Virtual power plant reliability determination method, apparatus and device, and storage medium

Abstract

The invention discloses a virtual power plant reliability determination method and device, equipment and a storage medium. The method comprises the following steps: determining reason context information of a target event occurring in a current virtual power plant; performing curve fitting by using the fuzzy representation of the occurrence probability of the indirect reason event to obtain a probability distribution function of the indirect reason event, and performing multi-round simulation test on the current virtual power plant by using a fault tree generated by the reason context information to obtain a candidate reliability index of the current virtual power plant; and determining a theoretical probability of occurrence of the target event according to the probability distribution function, and determining a reliability index of the current virtual power plant according to a matching degree between the theoretical probability and the candidate reliability index. According to the technical scheme of the embodiment of the invention, mathematical modeling of the fault factors influencing the reliability of the virtual power plant is realized, and the accuracy of reliability evaluation of the virtual power plant is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment and storage medium for determining the reliability of a virtual power plant. Background Art

[0002] With the reform of electricity spot market and the increase of new energy penetration, virtual power plant has become a new direction for the development of integrated energy business with its advantage of low initial fixed asset investment cost. In terms of technical implementation, virtual power plant can be understood as a power coordination management system with the ability to participate in the power market and power grid operation. Through advanced communication technology and software system, it aggregates and coordinates the load-side energy such as distributed photovoltaic, energy storage system, heat pump, electric vehicle, cold and heat storage, flexible load, etc. on the user side.

[0003] Virtual power plant operation and dispatching rules place higher demands on system reliability, so it is necessary for virtual power plant operators to analyze the reliability of virtual power plant systems. After reliability modeling and analysis of virtual power plant systems, operators can identify the main factors affecting reliability and predict failure rates, thereby providing a theoretical basis for improving operation and maintenance management and reducing penalty fees caused by system failures.

[0004] However, in actual work, when conducting reliability assessment of virtual power plant systems, there is often a problem of difficulty in modeling fault influencing factors, which leads to inaccurate reliability assessment of virtual power plants. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for determining the reliability of a virtual power plant to solve the problem of inaccurate reliability assessment of a virtual power plant.

[0006] In a first aspect, the present invention provides a method for determining reliability of a virtual power plant, comprising:

[0007] Determine the cause context information of the target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes a direct cause event and an indirect cause event of the target event, and there is a causal relationship between the indirect cause event and the direct cause event;

[0008] Performing curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain the probability distribution function of the indirect cause event, and performing multiple rounds of simulation tests on the current virtual power plant using the fault tree generated by the cause context information to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes the causal relationship between the target event and the cause context information;

[0009] The theoretical probability of the target event occurring is determined according to the probability distribution function, and the reliability index of the current virtual power plant is determined according to the matching degree between the theoretical probability and the candidate reliability index, wherein the candidate reliability index includes the simulation probability of the target event occurring.

[0010] In a second aspect, the present invention provides a virtual power plant reliability determination device, comprising:

[0011] An event context determination module is used to determine the cause context information of a target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes a direct cause event and an indirect cause event of the target event, and there is a causal relationship between the indirect cause event and the direct cause event;

[0012] a candidate indicator determination module, configured to perform curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain a probability distribution function of the indirect cause event, and to perform multiple rounds of simulation tests on the current virtual power plant using a fault tree generated by the cause context information to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes a causal relationship between the target event and the cause context information;

[0013] A reliability index determination module is used to determine the theoretical probability of the target event occurring according to the probability distribution function, and to determine the reliability index of the current virtual power plant according to the matching degree between the theoretical probability and the candidate reliability index, wherein the candidate reliability index includes the simulation probability of the target event occurring.

[0014] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0015] at least one processor;

[0016] and a memory communicatively coupled to the at least one processor;

[0017] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the virtual power plant reliability determination method of the first aspect mentioned above.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which are used to enable a processor to implement the virtual power plant reliability determination method of the first aspect when executed.

[0019] The virtual power plant reliability determination scheme provided by the present invention determines the cause context information of a target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes a direct cause event and an indirect cause event of the target event, and there is a causal relationship between the indirect cause event and the direct cause event. A fuzzy representation of the probability of occurrence of the indirect cause event is used to perform curve fitting to obtain a probability distribution function of the indirect cause event, and a fault tree generated by the cause context information is used to perform multiple rounds of simulation tests on the current virtual power plant to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes the causal relationship between the target event and the cause context information, and the theoretical probability of the target event occurring is determined according to the probability distribution function, and the reliability indicator of the current virtual power plant is determined according to the matching degree between the theoretical probability and the candidate reliability indicator, wherein the candidate reliability indicator includes the simulation probability of the target event occurring. By adopting the above technical scheme, the impact of indirect cause events caused by human and management dimensions is quantified through fuzzy representation, and the prediction of the failure rate distribution function and the theoretical probability of event occurrence is realized through fitting. Then, according to the matching degree of the candidate reliability index of the theoretical probability and the simulation probability, it can be determined whether the candidate reliability index is the reliability index of the current virtual power plant. This method combines the advantages of fuzzy mathematics, realizes the mathematical modeling of the fault factors affecting the reliability of the virtual power plant, and improves the accuracy of the reliability assessment of the virtual power plant.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a method for determining reliability of a virtual power plant provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a method for determining reliability of a virtual power plant provided according to Embodiment 2 of the present invention;

