartment, the fuzzy probability of the energy storage battery compartment in each performance state is determined using fuzzy mathematics theory.

[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 collection line-energy storage power station; the energy storage power station includes multiple energy storage collection lines; the energy storage collection line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments.

[0038] Step 204: construct a general generation function of the energy storage battery compartment according to the fuzzy probability of the energy storage battery compartment under each performance state and the SOH corresponding to each performance state.

[0039] Step 205: Construct a multi-state mixed precision model of the energy storage power station according to the universal generation function of the energy storage battery compartment and the topological structure of the energy storage power station; the multi-state mixed precision model of the energy storage power station includes the universal generation function of the energy storage battery compartment, the universal generation function of the energy storage subsystem, the universal generation function of the energy storage collection line and the universal generation function of the energy storage power station.

[0040] Step 206: Based on the multi-state mixed precision 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 to obtain a reliability evaluation result of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indicators, static indicators and importance indicators.

[0041] By implementing the above steps 201 to 206, the state of health (SOH) of the energy storage battery compartment is first defined, and on this basis, a capacity decay model of the energy storage battery compartment SOH is established. Then, according to the SOH value of the energy storage battery compartment, four performance states of "excellent-degraded-warning-failure" are divided. Combined with the working conditions of the battery compartment and the working environment temperature, the mean and standard deviation of the performance level distribution of the battery compartment at each moment are calculated to obtain the fuzzy probability of the battery compartment in different performance states. Secondly, according to the typical topological structure of the large-scale energy storage power station, based on the fuzzy multi-state model of the energy storage battery compartment considering performance decay, the universal generation function method (UGF) is used to expand the multi-state mixed precision model of the energy storage power station of the energy storage battery compartment-energy storage subsystem-energy storage collection line-energy storage power station. Finally, the reliability index system of the energy storage power station is constructed from the three aspects of static, dynamic and importance, and then the Monte Carlo simulation method is used to simulate the operation scenarios of the energy storage power station for a specified number of times. Combined with the calculation results of the reliability indicators under each operation scenario and the probability of occurrence, the reliability evaluation results of the energy storage power station are obtained.

[0042] The energy storage battery compartment is an integrated system that uses electrochemical methods to store electrical energy in multiple battery cells and combines these battery cells together in a specific series or parallel connection. The SOH of the energy storage battery compartment is an important indicator for measuring the health and performance of the battery compartment. From the perspective of capacity, the SOH of the battery compartment can be defined as the ratio of the rated capacity of the battery compartment at the current stage to the rated capacity at the initial stage. By monitoring the SOH, we can understand the gradual degradation or sudden performance decline of the battery compartment, which is an effective method to reflect the performance and health status of the battery compartment. At the initial moment, if the rated capacity of the battery compartment is the same as the rated available capacity at the current stage, the SOH at this time is 100%. If the SOH of the battery compartment drops to a specific range during continuous use, it is considered that the energy storage battery compartment is in a faulty state.

[0043]

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

[0045] According to the above definition of battery compartment SOH, 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 final capacity decay expression can be obtained as the following formula (2).

[0046]

[0047] Where, ΔC dy is the capacity loss rate, that is, the capacity loss amount C dy With maximum charge capacity C ne 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 is the total amount of current flowing.

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

[0049]

[0050] Where SoH(0) is the SOH of the energy storage battery compartment when it is brand new and unused; C ne It is the capacity of the energy storage battery compartment when it is new and unused, in kwh; Pr b (δ) is the energy storage battery compartment power under the working conditions of the bth charge and discharge cycle; M bis the equivalent number of charge and discharge cycles before the EOL of the energy storage battery compartment under the bth operating condition. b It can be calculated using the following equation.

[0051]

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

[0053] Combining the above equations, the derivative of SOH with respect to time under the bth operating condition can be obtained, which is expressed as the following formula.

[0054]

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

[0056]

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

[0058] Calculating the capacity decay of the battery compartment by calculating the SOH of the energy storage battery compartment cannot fully describe the changes in the reliability of the battery compartment during the performance decay process. In order to more carefully evaluate the changes in the reliability of battery energy storage during the life aging process, the performance status of the battery compartment is divided into four performance states: excellent-degraded-warning-failure based on the SOH status of the battery compartment, to reflect 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 attenuation change of its SOH, the performance status of the battery compartment is divided into four states: excellent-degraded-warning-failure. The four performance states are divided into excellent: 80%-100%, degraded: 60%-80%, warning: 20%-60%, and failure 0%-20%. G1, G2, G3, and G4 represent different battery compartment states. For each distinguished state, it corresponds to a certain energy storage battery compartment SOH state range, which can be expressed as the following formula.

