Reliability evaluation method and system for light-gas-electricity micro-grid of gas field station

Abstract

The invention discloses a reliability evaluation method and system for a light-gas-electricity micro-grid of a gas station, and relates to the technical field of reliability evaluation for the light-gas-electricity micro-grid, and the method comprises the steps: building a reliability evaluation index system for the light-gas-electricity micro-grid of the gas station; determining the weight of each evaluation index of each level in the reliability evaluation index system; according to the reliability evaluation index system and the weight of each evaluation index of each layer in the reliability evaluation index system, constructing a fuzzy comprehensive evaluation model; and performing reliability evaluation on the light-gas-electricity micro-grid of the target gas field station by using the fuzzy comprehensive evaluation model to obtain a reliability evaluation result of the light-gas-electricity micro-grid of the target gas field station. According to the method, a more scientific and reasonable reliability evaluation result can be obtained, and powerful support is provided for optimized operation and reliable power supply of the light-gas-electricity micro-grid of the gas field station.

Description

Technical Field

[0001] This invention relates to the field of reliability evaluation technology for solar-gas-electric microgrids, and more particularly to a reliability evaluation method and system for solar-gas-electric microgrids in gas stations. Background Technology

[0002] Currently, after photovoltaic power generation and various energy supply equipment are added to the energy supply system of gas stations, the fluctuation characteristics of each device will affect the voltage, protection mechanism and mechanism of the solar-gas-electricity microgrid of the gas station. For example, the photovoltaic power generation system is greatly affected by the environment, and its power generation is closely related to the amount of solar radiation. The random and intermittent power fluctuations brought about by the connection of the photovoltaic system will affect the reliable and stable operation of the solar-gas-electricity microgrid of the gas station. Random faults such as natural gas pipeline leaks and gas supply interruptions may cause the output of gas turbine units to decrease rapidly due to insufficient natural gas supply, thereby threatening the reliable and stable operation of the solar-gas-electricity microgrid of the gas station. The local faults and risks hidden in the operation of the solar-gas-electricity microgrid of the gas station will induce a chain reaction. For example, faults such as component underload, overload, and voltage over-limit may cause other component faults, thereby expanding the scope and extent of the accident. To improve system economy, the operation of the solar-gas-electric microgrid at gas-fired power plants often approaches its limits. The coupling of multiple devices increases the probability of system failures. For example, the randomness, intermittency, and frequent switching of distributed photovoltaic power generation can affect the stability of the solar-gas-electric microgrid at gas-fired power plants. The extensive use of electronic conversion devices can introduce harmonic pollution and reactive power losses into the solar-gas-electric microgrid, leading to a significant deterioration in power quality and seriously jeopardizing its safety. Due to the special nature of gas-fired power plants, accidents can cause huge losses and even endanger personal safety. For example, natural disasters can cause widespread failure or damage to photovoltaic modules, resulting in voltage differences and energy fluctuations, which in turn can lead to instability in energy storage equipment and affect the safety of the solar-gas-electric microgrid at gas-fired power plants. Moreover, traditional reliability evaluation indicators are relatively singular and are greatly affected by random errors, making it difficult to comprehensively measure the safety level of the solar-gas-electric microgrid at gas-fired power plants. Summary of the Invention

[0003] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a reliability evaluation method and system for a solar-gas-electricity microgrid in a gas-fired power plant, as detailed below: 1) In a first aspect, the present invention provides a reliability evaluation method for a solar-gas-electricity microgrid in a gas-fired power plant, the specific technical solution of which is as follows: Establish a reliability evaluation index system for solar-gas-electric microgrids in gas-fired power plants; Determine the weight of each evaluation indicator at each level in the reliability evaluation index system; Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, a fuzzy comprehensive evaluation model is constructed. The reliability of the solar-gas-electric microgrid of the target gas station was evaluated using a fuzzy comprehensive evaluation model, and the reliability evaluation results of the solar-gas-electric microgrid of the target gas station were obtained.

[0004] The beneficial effects of the reliability evaluation method for a solar-gas-electric microgrid in a gas-fired power plant provided by this invention are as follows: Traditional reliability assessment indicators are singular and susceptible to random errors. This application, however, establishes a reliability evaluation indicator system for the solar-gas-electricity microgrid of gas-fired power plants. This system comprehensively measures the reliability of the microgrid from multiple dimensions, effectively overcoming the problem of singular evaluation indicators and improving the comprehensiveness and accuracy of the assessment. By determining the weights of evaluation indicators at each level and constructing a fuzzy comprehensive evaluation model, the system can comprehensively consider the impact of various factors on the reliability of the solar-gas-electricity microgrid of gas-fired power plants, such as the randomness and intermittency of distributed photovoltaic power generation, as well as harmonic pollution and reactive power loss caused by electronic conversion devices. This solves the problem that traditional methods cannot comprehensively evaluate complex coupled systems and better addresses the failure risks of the solar-gas-electricity microgrid of gas-fired power plants when its operating state approaches its limits. Regarding the risks brought about by the special characteristics of gas-fired power plants, such as photovoltaic module failures caused by natural disasters and instability of energy storage equipment, this application can identify potential risks in advance, providing a basis for formulating preventive measures, reducing the probability of accidents, ensuring the safe and stable operation of the solar-gas-electricity microgrid of gas-fired power plants, and reducing potential personal injury and huge losses caused by accidents. Using a fuzzy comprehensive evaluation model for reliability assessment, compared to traditional methods, can more flexibly handle uncertainties in the evaluation process and obtain more scientific and reasonable reliability assessment results, providing strong support for the optimized operation and reliable power supply of the solar-gas-electric microgrid of gas stations.

[0005] Based on the above scheme, the reliability evaluation method of the solar-gas-electric microgrid for gas stations of the present invention can be further improved as follows.

[0006] Furthermore, the weight of each evaluation indicator at each level in the reliability evaluation indicator system is determined, including: Using the analytic hierarchy process (AHP), the weight of each evaluation index at each level in the reliability evaluation index system is determined.

[0007] The beneficial effects of adopting the above-mentioned further scheme are as follows: The Analytic Hierarchy Process (AHP) combines qualitative and quantitative analysis, which can accurately quantify the weights of each level and each evaluation indicator in the reliability evaluation index system, avoid subjective arbitrariness, and make the weight determination more scientific, providing a reliable basis for the subsequent construction of the fuzzy comprehensive evaluation model. Through layer-by-layer decomposition and comparison, the degree of influence of each evaluation indicator on the system reliability is clearly presented, highlighting the weights of key factors, which is conducive to targeted optimization of the operation of the solar-gas-electricity microgrid, improving its reliability and stability, accurately focusing on weak links, and strengthening risk management.

[0008] Furthermore, based on the reliability evaluation index system and the weight of each evaluation index at each level within the reliability evaluation index system, a fuzzy comprehensive evaluation model is constructed, including: Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, determine the factor set, the comment set, and the weight set, and establish a fuzzy membership matrix. A fuzzy comprehensive evaluation model is constructed based on the factor set, comment set, weight set, and fuzzy membership matrix.