[0024] Figure 3This is a schematic diagram of the change of the failure rate of a hardware product over time according to the second embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of the change of the failure rate of a software product over time according to the second embodiment of the present invention;

[0026] Figure 5 is a structural schematic diagram of a fault tree provided according to Embodiment 2 of the present invention;

[0027] Figure 6 is a simulation flow chart provided according to the second embodiment of the present invention;

[0028] Figure 7 is a structural schematic diagram of a virtual power plant reliability determination device provided according to Embodiment 3 of the present invention;

[0029] Figure 8 It is a structural schematic diagram of an electronic device provided according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In the description of the present invention, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are an "or" relationship. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] In the process of responding to dispatch, the virtual power plant system based on the "cloud-edge-end" architecture needs to first receive the grid dispatch control instructions through the operator management platform, and then decompose the control instructions to the corporate customers in the subordinate park. After receiving the control instructions issued by the operator management platform in the cloud, the local energy management system installed in the park will further decompose the instructions and issue them to the load-side resources within the park, and then close the loop to monitor the load-side resources according to the target power curve. When the control instructions are executed, the local energy management system will upload the response curve data to the cloud.

[0033] Embodiment 1

[0034] Figure 1 A flowchart of a method for determining the reliability of a virtual power plant is provided for the first embodiment of the present invention. This embodiment is applicable to situations in which the reliability of a virtual power plant is determined. The method can be executed by a virtual power plant reliability determination device. The virtual power plant reliability determination device can be implemented in the form of hardware and / or software. The virtual power plant reliability determination device can be configured in an electronic device. The electronic device can be composed of two or more physical entities or one physical entity.

[0035] like Figure 1 As shown, a method for determining the reliability of a virtual power plant provided in the first embodiment of the present invention specifically includes the following steps:

[0036] S101. Determine the cause context information of a target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes direct cause events and indirect cause events of the target event, and there is a causal relationship between the indirect cause events and the direct cause events.

[0037] In this embodiment, the cause context information of the target event occurring in the current virtual power plant can be determined based on prior knowledge or historical work orders, etc. The target event can be understood as an event that affects the reliability of the current virtual power plant, such as the inability to execute a dispatch control instruction, etc. The cause context information includes at least the direct cause event and the indirect cause event of the target event, and the indirect cause event can include an event that directly causes the direct cause event to occur, and can also include a second event that indirectly causes the direct cause event to occur.

[0038] S102. Perform curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain the probability distribution function of the indirect cause event, and perform multiple rounds of simulation tests on the current virtual power plant using the fault tree generated by the cause context information to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes the causal relationship between the target event and the cause context information.

[0039] In this embodiment, a fuzzy representation of the probability of occurrence of an indirect cause event can be determined by using fuzzy evaluation or the like. The fuzzy representation can be a fuzzy matrix, which can contain multiple fuzzy values ​​of the probability of occurrence of an indirect cause event. Then, a curve fitting can be performed on the fuzzy representation to obtain a probability distribution function that the probability of occurrence of an indirect cause event obeys. A fault tree can be generated based on the cause context information, and the fault tree can be used to perform multiple rounds of simulation tests on the current virtual power plant to obtain candidate reliability index data of the current virtual power plant through simulation.

[0040] S103. Determine the theoretical probability of the target event occurring according to the probability distribution function, and determine the reliability index of the current virtual power plant according to the matching degree between the theoretical probability and the candidate reliability index, wherein the candidate reliability index includes the simulation probability of the target event occurring.

[0041] In this embodiment, the theoretical probability of the occurrence of an indirect cause event can be estimated based on the probability distribution function, so that the theoretical probability of the occurrence of the target event can be deduced based on the theoretical probability of the indirect cause event and the causal relationship in the fault tree. If the matching degree between the simulation probability of the occurrence of the target event in the candidate reliability index and the theoretical probability of the occurrence of the target event meets the preset requirements, such as the matching degree is greater than the preset threshold, the candidate reliability index can be determined as the reliability index of the current virtual power plant. If not, it is necessary to adjust the fault tree and / or probability distribution function, etc.

[0042] The technical solution of the embodiment of the present invention quantifies the impact of human and management dimensions on the occurrence of indirect cause events through fuzzy representation, and realizes the prediction of the failure rate distribution function and the theoretical probability of event occurrence through fitting. Then, according to the matching degree of the candidate reliability index of the theoretical probability and the simulation probability, it can be determined whether the candidate reliability index is the reliability index of the current virtual power plant. This method combines the advantages of fuzzy mathematics, realizes the mathematical modeling of the fault factors affecting the reliability of the virtual power plant, and improves the accuracy of the reliability assessment of the virtual power plant.

[0043] Optionally, the indirect cause events include first-level indirect cause events and second-level indirect cause events, the first-level indirect cause events are events that directly lead to the occurrence of the direct cause events, the second-level indirect cause events are events that directly lead to the occurrence of the first-level indirect cause events, and the second-level indirect cause events include events whose human subjectivity and / or randomness exceeds a preset range.

[0044] Exemplarily, if the target event is failure to execute the scheduling control instruction, the direct cause event may be as shown in the following table 1:

[0045] Table 1 Direct cause event table

[0046] Serial number serial number Event Description causation 1 F1 The equipment itself may have mechanical failure Lead to DO 2 F2 Failures in use due to management and operation Lead to DO 3 F3 Failures caused by complex working conditions Lead to DO

[0047] The first-level indirect cause events can be shown in Table 2 below:

[0048] Table 2 First-level indirect cause event table

[0049]

[0050] The secondary indirect cause events can be shown in Table 3 as follows:

[0051] Table 3 Secondary indirect cause event table

[0052]

[0053]

[0054] Among them, the events whose human subjectivity and / or randomness exceeds the preset range may be the events corresponding to X20, X21, ..., X29 respectively.