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

[0061] Among them, SOH high ,SOH low The upper and lower bounds of the SOH level corresponding to the bth state of the energy storage battery compartment. The values ​​of b are 1, 2, 3, and 4, representing the four performance states of the energy storage battery compartment: excellent, degraded, warning, and failure.

[0062] In order 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 SOH at any time obeys N(μ,σ 2 ) (except when the SOH of a brand new battery compartment is 1), then the battery compartment corresponds to different distribution probabilities in different states. Then the probability set of the battery compartment a in each performance state can be expressed as the following formula.

[0063]

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

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

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

[0067]

[0068] Among them, 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 and lower bounds of the SOH level interval are the maximum and minimum values ​​of the SOH when the energy storage battery compartment is in the four states of excellent, degraded, warning, and failure.

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

[0072]

[0073] After that, the battery compartment is in interval G b =[SoH low-b ,SoH high-b ] 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 the above step 201, 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 accurately obtain and describe. Therefore, the battery's affiliation with each performance state at a certain moment is not either this or that. In order to describe the fuzzy phenomenon that the battery compartment is in a state that is both this and that and difficult to accurately distinguish, based on fuzzy mathematics theory, fuzzy numbers can be used to represent the mean and standard deviation of the battery compartment performance level distribution.

[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 the energy storage battery compartment a belongs to state k at time t is Use fuzzy numbers instead, that is, the probability at this time is a fuzzy set The values ​​of k are 1, 2, 3, and 4, representing the four performance states of the energy storage battery compartment: excellent, degraded, warning, and failure. Different membership functions are used to represent and calculate fuzzy numbers. Considering that the constructed probabilities are all in the real number domain, the corresponding membership function can also be called fuzzy distribution. For example, the degree to which each element in the fuzzy set belongs to the fuzzy number is represented by its membership function. as well as For fuzzy numbers The lowest degree of membership can be The lower and upper limits of the value. For fuzzy numbers The best membership degree is 1, which means it is the most likely value of the fuzzy number.

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

[0078]

[0079] Correspondingly, at this time, for any energy storage battery compartment a, the probability of its performance in each state is The probability power set can be expressed as follows.

[0080]

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

[0082]

[0083] Among them, Pr refers to the elements that have a membership relationship with the fuzzy set. is the corresponding membership function. When the membership function adopts normal fuzzy distribution, It can be expressed as the following formula.

[0084]

[0085] Among them, μ is the mean, that is, the element with the strongest membership.

[0086] For the mean and variance of the distribution, fuzzy numbers are used to represent them. It can be obtained by the following formula.

[0087]

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

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

[0090]

[0091] According to the calculation method of the above steps, a fuzzy multi-state model of the energy storage battery compartment considering the SOH performance attenuation can be obtained. According to the typical topological structure 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 then to the energy storage power station. The general generating function method is used to form the state distribution of the energy storage power station from the state distribution of the energy storage battery compartment.

[0092] In another exemplary embodiment of the present 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) is the universal generating function value of the a-th energy storage battery compartment, m is the power coefficient, 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), It represents the SOH performance level corresponding to the bth performance state of the ath energy storage battery compartment. For the energy storage battery compartment models with four performance states, the value of b is 1, 2, 3, and 4.

[0095] Since the large-scale energy storage power station is composed of various energy storage subsystems that are combined in parallel, the corresponding UGF operator operation rules that reflect the series-parallel relationship are designed. For the parallel connection between two energy storage battery compartments, according to the characteristics of the parallel structure, the reliability of the entire structure should be determined by the energy storage battery compartment with the best performance. However, considering that the faulty parallel battery module will be removed, it will affect the output power of the entire parallel structure, which is equivalent to a sudden capacity decay of the battery compartment. Therefore, for the parallel structure, when considering the capacity decay of the battery, only the battery compartment with the best performance is used to decide, which is contrary to the original multi-state idea and is not reasonable in reflecting the reliability. Therefore, a new weighted operation rule is formulated based on the universal generating function operator, and finally the different states of each parallel structure of the energy storage power station are obtained by weighted combination of each part according to the following operation rules. The specific operation of the universal generating function operator is shown in the following formula.

