A vulnerability assessment method for multi-energy systems in a region

Abstract

The application provides a vulnerability evaluation method for a multi-energy system in a region, comprising: acquiring topological structure data, measurement data and historical data of a multi-energy system in a region to be evaluated; performing simulation scene simulation and power flow calculation on the multi-energy system in the region to be evaluated according to the topological structure data, the measurement data and the historical data, to obtain a plurality of groups of vulnerability evaluation indexes; composing an evaluation index matrix according to a plurality of groups of vulnerability evaluation indexes in a preset format; calculating a correlation coefficient of each index and an optimal index according to the evaluation index matrix, and calculating a comprehensive evaluation result according to the correlation coefficient; and outputting the comprehensive evaluation result as a final vulnerability evaluation result of the multi-energy system in the region. The application can accurately calculate vulnerability indexes of the multi-energy system in the region under various operation scenes and configuration schemes, and perform vulnerability sorting on different configuration schemes, thereby improving the safety and stability of the system.

Description

Technical Field

[0001] This invention relates to the field of vulnerability assessment technology for multi-energy systems, and in particular to a vulnerability assessment method for multi-energy systems within a region. Background Technology

[0002] With the grid connection of numerous distributed power sources, power electronic devices, and controllable loads, distribution networks have gradually evolved into large-scale, dynamically complex, AC / DC interconnected regional multi-energy systems. These regional multi-energy systems, combining physical simulation models, advanced metering infrastructure, and artificial intelligence algorithms, form a smart grid with bidirectional interaction between energy flow, information flow, and business flow, and have been widely applied.

[0003] Compared to traditional power distribution networks, regional multi-energy systems have more complex mathematical models and higher coupling between different devices, making simulation studies of regional multi-energy systems more challenging. At the same time, regional multi-energy systems contain various types of distributed power sources such as wind turbines and photovoltaics. While large-scale grid integration of new energy sources improves the system's cleanliness and sustainable development characteristics, it also introduces more random factors and uncertainties, making the system's operating state more complex.

[0004] Vulnerability assessment evaluates the defects and weaknesses of regional multi-energy systems. Grid vulnerability is generally analyzed from the perspective of the system's topology and operational risks. Because regional multi-energy systems contain a large number of intermittent, random, and unstable distributed power sources, the safe and stable operation of the system will inevitably be affected to some extent. At the same time, their intermittent and random characteristics will inevitably lead to certain unpredictable vulnerabilities in the system's structure and operation. Conducting vulnerability assessments on regional multi-energy systems with large-scale renewable energy sources can determine distributed power source grid connection strategies that are more conducive to the safe and stable operation of the system, and control or take corresponding preventive measures against vulnerabilities that threaten the system's safe operation. Therefore, conducting vulnerability assessments of regional multi-energy systems has significant theoretical and practical implications.

[0005] Existing technologies include a vulnerability assessment method based on operational risk, which focuses on evaluating branch load risk rate, node voltage deviation rate, and line network loss variation rate. However, existing regional multi-energy systems contain highly pervasive distributed power sources, and current vulnerability assessment methods do not consider the impact of these distributed sources. They also fail to comprehensively assess the vulnerability of regional multi-energy systems from the perspectives of complex network theory and risk assessment theory, and their algorithms are complex and unsuitable for practical engineering applications. Therefore, existing algorithms still have certain limitations for regional multi-energy systems, and a vulnerability evaluation index system and comprehensive assessment method need further development and improvement. Summary of the Invention

[0006] The purpose of this invention is to propose a vulnerability assessment method for multi-energy systems within a region, thereby solving the technical problem that existing methods cannot fully consider the random factors and uncertainties of multi-energy systems within a region during the assessment process.

[0007] On the one hand, a vulnerability assessment method for multi-energy systems within a region is provided, including: Acquire topological data, measurement data, and historical data of multi-energy systems within the area to be evaluated; Based on the topology data, the measurement data, and the historical data, a simulation scenario is performed on the multi-energy system within the area to be evaluated, and power flow calculation is carried out to obtain multiple sets of vulnerability assessment indicators. Multiple vulnerability assessment indicators are combined into an assessment indicator matrix according to a preset format; The correlation coefficient between each indicator and the optimal indicator is calculated based on the evaluation index matrix, and the comprehensive evaluation result is calculated based on the correlation coefficient. The comprehensive evaluation result is then output as the final vulnerability assessment result of the multi-energy system in the region.