[0009] The beneficial effects of adopting the above-mentioned further scheme are as follows: It integrates the reliability evaluation index system and weights into the fuzzy comprehensive evaluation model, determines the factor set, evaluation set, weight set, and fuzzy membership matrix, comprehensively considers all factors in the solar-gas-electricity microgrid, forms a systematic evaluation framework, and improves the reliability and accuracy of the evaluation. Based on the weight set and fuzzy membership matrix, it accurately quantifies the impact of each factor of the microgrid on the system reliability, transforms the fuzzy evaluation into specific data, enhances the credibility of the evaluation results, and provides a strong basis for optimizing microgrid operation and formulating maintenance strategies.

[0010] Furthermore, establish a reliability evaluation index system for the solar-gas-electric microgrid at gas-fired power plants, including: Based on the operation, control, and process characteristics of the solar-gas-electric microgrid, a reliability evaluation index system is established to characterize the internal and external reliability of the solar-gas-electric microgrid in gas stations.

[0011] The beneficial effects of adopting the above-mentioned further scheme are as follows: Firstly, it accurately characterizes reliability. Based on the microgrid's operational characteristics, an evaluation index system is established to comprehensively and accurately reflect the reliability status of each link within the solar-gas-electricity microgrid and its interaction with the external environment. This covers reliability characteristics in multiple aspects, including power generation, gas supply, and electricity consumption, providing rich information for in-depth analysis of system reliability. Secondly, it effectively supports subsequent evaluations. This evaluation index system lays a solid foundation for subsequent operations such as determining weights and constructing fuzzy comprehensive evaluation models, ensuring that microgrid reliability can be scientifically and quantitatively assessed from multiple dimensions, thus helping to improve the reliable and stable operation level of gas station microgrids.

[0012] 2) Secondly, the present invention also provides a reliability evaluation system for a solar-gas-electricity microgrid in a gas-fired power plant, the specific technical solution of which is as follows: It includes a system establishment module, a weight determination module, a model building module, and a reliability evaluation module; The system establishment module is used to: establish a reliability evaluation index system for the solar-gas-electric microgrid of gas stations; The weight determination module is used to: determine the weight of each evaluation indicator at each level in the reliability evaluation index system; The model building module is used to construct a fuzzy comprehensive evaluation model based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system. The reliability evaluation module is used to evaluate the reliability of the solar-gas-electric microgrid of the target gas station using a fuzzy comprehensive evaluation model, and to obtain the reliability evaluation results of the solar-gas-electric microgrid of the target gas station.

[0013] Based on the above scheme, the reliability evaluation system of the solar-gas-electric microgrid for gas stations of the present invention can be further improved as follows.

[0014] Furthermore, the weight determination module is specifically used for: Using the analytic hierarchy process (AHP), the weight of each evaluation index at each level in the reliability evaluation index system is determined.

[0015] Furthermore, the model building module is specifically used for: Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, determine the factor set, the comment set, and the weight set, and establish a fuzzy membership matrix. A fuzzy comprehensive evaluation model is constructed based on the factor set, comment set, weight set, and fuzzy membership matrix.

[0016] Furthermore, the system establishment module is specifically used for: Based on the operation, control, and process characteristics of the solar-gas-electric microgrid, a reliability evaluation index system is established to characterize the internal and external reliability of the solar-gas-electric microgrid in gas stations.

[0017] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor, so as to enable the electronic device to implement any of the above-mentioned methods for evaluating the reliability of the solar-gas-electric microgrid of a gas station.

[0018] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the reliability evaluation method for the photovoltaic-gas-electricity microgrid of any of the above-mentioned gas stations.

[0019] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below: Figure 1 This is a flowchart illustrating a reliability evaluation method for a solar-gas-electric microgrid at a gas station according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a reliability evaluation system for a solar-gas-electric microgrid in a gas station, according to an embodiment of the present invention. Detailed Implementation

[0021] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0022] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0023] like Figure 1 As shown in the figure, a reliability evaluation method for a solar-gas-electric microgrid in a gas-fired power plant according to an embodiment of the present invention includes the following steps: S1. Establish a reliability evaluation index system for the solar-gas-electric microgrid of gas stations; Among them, the solar-gas-electricity microgrid of the gas station is an integrated energy system that combines photovoltaic power generation, natural gas power supply and conventional power system within the gas station. Through energy conversion and distribution equipment, it realizes the mutual conversion and coordinated operation between multiple energy forms, meets the electricity and gas demand of the station and its surroundings, improves energy utilization efficiency, enhances the reliability and flexibility of energy supply, and reduces dependence on a single energy source.

[0024] In order to accurately assess the reliability of the solar-gas-electric microgrid in gas stations, it is necessary to construct appropriate reliability evaluation indicators. Specifically, based on the operation, control and process characteristics of the solar-gas-electric microgrid, a reliability evaluation indicator system should be established to characterize the internal and external reliability of the solar-gas-electric microgrid in gas stations.

[0025] The reliability evaluation index system is explained as follows: Internal reliability evaluation index B1 and external reliability evaluation index B2 are selected as primary evaluation indicators in the reliability evaluation index system. System reliability evaluation index C11, static reliability evaluation index C12, and transient reliability evaluation index C13 are selected as secondary evaluation indicators of internal reliability evaluation index B1. Operational management evaluation index C21 and communication security evaluation index C22 are selected as secondary evaluation indicators of external reliability evaluation index B2. System runtime evaluation index D11, operating environment evaluation index D12, and scheduling control evaluation index D13 are selected as secondary evaluation indicators of system reliability evaluation index C11. The following are the three-level evaluation indicators for the reliability evaluation index C12: Load shedding evaluation index D21, overload evaluation index D22, and voltage limit exceedance evaluation index D23 are selected as lower-level evaluation indicators for static reliability evaluation index C12. In other words, load shedding evaluation index D21, overload evaluation index D22, and voltage limit exceedance evaluation index D23 are the three-level evaluation indicators in the reliability evaluation index system. Similarly, photovoltaic power generation fluctuation evaluation index D31 and gas-fired power generation fluctuation evaluation index D32 are selected as lower-level evaluation indicators for photovoltaic power generation fluctuation evaluation index D31 and gas-fired power generation fluctuation evaluation index D32. In other words, photovoltaic power generation fluctuation evaluation index D31 and gas-fired power generation fluctuation evaluation index D32 are the three-level evaluation indicators in the reliability evaluation index system. Operation management evaluation index C21 and communication security evaluation index C22 are selected as lower-level evaluation indicators for external reliability evaluation index B2. In other words, operation management evaluation index C21 and communication security evaluation index C22 are the two-level evaluation indicators in the reliability evaluation index system. It should be noted that lower-level evaluation indicators for the three-level evaluation indicators can be set according to actual conditions. The specific descriptions of each evaluation indicator are as follows: (a) Internal reliability evaluation index B1: 1. System reliability evaluation index C11: (1) System operation time evaluation index D11: As the solar-gas-electric microgrid of the gas station operates for a long time, various problems such as line aging and equipment failure will occur, and the probability of failure will increase. The reliability and stability of the solar-gas-electric microgrid of the gas station will decrease. At the same time, since there is a photovoltaic power generation system in the solar-gas-electric microgrid of the gas station, the power generation efficiency of the photovoltaic module will decrease with the operation time, which also leads to a decrease in the power supply capacity of the solar-gas-electric microgrid of the gas station.

