Power system probability risk assessment method considering new energy uncertainty

Abstract

The invention discloses an electric power system probability risk assessment method considering new energy uncertainty, and relates to the technical field of risk assessment, and the method comprises the following steps: constructing an electric power system cross-regional graph model, analyzing the cross-regional power flow change, and obtaining a first change value; performing first interference signal interference on the power system to obtain an interference path set, and screening the interference path set according to the cross-regional graph model of the power system in combination with equipment N-1 safety verification to obtain a first screening path set; screening the first screening path set according to the first change value to obtain a cross-region out-of-limit line set; and analyzing the risk probability of the cross-regional out-of-limit line set, and outputting the probability risk assessment level, thereby solving the problem that the traditional method cannot accurately quantify the comprehensive risk caused by the new energy fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment, and more specifically, to a probabilistic risk assessment method for a power system taking into account the uncertainty of new energy sources. Background Art

[0002] As the global energy structure transforms toward low-carbon and clean energy, the penetration rate of new energy (such as wind power, photovoltaics, etc.) in the power system continues to increase. However, new energy has significant randomness, intermittency and uncertainty, and its output is greatly affected by meteorological conditions, which brings many challenges to the stable operation of the power grid.

[0003] For example, the invention patent announcement with announcement number: CN112036718B discloses a method for assessing the safety risk of a power system taking into account the uncertainty of new energy. It clusters scenarios based on the weighted distance of uncertain variables weighted by the safety and stability impact factor of new energy plants and stations based on multiple possible scenarios by sampling the uncertain variables of new energy. This can largely avoid the possibility of "risk leakage" caused by missing low-probability high-risk scenarios and other key scenarios; for each type of scenario subset, the maximum and minimum operating scenarios are determined according to the impact of the uncertain variables of new energy on safety and stability, and the maximum and minimum values ​​of the safe and stable operation risk can be accurately assessed. The present invention realizes the online assessment of the safe and stable operation risk of the power system taking into account the uncertainty of new energy prediction, which can meet the requirements of calculation speed and accuracy.

[0004] For example, the invention patent with announcement number: CN116050838B discloses a distribution transformer operation risk assessment method and system based on wind power load correlation. As the penetration rate of new energy sources in new power systems continues to increase, the uncertainty of wind power output and its correlation with load have increasingly prominent effects on system operation, and traditional deterministic transformer risk assessment can no longer meet existing needs. The present invention discloses a joint probability prediction method for transformer operation risk considering wind power-load correlation. Based on the Copula function and the Susa model, it calculates indicators such as insulation degradation probability, uses the Monte Carlo method to calculate each risk index value and evaluates the transformer operation risk. On the basis of traditional equipment evaluation, the present invention introduces the uncertainty factors of wind power and load fluctuations brought about by the access of new energy sources, and considers the correlation between regional wind power and load, which greatly improves the accuracy of transformer operation risk level assessment in new power systems.

[0005] The above disclosed technical solutions have at least the following technical problems: In traditional methods, correlation analysis based on fixed sensitivity directions will ignore the polarity reversal of path sensitivity in wind-solar hybrid scenarios, and static N-1 verification is difficult to capture low-probability-high-loss cross-region chain limit-crossing events, resulting in the inability to accurately quantify the comprehensive risks caused by new energy fluctuations. In response to the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a probabilistic risk assessment method for a power system taking into account the uncertainty of new energy. By analyzing the changes in power flow, interference paths are screened and the probability of over-limit risk is evaluated, so as to solve the problem that traditional methods cannot accurately quantify the comprehensive risks caused by fluctuations in new energy.

[0007] To achieve the above object, the present invention provides the following technical solutions: A probabilistic risk assessment method for a power system taking into account the uncertainty of new energy sources comprises the following steps: constructing a cross-regional graph model of the power system, and analyzing the cross-regional power flow changes to obtain a first change value; performing a first interference signal interference on the power system to obtain an interference path set, and screening the interference path set according to the cross-regional graph model of the power system combined with the N-1 safety check of the equipment to obtain a first screened path set; screening the first screened path set according to the first change value to obtain a cross-regional over-limit line set; analyzing the risk probability of the cross-regional over-limit line set, and outputting a probabilistic risk assessment level.