[0008] Preferably, the topology data includes at least the system node voltage deviation rate after branch disconnection, steady-state voltage before branch fault, steady-state voltage after branch fault, number of nodes, branch line loss change rate after branch disconnection, network loss before branch fault, network loss after branch fault, system reference capacity, load loss rate after branch disconnection, load before branch fault, and load after branch fault. The measurement data includes at least wind speed and light intensity; The historical data includes at least the active power and reactive power of the load.

[0009] Preferably, the simulation of the multi-energy system within the area to be evaluated and the calculation of power flow specifically include: Wind power generation is simulated using a pre-defined wind power generation probability model, which includes at least a wind speed model and a generator model. The wind speed model is used to calculate the wind speed probability distribution, and the generator model is used to calculate the active power output and reactive power output of the wind turbine generator set. The wind speed model specifically includes:

[0010] in, Represents the probability of wind speed. Indicates wind speed. The shape parameter representing the Weibull distribution of wind speed. The scale parameter representing the Weibull distribution of wind speed; The wind turbine generator model specifically includes:

[0011]

[0012] in, Indicates the power factor. Indicates the rated capacity of the fan. Indicates the rated wind speed. Indicates the cut-in wind speed. Indicates the cut-out wind speed; This indicates the active power output of the wind turbine generator set; This indicates the reactive power output of the wind turbine generator set.

[0013] Preferably, the step of simulating the multi-energy system within the area to be evaluated and performing power flow calculations further includes: Photovoltaic power generation is simulated using a preset photovoltaic power generation probability model, which includes at least a light intensity model and a photovoltaic generator set model. The light intensity model is used to calculate the probability distribution of light intensity, and the photovoltaic generator set model is used to calculate the active power output and reactive power output of the photovoltaic generator set. The light intensity model specifically includes:

[0014] in, Represents the probability distribution of light intensity. Indicates light intensity. It is the maximum light intensity. It is the gamma function. and It is the shape parameter of the Beta distribution of light intensity; The photovoltaic generator model specifically includes:

[0015]

[0016] in, This indicates the reactive power output of the photovoltaic power generation unit. This indicates the active power output of the photovoltaic generator set. Indicates light intensity. This refers to the rated value of solar radiation intensity. The rated active power of photovoltaic power. This is the rated power factor angle for photovoltaics.

[0017] Preferably, the step of simulating the multi-energy system within the area to be evaluated and performing power flow calculations further includes: The load conditions are simulated using a preset load probability model, which specifically includes:

[0018]

[0019] in, This represents the probability of active power of the load. This represents the probability of reactive power from the load. This represents the active power of the load. This indicates the reactive power of the load; This represents the probabilistic mean of the active power of the load. The standard deviation represents the probability of active power output from a load. This represents the mean of the probability of reactive power from the load. This represents the mean of the probability of reactive power from the load.

[0020] Preferably, the step of simulating the multi-energy system within the area to be evaluated and performing power flow calculations further includes: Operational risk is calculated using a pre-defined operational risk probability model, which includes at least a node voltage deviation rate model, a branch line loss change rate model, and a load shedding rate model. The node voltage deviation rate model is used to calculate the probability that a fault in the system will cause the bus voltage in the system to be too high or too low. The branch line loss change rate model is used to calculate the probability that a fault in the system will lead to unreasonable power flow distribution and excessive branch line losses. The load shedding rate model is used to calculate the probability that a fault in the system will lead to the area of ​​power outage in the system. The node voltage deviation rate model specifically includes:

[0021] in, This represents the system node voltage deviation rate after branch ij is disconnected; This represents the steady-state voltage of branch ij before the fault. This represents the steady-state voltage after a fault in branch ij; Indicates the number of nodes; The branch line loss rate model specifically includes:

[0022] in, It is the rate of change of branch line loss after branch ij is disconnected. This represents the network loss before the branch ij fault. This refers to the network loss after a fault in branch ij. This is the system's baseline capacity; The load failure rate model specifically includes:

[0023] in, It is the load loss rate after branch ij is disconnected. The load before the fault in branch ij. The load after a fault in branch ij.