[0096]

[0097] Where Φ is the universal generating function operator, is the random variable function under the series structure mode, as shown in the following formula.

[0098]

[0099] In the formula, is a random variable function under a parallel structure, and its specific form is as shown in the following formula.

[0100]

[0101] The overall energy storage power station is first connected in parallel by multiple battery compartments, and then connected in series with the PCS (energy storage converter) and the step-up transformer to form an energy storage subsystem. After that, the energy storage subsystems are connected in parallel (the number of energy storage subsystems is 5 or 6), and then the collection lines are connected in parallel to form a complete energy storage power station. The PCS and the step-up transformer have little influence on the energy storage subsystem, so this embodiment does not consider the PCS and the step-up transformer, and the general generating function of the energy storage subsystem can be expressed as follows.

[0102]

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

[0104] An energy storage collection line is formed by a plurality of energy storage subsystems connected in parallel, and the general generating function of each energy storage collection line can be expressed as the following formula.

[0105]

[0106] Among them, u line (m) is the universal generating function value of the energy storage collection line, u system,1 (m) is the universal generating function value of the first energy storage subsystem in the energy storage collection line, u system,2 (m) is the universal generating function value of the second energy storage subsystem in the energy storage collection line, u system,a-1 (m) is the universal generating function value of the a-1th energy storage subsystem in the energy storage collection line, u system,a (m) is the universal generating function value of the ath energy storage subsystem in the energy storage collection line, u system,K-1 (m) is the universal generating function value of the K-1th energy storage subsystem in the energy storage collection line, u system,K (m) is the universal generating function value of the Kth energy storage subsystem in the energy storage collection line, K represents the number of energy storage subsystems in an energy storage collection line, and is 10 or 12; is the SOH performance level corresponding to the bth performance state of the energy storage power station, is the probability corresponding to the energy storage collection line in the bth 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) is the universal generating function value of 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) is the universal generating function value of the second energy storage collection line, u line,a (m) is the universal generating function value of the a-th energy storage collection line, u line,I-1 (m) is the universal generating function value of the I-1th energy storage collection line, u line,I (m) is the universal generating function value of the Ith energy storage collection line in the energy storage collection line, I represents the number of energy storage collection lines in the energy storage power station, is the SOH performance level corresponding to the bth performance state of the energy storage power station, is the probability corresponding to the energy storage power station in the bth performance state.

[0110] Based on the above steps, the mixed precision modeling method of different levels under different states of large-scale energy storage power stations has completed the refined modeling of large-scale energy storage power stations. Then, the reliability of the energy storage power station is evaluated. First, the reliability indicators of the energy storage power station need to be defined. For large-scale energy storage power stations, indicators are proposed from three aspects: dynamic indicators, static indicators, and importance indicators. The static state indicators are intended to reflect the basic state and state margin of the energy storage power station; the dynamic change indicators are intended to characterize the changes in the state of the energy storage power station and its ability to adapt to changes; the importance indicators reflect the connection and influence between the internal battery compartment of the energy storage power station and the overall, which can be used to explore the weak links in the power station.

[0111] 1) In terms of static indicators, they mainly 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 threshold is set. When the SOH value of the battery compartment is less than the threshold, it is considered that the battery compartment has failed. The sum of the probabilities that its SOH state is greater than the threshold is the reliability of the battery compartment.

[0113]

[0114] Among them, α is the threshold of the set SOH state, F BAT is the cumulative probability density function of the state distribution, SoH b is the SOH value of performance state b, Pr b is the probability of performance state b.

[0115] Expected capacity of energy storage battery compartment E BAT : A certain SOH state threshold is set. When the battery compartment SOH state is greater than the threshold, the expected value of the battery compartment SOH state is the expected health state of the battery compartment.

[0116]

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

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

[0119] Among them, C ini is the initial state capacity of the battery; E BAT {Ψ(SoH)} is the expected state of health of the battery, that is, 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 change rate of various battery performance parameters. They mainly include: reliability degradation rate, average SOH decay rate of power stations, expected decay rate of power station capacity, and average capacity decay rate of power stations.

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

[0122]

[0123] Among them, ΔR BAT is the total reliability decrease value during the simulation, and Δt is the total time.