[0008] In a preferred embodiment, the cross-regional graph model of the power system is constructed, and the cross-regional power flow changes are analyzed to obtain the first change value, specifically: the power system is divided into several sub-regions and a cross-regional graph model of the power system is constructed; a power flow sensitivity matrix is ​​constructed under a preset set of new energy scenarios; based on the power flow sensitivity matrix, the interconnection line power flow change value in each new energy scenario is calculated; and the maximum absolute value of the interconnection line power flow change value in all new energy scenarios is used as the first change value.

[0009] In a preferred embodiment, the power flow sensitivity matrix is ​​constructed under a preset new energy scenario set, specifically: a probability distribution model is constructed based on the historical output data of the new energy station; based on the probability distribution model, a generative adversarial network is used to obtain the new energy scenario set and calculate the scene probability density of each new energy scenario and the contribution weight of the new energy scenario; the power flow sensitivity of the new energy nodes under each new energy scenario is calculated to obtain the sensitivity matrix of the new energy nodes of each new energy scenario; the sensitivity matrix is ​​weightedly fused based on the probability density of the new energy scenario and the contribution weight of the new energy scenario to obtain the power flow sensitivity matrix.

[0010] In a preferred embodiment, the first interference signal interference is performed on the power system to obtain the interference path set, specifically: the first interference signal interference is performed on the power system, and power fluctuation data is collected; the power fluctuation amount of the node is calculated based on the power fluctuation data; the disturbance value of each line in the power system is calculated based on the power fluctuation amount of the node and the power flow sensitivity matrix; the disturbance value of each line in the power system is compared with a preset first threshold value to screen out the interference path set; In a preferred embodiment, the interference path set is screened according to the cross-regional graph model of the power system in combination with the device N-1 safety check to obtain a first screened path set, specifically: the interference path set is converted into an edge sequence set of the cross-regional graph model of the power system; based on the depth-first algorithm, each path and all sub-paths corresponding to each path in the edge sequence set of the cross-regional graph model of the power system are traversed; all sub-paths corresponding to each path are screened based on the device N-1 safety check to obtain the first screened path set.

[0011] In a preferred embodiment, all sub-paths corresponding to each path are screened based on the device N-1 safety check to obtain a first screening path set, specifically: disconnect each edge in the current sub-path in turn, simulate triggering N-1 faults, and update the topological connection state of the power system graph model to obtain an updated cross-regional graph model of the power system; calculate the flow distribution value of the updated cross-regional graph model of the power system based on the flow sensitivity matrix, and detect whether the flow exceeds the limit on the edges connecting different regions; if the flow exceeds the limit on the cross-regional edge, add the edge to the first screening path set; merge all candidate over-limit lines, and eliminate duplicate candidate over-limit lines to obtain the first screening path set.

[0012] In a preferred embodiment, the first screening path set is screened according to the first change value to obtain a cross-regional over-limit line set, specifically: calculating the power flow value of each first screening path in the first screening path set; calculating the sensitivity coefficient of each first screening path to the fluctuation of new energy output based on the power flow sensitivity matrix; correcting the sensitivity coefficient of each first screening path to the fluctuation of new energy output according to the first change value to obtain the maximum additional power flow change; superimposing the power flow value of each first screening path with the maximum additional power flow change to obtain the first maximum power flow value of the first screening path; comparing and analyzing the first maximum power flow value of the first screening path with the preset safety operation limit to screen out the cross-regional over-limit line set.

[0013] In a preferred embodiment, the risk probability of the cross-regional over-limit line set is analyzed and the probabilistic risk assessment level is output, specifically: the frequency of each cross-regional over-limit line triggering over-limit under the new energy scenario set is obtained and the over-limit probability of each cross-regional over-limit line is calculated; the flow over-limit amount of each cross-regional over-limit line under the new energy scenario set is calculated, and the expected severity of each cross-regional over-limit line is calculated based on the flow over-limit amount; the over-limit probability and the expected severity of each cross-regional over-limit line are multiplied to obtain the risk probability value of each cross-regional over-limit line; the probabilistic risk average and standard deviation of all cross-regional over-limit lines in the power system are calculated; the ratio of the probabilistic risk average and standard deviation is used as the comprehensive risk value of the power system; the comprehensive risk value of the power system is compared and analyzed with the preset second threshold value to construct a probabilistic risk assessment level of the power system.