[0024] Preferably, multiple vulnerability assessment indicators are combined into an assessment indicator matrix according to the following format:

[0025]

[0026] in, Represents the evaluation index matrix, This represents the original value of the nth indicator in the mth scheme. Represents the optimal index set. This represents the optimal value of the nth indicator.

[0027] Preferably, it further includes: The index values ​​in the evaluation index matrix are normalized according to the following formula:

[0028] in, This represents the standardized value of the k-th index in the i-th column of the index set. This represents the value of the k-th indicator in the i-th indicator set. This represents the minimum value of the k-th index among all possible solutions; This represents the maximum value of the k-th index among all possible solutions; All standardized indicator values ​​are combined into a post-planning evaluation indicator matrix:

[0029] in, This represents the evaluation index matrix after planning. Indicates a reference sequence, Let i represent the sequence being compared, i represent the number of index columns after planning, and k represent the number of index sequences after planning.

[0030] Preferably, the correlation coefficient between each indicator and the optimal indicator is calculated according to the following formula:

[0031] in, This represents the correlation coefficient of indicator k in the i-th column after planning. This represents the value of the k-th planned indicator in the i-th column of the indicator set. This represents the index value after planning for the kth time in the reference sequence.

[0032] Preferably, the comprehensive evaluation result is calculated according to the following formula:

[0033] in, This indicates the comprehensive evaluation results. W(k) denoted by k, where k represents the number of planned indicator sequences and n represents the total number of planned indicator columns.

[0034] In summary, implementing the embodiments of the present invention has the following beneficial effects: This invention provides a vulnerability assessment method for regional multi-energy systems. It establishes vulnerability assessment indicators and a comprehensive assessment framework for regional multi-energy systems, and constructs a vulnerability assessment model using the grey comprehensive evaluation method. A typical regional multi-energy system was modeled, and test results show that the model can accurately calculate vulnerability indicators under various operating scenarios and configuration schemes of the regional multi-energy system, and rank the vulnerabilities of different configuration schemes, thereby improving the system's security and stability. This method can provide valuable reference and support for the security and stability analysis of regional multi-energy systems. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0036] Figure 1 This is a schematic diagram of the main process of a vulnerability assessment method for multi-energy systems within a region, as described in an embodiment of the present invention.

[0037] Figure 2 This is a schematic diagram of the test system in an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram illustrating the improved node betweenness before and after grid connection of distributed power sources in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] like Figure 1The diagram shown is an embodiment of a vulnerability assessment method for multi-energy systems within a region provided by the present invention. In this embodiment, the method includes the following steps: The process involves acquiring topology data, measurement data, and historical data of the multi-energy systems within the area to be evaluated; that is, collecting data parameters of the multi-energy systems within the area. Specifically, the topology data includes at least the system node voltage deviation rate after branch disconnection, steady-state voltage before branch fault, steady-state voltage after branch fault, number of nodes, branch line loss change rate after branch disconnection, network loss before branch fault, network loss after branch fault, system baseline capacity, load loss rate after branch disconnection, load before branch fault, and load after branch fault; the measurement data includes at least wind speed and solar irradiance; and the historical data includes at least the active power and reactive power of the load.

[0041] Furthermore, based on the topology data, measurement data, and historical data, simulation scenarios are performed on the multi-energy system within the assessment area, and power flow calculations are conducted to obtain multiple sets of vulnerability assessment indicators. In other words, to achieve vulnerability assessment of the regional multi-energy system and improve system operational safety and stability, a comprehensive approach considering modeling and simulation theory and complex network theory is necessary. Regarding modeling and simulation theory, to accurately simulate the output characteristics of distributed power sources, probability distribution models of random variables such as wind turbines, photovoltaics, and loads need to be established. Then, random variables are sampled to determine different simulation scenarios. Power flow calculations are performed for each simulation scenario, and based on this, complex network theory is combined to achieve vulnerability assessment of the regional multi-energy system.