[0026] (2) Working environment evaluation index D12: In the solar-gas-electricity microgrid of the gas station, photovoltaic power generation is the main power generation method. Photovoltaic power generation is greatly affected by weather, mainly by solar radiation, which will directly affect the power supply capacity of the solar-gas-electricity microgrid of the gas station. At the same time, the photovoltaic modules are located outdoors, and natural disasters may cause large-scale failures or damage to the photovoltaic modules, causing voltage differences and energy fluctuations, which in turn lead to instability of the energy storage equipment in the solar-gas-electricity microgrid of the gas station, affecting the safety of the solar-gas-electricity microgrid of the gas station.

[0027] (3) Dispatch control evaluation index D13: The photovoltaic power generation system only outputs active power when connected to the grid during the daytime. Compared with traditional energy, its utilization rate is low. The significant randomness, intermittency and frequent switching of distributed photovoltaic power generation will also affect the stability of the power grid. Furthermore, the extensive use of electronic conversion devices has brought harmonic pollution and reactive power loss to the photovoltaic-gas-electric microgrid of the gas station, resulting in a serious decline in the power supply quality of the photovoltaic-gas-electric microgrid of the gas station and causing serious harm to the safety of the photovoltaic-gas-electric microgrid of the gas station. Therefore, it is necessary to carry out dispatch control of the photovoltaic-gas-electric microgrid of the entire gas station so that energy can be effectively delivered to users.

[0028] 2. Static Reliability Evaluation Index C12: The main task of static reliability analysis of power systems is to study the state of the solar-gas-electric microgrid at a gas-fired power plant under a given operating mode and after the main components of the microgrid are taken out of service. The purpose is to verify whether the microgrid can operate safely under a given power generation mode and wiring configuration, considering whether it will cause node overvoltage, equipment overload, and the resulting cascading failures. Generally, the evaluation index should not only quantify the impact of faults on the microgrid, but also compare the impact of different faults to rank the urgency of each fault in the fault cluster.

[0029] (1) Load loss evaluation index D21: Load loss caused by permanent faults is divided into two parts: fixed losses within the fault range and non-fixed losses within the non-fault power loss area. Fixed losses within the fault range are caused by fault isolation, and power cannot be restored until the fault is repaired; non-fixed losses within the non-fault power loss area are caused by the radial network structure of the gas station's solar-gas-electric microgrid itself. Due to fault isolation, its downstream power supply area will also lose power, and this part of the load loss is non-fixed. Component load loss risk reflects the possibility and severity of system failure losses caused by component accidents, as well as whether the gas station's solar-gas-electric microgrid can continue to operate.

[0030] (2) Overload Evaluation Index D22: The load of the solar-gas-electric microgrid of a gas-fired power plant can be represented by a fixed component (base load) and a variable component (peak load). The existence of the variable component leads to a large fluctuation in the load of the solar-gas-electric microgrid of the gas-fired power plant. Only when the base load units maintain stable power and the peak load units have the ability to periodically adjust power can the solar-gas-electric microgrid of the gas-fired power plant meet the real-time changing power grid load demand. Overload refers to the overload of equipment transmission power in the solar-gas-electric microgrid of the gas-fired power plant caused by a component failure. The component overload risk reflects the possibility and severity of the overload of equipment transmission power in the solar-gas-electric microgrid of the gas-fired power plant caused by a component failure.

[0031] (3) Voltage over-limit evaluation index D23 refers to the voltage over-limit of the bus in the solar-gas-electric microgrid of the gas station caused by a component accident. The component voltage over-limit risk reflects the possibility and degree of harm of the voltage over-limit of the bus in the solar-gas-electric microgrid of the gas station caused by a component accident. When the voltage exceeds the normal allowable operating range, not only will the operating efficiency of the electrical equipment decrease and deteriorate, but overcurrent or overvoltage phenomena will also occur, resulting in damage to the electrical equipment and seriously harming the safety performance and economic operation of the solar-gas-electric microgrid of the gas station. Distributed photovoltaic grid connection has a significant impact on the voltage safety of the solar-gas-electric microgrid of the gas station. When the photovoltaic penetration rate exceeds the maximum allowable value or the system is operating under light load, reverse power is likely to occur, causing a significant increase in voltage, which leads to a change in the operation mode of the solar-gas-electric microgrid of the gas station.

[0032] 3. Transient Reliability Evaluation Index C13: The key issue in transient reliability analysis is the stability of the solar-gas-electricity microgrid in the gas station, that is, whether the solar-gas-electricity microgrid can maintain stable operation after being subjected to various disturbances. Sub-indicators of the transient reliability evaluation index include: (1) Evaluation index D31 for photovoltaic power generation fluctuation: Photovoltaic power generation is greatly affected by the environment. Its power generation is closely related to the amount of solar radiation. The randomness and intermittent fluctuations in power brought about by the access of photovoltaic systems will affect the reliable and stable operation of the power system.

[0033] (2) Evaluation index D32 for gas-fired power generation fluctuations: Random failures such as leaks in natural gas pipelines and interruptions in gas supply may cause the output of gas turbine units to decrease rapidly due to insufficient natural gas supply, thereby threatening the reliable and stable operation of the solar-gas-electric microgrid of the gas station.

[0034] (ii) External reliability evaluation index B2: 1. Operational Management Evaluation Indicator C21: This mainly involves various aspects of safety management, among which operation and maintenance, safety protection and monitoring, and emergency management are the most prominent. Good operational management is essential to ensure the safe operation of the solar-gas-electricity microgrid at the gas station. It requires full consideration of the safety of personnel, the surrounding environment, the internal physical environment, and related information systems of the intelligent systems. The ability to promptly report and control potential risks directly impacts the reliable operation of the solar-gas-electricity microgrid at the gas station.

[0035] 2. Communication Security Evaluation Index C22: This mainly involves communication information security and smart terminal security. In gas-powered microgrids, the front-end acquisition module transmits measured data to the control center via transmission protocols, or uploads it through IP protocols connecting to the intelligent system and core switch. However, differences in protocol standards used by various equipment brands can cause data distortion. Furthermore, communication equipment malfunctions can prevent information transmission.

[0036] S2. Determine the weight of each evaluation indicator at each level in the reliability evaluation index system; S3. Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, construct a fuzzy comprehensive evaluation model. S4. The reliability of the solar-gas-electric microgrid of the target gas station is evaluated using a fuzzy comprehensive evaluation model, and the reliability evaluation results of the solar-gas-electric microgrid of the target gas station are obtained.