[0055] For example, the above text can be used to determine the occurrence probability prediction values ​​corresponding to X20, X21, ..., X29, respectively, as shown in the occurrence probability prediction value table in Table 4 below:

[0056] Table 4 Occurrence probability prediction value table

[0057]

[0058]

[0059] Optionally, the use of the fuzzy representation of the probability of occurrence of the indirect cause event to perform curve fitting to obtain the probability distribution function of the indirect cause event includes: performing fuzzy evaluation on the probability of occurrence of a first target event to obtain a trapezoidal fuzzy number, wherein the indirect cause event includes the first target event and a second target event, the first target event is an event whose human subjectivity and / or randomness exceeds a preset range, and the second target event is an event among the indirect cause events other than the first target event; defuzzifying the trapezoidal fuzzy number to obtain a predicted value of the occurrence probability of the first target event; performing curve fitting on the predicted value of the occurrence probability to determine, based on the curve fitting result, a first probability distribution function that the first target event obeys, and determine a second probability distribution function that the second target event obeys.

[0060] Specifically, the evaluation of experts in the field of virtual power plants can be obtained through questionnaires and other methods, so as to obtain a fuzzy evaluation of the probability of occurrence of each first target event, and the probability of occurrence can be divided into 7 levels, including: very low, low, relatively low, medium, relatively high, high and very high, which are represented by symbols VL, L, FL, M, FH, H and VH respectively. The evaluation can be converted into a fuzzy number using a membership function, as shown in the fuzzy number table in Table 5 below:

[0061] Table 5 Fuzzy number table

[0062] Serial number Qualitative evaluation Symbolic representation Fuzzy Number 1 Very low V L (0,0,0,1,0.2) 2 Low L (0.1,0.2,0,2,0.3) 3 Relatively low FL (0.2,0.3,0,4,0.5) 4 medium M (0.4,0.5,0,5,0.6) 5 Relatively high FH (0.5,0.6,0,7,0.8) 6 high H (0.7,0.8,0,8,0.9) 7 Very high VH (0.8,0.9,1,1,1)

[0063] Due to the differences in the cognitive level and management experience of each expert, the expert evaluation and fuzzy number are weighted. The evaluation weights and fuzzy number weights of the 10 experts are shown in the following table 6 fuzzy number weight table:

[0064] Table 6 Fuzzy number weight table

[0065] Serial number serial number Expert Role Evaluation weight Fuzzy number weight 1 C1 university professor 1 0.1190 2 C2 university professor 1 0.1190 3 C3 Operator Manager 0.9 0.1071 4 C4 Operator Manager 0.9 0.1071 5 C5 Operator operation and maintenance personnel 0.8 0.0952 6 C6 Operator operation and maintenance personnel 0.8 0.0952 7 C7 Operator operation and maintenance personnel 0.8 0.0952 8 C8 Operator operation and maintenance personnel 0.8 0.0952 9 C9 Enterprise Field Engineer 0.7 0.0833 10 C10 Enterprise Field Engineer 0.7 0.0833

[0066] Specifically, the trapezoidal fuzzy number R xi It can be expressed as:

[0067]

[0068] Where N is the number of experts, i is the ordinal number of the first target event in the indirect cause event (e.g., ranging from 20 to 29), j is the ordinal number of the expert (j = 1, 2, ..., 10), (l i,j ,m i,j ,u i,j ,t i,j ) is the fuzzy number corresponding to the expert evaluation of the jth expert on the i-th indirect cause event, w i,j is the fuzzy number weight of the j-th expert (corresponding to the expert evaluation of the i-th indirect cause event).

[0069] Then, the trapezoidal fuzzy number can be defuzzified to obtain the predicted probability value FP of the first target event: i , including:

[0070]

[0071] Among them, FP i is the predicted value of the occurrence probability of the i-th first target event.

[0072] For example, if i=20, the corresponding first target event is "untimely replacement of consumable parts", and the fuzzy evaluation of the event by 10 experts is shown in the following fuzzy evaluation table 7:

[0073] Table 7 Fuzzy evaluation table

[0074] Serial number Expert Role Qualitative evaluation Fuzzy Number Fuzzy number calculation weight 1 university professor Very low (0,0,0,1,0.2) 0.1190 2 university professor Low (0.1,0.2,0,2,0.3) 0.1190 3 Operator Manager Relatively low (0.2,0.3,0,4,0.5) 0.1071 4 Operator Manager Very low (0,0,0,1,0.2) 0.1071 5 Operator operation and maintenance personnel Relatively low (0.2,0.3,0,4,0.5) 0.0952 6 Operator operation and maintenance personnel Low (0.1,0.2,0,2,0.3) 0.0952 7 Operator operation and maintenance personnel Very low (0,0,0,1,0.2) 0.0952 8 Operator operation and maintenance personnel medium (0.4,0.5,0,5,0.6) 0.0952 9 Enterprise Field Engineer Relatively low (0.2,0.3,0,4,0.5) 0.0833 10 Enterprise Field Engineer Relatively low (0.2,0.3,0,4,0.5) 0.0833

[0075] Using the above formula, we can determine that the predicted probability of the event is 0.207%, that is, FP 20 =0.207%.