[0042] In a specific embodiment, power flow calculation includes: First, wind power generation is simulated using a pre-defined wind power generation probability model, which includes at least a wind speed model and a generator model. The wind speed model is used to calculate the wind speed probability distribution, and the generator model is used to calculate the active power output and reactive power output of the wind turbine generator set. In other words, the wind power generation probability model consists of a wind speed model and a generator model.

[0043] Specifically, the wind speed probability distribution mainly satisfies a two-parameter Weibull distribution, and the wind speed model specifically includes:

[0044] in, Represents the probability of wind speed. Indicates wind speed. The shape parameter representing the Weibull distribution of wind speed. The scale parameter representing the Weibull distribution of wind speed; Wind turbine generators typically use asynchronous generators and are modeled as PQ nodes in power flow calculations; the wind turbine generator model specifically includes:

[0045]

[0046] in, Indicates the power factor. Indicates the rated capacity of the fan. Indicates the rated wind speed. Indicates the cut-in wind speed. Indicates the cut-out wind speed; This indicates the active power output of the wind turbine generator set; This indicates the reactive power output of the wind turbine generator set.

[0047] Secondly, photovoltaic power generation is simulated using a preset photovoltaic power generation probability model, which includes at least a light intensity model and a photovoltaic generator set model. The light intensity model is used to calculate the probability distribution of light intensity, and the photovoltaic generator set model is used to calculate the active power output and reactive power output of the photovoltaic generator set. The light intensity model follows a Beta distribution, and the light intensity model specifically includes:

[0048] in, Represents the probability distribution of light intensity. Indicates light intensity. It is the maximum light intensity. It is the gamma function. and It is the shape parameter of the Beta distribution of light intensity; The photovoltaic generator model specifically includes:

[0049]

[0050] in, This indicates the reactive power output of the photovoltaic power generation unit. This indicates the active power output of the photovoltaic generator set. Indicates light intensity. This refers to the rated value of solar radiation intensity. The rated active power of photovoltaic power. This is the rated power factor angle for photovoltaics.

[0051] Next, the load situation is simulated using a preset load probability model, which specifically includes:

[0052]

[0053] in, This represents the probability of active power of the load. This represents the probability of reactive power from the load. This represents the active power of the load. This indicates the reactive power of the load; This represents the probabilistic mean of the active power of the load. The standard deviation represents the probability of active power output from a load. This represents the mean of the probability of reactive power from the load. This represents the mean of the probability of reactive power from the load.

[0054] Furthermore, operational risk is calculated using a pre-defined operational risk probability model. This model includes at least a node voltage deviation rate model, a branch line loss change rate model, and a load shedding rate model. The node voltage deviation rate model calculates the probability that a fault in the system will cause excessively high or low bus voltage. The branch line loss change rate model calculates the probability that a fault in the system will lead to unreasonable power flow distribution and excessive branch line losses. The load shedding rate model calculates the probability that a fault in the system will result in a significant power outage area. In other words, the vulnerability assessment index for regional multi-energy systems based on topology primarily analyzes the original system structure, neglecting the impact of system faults. Operational risk assessment determines the operational status and extent of impact of a fault in a regional multi-energy system. This paper mainly assesses the operational risk of a regional multi-energy system from three aspects: node voltage deviation rate, branch line loss change rate, and load shedding rate.

[0055] Specifically, regional multi-energy systems contain a large number of distributed power sources. The randomness and intermittency of these distributed power sources affect the system's vulnerability, making it impossible to directly assess the vulnerability of regional multi-energy systems from a topology perspective. Therefore, it is necessary to consider the impact of distributed power sources and improve traditional topology-based vulnerability indices to obtain a vulnerability assessment index system for regional multi-energy systems. This includes: improving node betweenness. The traditional distribution network node betweenness is defined as the number of shortest paths from a generator node to a load node through that node. Considering that the system loses different loads when different nodes are disconnected, and that distributed power sources can improve system reliability to some extent, the improved node betweenness index for regional multi-energy systems is defined as follows:

[0056] In the formula, V1 is the set of equivalent power supply nodes; V2 is the set of equivalent load nodes; The rated power consumed by the load; This represents the load lost by the system after node k is disconnected. This is a function to determine whether the shortest path between node i and node j passes through node k. If it does, its value is 1; otherwise, it is 0.