[0037] Optionally, in S2, the weight of each evaluation index at each level in the reliability evaluation index system is determined, which can be achieved in the following way: 1) The first implementation method: use the analytic hierarchy process to determine the weight of each evaluation index at each level in the reliability evaluation index system.

[0038] The Analytic Hierarchy Process (AHP) is a decision-making method that combines qualitative and quantitative approaches to handle multi-objective, multi-criteria, multi-factor, and multi-level decision problems. It provides an efficient and simple decision-making tool for complex problems. The basic principle is as follows: First, the complex decision problem (objective level) is decomposed into a hierarchical structure of multiple evaluation indicators (criteria level). Then, relevant experts are invited to score each evaluation indicator at the same level pairwise using a scale of 1 to 9 to determine the relative importance of each indicator. Next, mathematical matrix calculations are used to calculate the weights of each evaluation indicator at each level and rank them. Finally, the ranking results are analyzed to draw conclusions. The specific implementation process is as follows: S20. Use scaling to compare the evaluation indicators of each level pairwise and construct the judgment matrix for each level.

[0039] The principles followed in constructing the judgment matrix at each level (also known as the judgment matrix scaling criteria) are shown in Table 1.

[0040] Table 1: Based on the principles in Table 1, experts were asked to score each level of evaluation indicators in pairs using the scaling method. The average scores were then used to construct the judgment matrices for each level. Table 2 shows the judgment matrices for the first-level evaluation indicators; Table 3 shows the judgment matrices for the lower-level evaluation indicators of internal reliability evaluation indicator B1; Table 4 shows the judgment matrices for the lower-level evaluation indicators of system reliability evaluation indicator C11; Table 5 shows the judgment matrices for the lower-level evaluation indicators of static reliability evaluation indicator C12; Table 6 shows the judgment matrices for the lower-level evaluation indicators of transient reliability evaluation indicator C13; Table 7 shows the judgment matrices for the lower-level evaluation indicators of external reliability evaluation indicator B2; Table 8 shows the judgment matrices for the lower-level evaluation indicators of underload evaluation indicator D21; Table 9 shows the judgment matrices for the lower-level evaluation indicators of overload evaluation indicator D22; and Table 10 shows the judgment matrices for the lower-level evaluation indicators of voltage over-limit evaluation indicator D23.

[0041] Table 2: Table 3: Table 4: Table 5: Table 6: Table 7: Table 8: Table 9: Table 10: S21. Solve for the weights of each evaluation index and perform consistency checks, specifically: 1) Weight calculation steps: Normalize the columns of the judgment matrix, then sum the rows of the normalized matrix to obtain the row sum vector, and finally average the row sum vector to obtain the final weight of each evaluation index.

[0042] 2) Consistency check: ① Calculate the largest eigenvalue of the judgment matrix (by summing the products of the column totals of the judgment matrix and the corresponding index weights). ② Calculate the consistency index CI (formula: (maximum eigenvalue - number of indices) / (number of indices - 1)); ③ Calculate the consistency ratio CR (formula: CI / RI, where RI is the average random consistency index, and the values ​​are shown in Table 11).

[0043] When CR < 0.1, the judgment matrix has good consistency; otherwise, the judgment matrix needs to be modified until it passes the test.

[0044] 3) Weighting results: The weights of each evaluation index obtained by the above method are shown in Table 12.

[0045] Table 11: Table 12: 2) The second implementation method: S22. For the three-level evaluation indicators in the reliability evaluation index system, collect the historical operation dataset of each evaluation indicator within a preset period; calculate the entropy value of each three-level evaluation indicator, specifically by normalizing the historical data to obtain the probability distribution of the three-level evaluation indicators, and calculate the dispersion of the three-level evaluation indicators according to the information entropy formula; solve the coefficient of variation of each three-level evaluation indicator based on the entropy value results, and obtain the initial weight of the three-level evaluation indicators after normalizing the coefficient of variation, ensuring that the weight allocation is completely dependent on the objectivity of the data.

[0046] If the preset period is one month, the operating data of each tertiary evaluation indicator will be collected over the past month. This data will provide the basis for subsequent weight calculations.

[0047] The specific implementation process for calculating the entropy value of each tertiary evaluation indicator is as follows: The collected historical data is normalized to obtain the probability distribution of each evaluation index. Let the collected data sequence be denoted as... (i represents different time points or different samples), the normalized probability distribution is denoted as The calculation formula is as follows: Where W is the sum of the data sequence. After obtaining the probability distribution, the entropy value is calculated according to the information entropy formula. : in, As a constant factor, it is usually taken as ( (where the number of data points is 0), to ensure that the entropy value is within the range of [0,1].

[0048] The process of calculating the coefficient of variation for each of the three-level evaluation indicators is as follows: The coefficient of variation for each of the three-level evaluation indicators is calculated based on the entropy values. The coefficient of variation is an indicator that measures the relative dispersion of data and reflects its volatility. The calculation formula is: in, The standard deviation of the data reflects the degree of dispersion of the data; The average value of the data indicates the central tendency of the data.

[0049] The process of determining the initial weights of the three-level evaluation indicators is as follows: The coefficients of variation are normalized to obtain the initial weights of the three-level evaluation indicators. The purpose of normalization is to ensure that the sum of all weights is 1, so as to facilitate subsequent comprehensive evaluation. The normalization formula is: in, Indicates the first Initial weights for each of the three-level evaluation indicators; For the first The coefficient of variation of each of the three-level evaluation indicators; This is the sum of the coefficients of variation for all three levels of evaluation indicators. Through the above process, we ensure that the weight allocation relies entirely on the objectivity of the data, avoiding interference from subjective human factors.

[0050] S23. Aggregate the initial weights of tertiary evaluation indicators belonging to the same secondary evaluation indicator; construct a linear weighted aggregation model based on the correlation between the tertiary evaluation indicators, with the tertiary indicator weights as input and the secondary indicator weights as the target output; fit the aggregation coefficient matrix using the least squares method, obtain the unnormalized weights of the secondary evaluation indicators by solving the weight transfer equation, and finally perform normalization processing to generate the aggregated weights of the secondary evaluation indicators.

[0051] The initial weights of each third-level evaluation index were obtained through S22. For example, under the system reliability evaluation index C11, the initial weights of the system runtime evaluation index D11, the operating environment evaluation index D12, and the scheduling control evaluation index D13 are respectively... , , At this point, it is necessary to aggregate the initial weights of these tertiary indicators belonging to the same secondary evaluation indicator (such as C11) to determine the comprehensive weight of the secondary evaluation indicator (such as C11).