[0076] Optionally, the above method also includes: obtaining the future day's operation information of the current virtual power plant, and using the operation information to update the cause context information, and using the updated cause context information to correct the fault tree, wherein the future day's operation information includes new direct cause events and / or new indirect cause events.

[0077] Specifically, in the operation of the virtual power plant, the events in the cause context information may not be fully recognized due to human cognition and other reasons, which will affect the prediction of the probability of occurrence of the target event and the accuracy of the reliability index. Therefore, it is necessary to establish a process and mechanism for correcting the cause context information and fault tree during the operation of the virtual power plant, as shown in the cause context information correction table in Table 8 below:

[0078] Table 8 Cause context information correction table

[0079]

[0080]

[0081] The advantage of this setting is that the operational information generated during the operation of the virtual power plant is used to correct the causal logic relationship of the fault tree and the events included in the fault tree, making up for the limitations of fault tree construction due to insufficient human cognition.

[0082] Embodiment 2

[0083] Figure 2 A flowchart of a method for determining the reliability of a virtual power plant is provided in the second embodiment of the present invention. The technical solution of the embodiment of the present invention is further optimized on the basis of the above-mentioned optional technical solutions, and a specific method for determining the reliability of a virtual power plant is given.

[0084] Optionally, the curve fitting is performed on the predicted value of the occurrence probability to determine the first probability distribution function that the first target event obeys according to the curve fitting result, including: curve fitting is performed on the predicted value of the occurrence probability to obtain a curve fitting result; the probability function of the first target event is determined according to the curve fitting result, and the theoretical probability of the event of the device corresponding to the first target event at a preset time point is obtained; the first probability distribution function that the first target event obeys is determined using the theoretical probability of the event and the probability distribution. The advantage of this setting is that by solving the probability distribution using the theoretical probability of the event, the first probability distribution function that the first target event obeys is obtained, and the status of different equipment life cycles is predicted. It can solve the problem of unclear equipment status caused by the mixing of old and new equipment, diverse manufacturers, and diverse technologies in actual business, and the predicted equipment status is closer to the fault status of the actual business scenario.

[0085] Optionally, the fault tree generated by using the cause context information performs multiple rounds of simulation tests on the current virtual power plant to obtain candidate reliability indicators of the current virtual power plant, including: using the Monte Carlo method and the fault tree to perform multiple rounds of simulation tests on the current virtual power plant to obtain simulation results, wherein the simulation results include the simulation rounds when the target event is triggered, the total simulation rounds when the target event is triggered, and the number of times each indirect cause event triggers the target event; using the simulation results to determine the candidate reliability indicators of the current virtual power plant, wherein the candidate reliability indicators include the simulation probability of the target event, the mean time between failures, and the ranking of the indirect cause events that trigger the target event, and the ranking is determined according to the number of times the indirect cause event triggers the target event. The advantage of this setting is that by using the Monte Carlo method and the fault tree to perform multiple rounds of simulation on the virtual power plant, the deviation of the target event trigger prediction caused by the analyst's cognitive limitations on the virtual power plant is solved, which has good engineering application practical significance, helps virtual power plant operators accurately control the state of the virtual power plant, and can help virtual power plant operators avoid losses caused by failure risks.

[0086] Optionally, the method of determining the theoretical probability of the target event occurring according to the probability distribution function, and determining the reliability index of the current virtual power plant according to the matching degree between the theoretical probability and the candidate reliability index, includes: using the probability distribution function and Boolean algebra to determine the theoretical probability of the target event occurring; if the theoretical probability matches the simulation probability in the candidate reliability index, then determining the candidate reliability index as the reliability index of the current virtual power plant. The advantage of this setting is that the theoretical probability of the target event occurring is accurately predicted by the probability distribution function and Boolean algebra, and then by determining whether the theoretical probability matches the simulation probability in the candidate reliability index, the accuracy of the reliability index of the current virtual power plant is guaranteed.

[0087] like Figure 2 As shown, a method for determining the reliability of a virtual power plant provided by Embodiment 2 of the present invention specifically includes the following steps:

[0088] S201. Determine the cause context information of the target event occurring in the current virtual power plant, wherein the cause context information includes the direct cause event and indirect cause event of the target event, the indirect cause event includes the first target event and the second target event, the first target event is an event whose human subjectivity and / or randomness exceeds a preset range, and the second target event is an event among the indirect cause events except the first target event.

[0089] S202, performing fuzzy evaluation on the occurrence probability of the first target event to obtain a trapezoidal fuzzy number; and performing defuzzification processing on the trapezoidal fuzzy number to obtain a predicted value of the occurrence probability of the first target event.

[0090] S203: Perform curve fitting on the predicted value of the occurrence probability to obtain a curve fitting result.

[0091] S204: Determine a probability function of the first target event according to the curve fitting result, and obtain a theoretical probability of an event of the device corresponding to the first target event at a preset time point.