[0057] Improved line betweenness: The traditional concept of line betweenness in distribution networks is the number of shortest paths from generator nodes to load nodes along that line. A higher line betweenness indicates greater importance of the line, and a greater impact of its failure on the system. Traditional line betweenness definitions do not consider the influence of distributed generation output. Considering the mitigating effect of distributed generation on system vulnerability, an improved line betweenness index for regional multi-energy systems is defined as follows:

[0058] In the formula, This represents the load loss of the system after branch ij is disconnected.

[0059] Specifically, to reflect the importance of lines in power transmission and distribution, line transmission power is proposed as a vulnerability assessment index for branches. The greater the power transmitted by a line, the greater the impact on the system if that line is disconnected due to a fault. The grid connection of numerous distributed power sources can alter the power flow distribution and even the direction of power flow; therefore, line transmission power is proposed as a vulnerability assessment index for regional multi-energy systems.

[0060] More specifically, the node voltage deviation rate reflects the possibility and harm of a fault in the system causing excessively high or low bus voltage. It primarily considers the system's operating state under a line break fault. Under this fault, the node voltage deviation rate of the regional multi-energy system is defined by the following model:

[0061] in, This represents the system node voltage deviation rate after branch ij is disconnected; This represents the steady-state voltage of branch ij before the fault. This represents the steady-state voltage after a fault in branch ij; Indicates the number of nodes; Branch line loss rate reflects the situation where faults in the regional multi-energy system lead to unreasonable power flow distribution and excessive branch line losses; it considers the rate of change of branch line losses when a system disconnection fault occurs. The specific model for the branch line loss rate includes:

[0062] in, It is the rate of change of branch line loss after branch ij is disconnected. This represents the network loss before the branch ij fault. This refers to the network loss after a fault in branch ij. This is the system's baseline capacity; The load shedding rate reflects the area of ​​power outage caused by a fault in a regional multi-energy system. It considers the load shedding rate of the regional multi-energy system when a line break occurs. The specific load shedding rate model includes:

[0063] in, It is the load loss rate after branch ij is disconnected. The load before the fault in branch ij. The load after a fault in branch ij.

[0064] Furthermore, multiple vulnerability assessment indicators are combined into an assessment indicator matrix according to a preset format; that is, based on determining the vulnerability assessment indicators for regional multi-energy systems, a regional multi-energy system vulnerability assessment model is constructed using the grey comprehensive evaluation method. Correlation analysis is the foundation of grey system analysis, evaluation, and decision-making. Grey correlation analysis is a multi-factor statistical analysis method that uses grey correlation degree to describe the strength, magnitude, and order of relationships between factors.

[0065] The grey comprehensive evaluation mainly determines the ranking of each scheme using the following formula:

[0066] In the formula: Let m be the correlation index of the evaluated objects. The weighting coefficients are the weighting coefficients for the n evaluation indicators.

[0067] E is the evaluation matrix for each indicator:

[0068] In the formula: Let be the correlation coefficient of index n in scheme m.

[0069] In a specific embodiment, multiple vulnerability assessment indicators are combined into an assessment indicator matrix according to the following format:

[0070]

[0071] in, Represents the evaluation index matrix, This represents the original value of the nth indicator in the mth scheme. Represents the optimal index set. This represents the optimal value of the nth indicator. In other words, first, we determine the optimal set of indicators. After determining the optimal set of indicators, construct the evaluation indicator matrix D.

[0072] Furthermore, the correlation coefficients between each indicator and the optimal indicator are calculated based on the evaluation indicator matrix, and the comprehensive evaluation result is calculated based on the correlation coefficients. The comprehensive evaluation result is then output as the final vulnerability assessment result for the multi-energy system within the region. That is, since different evaluation indicators have different dimensions and orders of magnitude, the original indicator values ​​need to be normalized to ensure the reliability of the results; subsequently, the correlation coefficient between the k-th indicator of scheme i and the optimal indicator can be obtained. .