[0052] A linear weighted aggregation model is constructed based on the correlation between the three-level evaluation indicators. Specifically, the weights of each tertiary indicator are summed in a weighted manner to obtain the corresponding weights of the secondary indicators. Mathematically, this can be expressed as: in, Indicates the first Unnormalized weights of each secondary evaluation indicator. To belong to the The number of tertiary evaluation indicators for each secondary evaluation indicator. Indicates the first The first secondary evaluation indicator The initial weights of the three-level evaluation indicators. Represents the polymerization coefficient, used to measure the... The first secondary evaluation indicator The contribution of each tertiary evaluation indicator to the weight of the secondary indicators. The aggregation coefficient matrix here. It needs to be determined by fitting using the least squares method.

[0053] The specific process of fitting the aggregation coefficient matrix using the least squares method is as follows: To determine the aggregation coefficient matrix This requires collecting several sample data points. Each sample includes the initial weights of each of the three-level evaluation indicators. And the target weights of secondary evaluation indicators determined by expert assessment or historical data. .

[0054] Define the error function as: in, For the sample size, and They represent the first Secondary evaluation indicators in each sample The target weight and the initial weight of each of the three levels of evaluation indicators.

[0055] By minimizing the error function The optimal aggregation coefficient matrix is ​​solved using the least squares method. Specifically, regarding the error function... Taking the partial derivatives and setting them to zero yields a system of normal equations. Solving this system of equations gives the aggregation coefficient matrix. The estimated value.

[0056] The unnormalized weights of the secondary evaluation indicators are obtained by solving the weight transfer equation, and then normalized to generate the aggregated weights of the secondary evaluation indicators. The specific implementation process is as follows: Once the aggregation coefficient matrix is ​​determined Then, the initial weights of each of the three-level evaluation indicators can be substituted into the linear weighted aggregation model: The unnormalized weights of the secondary evaluation indicators were calculated. To ensure that the sum of the weights of all secondary evaluation indicators is 1, normalization is required. The normalization formula is: in, Indicates the first The aggregate weights of the secondary evaluation indicators, This represents the number of secondary evaluation indicators. Through the above process, the initial weights of the tertiary evaluation indicators can be aggregated to the secondary evaluation indicator level, resulting in the aggregated weights of each secondary evaluation indicator.

[0057] S24. Repeat the entropy weight calculation process for the secondary evaluation index layer, collect the system-level historical performance dataset corresponding to the secondary index; calculate the entropy value and coefficient of variation of each secondary evaluation index, and generate objective weights based on system-level data; weight this weight and the aggregate weight obtained in S23 are weighted and fused, and the fusion weight coefficient is dynamically adjusted according to the index type. Specifically, static indexes are dominated by aggregate weight, and transient indexes are dominated by entropy weight, to generate the corrected comprehensive weight of the secondary evaluation index.

[0058] To calculate the objective weights of the secondary evaluation indicators, it is necessary to collect the system-level historical performance data corresponding to each secondary indicator. This data reflects the historical performance of the secondary evaluation indicators at the system level. For example, for the system reliability evaluation indicator C11, its corresponding system-level performance data may include indicators such as the overall operational stability and failure rate of the system over a period of time.

[0059] The specific implementation process for calculating the entropy value and coefficient of variation of each secondary evaluation index is as follows: The collected system-level historical performance data is normalized to obtain the probability distribution of each secondary indicator, and then its entropy value is calculated. Similarly, the coefficient of variation of each secondary evaluation indicator is solved based on the entropy value results. The calculation method of entropy value and coefficient of variation is similar to the calculation method of tertiary indicators in S22, except that the data level is changed to system-level performance data.

[0060] The specific implementation process for generating objective weights based on system-level data is as follows: After normalizing the coefficient of variation, the objective weights of the secondary evaluation indicators are obtained. These weights reflect the importance of the secondary indicators at the system level, and their calculation formula is similar to that of the tertiary weights in step S22, namely: in, Indicates the first The system-level objective weights of each secondary evaluation indicator; For the first The coefficient of variation of each secondary evaluation indicator; This is the sum of the coefficients of variation of all secondary evaluation indicators.

[0061] This weight is then weighted and fused with the aggregated weight obtained in S23. The specific implementation process is as follows: To comprehensively consider the aggregated weights of the three-level indicators and the objective weights at the system level, a weighted fusion method is adopted. The fusion weight coefficient is dynamically adjusted according to the indicator type: for static indicators (such as relatively stable indicators like system reliability evaluation indicator C11 and load shedding evaluation indicator D21 in static reliability evaluation indicator C12), the aggregated weight is dominant, i.e., a larger fusion coefficient is assigned to the aggregated weight; for transient indicators (such as highly volatile indicators like photovoltaic power generation fluctuation evaluation indicator D31 and gas-fired power generation fluctuation evaluation indicator D32 under transient reliability evaluation indicator C13), the entropy weight is dominant, i.e., a larger fusion coefficient is assigned to the objective weights at the system level.

[0062] The specific implementation process for generating the revised comprehensive weights of the secondary evaluation indicators is as follows: The revised comprehensive weights of the secondary evaluation indicators are calculated using a weighted fusion formula. Assume the fusion coefficients are as follows: (The coefficients corresponding to the aggregation weights) and (The coefficient corresponding to the objective weight of the system layer), then the comprehensive weight The calculation formula is: in, The value is dynamically determined based on the indicator type. For static indicators, Larger values ​​(e.g., 0.7-0.8); for transient indicators, The weights are relatively small (e.g., 0.3-0.4). The revised comprehensive weights of the secondary evaluation indicators more comprehensively reflect the importance of the secondary indicators at different levels, improving the rationality and accuracy of the weights.

[0063] S25. Aggregate the comprehensive weights of secondary evaluation indicators belonging to the same primary evaluation indicator across levels; establish a primary indicator weight allocation model, with secondary indicator weights and preset level contribution factors as inputs; calculate the contribution of secondary weights to primary indicators through vector projection method, perform weight correction in combination with the coupling strength matrix between indicators, output unnormalized primary evaluation indicator weights, and generate the final weights of primary evaluation indicators after normalization. The specific implementation process is as follows: The comprehensive weights of secondary evaluation indicators belonging to the same primary evaluation indicator are aggregated across levels. The comprehensive weights of secondary evaluation indicators belonging to the same primary evaluation indicator are aggregated across levels. The specific implementation process is as follows: Taking internal reliability evaluation index B1 as an example, it includes the comprehensive weights of system reliability evaluation index C11, static reliability evaluation index C12, and transient reliability evaluation index C13. First, the weight allocation ratio of each secondary index to the primary index is determined, i.e., the hierarchical contribution factor, which can be preset based on prior knowledge or expert experience. Then, using the vector projection method, the secondary weight vectors are projected onto the primary index space, and the contribution of each secondary weight to the primary index is calculated. Next, an inter-index coupling strength matrix is ​​introduced to correct the contribution, considering the mutual coupling influence between different secondary indicators. Finally, the corrected contribution is normalized so that the sum of its weights is 1, thus obtaining the final weights of the primary evaluation index.