[0014] Technical effects and advantages of the power system probabilistic risk assessment method taking into account the uncertainty of new energy sources of the present invention: 1. The present invention accurately divides the power system into multiple sub-regions and intuitively presents the power connection between different regions in the form of a cross-regional graph model, thereby improving the operability and computational efficiency of power flow analysis. In addition, the generative adversarial network method based on the probability distribution of new energy output is adopted to make the constructed new energy scenario set more in line with the actual operating conditions and enhance the adaptability of power flow analysis. The first change value finally calculated fully considers the uncertainty of new energy output and provides a reasonable reference benchmark for subsequent risk assessment. This step ensures a comprehensive assessment of the impact of new energy output fluctuations on cross-regional power system power flows, and improves the scientificity and accuracy of risk analysis; 2. The present invention ensures the quantitative evaluation of the impact of new energy uncertainty on the power grid power distribution by calculating the sensitivity coefficient of the power flow value and the impact of new energy fluctuations. Through the correction calculation of the maximum additional power flow change, the screened out-of-limit lines are more in line with the actual operating conditions, avoiding potential safety hazards caused by new energy fluctuations. At the same time, this step effectively identifies the lines with higher risks by comparing the safe operating limits, providing a scientific basis for subsequent risk probability analysis. Finally, this step improves the accuracy of out-of-limit line screening, ensures the safety and stability of power system operation, and improves the reliability of risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the method for probabilistic risk assessment of power system taking into account the uncertainty of new energy sources according to the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1, Figure 1 The present invention provides a probabilistic risk assessment method for a power system taking into account the uncertainty of new energy sources, comprising the following steps: S1, constructing a cross-regional graph model of the power system, and analyzing the cross-regional power flow changes to obtain a first change value; In this example, a cross-regional graph model of the power system is constructed, and the cross-regional power flow changes are analyzed to obtain the first change value, which is specifically: Divide the power system into several sub-regions and construct a cross-regional graph model of the power system; Construct a power flow sensitivity matrix under a preset set of new energy scenarios; Based on the power flow sensitivity matrix, calculate the interconnection line power flow change value in each new energy scenario; The maximum absolute value of the interconnection line power flow change value in all new energy scenarios is taken as the first change value.

[0018] In this example, a power flow sensitivity matrix is ​​constructed under the preset new energy scenario set, specifically: Construct a probability distribution model based on the historical output data of new energy stations; Based on the probability distribution model, a generative adversarial network is used to obtain a set of new energy scenarios and calculate the scenario probability density and contribution weight of each new energy scenario. Calculate the power flow sensitivity of the new energy nodes in each new energy scenario to obtain the sensitivity matrix of the new energy nodes in each new energy scenario; The sensitivity matrix is ​​weighted and fused based on the probability density of new energy scenarios and the contribution weights of new energy scenarios to obtain the power flow sensitivity matrix.

[0019] Among them, the sensitivity matrix is ​​weighted and fused based on the probability density of new energy scenarios and the contribution weights of new energy scenarios to obtain the power flow sensitivity matrix. The specific calculation formula is as follows: The contribution weight calculation formula of the new energy scenario is as follows:

[0020] The calculation formula of the power flow sensitivity matrix is ​​as follows:

[0021] in, is the mth new energy scenario, is the contribution weight of the mth new energy scenario, is the probability density value of the mth new energy field, is the probability density value of the jth new energy field, is the total number of new energy scenario groups in the new energy scenario group, is the sensitivity matrix, is the power flow sensitivity matrix.

[0022] It should be noted that by constructing a cross-regional graph model of the power system and analyzing the cross-regional power flow changes to obtain the first change value, the safety assessment capability of the power system under the influence of new energy uncertainty is effectively improved. First, the power system is divided into several sub-regions, and a cross-regional graph model is constructed, so that the complexity of the system is reasonably simplified, while ensuring that the power interconnection relationship between regions can be clearly expressed, which is conducive to the calculation efficiency and accuracy of subsequent power flow analysis. Secondly, a power flow sensitivity matrix is ​​constructed under the preset new energy scenario set, which fully considers the power flow distribution characteristics under different new energy output conditions, and uses sensitivity analysis to quantify the impact of new energy fluctuations on the power system power flow, thereby providing accurate basic data for further risk assessment. In addition, based on the power flow sensitivity matrix, the power flow change value of the interconnection line in each new energy scenario is calculated, so that the power flow fluctuations in different scenarios can be quantified and presented, providing a more targeted assessment method for power grid operation. Finally, the maximum absolute value of the interconnection line power flow change value in all new energy scenarios is taken as the first change value, which effectively captures the most extreme impact of new energy uncertainty on the power grid power flow, thereby ensuring the rigor and safety of subsequent risk assessment. Through this method, power grid operators can more accurately identify high-risk areas, formulate targeted control strategies in advance, reduce the operational risks brought by new energy access, and ensure the stable operation of the power grid.