[0073] In a specific embodiment, the index values ​​in the evaluation index matrix are normalized according to the following formula:

[0074] in, This represents the standardized value of the k-th index in the i-th column of the index set. This represents the value of the k-th indicator in the i-th indicator set. This represents the minimum value of the k-th index among all possible solutions; This represents the maximum value of the k-th index among all possible solutions; All standardized indicator values ​​are combined into a post-planning evaluation indicator matrix:

[0075] in, This represents the evaluation index matrix after planning. Indicates a reference sequence, Let i represent the sequence being compared, i represent the number of index columns after planning, and k represent the number of index sequences after planning.

[0076] More specifically, the correlation coefficient between each indicator and the optimal indicator is calculated using the following formula:

[0077] in, This represents the correlation coefficient of indicator k in the i-th column after planning. This represents the value of the k-th planned indicator in the i-th column of the indicator set. This represents the index value after planning for the kth time in the reference sequence.

[0078] Finally, the comprehensive evaluation result is calculated according to the following formula:

[0079] in, This indicates the comprehensive evaluation results. W(k)This represents the weight coefficients of the k evaluation indicators, where k represents the number of indicator sequences after planning, and n represents the total number of indicator columns after planning. (Relevance) The larger the value, the better the stability of the solution.

[0080] In this embodiment of the invention, to investigate the impact of random output from distributed power sources on the vulnerability of regional multi-energy systems, an improved IEEE-37 node test system was built on the CloudPSS platform. This system includes distributed power sources such as photovoltaics, wind turbines, and gas turbines, forming a typical regional multi-energy system. The system has a complex structure with voltage levels of 35kV, 4.8kV, and 0.48kV, and a base capacity of 10MW. Photovoltaic power generation is connected to bus 728, and wind turbine power generation is connected to bus 722.

[0081] like Figure 2 As shown, a probabilistic model of distributed power sources and random loads is established by measuring meteorological data such as temperature and light intensity in the measurement system, and the entire sample space is sampled by Latin hypercube sampling method to determine typical operating scenarios.

[0082] The wind turbine's cut-in wind speed, rated wind speed, and cut-out wind speed are 3, 13, and 25 m / s, respectively; the photovoltaic power generation has a rated irradiance of 4.5 kW / m², a maximum irradiance of 6.5 kW / m², and a photoelectric conversion efficiency of 0.15.

[0083] In the results analysis, the vulnerability impact of distributed power sources was discussed. To verify the impact of distributed generation grid connection on the vulnerability of regional multi-energy systems, different types of distributed generation were connected to the system. The specific grid connection scheme is as follows: 0.2MW wind turbines were configured at node 14, 0.225MW photovoltaic power was configured at node 29, and 0.21MW and 0.22MW gas turbines were configured at nodes 33 and 28, respectively.

[0084] For this test case, a comprehensive evaluation index system was established, and the vulnerability assessment indexes of the system before and after distributed power generation grid connection were calculated. The top ten key index values ​​for vulnerability of each node or branch were averaged, and the final comparison results are shown in Table 1. Table 1 Comparison of Vulnerability Indicators Before and After Distributed Power Generation Grid Connection

[0085] As shown in Table I, Bk is 1.83, a decrease of 46.64% compared to 3.43 before grid connection of distributed power sources. Bij is 0.86, a decrease of 51.41% compared to 1.77. Nij is 2.51%, a decrease of 42.29% compared to 4.35%. Vij is 4.40%, a decrease of 6.58% compared to 4.71%. Rij is 6.4%, a decrease of 32.63% compared to 9.5%. Dij is 8.43%, a decrease of 45.85% compared to 15.57%. This indicates that a reasonable grid connection scheme for distributed power sources can reduce the vulnerability of the system topology and operational risks, and improve the security and stability of regional multi-energy systems. The comparison of improved node betweenness numbers before and after grid connection of distributed power sources is shown in the table. Figure 3 As shown, the closer to the voltage source, the larger the node betweenness number, and the greater the impact on the system when a node fails and disconnects. Furthermore, grid connection of distributed generation can significantly reduce the improved node betweenness number.