[0064] The establishment of a primary indicator weight allocation model includes: A weight allocation model for primary indicators is established using the weights of secondary indicators and preset hierarchical contribution factors as inputs. The hierarchical contribution factors reflect the relative contribution of secondary indicators to primary indicators and can be preset based on prior knowledge or expert experience. For example, for internal reliability evaluation indicator B1, the hierarchical contribution factors for its subordinate system reliability evaluation indicators C11, static reliability evaluation indicator C12, and transient reliability evaluation indicator C13 can be set as follows: , , ,and .

[0065] The contribution of secondary weights to primary indicators is calculated using the vector projection method, including: Vector projection is a method that projects a second-level weight vector onto a first-level indicator space to calculate the contribution of the second-level weights to the first-level indicators. Assume the second-level weight vector is... The hierarchical contribution factor vector is Then the projection length of the secondary weights onto the direction of the primary indicator is the contribution. The calculation formula is: in, The number of secondary evaluation indicators. This represents the unnormalized contribution of the secondary weights to the primary indicator.

[0066] The weight correction is performed by combining the coupling strength matrix between indicators, and the specific implementation process is as follows: Considering the potential coupling and influence between different secondary indicators, a coupling strength matrix is ​​introduced. To adjust the contribution. Elements in the coupling strength matrix. Indicates the first The second-level indicator and the first The coupling strength between the secondary indicators ranges from [0,1]. Weight correction is performed by adjusting the contribution formula and combining it with the coupling strength matrix. in, The corrected weights are the unnormalized first-level evaluation indicators.

[0067] The final weights of the primary evaluation indicators, after normalization, include: The corrected unnormalized weights are then normalized so that their sum equals 1. The normalization formula is: in, This indicates the final weight of the primary evaluation indicator. This represents the sum of the unnormalized weights of all primary evaluation indicators after correction. Through the above process, the final weights of the primary evaluation indicators are obtained, providing a higher-level weight allocation for subsequent overall reliability evaluation.

[0068] S26. For the operation management evaluation index C21 and communication security evaluation index C22 under the external reliability evaluation index B2, independently construct a weighting model based on the security event database; statistically analyze the occurrence frequency, impact duration, and repair cost of historical operation failure events and communication attack events; calculate the weight vectors of the three sets of parameters respectively using the entropy weight method, and generate independent weights for the external secondary evaluation index after weighted fusion, which are directly used as the final weights of B2.

[0069] Because the secondary indicators (C21 and C22) under the external reliability evaluation indicator B2 have special characteristics, their weight determination methods are different from those under the secondary indicators of the internal reliability evaluation indicator B1. Therefore, it is necessary to independently construct a weighting model based on the security event database.

[0070] Historical event data related to operational management evaluation indicator C21 and communication security evaluation indicator C22 are collected from the security event database. Specifically, for operational management evaluation indicator C21, the frequency of historical operational failure events (i.e., the number of failures per unit time), the duration of each failure's impact (the time from failure occurrence to recovery), and the repair cost (the expense required to repair the failure) are statistically analyzed. Similarly, for communication security evaluation indicator C22, the frequency of communication attack events, their duration of impact, and the repair cost are statistically analyzed. These parameters reflect the degree of influence of external factors on system reliability.

[0071] The weight vectors for the three sets of parameters are calculated using the entropy weight method. The specific implementation process is as follows: For each set of parameters (occurrence frequency, duration of impact, and repair cost), the entropy weight method is used to calculate the respective weight vector. Taking the operation management evaluation index C21 as an example, assuming its three sets of parameters are occurrence frequency... Duration of impact Repair costs The probability distribution is obtained by normalizing each set of parameters, and then the entropy and coefficient of variation are calculated to obtain the weight vector of each parameter. , = Similarly, the three sets of parameter weight vectors for the communication security evaluation index C22 are obtained. , .

[0072] The weight vectors of each set of parameters are weighted and fused with their corresponding parameter values ​​to obtain the independent weights of the operation management evaluation index C21 and the communication security evaluation index C22. For example, the independent weights of the operation management evaluation index C21... The calculation formula is: Similarly, the independent weights of the communication security evaluation index C22 The calculation formula is: The weighted fusion operation here combines the importance of each parameter with its actual value to generate independent weights that comprehensively reflect the influence of external factors. Finally, these two independent weights are directly used as the final weights for the external reliability evaluation index B2, i.e. However, it is important to note that normalization processing is required to ensure that its weight is on the same dimension as the internal reliability evaluation index B1.

[0073] S27. Perform hierarchical chain multiplication to synthesize the weights of the first-level evaluation indicators, the comprehensive weights of the second-level evaluation indicators, and the initial weights of the third-level evaluation indicators; perform global sensitivity analysis on the synthesis results: randomly disturb the third-level indicator data and observe the rate of change of the weights. If the rate of change exceeds the threshold, return to S22 to adjust the entropy weight calculation parameters until the rate of change of all indicator weights stabilizes within the preset tolerance range, and output the weight vector of the entire system as the reliability evaluation benchmark.

[0074] Hierarchical chain multiplication synthesis is a method for combining weights from different levels. Its basic idea is to multiply the first-level, second-level, and third-level weights sequentially according to their hierarchical relationship to obtain the final composite weight vector. Specifically, for each third-level evaluation indicator... (belongs to secondary indicators) Primary indicators ), its comprehensive weight The calculation formula is: in, The final weight of the primary evaluation indicator. The comprehensive weight of the secondary evaluation indicators. The initial weights for the three-level evaluation indicators.

[0075] In this way, weight information from different levels is integrated to build a complete weight system.

[0076] The global sensitivity analysis of the synthesis results is implemented as follows: Global sensitivity analysis aims to assess the sensitivity of the weighting system to changes in input data, ensuring the stability of the weights. The specific procedure involves randomly perturbing the tertiary indicator data (e.g., randomly changing the initial weights or historical data of the tertiary indicators within a certain range), and then repeatedly calculating the entire weighting system starting from S22, observing the rate of weight change. The formula for calculating the rate of weight change is: in, The weights after perturbation. The weights before the disturbance.

[0077] The specific process for determining whether parameter adjustment is needed is as follows: If the rate of change of weights exceeds a preset threshold (e.g., the rate of change exceeds 5%), it indicates that the current weight system is too sensitive to data changes, and its reliability may be affected. At this time, it is necessary to return to S22, adjust the entropy weight calculation parameters (such as the normalization method, the constant factor in the entropy value calculation, etc.), and recalculate and synthesize the weights until the rate of change of all indicator weights stabilizes within the preset tolerance range (e.g., the rate of change is less than 3%).

[0078] Once the global sensitivity analysis indicates that the weighting system is stable, the final overall system weight vector is output. This weight vector includes the weights of all primary, secondary, and tertiary evaluation indicators, serving as the benchmark for the reliability evaluation of the gas-powered solar-electric microgrid at the gas plant. Subsequent reliability evaluations will be based on this weight vector to ensure the objectivity, accuracy, and stability of the evaluation results.