[0124] SOH decay rate I dSoH :SOH decay rate is defined as the change in SOH of the energy storage battery compartment per unit time. It is used to describe the decay speed of the health status of the battery compartment.

[0125]

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

[0127] Average SOH attenuation rate of power station I adSoH : The average value of the 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 is the duration of the kth state, ΔSoH k is the SOH drop value of the kth state.

[0130] Expected attenuation rate of power station capacity I dC :Expected capacity of energy storage power station C s The differential operation with respect to time reflects how fast the power station capacity decays.

[0131]

[0132] Average capacity attenuation rate of power station I C : The ratio of the attenuation of the energy storage power station capacity to time during the statistical period, used to reflect the average attenuation rate of the power station capacity.

[0133]

[0134] Where, ΔC re Indicates that the energy storage station has reached the capacity threshold C α Total capacity attenuation; T α Indicates the time it takes for the energy storage power station capacity to decay to the capacity threshold.

[0135] 3) Considering the difference between large-scale energy storage power stations and mechanical energy storage power stations such as pumped storage and compressed air: a battery energy storage power station contains multiple energy storage battery compartments. In order to reflect the connection between the energy storage battery compartment and the energy storage power station as a whole, analyze its weak links and define importance indicators, including reliability change contribution, expected capacity change contribution and reliability sensitivity.

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

[0137]

[0138] Among them, Γ(SoH|d SoH,k ) indicates that the performance of the kth energy storage battery compartment increases by d SoH,k Rear battery compartment assembly.

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

[0140]

[0141] Reliability sensitivity d Rp,k :This indicator is used to reflect the impact of changes in the SOH of the energy storage battery compartment on the reliability index of the overall energy storage power station. It is defined as the differential calculation of the overall reliability of the energy storage power station to the SOH of the battery compartment.

[0142]

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

[0144] In another exemplary embodiment of the present application, the above-mentioned step 206 specifically includes: based on the multi-state mixed precision 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 scenario of the energy storage power station, and obtain the calculation results and occurrence probabilities of the reliability indicators in each operation scenario; according to the calculation results and occurrence probabilities of the reliability indicators in each operation scenario, the reliability of the energy storage power station is evaluated to obtain the reliability evaluation results of the energy storage power station.

[0145] After multi-state mixed precision modeling of large-scale energy storage power stations and construction of the energy storage power station reliability index system, 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 attenuation is established based on the reliability test data of the energy storage battery compartment in the energy storage power station. Combined with 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, and the fuzzy probability of the energy storage battery compartment in each performance state is obtained to 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 indicators of the energy storage power station in three aspects, namely dynamic indicators, static indicators and importance indicators, are calculated according to the reliability index system to complete the reliability evaluation of the energy storage power station. The specific description of each step is shown in (1) to (5) below.

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

[0147] (2) Set up a reliability assessment environment and determine parameters such as ambient temperature T, battery charge and discharge rate K, and the typical topology structure 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, complete the mixed precision modeling of different levels under different states of the energy storage power station. Then use the Monte Carlo simulation method to simulate the operation scenario of the energy storage power station for a specified number of times. In each simulation, record the performance distribution of the power station at each moment G Tb , And the reliability index is calculated based on the reliability index system of the energy storage power station.

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

[0151] This application has the following beneficial effects.

[0152] (1) This application considers the SOH of the energy storage battery compartment and divides the battery compartment into four performance states: "excellent-degraded-warning-failure" according to the SOH value. Combined with the working conditions and ambient temperature of the battery compartment, the mean and standard deviation of the performance level distribution of the battery compartment at each moment are calculated to obtain the fuzzy probability of the battery compartment in different performance states, which can accurately evaluate the performance and health status of the battery compartment and provide accurate reliability detection.

[0153] (2) This application adopts a general generating function method to expand the fuzzy multi-state model of the energy storage battery compartment considering performance degradation, forming a multi-state, multi-level mixed precision model of the energy storage battery compartment-energy storage subsystem-energy storage collection line-energy storage power station, which can comprehensively reflect the probabilities of different levels under different states of the energy storage power station, and effectively reflect 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. It also uses the Monte Carlo simulation method to simulate multiple operating scenarios of energy storage power stations. It combines the calculation results of reliability indicators under each operating scenario and their probability of occurrence to obtain the reliability evaluation results of energy storage power stations. It is possible to obtain a more comprehensive reliability evaluation through multiple simulations of indicators from different angles, which enhances the credibility and practicality of the evaluation results.