[0086] In the vulnerability ranking of different schemes, the vulnerability index of different distributed power grid connection schemes is calculated, and the grey comprehensive correlation method is used for evaluation. Distributed power grid connection schemes are shown in Table 2: Table 2 Distributed Grid Connection Schemes with Different Approaches

[0087] The vulnerability indicators for different schemes are shown in Table 3: Table 3 Vulnerability Indicators for Different Schemes

[0088] The weighting coefficients for each indicator are shown in Table 4: Table 4 Weighting coefficients for different indicators

[0089] The correlation results of each scheme are shown in Table 5: Table 5. Relevance Assessment Results of Each Option

[0090] As can be seen from Table 5, Solution A (the technical solution of this invention) has the best relevance among the solutions. Therefore, Solution A has the best safety and stability.

[0091] In summary, implementing the embodiments of the present invention has the following beneficial effects: This invention provides a vulnerability assessment method for regional multi-energy systems. It establishes vulnerability assessment indicators and a comprehensive assessment framework for regional multi-energy systems, and constructs a vulnerability assessment model using the grey comprehensive evaluation method. A typical regional multi-energy system was modeled, and test results show that the model can accurately calculate vulnerability indicators under various operating scenarios and configuration schemes of the regional multi-energy system, and rank the vulnerabilities of different configuration schemes, thereby improving the system's security and stability. This method can provide valuable reference and support for the security and stability analysis of regional multi-energy systems.

[0092] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A vulnerability assessment method for multi-energy systems within a region, characterized in that, include: Acquire topological data, measurement data, and historical data of multi-energy systems within the area to be evaluated; Based on the topology data, the measurement data, and the historical data, a simulation scenario is performed on the multi-energy system within the area to be evaluated, and power flow calculation is carried out to obtain multiple sets of vulnerability assessment indicators. Multiple vulnerability assessment indicators are combined into an assessment indicator matrix according to a preset format; Calculate the correlation coefficient between each indicator and the optimal indicator based on the evaluation index matrix, and calculate the comprehensive evaluation result based on the correlation coefficient. The comprehensive assessment results are output as the final vulnerability assessment results for multi-energy systems within the region; The improved node betweenness index for a regional multi-energy system is defined by the following formula: In the formula, V1 is the set of equivalent power supply nodes; V2 is the set of equivalent load nodes; The rated power consumed by the load; This represents the load lost by the system after node k is disconnected. This is a function to determine whether the shortest path between node i and node j passes through node k. The improved line betweenness index for a regional multi-energy system is defined by the following formula: In the formula, This represents the load lost by the system after branch ij is disconnected; The simulation of the multi-energy system within the area to be evaluated and the calculation of power flow specifically include: Operational risk is calculated using a pre-defined operational risk probability model. This operational risk probability model includes at least a node voltage deviation rate model, a branch line loss change rate model, and a load shedding rate model. The node voltage deviation rate model is used to calculate the probability that a fault in the system will cause the bus voltage to be too high or too low. The branch line loss change rate model is used to calculate the probability that a fault in the system will lead to unreasonable power flow distribution and excessive branch line losses. The load shedding rate model is used to calculate the probability that a fault in the system will cause the area of ​​power outage. The node voltage deviation rate model specifically includes: in, This represents the system node voltage deviation rate after branch ij is disconnected; This represents the steady-state voltage of branch ij before the fault. This represents the steady-state voltage after a fault in branch ij; Indicates the number of nodes; The branch line loss rate model specifically includes: in, It is the rate of change of branch line loss after branch ij is disconnected. This represents the network loss before the branch ij fault. This refers to the network loss after a fault in branch ij. This is the system's baseline capacity; The load failure rate model specifically includes: in, It is the load loss rate after branch ij is disconnected. The load before the fault in branch ij. The load after a fault in branch ij; Combine multiple vulnerability assessment indicators into an assessment indicator matrix according to the following format: in, Represents the evaluation index matrix, This represents the original value of the nth indicator in the mth scheme. Represents the optimal index set. This represents the optimal value of the nth indicator.