[0092] Specifically, Figure 3 This is a schematic diagram of the failure rate of a hardware product changing over time. Figure 3 As shown in the figure, hardware products will have a short running-in period when they are first used. Most of the failures that occur at this time are related to usage habits and proficiency. After entering the normal use period, the failure rate will decrease and remain stable for a long time. When the equipment ages to the end of its service life, the failure rate will increase again. The closer it is to scrapping, the higher the failure rate. Therefore, the failure rate distribution of related hardware products in virtual power plants usually follows the characteristics of the "bathtub curve". Figure 4 This is a schematic diagram of the failure rate of a software product changing over time. Figure 4As shown in the figure, the software needs to be upgraded and maintained during use. As the number of system upgrades increases, a large amount of redundant code and business logic will be superimposed, which will cause the software failure rate to gradually increase over time. Therefore, the failure rate distribution of related software products in virtual power plants usually follows the "check mark curve" feature.

[0093] Exemplarily, if the probability function of the first target event determined according to the curve fitting result is a bathtub curve function, the probability of occurrence r1 of the first target event can be expressed as:

[0094]

[0095] Where t is the number of years of use, η and β are function constants. The theoretical probability of the event corresponding to the first target event at the first preset time point can be obtained through manufacturer surveys, such as the first-year failure rate r(t=1) and the useful life failure rate r(t=1). And the failure rate r during the wear-out failure period (t = design life).

[0096] Exemplarily, if the probability function of the first target event determined according to the curve fitting result is a check curve function, the probability of occurrence r2 of the first target event can be expressed as:

[0097]

[0098] Among them, α (α>0), θ (θ>0) and δ (δ>0) are function constants. The theoretical probability of the event corresponding to the first target event at the second preset time point can be obtained through manufacturer surveys, such as the first-year failure rate r (t=1), useful life failure rate r (t=3) and wear failure rate r (t=design life) of the equipment.

[0099] S205. Using the theoretical probability of the event and the probability distribution, determine a first probability distribution function that the first target event obeys, and determine a second probability distribution function that the second target event obeys.

[0100] Specifically, the above-mentioned theoretical probability of events can be used to solve η and β, as well as α, θ and δ, so as to determine the r1(t; η, β) function and r2(t; α, δ, θ), that is, to determine the first probability distribution function that the first target event obeys. The second probability distribution function that the second target event obeys and other information can be obtained through methods such as manufacturer research.

[0101] Exemplarily, the curve type corresponding to the second probability distribution function obeyed by the second target event is shown in the curve type table in Table 9 below:

[0102] Table 9 Curve type table

[0103]

[0104]

[0105] Among them, for the situation where the equipment in the virtual power plant is put into operation at different times, the probability of the event occurring at the current time can be obtained by substituting the time already put into operation into the probability distribution function.

[0106] S206. Using the Monte Carlo method and the fault tree, perform multiple rounds of simulation tests on the current virtual power plant to obtain simulation results.

[0107] The simulation result includes the simulation round when the target event is triggered, the total simulation rounds when the target event is triggered, and the number of times each indirect cause event leads to the triggering of the target event.

[0108] Specifically, the Monte Carlo method is a simulation method that evaluates complex systems by generating random numbers. The Monte Carlo method can be used to analyze the fault tree of the virtual power plant to conduct multiple rounds of simulation tests on the current virtual power plant to obtain simulation results. The specific process includes the following two steps:

[0109] 1) Build the model

[0110] Figure 5 A schematic diagram of the structure of a fault tree. Figure 5 In the example, D0 is the target event, F1 to F3 are the direct cause events of the target event, M1 to M16 are the first-level indirect cause events, and X1 to X29 are the second-level indirect cause events. Figure 5 As shown in the figure, there are 29 secondary indirect cause events in the fault tree of the current virtual power plant. Based on the business scenario, it can be assumed that the secondary indirect cause events all obey exponential distribution and are independent of each other. Assuming that the states of all events in the fault tree are only "fault" (i.e. "occurrence") and "normal" (i.e. "non-occurrence"), the structural function of the fault tree is expressed as follows:

[0111] Y(t)=[x 1 (t),x 2 (t),…x i (t)…,x 29 (t)]

[0112] Where t is time, x i (t) represents the secondary indirect cause event x i The state value of the target event is represented by Y(t). In order to solve the problem of mixing old and new equipment in the current virtual power plant, when generating x i (t), the first probability distribution function and the second probability distribution function can be used to determine the corresponding state value by rounding up the year in which the equipment has been put into operation and then inserting it into the function.

[0113] The Monte Carlo method can be used to generate simulation parameters. The simulation parameters include: time interval m = 1, indicating that a random number is generated every hour, maximum operation time T_MAX = 175200, indicating the total number of hours in the 20-year virtual power plant operation period, simulation times NS = 1000, indicating that a total of 1000 simulation calculations are performed.

[0114] 2) Random Sampling

[0115] Figure 6 It is a simulation flow chart. Monte Carlo method can be used for random sampling, and then the fault tree can be used to simulate the reliability of the current virtual power plant. The specific process is as follows: Figure 6 As shown, first import the structure function x i (t) and initialize the number of simulations N = 1 and the total number of simulation hours T = 1, and then use the Monte Carlo method to randomly generate a set of failure times (i.e., state values) of the bottom events (i.e., the secondary indirect cause events) [x 1 (t),x 2 (t),…x i (t)…,x 29 (t)]. Then, according to the event causal relationship in the fault tree, determine whether the current time T will trigger the top event (i.e., the target event). If triggered, record T, the sequence of the bottom events (i.e., the serial number), and the cumulative value of the top event (i.e., the total number of simulation rounds when the target event is triggered). Then, determine whether the number of simulations and the total number of simulation hours are less than or equal to their respective thresholds. If so, continue the simulation. If not, output the simulation results, i.e., the simulation round set tk when the target event is triggered, the total number of simulation rounds NK when the target event is triggered, and the number of times each indirect cause event causes the target event to be triggered.