[0155] The present application also provides an application scenario, which applies the above-mentioned battery energy storage power station reliability assessment method. Specifically: The battery energy storage power station reliability assessment method provided in this embodiment can be applied in the energy storage power station assessment scenario. The energy storage power station assessment scenario includes an information acquisition link and an energy storage power station assessment link; the energy storage power station assessment parameters enter the energy storage power station assessment link from the information acquisition link, and the corresponding reliability assessment results are obtained through human-computer 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 link process for the energy storage power station, the performance state of the energy storage battery compartment can be divided into four performance states based on the SOH of the energy storage battery compartment, and the fuzzy probability of the energy storage battery compartment in each performance state can be determined by using fuzzy mathematics theory. The energy storage power station is divided into a four-level model, and a universal generation function of the energy storage battery compartment is constructed according to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state. According to the universal generation function of the energy storage battery compartment and the topological structure of the energy storage power station, a multi-state mixed precision model of the energy storage power station is constructed. Based on the multi-state mixed precision 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, the embodiment of the present application also provides a battery energy storage power station reliability assessment device for implementing the battery energy storage power station reliability assessment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the battery energy storage power station reliability assessment device provided below can refer to the limitations of the battery energy storage power station reliability assessment method above, and will not be repeated here.

[0157] In an exemplary embodiment, Figure 5 As shown, a battery energy storage power station reliability assessment device is provided, including 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 states based on the SOH of the energy storage battery compartment; the four performance states include excellent, degraded, warning and failure.

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

[0160] The four-level model division 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 collection line-energy storage power station; the energy storage power station includes multiple energy storage collection lines; the energy storage collection line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments.

[0161] The universal generation function construction module T4 of the energy storage battery compartment is used to construct a universal generation function of the energy storage battery compartment according to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state.

[0162] The multi-state mixed precision model construction module T5 of the energy storage power station is used to: construct a multi-state mixed precision model of the energy storage power station according to the universal generation function of the energy storage battery compartment and the topological structure of the energy storage power station; the multi-state mixed precision model of the energy storage power station includes the universal generation function of the energy storage battery compartment, the universal generation function of the energy storage subsystem, the universal generation function of the energy storage collection line and the universal generation function of the energy storage power station.

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

[0164] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store energy storage power station evaluation data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a reliability evaluation method for a battery energy storage power station is implemented.

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

[0166] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0167] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0169] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0170] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A reliability assessment method for a battery energy storage power station, characterized in that: The battery energy storage power station reliability assessment method comprises: Based on the SOH of the energy storage battery compartment, the performance status of the energy storage battery compartment is divided into four performance states; the four performance states include excellent, degraded, warning and failure; Based on the four performance states of the energy storage battery compartment, the fuzzy probability of the energy storage battery compartment in 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 collection line-energy storage power station; the energy storage power station includes multiple energy storage collection lines; the energy storage collection line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments; According to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state, a universal generation function of the energy storage battery compartment is constructed; According to the universal generation function of the energy storage battery compartment and the topological structure of the energy storage power station, a multi-state mixed precision model of the energy storage power station is constructed; the multi-state mixed precision model of the energy storage power station includes the universal generation function of the energy storage battery compartment, the universal generation function of the energy storage subsystem, the universal generation function of the energy storage collection line and the universal generation function of the energy storage power station; Based on the multi-state mixed precision 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 to obtain the reliability evaluation result of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indicators, static indicators and importance indicators.

2. The reliability assessment method of a battery energy storage power station according to claim 1, characterized in that: The general generation function of the energy storage battery compartment is: Among them, u a (m) is the universal generating function value of the a-th energy storage battery compartment, m is the power coefficient, represents the probability that the a-th energy storage battery compartment corresponds to the b-th performance state, Indicates the SOH performance level corresponding to the bth performance state of the ath energy storage battery compartment.

3. The reliability assessment method of a battery energy storage power station according to claim 1, characterized in that: The general generating function of the energy storage subsystem can be expressed as: Among them, u system (m) is the universal generating function value of the energy storage subsystem, Φ represents the universal generating function operator, u1(m) is the universal generating function value of the first energy storage battery compartment in the energy storage subsystem, u2(m) is the universal generating function value of the second energy storage battery compartment in the energy storage subsystem, and u a (m) is the universal generating function value of the ath energy storage battery compartment in the energy storage subsystem, u L (m) is the universal generating function value of the Lth energy storage battery compartment in the energy storage subsystem, is the SOH performance level corresponding to the a-th energy storage battery compartment in the b-th performance state, is the probability corresponding to the a-th energy storage battery compartment in the b-th performance state.