[0079] Through the specific processes outlined above, the weights of each level of indicators in the reliability evaluation index system for gas-fired power plant solar-gas-electricity microgrids can be systematically and scientifically determined, providing a solid foundation for subsequent reliability evaluations. Each step strictly adheres to technical logic, ensuring that weight allocation considers both the objectivity of the data and the correlation between indicators and the overall characteristics of the system. Furthermore, sensitivity analysis guarantees the reliability of the weighting system.

[0080] Optionally, in S3, a fuzzy comprehensive evaluation model is constructed based on the reliability evaluation index system and the weight of each evaluation index at each level of the reliability evaluation index system. This is specifically implemented in the following way: S30. Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, determine the factor set, the comment set, and the weight set, and establish a fuzzy membership matrix. The factor set includes all evaluation indicators in the reliability evaluation index system of the gas station solar-gas-electric microgrid in this application. These indicators can comprehensively reflect the reliability characteristics of the gas station solar-gas-electric microgrid. Specifically, it includes internal reliability evaluation index B1 and external reliability evaluation index B2, as well as their subordinate secondary and tertiary evaluation indicators. The comment set is a set for classifying the reliability of the solar-gas-electric microgrid, namely {very reliable, reliable, average, dangerous, very dangerous}, with corresponding 1-point evaluation scores and judgment intervals as follows: Very reliable (1 point, >0.9), Reliable (0.8 points, >0.7 and ≤0.9), Average (0.6 points, >0.5 and ≤0.7), Dangerous (0.4 points, >0.3 and ≤0.5), Very dangerous (0.2 points, ≤0.3). The weight set is the set of weights for each level of evaluation indicators determined in S2, reflecting the importance of each indicator in the reliability evaluation. These weights are determined comprehensively through multiple methods such as the analytic hierarchy process (AHP) and entropy weight method, taking into account both expert experience and objective data. The elements of the fuzzy membership matrix represent the fuzzy membership degree of each evaluation indicator at each reliability level, that is, the degree to which each evaluation indicator belongs to different reliability levels. The process of establishing the fuzzy membership matrix is ​​determined using certain fuzzy mathematical methods based on the actual values ​​of each evaluation indicator and the reliability level classification standards. For example, for the system uptime evaluation indicator D11, if its actual value is 5 years, according to the level classification in Table 13, its membership degree at the "very reliable" level is 1, and its membership degree at other levels is 0.

[0081] S31. Based on the factor set, comment set, weight set, and fuzzy membership matrix, construct a fuzzy comprehensive evaluation model, specifically: First, determine the indicators to be evaluated based on the factor set; second, clarify the reliability level classification based on the evaluation set; then, determine the weight of each indicator using the weight set; finally, combine the fuzzy membership matrix to perform a weighted sum of the membership degree and weight of each indicator at different reliability levels to obtain the comprehensive score of the solar-gas-electric microgrid at each reliability level.

[0082] Let the factor set be U = {u1, u2, ..., u} n The set of comments is V={v1,v2,…,v}. m The weight set is A = (a1, a2, ..., a...). n The fuzzy membership matrix is ​​R=(r ij ) n×m Then the fuzzy comprehensive evaluation result B = A × R = (b1, b2, ..., b m ), where b j This represents the overall score of the optical-gas-electric microgrid at the j-th reliability level. By comparing each b... jThe size of the value can determine the reliability level of the optical-gas-electric microgrid, i.e., selecting b. j The highest corresponding reliability level is used as the evaluation result.

[0083] For example, in a specific embodiment of this application, by comprehensively calculating the fuzzy membership degree and weight of each evaluation index, the final comprehensive evaluation result of the solar-gas-electric microgrid is 0.895. According to the judgment interval, this result belongs to the "reliable" range, thereby realizing a scientific and reasonable evaluation of the reliability of the solar-gas-electric microgrid of the gas station.

[0084] The reliability of the solar-gas-electric microgrid is divided into five levels: Very Reliable, Reliable, Average, Hazardous, and Very Hazardous. A 1-point scale is used, with corresponding evaluation scores of 1, 0.8, 0.6, 0.4, and 0.2 for each level, respectively. The judgment intervals are: >0.9, >0.7 and ≤0.9, >0.5 and ≤0.7, >0.3 and ≤0.5, and ≤0.3.

[0085] Based on specific practical situations, the reliability evaluation index levels for solar-gas-electric microgrids are classified as shown in Tables 13 to 18. Table 13 shows the classification of system reliability evaluation index (C11) within the reliability evaluation index system for gas-fired power plant solar-gas-electric microgrids, including the specific performance of system uptime evaluation index (D11), operating environment evaluation index (D12), and dispatch control evaluation index (D13) at different reliability levels. Table 14 shows the classification of load shedding evaluation index (D21), specifically the frequency of photovoltaic module load shedding (E11), photovoltaic inverter load shedding (E12), gas turbine load shedding (E13), and energy storage system load shedding (E14) at different reliability levels. Table 15 shows the classification of overload evaluation index (D22), covering the specific performance of photovoltaic module overload (E21), photovoltaic inverter overload (E22), gas turbine overload (E23), and energy storage system overload (E24) at different reliability levels. Table 16 shows the classification of voltage overrun evaluation index (D23), including the frequency of photovoltaic module voltage overruns (E31), photovoltaic inverter voltage overruns (E32), gas turbine voltage overruns (E33), and energy storage system voltage overruns (E34) at different reliability levels. Table 17 shows the classification of transient reliability evaluation index (C13), specifically the performance of photovoltaic power generation fluctuations (D31) and gas turbine power generation fluctuations (D32) at different reliability levels. Table 18 shows the classification of operation management (C21) and communication security (C22) within the external reliability evaluation index (B2).

[0086] Table 13: Table 14: Table 15: Table 16: Table 17: Table 18: Based on the comprehensive evaluation system established in the previous section, taking a gate station with relatively abundant solar energy resources as an example, a reliability evaluation of the entire gas station is conducted. Table 19 shows the weights of each tertiary evaluation indicator in the secondary evaluation index system reliability evaluation (C11) and their performance under different reliability levels. Table 20 shows the weights of each quaternary evaluation indicator in the underload evaluation index (D21) and their performance under different reliability levels. Table 21 shows the weights of each quaternary evaluation indicator in the overload evaluation index (D22) and their performance under different reliability levels. Table 22 shows the weights of each quaternary evaluation indicator in the voltage limit exceedance evaluation index (D23) and their performance under different reliability levels. Table 23 shows the weights of each tertiary evaluation indicator in the transient reliability evaluation index (C13) and their performance under different reliability levels. Table 24 shows the weights of each secondary evaluation indicator in the external reliability evaluation index (B2) and their performance under different reliability levels.

[0087] Table 19: According to Table 19, C11= = .

[0088] Table 20: According to Table 20, we can obtain: = .

[0089] Table 21: According to Table 21, we can obtain: = .

[0090] Table 22: According to Table 22, we can obtain: = .