[0116] S207. Determine candidate reliability indicators of the current virtual power plant using the simulation results, wherein the candidate reliability indicators include the simulation probability of the target event occurring, the mean time between failures, and the ranking of indirect cause events that trigger the target event, and the ranking is determined based on the number of times the indirect cause event triggers the target event.

[0117] Specifically, the method for determining the simulation probability SFR of the target event occurring includes:

[0118]

[0119] Here, SFR can also be understood as simulated failure rate.

[0120] The determination methods of mean time between failures (MTBF) include:

[0121]

[0122] Among them, tk v is the vth value in the simulation round set when the target event is triggered, It represents the total number of hours before the target event is triggered, that is, the total number of hours that the target event is not triggered, and D is the total number of values ​​in the simulation round set when the target event is triggered.

[0123] The order of indirect cause events that trigger the target event is determined by:

[0124] According to the number of times each indirect cause event leads to the triggering of the target event, the indirect cause events that trigger the target event are sorted to obtain the sorting of the indirect cause events that trigger the target event, and the important indirect cause events are screened out, such as determining the indirect cause events ranked in a preset sorting position as important indirect cause events.

[0125] S208: Determine the theoretical probability of the target event occurring by using the probability distribution function and Boolean algebra.

[0126] Specifically, the fault tree can be simplified by Boolean algebra to obtain the minimum cut set. All secondary indirect cause events can be regarded as independent events, and the methods for determining the theoretical probability g of the target event include:

[0127]

[0128] Among them, r is the minimum cut set ordinal number, i is the ordinal number of the secondary indirect cause event, N G is the minimum number of cut sets, x i ∈G r Indicates that the i-th secondary indirect cause event belongs to the r-th minimum cut set, q i is the probability of occurrence of the i-th secondary indirect cause event, which can be determined according to the first probability distribution function and the second probability distribution function, Π represents cumulative multiplication, and ∪ represents a union operation.

[0129] S209. If the theoretical probability matches the simulation probability in the candidate reliability index, the candidate reliability index is determined as the reliability index of the current virtual power plant.

[0130] Specifically, if the theoretical probability g matches the simulation probability SFR in the candidate reliability index, it means that the simulation result is accurate, and the candidate reliability index can be determined as the reliability index of the current virtual power plant.

[0131] The virtual power plant reliability determination method provided in the embodiment of the present invention obtains the first probability distribution function obeyed by the first target event by solving the probability distribution by using the theoretical probability of events, and realizes the prediction of the status of different equipment life cycles. It can solve the problem of unclear equipment status caused by the mixing of old and new equipment, diverse manufacturers and diverse technologies in actual business. The predicted equipment status is closer to the fault status of the actual business scenario, and multiple rounds of simulation of the virtual power plant are performed by using the Monte Carlo method and the fault tree, which solves the deviation of the target event trigger prediction caused by the analyst's cognitive limitations on the virtual power plant. It has good engineering application practical significance, helps the virtual power plant operator to accurately control the virtual power plant status, and can help the virtual power plant operator avoid losses caused by failure risks. The probability distribution function and the Boolean algebra method are used to accurately predict the theoretical probability of the target event, and by determining whether the theoretical probability matches the simulation probability in the candidate reliability index, the accuracy of the reliability index of the current virtual power plant is guaranteed.

[0132] Embodiment 3

[0133] Figure 7 This is a schematic diagram of the structure of a virtual power plant reliability determination device provided in Embodiment 3 of the present invention. Figure 7 As shown, the device includes: an event context determination module 301, a candidate index determination module 302 and a reliability index determination module 303, wherein:

[0134] An event context determination module is used to determine the cause context information of a target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes a direct cause event and an indirect cause event of the target event, and there is a causal relationship between the indirect cause event and the direct cause event;

[0135] a candidate indicator determination module, configured to perform curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain a probability distribution function of the indirect cause event, and to perform multiple rounds of simulation tests on the current virtual power plant using a fault tree generated by the cause context information to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes a causal relationship between the target event and the cause context information;

[0136] A reliability index determination module is used to determine the theoretical probability of the target event occurring according to the probability distribution function, and to determine the reliability index of the current virtual power plant according to the matching degree between the theoretical probability and the candidate reliability index, wherein the candidate reliability index includes the simulation probability of the target event occurring.

[0137] The virtual power plant reliability determination device provided by the embodiment of the present invention quantifies the impact of indirect cause events caused by human and management dimensions through fuzzy representation, and realizes the prediction of the failure rate distribution function and the theoretical probability of event occurrence through fitting. Then, according to the matching degree of the candidate reliability index of the theoretical probability and the simulation probability, it can be determined whether the candidate reliability index is the reliability index of the current virtual power plant. This method combines the advantages of fuzzy mathematics, realizes the mathematical modeling of the fault factors affecting the reliability of the virtual power plant, and improves the accuracy of the reliability assessment of the virtual power plant.

[0138] Optionally, the candidate indicator determination module includes:

[0139] a trapezoidal fuzzy number determination unit, configured to perform fuzzy evaluation on the occurrence probability of a first target event to obtain a trapezoidal fuzzy number, wherein the indirect cause event includes the first target event and a second target event, the first target event is an event whose human subjectivity and / or randomness exceeds a preset range, and the second target event is an event in the indirect cause event other than the first target event;

[0140] A probability prediction unit, configured to perform a defuzzification process on the trapezoidal fuzzy number to obtain a predicted value of the occurrence probability of the first target event;

[0141] A probability distribution determination unit is used to perform curve fitting on the occurrence probability prediction value to determine a first probability distribution function that the first target event obeys according to the curve fitting result, and to determine a second probability distribution function that the second target event obeys.