[0023] In addition, a probability distribution model is constructed through the historical output data of new energy stations, so that the volatility of new energy output can be quantified, thereby ensuring the rationality and accuracy of the generated new energy scenarios. Secondly, based on the probability distribution model, a generative adversarial network (GAN) is used to generate a set of new energy scenarios, and the probability density and contribution weight of each scenario are calculated. Compared with the traditional random sampling method, GAN can simulate the complex changes of new energy output more realistically, improve the representativeness and diversity of the scenario, and avoid the possible deviation problems of traditional methods. In addition, by calculating the power flow sensitivity of new energy nodes under each new energy scenario, the sensitivity matrix of new energy nodes is obtained, so that the impact of new energy fluctuations on power grid power flow can be accurately characterized, providing fine-grained data support for subsequent safety assessment. Finally, by weighted fusion of the sensitivity matrix based on the probability density and contribution weight of the new energy scenario, the global power flow sensitivity matrix is ​​obtained, thereby ensuring that the importance of different scenarios can be reasonably reflected, and improving the comprehensiveness and scientificity of power flow sensitivity analysis. This method significantly improves the analysis accuracy of the impact of new energy uncertainty on power grid power flow, provides more reliable data support for power grid operators, helps to optimize scheduling strategies, reduce the uncertainty risks brought by new energy access, and improve the stability and safety of the power system.

[0024] S2, performing interference with the power system using a first interference signal to obtain an interference path set, and screening the interference path set according to a cross-regional graph model of the power system combined with a safety check of device N-1 to obtain a first screened path set; In this example, the first interference signal is interfered with the power system to obtain an interference path set, which is specifically: Performing a first interference signal interference on the power system and collecting power fluctuation data; Calculate the power fluctuation amount of the node based on the power fluctuation data; Calculate the disturbance value of each line in the power system based on the power fluctuation amount and power flow sensitivity matrix of the node; The disturbance value of each line in the power system is compared with a preset first threshold value to screen out a set of interference paths.

[0025] Among them, the disturbance value of each line in the power system is calculated based on the power fluctuation amount of the node and the power flow sensitivity matrix. The specific calculation formula is as follows:

[0026] in, is the disturbance value of line l in the power system, is the power sensitivity of line l to node i in the power flow sensitivity matrix, is the power fluctuation of the node, is the total number of power system nodes.

[0027] It should be noted that by applying the first interference signal to the power system and collecting power fluctuation data in real time, the dynamic response characteristics of the power grid under external disturbances can be fully captured, providing real operating data support for subsequent risk analysis. Secondly, based on the collected power fluctuation data, the power fluctuation amount of each node is calculated, so as to quantify the sensitivity of different nodes to external disturbances, so that key areas and vulnerable nodes can be effectively identified. In addition, combined with the power flow sensitivity matrix, the disturbance value of each line in the power system is further calculated, so that the impact of new energy fluctuations or external disturbances on the system power flow can be accurately evaluated to ensure the comprehensiveness of risk analysis. Finally, the calculated disturbance value is compared and screened with the preset first threshold to ensure that only the interference paths that may really affect the safe operation of the power grid are identified, avoiding misjudgment due to data noise or small fluctuations, and improving the reliability and practicality of the screening results. This method can not only effectively identify key lines that are greatly affected by the uncertainty of new energy, but also provide more targeted risk prevention and control measures for power dispatchers, improve the safety and stability of the system, and reduce the adverse effects of new energy access on the operation of the power grid, thereby ensuring the reliability and operation efficiency of the power system.

[0028] In this example, the interference path set is screened according to the cross-regional graph model of the power system combined with the safety check of equipment N-1 to obtain the first screened path set, which is specifically: The interference path set is converted into a set of edge sequences of a cross-regional graph model of the power system; Based on the depth-first algorithm, each path and all sub-paths corresponding to each path in the edge sequence set of the cross-regional graph model of the power system are traversed; All sub-paths corresponding to each path are screened based on the security check of device N-1 to obtain a first screened path set.