4. The reliability assessment method of a battery energy storage power station according to claim 1, characterized in that: The general generating function of the energy storage collection line is: Among them, u line (m) is the universal generating function value of the energy storage collection line, u system,1 (m) is the universal generating function value of the first energy storage subsystem in the energy storage collection line, u system,2 (m) is the universal generating function value of the second energy storage subsystem in the energy storage collection line, u system,a-1 (m) is the universal generating function value of the a-1th energy storage subsystem in the energy storage collection line, u system,a (m) is the universal generating function value of the ath energy storage subsystem in the energy storage collection line, u system,K-1 (m) is the universal generating function value of the K-1th energy storage subsystem in the energy storage collection line, u system,K (m) is the universal generating function value of the Kth energy storage subsystem in the energy storage collection line, K represents the number of energy storage subsystems in an energy storage collection line, is the SOH level corresponding to the bth performance state of the energy storage power station, is the probability corresponding to the energy storage collection line in the bth performance state.

5. The reliability assessment method of a battery energy storage power station according to claim 1, characterized in that: The general generating function of the energy storage power station is: Among them, u station (m) is the universal generating function value of 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) is the universal generating function value of the second energy storage collection line, u line,a (m) is the universal generating function value of the mth energy storage collection line, u line,I-1 (m) is the universal generating function value of the I-1th energy storage collection line, u line,I (m) is the universal generating function value of the Ith energy storage collection line in the energy storage collection line, I represents the number of energy storage collection lines in the energy storage power station, is the SOH performance level corresponding to the bth performance state of the energy storage power station, is the probability corresponding to the energy storage power station in the bth performance state.

6. The reliability assessment method of a battery energy storage power station according to claim 1, characterized in that: Based on the multi-state mixed precision 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 results of the energy storage power station are obtained, including: Based on the multi-state mixed precision model of energy storage power station and the reliability index system of energy storage power station, the Monte Carlo simulation method is used to simulate the operation scenarios of energy storage power station, and the calculation results and occurrence probabilities of reliability indicators under various operation scenarios are obtained; According to the calculation results and occurrence probabilities of reliability indicators under various operation scenarios, the reliability of the energy storage power station is evaluated to obtain the reliability evaluation results of the energy storage power station.

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

8. A reliability assessment device for a battery energy storage power station, characterized in that: The battery energy storage power station reliability assessment device comprises: The performance state classification module is used to: classify the performance state of the energy storage battery compartment into four performance states based on the SOH of the energy storage battery compartment; the four performance states include excellent, degraded, warning and failure; A fuzzy probability determination module is used to: determine the fuzzy probability of the energy storage battery compartment in each performance state based on the four performance states of the energy storage battery compartment using fuzzy mathematics theory; The four-level model division 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-energy storage subsystem-energy storage collection line-energy storage power station; the energy storage power station includes multiple energy storage collection lines; the energy storage collection line includes multiple energy storage subsystems; the energy storage subsystem includes multiple energy storage battery compartments; The universal generation function construction module of the energy storage battery compartment is used to: construct the universal generation function of the energy storage battery compartment according to the fuzzy probability of the energy storage battery compartment in each performance state and the SOH corresponding to each performance state; The multi-state mixed precision model building module of the energy storage power station is used to: build a multi-state mixed precision model of the energy storage power station according to the universal generation function of the energy storage battery compartment and the topological structure of the energy storage power station; the multi-state mixed precision model of the energy storage power station includes the universal generation function of the energy storage battery compartment, the universal generation function of the energy storage subsystem, the universal generation function of the energy storage collection line and the universal generation function of the energy storage power station; The reliability assessment module of the energy storage power station is used to: perform reliability assessment on the energy storage power station based on the multi-state mixed precision model of the energy storage power station and the reliability index system of the energy storage power station to obtain the reliability assessment result of the energy storage power station; the reliability index system of the energy storage power station includes dynamic indicators, static indicators and importance indicators.

9. A computer device comprising: 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 battery energy storage power station reliability assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the battery energy storage power station reliability assessment method described in any one of claims 1 to 7 is implemented.

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