[0142] Optionally, the indirect cause events include first-level indirect cause events and second-level indirect cause events, the first-level indirect cause events are events that directly lead to the occurrence of the direct cause events, the second-level indirect cause events are events that directly lead to the occurrence of the first-level indirect cause events, and the second-level indirect cause events include events whose human subjectivity and / or randomness exceeds a preset range.

[0143] Optionally, performing curve fitting on the occurrence probability prediction value to determine the first probability distribution function that the first target event obeys according to the curve fitting result includes: performing curve fitting on the occurrence probability prediction value to obtain a curve fitting result; determining the probability function of the first target event according to the curve fitting result, and obtaining the event theoretical probability of the device corresponding to the first target event at a preset time point; and determining the first probability distribution function that the first target event obeys using the event theoretical probability and the probability distribution.

[0144] Optionally, the candidate indicator determination module includes:

[0145] A simulation result determination unit, configured to perform multiple rounds of simulation tests on the current virtual power plant using the Monte Carlo method and the fault tree to obtain simulation results, wherein the simulation results include the simulation rounds when the target event is triggered, the total simulation rounds when the target event is triggered, and the number of times each of the indirect cause events causes the target event to be triggered;

[0146] A reliability index determination unit is used to determine candidate reliability indexes of the current virtual power plant using the simulation results, wherein the candidate reliability indexes include the simulation probability of the target event occurring, the mean time between failures, and the ranking of indirect cause events that trigger the target event, and the ranking is determined according to the number of times the indirect cause event triggers the target event.

[0147] Optionally, the reliability index determination module includes:

[0148] A theoretical probability determination unit, used to determine the theoretical probability of the target event occurring by using the probability distribution function and Boolean algebra;

[0149] A reliability index determination unit is used to determine the candidate reliability index as the reliability index of the current virtual power plant if the theoretical probability matches the simulation probability in the candidate reliability index.

[0150] Optionally, the device further comprises:

[0151] A correction module is used to obtain the future day operation information of the current virtual power plant, and use the operation information to update the cause context information, and use the updated cause context information to correct the fault tree, wherein the future day operation information includes new direct cause events and / or new indirect cause events.

[0152] The virtual power plant reliability determination device provided in the embodiment of the present invention can execute the virtual power plant reliability determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0153] Embodiment 4

[0154] Figure 8A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0155] like Figure 8 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0156] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0157] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a virtual power plant reliability determination method.

[0158] In some embodiments, the virtual power plant reliability determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the virtual power plant reliability determination method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the virtual power plant reliability determination method in any other appropriate manner (e.g., by means of firmware).

[0159] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0160] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0161] The computer device provided above can be used to execute the virtual power plant reliability determination method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0162] Embodiment 5

[0163] In the context of the present invention, a computer readable storage medium may be a tangible medium, wherein the computer executable instructions, when executed by a computer processor, are used to perform a virtual power plant reliability determination method, the method comprising:

[0164] Determine the cause context information of the target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes a direct cause event and an indirect cause event of the target event, and there is a causal relationship between the indirect cause event and the direct cause event;

[0165] Performing curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain the probability distribution function of the indirect cause event, and performing multiple rounds of simulation tests on the current virtual power plant using the fault tree generated by the cause context information to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes the causal relationship between the target event and the cause context information;

[0166] The theoretical probability of the target event occurring is determined according to the probability distribution function, and the reliability index of the current virtual power plant is determined according to the matching degree between the theoretical probability and the candidate reliability index, wherein the candidate reliability index includes the simulation probability of the target event occurring.

[0167] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by an instruction execution system, device or equipment or used with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0168] The computer device provided above can be used to execute the virtual power plant reliability determination method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0169] It is worth noting that in the above-mentioned embodiment of the virtual power plant reliability determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0170] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining the reliability of a virtual power plant, characterized in that: include: Determine the cause context information of the target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes a direct cause event and an indirect cause event of the target event, and there is a causal relationship between the indirect cause event and the direct cause event; Performing curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain the probability distribution function of the indirect cause event, and performing multiple rounds of simulation tests on the current virtual power plant using the fault tree generated by the cause context information to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes the causal relationship between the target event and the cause context information; The theoretical probability of the target event occurring is determined according to the probability distribution function, and the reliability index of the current virtual power plant is determined according to the matching degree between the theoretical probability and the candidate reliability index, wherein the candidate reliability index includes the simulation probability of the target event occurring.

2. The method according to claim 1, characterized in that The method of performing curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain the probability distribution function of the indirect cause event includes: Performing fuzzy evaluation on the occurrence probability of a first target event to obtain a trapezoidal fuzzy number, wherein the indirect cause event includes the first target event and a second target event, the first target event is an event whose human subjectivity and / or randomness exceeds a preset range, and the second target event is an event among the indirect cause events except the first target event; Defuzzifying the trapezoidal fuzzy number to obtain a predicted value of the occurrence probability of the first target event; Curve fitting is performed on the occurrence probability prediction value to determine a first probability distribution function that the first target event obeys according to the curve fitting result, and to determine a second probability distribution function that the second target event obeys.