[0091] From D21, D22, and D23, we can obtain: = .

[0092] Table 23: According to Table 23, C13 = = .

[0093] Calculated from C11, C12, and C13: B1= =( .

[0094] Table 24: According to Table 24, B2 = = .

[0095] In summary, we can conclude that: A= = .

[0096] After weighting and averaging according to the fixed-score principle, the specific quantitative result can be obtained as follows: A= =0.895, therefore, after reliability evaluation and analysis, the gate station's optical-gas-electric microgrid scheme falls within the "reliable" range.

[0097] This invention can comprehensively and accurately assess the reliability of solar-gas-electricity microgrids in gas stations, overcoming the limitations of traditional assessments and providing a scientific basis for the safe operation of stations. It can promptly identify potential problems and risks in microgrid operation, allowing for proactive preventative and improvement measures to enhance microgrid stability and reliability. It effectively reduces the probability of accidents at gas stations, minimizing accident losses and ensuring personal and property safety. It provides decision support for station operation management, optimizes resource allocation, improves energy efficiency, and achieves a win-win situation for both economic and social benefits. Furthermore, it promotes the development and application of solar-gas-electricity microgrid technology, driving gas stations towards intelligent and efficient operation.

[0098] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0099] like Figure 2 As shown, a reliability evaluation system 200 for a solar-gas-electric microgrid in a gas station according to an embodiment of the present invention includes a system establishment module 201, a weight determination module 202, a model construction module 202, and a reliability evaluation module 204. System establishment module 201 is used to: establish a reliability evaluation index system for the solar-gas-electric microgrid of gas stations; The weight determination module 202 is used to: determine the weight of each evaluation index at each level in the reliability evaluation index system; The model building module 203 is used to: construct a fuzzy comprehensive evaluation model based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system; The reliability evaluation module 204 is used to: evaluate the reliability of the solar-gas-electric microgrid of the target gas station using a fuzzy comprehensive evaluation model, and obtain the reliability evaluation results of the solar-gas-electric microgrid of the target gas station.

[0100] Optionally, in the above technical solution, the weight determination module 202 is specifically used for: Using the analytic hierarchy process (AHP), the weight of each evaluation index at each level in the reliability evaluation index system is determined.

[0101] Optionally, in the above technical solution, the model building module 203 is specifically used for: Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, determine the factor set, the comment set, and the weight set, and establish a fuzzy membership matrix. A fuzzy comprehensive evaluation model is constructed based on the factor set, comment set, weight set, and fuzzy membership matrix.

[0102] Optionally, in the above technical solution, the system establishment module 201 is specifically used for: Based on the operation, control, and process characteristics of the solar-gas-electric microgrid, a reliability evaluation index system is established to characterize the internal and external reliability of the solar-gas-electric microgrid in gas stations.

[0103] It should be noted that the beneficial effects of the reliability evaluation system 200 for a gas-fired power plant's solar-gas-electricity microgrid provided in the above embodiments are the same as those of the reliability evaluation method for a gas-fired power plant's solar-gas-electricity microgrid described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0104] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the reliability evaluation method for the solar-gas-electric microgrid of any of the above-mentioned gas stations. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the reliability evaluation method for the solar-gas-electric microgrid of any embodiment of the present invention by calling the computer program.

[0105] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned methods for evaluating the reliability of a solar-gas-electric microgrid at a gas station.

[0106] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0107] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0108] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A reliability evaluation method for a solar-gas-electricity microgrid in a gas-fired power plant, characterized in that, include: Establish a reliability evaluation index system for solar-gas-electric microgrids in gas-fired power plants; Determine the weight of each evaluation index at each level in the reliability evaluation index system; Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, a fuzzy comprehensive evaluation model is constructed. The reliability of the solar-gas-electric microgrid of the target gas station is evaluated using the fuzzy comprehensive evaluation model, and the reliability evaluation results of the solar-gas-electric microgrid of the target gas station are obtained.

2. The reliability evaluation method for a solar-gas-electric microgrid in a gas-fired power plant according to claim 1, characterized in that, Determine the weight of each evaluation index at each level in the reliability evaluation index system, including: The weight of each evaluation index at each level in the reliability evaluation index system is determined using the analytic hierarchy process (AHP).

3. The reliability evaluation method for a solar-gas-electric microgrid in a gas-fired power plant according to claim 1, characterized in that, Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, a fuzzy comprehensive evaluation model is constructed, including: Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, determine the factor set, the comment set, and the weight set, and establish a fuzzy membership matrix. A fuzzy comprehensive evaluation model is constructed based on the factor set, the comment set, the weight set, and the fuzzy membership matrix.

4. A reliability evaluation method for a solar-gas-electric microgrid at a gas-fired power plant according to any one of claims 1 to 3, characterized in that, Establish a reliability evaluation index system for the solar-gas-electric microgrid of gas stations, including: Based on the operation, control, and process characteristics of the solar-gas-electric microgrid, a reliability evaluation index system is established to characterize the internal and external reliability of the solar-gas-electric microgrid in gas stations.

5. A reliability evaluation system for a solar-gas-electric microgrid at a gas power station, characterized in that, It includes a system establishment module, a weight determination module, a model building module, and a reliability evaluation module; The system establishment module is used to: establish a reliability evaluation index system for the solar-gas-electric microgrid of gas stations; The weight determination module is used to: determine the weight of each evaluation index at each level in the reliability evaluation index system; The model building module is used to: construct a fuzzy comprehensive evaluation model based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system; The reliability evaluation module is used to: evaluate the reliability of the solar-gas-electric microgrid of the target gas station using the fuzzy comprehensive evaluation model, and obtain the reliability evaluation result of the solar-gas-electric microgrid of the target gas station.

6. The reliability evaluation system for a solar-gas-electric microgrid at a gas station according to claim 5, characterized in that, The weight determination module is specifically used for: The weight of each evaluation index at each level in the reliability evaluation index system is determined using the analytic hierarchy process (AHP).

7. The reliability evaluation system for a solar-gas-electric microgrid at a gas station according to claim 5, characterized in that, The model building module is specifically used for: Based on the reliability evaluation index system and the weight of each evaluation index at each level in the reliability evaluation index system, determine the factor set, the comment set, and the weight set, and establish a fuzzy membership matrix. A fuzzy comprehensive evaluation model is constructed based on the factor set, the comment set, the weight set, and the fuzzy membership matrix.

8. A reliability evaluation system for a solar-gas-electric microgrid at a gas-fired power station according to any one of claims 5 to 7, characterized in that, The system establishment module is specifically used for: Based on the operation, control, and process characteristics of the solar-gas-electric microgrid, a reliability evaluation index system is established to characterize the internal and external reliability of the solar-gas-electric microgrid in gas stations.

9. An electronic device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the reliability evaluation method for a solar-gas-electric microgrid at a gas station as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the reliability evaluation method for a solar-gas-electric microgrid at a gas station as described in any one of claims 1 to 4.

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