[0029] It should be noted that the interference path set is converted into an edge sequence set of the cross-regional graph model of the power system, so that the complex power grid topology can be intuitively expressed in the form of a graph model, providing a clearer path relationship for subsequent calculations. At the same time, this conversion method can effectively reduce computational redundancy and improve data processing efficiency. Secondly, based on the depth-first algorithm (DFS), the edge sequence set in the graph model is traversed to ensure that all possible paths and their corresponding sub-paths are fully searched to avoid missing potential risk paths due to insufficient screening range. DFS has high search efficiency, can quickly lock the key path, and reduce the computational complexity. In addition, all sub-paths corresponding to each path need to undergo N-1 safety verification, that is, disconnecting a single device in the system (such as a transmission line or transformer) in turn, simulating the changes in the system flow after its failure, and ensuring that the remaining network can still maintain stable operation. This process can effectively screen out paths that may cause unsafe operation of the power grid under N-1 fault conditions, thereby obtaining a more risk-oriented first screening path set. This method not only improves the accuracy of power system safety verification and avoids unnecessary interference path intervention, but also helps power dispatchers accurately identify high-risk areas, provides strong data support for grid optimization and fault prevention, and ultimately improves the stability and safety of the power system.

[0030] S3, filtering the first screening path set according to the first change value to obtain a cross-regional over-limit route set; In this example, the first screening path set is screened according to the first change value to obtain a cross-regional over-limit route set, which is specifically: Calculating the power flow value of each first screening path in the first screening path set; Based on the power flow sensitivity matrix, the sensitivity coefficient of each first screening path to the fluctuation of renewable energy output is calculated; Correcting the sensitivity coefficient of each first screening path to the fluctuation of the new energy output according to the first change value to obtain the maximum additional power flow change; The power flow value of each first screening path is superimposed with the maximum additional power flow change to obtain the first maximum power flow value of the first screening path; The first maximum power flow value of the first screening path is compared and analyzed with the preset safe operation limit to screen out the cross-regional over-limit line set.

[0031] It should be noted that the power flow value of each screening path is calculated to obtain the load level of the power grid under the current working conditions, providing benchmark data for changes that may be caused by fluctuations in new energy sources. Subsequently, the power flow sensitivity matrix is ​​used to calculate the degree of influence of fluctuations in new energy output on each screening path, avoiding the misjudgment problem caused by ignoring the sensitivity of local nodes in traditional methods. Next, the sensitivity coefficient is corrected according to the first change value to quantify the worst case that may be caused by fluctuations in new energy sources, ensuring that the calculation results can cover extreme situations, thereby effectively making up for the shortcomings of traditional static assessment methods in dealing with the randomness of new energy sources. Then, the power flow value is superimposed with the maximum additional power flow change to calculate the first maximum power flow value, and the possible impact of new energy fluctuations is evaluated from a dynamic perspective, avoiding the limitations of relying solely on a single working condition analysis. Finally, by comparing and analyzing the first maximum power flow value with the safe operation limit, high-risk cross-regional over-limit lines are accurately screened, so that the potential risks caused by fluctuations in new energy sources are quantitatively evaluated, overcoming the problem that traditional methods rely on empirical judgment and lack data support. This method not only improves the accuracy of risk assessment, but also provides a scientific basis for power grid operation and dispatching, optimizes the strategy for connecting new energy to the grid, and improves the safety and stability of the power system under high-proportion new energy access.

[0032] S4, analyze the risk probability of the cross-regional over-limit line set and output the probabilistic risk assessment level.

[0033] In this example, the risk probability of the cross-regional over-limit line set is analyzed, and the probabilistic risk assessment level is output, specifically: Obtain the frequency of each cross-regional over-limit line triggering over-limit under the new energy scenario set and calculate the over-limit probability of each cross-regional over-limit line; Calculate the power flow exceeding the limit for each cross-regional over-limit line under the new energy scenario set, and calculate the expected severity of each cross-regional over-limit line based on the power flow exceeding the limit; Multiply the crossing probability and expected severity of each cross-region crossing route to obtain the risk probability value of each cross-region crossing route; Calculate the mean and standard deviation of the probability risk of all cross-regional over-limit lines in the power system; The ratio of the mean value and standard deviation of probability risk is taken as the comprehensive risk value of the power system; The comprehensive risk value of the power system is compared and analyzed with the preset second threshold to construct a probabilistic risk assessment level of the power system.