2. The method as described in claim 1, characterized in that, The topology data includes at least the system node voltage deviation rate after branch disconnection, steady-state voltage before branch fault, steady-state voltage after branch fault, number of nodes, branch line loss change rate after branch disconnection, network loss before branch fault, network loss after branch fault, system baseline capacity, load loss rate after branch disconnection, load before branch fault, and load after branch fault. The measurement data includes at least wind speed and light intensity; The historical data includes at least the active power and reactive power of the load.

3. The method as described in claim 2, characterized in that, The simulation of the multi-energy system within the area to be evaluated and the calculation of power flow specifically include: Wind power generation is simulated using a pre-defined wind power generation probability model, which includes at least a wind speed model and a generator model. The wind speed model is used to calculate the wind speed probability distribution, and the generator model is used to calculate the active power output and reactive power output of the wind turbine generator set. The wind speed model specifically includes: in, Represents the probability of wind speed. Indicates wind speed. The shape parameter representing the Weibull distribution of wind speed. The scale parameter representing the Weibull distribution of wind speed; The wind turbine generator model specifically includes: in, Indicates the power factor. Indicates the rated capacity of the fan. Indicates the rated wind speed. Indicates the cut-in wind speed. Indicates the cut-out wind speed; This indicates the active power output of the wind turbine generator set; This indicates the reactive power output of the wind turbine generator set.

4. The method as described in claim 3, characterized in that, The simulation of multi-energy system scenarios and power flow calculation within the area to be evaluated specifically includes: Photovoltaic power generation is simulated using a preset photovoltaic power generation probability model, which includes at least a light intensity model and a photovoltaic generator set model. The light intensity model is used to calculate the probability distribution of light intensity, and the photovoltaic generator set model is used to calculate the active power output and reactive power output of the photovoltaic generator set. The light intensity model specifically includes: in, Represents the probability distribution of light intensity. Indicates light intensity. It is the maximum light intensity. It is the gamma function. and It is the shape parameter of the Beta distribution of light intensity; The photovoltaic generator model specifically includes: in, This indicates the reactive power output of the photovoltaic power generation unit. This indicates the active power output of the photovoltaic generator set. Indicates light intensity. This is the rated value for solar radiation intensity. The rated active power of photovoltaic power. This is the rated power factor angle for photovoltaics.

5. The method as described in claim 4, characterized in that, The simulation of multi-energy system scenarios and power flow calculation within the area to be evaluated specifically includes: The load conditions are simulated using a preset load probability model, which specifically includes: in, This represents the probability of active power of the load. This represents the probability of reactive power from the load. This represents the active power of the load. This indicates the reactive power of the load; This represents the probabilistic mean of the active power of the load. The standard deviation represents the probability of active power output from a load. This represents the mean of the probability of reactive power from the load. This represents the mean of the probability of reactive power from the load.

6. The method as described in claim 1, characterized in that, Also includes: The index values ​​in the evaluation index matrix are normalized according to the following formula: in, This represents the standardized value of the k-th index in the i-th column of the index set. This represents the value of the k-th indicator in the i-th indicator set. This represents the minimum value of the k-th index among all possible solutions; This represents the maximum value of the k-th indicator among all possible solutions; All standardized indicator values ​​are combined into a post-planning evaluation indicator matrix: in, This represents the evaluation index matrix after planning. Indicates a reference sequence, Let i represent the sequence being compared, i represent the number of index columns after planning, and k represent the number of index sequences after planning.

7. The method as described in claim 6, characterized in that, The correlation coefficient between each indicator and the optimal indicator is calculated using the following formula: in, This represents the correlation coefficient of indicator k in the i-th column after planning. This represents the value of the k-th planned indicator in the i-th column of the indicator set. This represents the index value after planning for the kth time in the reference sequence.

8. The method as described in claim 7, characterized in that, The comprehensive evaluation result is calculated using the following formula: in, This indicates the comprehensive evaluation results. W(k) denoted by k, where k represents the number of planned indicator sequences and n represents the total number of planned indicator columns.

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