3. The method according to claim 1 or 2, characterized in that: The indirect cause events include first-level indirect cause events and second-level indirect cause events. The first-level indirect cause events are events that directly lead to the occurrence of the direct cause events, and the second-level indirect cause events are events that directly lead to the occurrence of the first-level indirect cause events. The second-level indirect cause events include events whose human subjectivity and / or randomness exceeds a preset range.

4. The method according to claim 2, characterized in that: The performing curve fitting on the predicted value of the occurrence probability to determine a first probability distribution function obeyed by the first target event according to the curve fitting result includes: Performing curve fitting on the predicted value of the occurrence probability to obtain a curve fitting result; Determine a probability function of the first target event according to the curve fitting result, and obtain a theoretical probability of an event of a device corresponding to the first target event at a preset time point; The first probability distribution function obeyed by the first target event is determined by using the theoretical probability of the event and the probability distribution.

5. The method according to claim 1, characterized in that The fault tree generated by using the cause context information performs multiple rounds of simulation tests on the current virtual power plant to obtain candidate reliability indicators of the current virtual power plant, including: Using the Monte Carlo method and the fault tree, multiple rounds of simulation tests are performed on the current virtual power plant to obtain simulation results, wherein the simulation results include the simulation rounds when the target event is triggered, the total simulation rounds when the target event is triggered, and the number of times each indirect cause event causes the target event to be triggered; The simulation results are used to determine candidate reliability indicators of the current virtual power plant, wherein the candidate reliability indicators include the simulation probability of the target event occurring, the mean time between failures, and the ranking of indirect cause events that trigger the target event, and the ranking is determined based on the number of times the indirect cause events trigger the target event.

6. The method according to claim 1 or 5, characterized in that: Determining the theoretical probability of the target event occurring according to the probability distribution function, and determining the reliability index of the current virtual power plant according to the matching degree between the theoretical probability and the candidate reliability index, includes: Determine the theoretical probability of the target event occurring by using the probability distribution function and Boolean algebra; If the theoretical probability matches the simulation probability in the candidate reliability index, the candidate reliability index is determined as the reliability index of the current virtual power plant.

7. The method according to claim 1, characterized in that Also includes: Obtain future day operation information of the current virtual power plant, use the operation information to update the cause context information, and use the updated cause context information to correct the fault tree, wherein the future day operation information includes new direct cause events and / or new indirect cause events.

8. A virtual power plant reliability determination device, characterized in that: include: An event context determination module is used to determine the cause context information of a target event occurring in the current virtual power plant, wherein the target event includes an event that affects the reliability of the current virtual power plant, and the cause context information includes a direct cause event and an indirect cause event of the target event, and there is a causal relationship between the indirect cause event and the direct cause event; a candidate indicator determination module, configured to perform curve fitting using the fuzzy representation of the probability of occurrence of the indirect cause event to obtain a probability distribution function of the indirect cause event, and to perform multiple rounds of simulation tests on the current virtual power plant using a fault tree generated by the cause context information to obtain candidate reliability indicators of the current virtual power plant, wherein the fault tree includes a causal relationship between the target event and the cause context information; A reliability index determination module is used to determine the theoretical probability of the target event occurring according to the probability distribution function, and to determine the reliability index of the current virtual power plant according to the matching degree between the theoretical probability and the candidate reliability index, wherein the candidate reliability index includes the simulation probability of the target event occurring.

9. The device according to claim 8, characterized in that The candidate indicator determination module comprises: a trapezoidal fuzzy number determination unit, configured to perform fuzzy evaluation on the occurrence probability of a first target event to obtain a trapezoidal fuzzy number, wherein the indirect cause event includes the first target event and a second target event, the first target event is an event whose human subjectivity and / or randomness exceeds a preset range, and the second target event is an event in the indirect cause event other than the first target event; A probability prediction unit, configured to perform a defuzzification process on the trapezoidal fuzzy number to obtain a predicted value of the occurrence probability of the first target event; A probability distribution determination unit is used to perform curve fitting on the occurrence probability prediction value to determine a first probability distribution function that the first target event obeys according to the curve fitting result, and to determine a second probability distribution function that the second target event obeys.

10. The device according to claim 8, characterized in that The candidate indicator determination module comprises: A simulation result determination unit, configured to perform multiple rounds of simulation tests on the current virtual power plant using the Monte Carlo method and the fault tree to obtain simulation results, wherein the simulation results include the simulation rounds when the target event is triggered, the total simulation rounds when the target event is triggered, and the number of times each of the indirect cause events causes the target event to be triggered; A reliability index determination unit is used to determine candidate reliability indexes of the current virtual power plant using the simulation results, wherein the candidate reliability indexes include the simulation probability of the target event occurring, the mean time between failures, and the ranking of indirect cause events that trigger the target event, and the ranking is determined according to the number of times the indirect cause event triggers the target event.

11. The device according to claim 8 or 10, characterized in that The reliability index determination module comprises: A theoretical probability determination unit, used to determine the theoretical probability of the target event occurring by using the probability distribution function and Boolean algebra; A reliability index determination unit is used to determine the candidate reliability index as the reliability index of the current virtual power plant if the theoretical probability matches the simulation probability in the candidate reliability index.

12. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the virtual power plant reliability determination method according to any one of claims 1 to 7.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the virtual power plant reliability determination method according to any one of claims 1 to 7 when executed.

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