[0034] It should be noted that by systematically analyzing the risk probability of the cross-regional over-limit line set and outputting the probabilistic risk assessment level, the accuracy of the comprehensive risk assessment of the power system caused by the fluctuation of new energy sources has been significantly improved, and the problem that the traditional method cannot accurately quantify the risks brought by the fluctuation of new energy sources has been solved. First, the frequency of each cross-regional over-limit line triggering the over-limit under the new energy scenario set is obtained, and its over-limit probability is calculated. This process can dynamically reflect the risks of the line under different scenarios based on the actual fluctuation data under various new energy scenarios, avoiding the static analysis of a single scenario usually adopted by the traditional method, which cannot cover the uncertainty brought by the fluctuation of new energy sources. Then, by calculating the over-limit amount of the flow of each cross-regional over-limit line and quantifying its expected severity based on this, a quantitative basis is provided for the size of the risk that each line may cause, thereby ensuring that the risk assessment results include not only the probability of occurrence, but also the degree of possible impact, avoiding the one-sidedness of pure probability analysis.

[0035] Then, the probability of crossing the limit and the expected severity are multiplied to obtain the risk probability value of each line, which further refines the risk level of each line in different scenarios. By calculating the average and standard deviation of the probability risk of all cross-regional crossing lines and using their ratio as the comprehensive risk value of the power system, the risk situation of the power system can be evaluated globally, providing more comprehensive and reliable risk analysis results. Finally, by comparing and analyzing with the preset second threshold, the probability risk assessment level of the power system is constructed, which effectively distinguishes high, medium and low risk areas, and provides accurate support for grid dispatching and operation decisions. The advantage of this method is that it integrates multiple risk factors brought about by the uncertainty of new energy, can accurately quantify and dynamically adjust the risk assessment results, significantly improves the ability of the power system to cope with new energy fluctuations, and makes up for the shortcomings of traditional methods in risk assessment of new energy fluctuations.

[0036] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0037] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0038] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0039] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0040] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0041] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A probabilistic risk assessment method for power systems taking into account the uncertainty of renewable energy, characterized in that: The following steps are involved: Constructing a cross-regional graph model of the power system, and analyzing the cross-regional power flow changes to obtain a first change value; Performing a first interference signal interference on the power system to obtain an interference path set, and screening the interference path set according to a cross-regional graph model of the power system combined with a safety check of equipment N-1 to obtain a first screening path set; The first screening path set is screened according to the first change value to obtain a cross-regional over-limit route set; Analyze the risk probability of cross-regional over-limit line sets and output the probabilistic risk assessment level.

2. The method for probabilistic risk assessment of a power system taking into account the uncertainty of new energy sources according to claim 1 is characterized in that: The constructing of the cross-regional graph model of the power system and analyzing the cross-regional power flow changes to obtain the first change value is specifically as follows: Divide the power system into several sub-regions and construct a cross-regional graph model of the power system; Construct a power flow sensitivity matrix under a preset set of new energy scenarios; Based on the power flow sensitivity matrix, calculate the interconnection line power flow change value in each new energy scenario; The maximum absolute value of the interconnection line power flow change value in all new energy scenarios is taken as the first change value.

3. The method for probabilistic risk assessment of a power system taking into account the uncertainty of new energy sources according to claim 2 is characterized in that: The power flow sensitivity matrix is ​​constructed under the preset new energy scenario set, specifically: Construct a probability distribution model based on the historical output data of new energy stations; Based on the probability distribution model, a generative adversarial network is used to obtain a set of new energy scenarios and calculate the scenario probability density and contribution weight of each new energy scenario. Calculate the power flow sensitivity of the new energy nodes in each new energy scenario to obtain the sensitivity matrix of the new energy nodes in each new energy scenario; The sensitivity matrix is ​​weighted and fused based on the probability density of new energy scenarios and the contribution weights of new energy scenarios to obtain the power flow sensitivity matrix.

4. The method for probabilistic risk assessment of a power system taking into account the uncertainty of new energy sources according to claim 3 is characterized in that: The first interference signal is interfered with the power system to obtain an interference path set, specifically: Performing a first interference signal interference on the power system and collecting power fluctuation data; Calculate the power fluctuation amount of the node based on the power fluctuation data; Calculate the disturbance value of each line in the power system based on the power fluctuation amount and power flow sensitivity matrix of the node; The disturbance value of each line in the power system is compared with a preset first threshold value to screen out a set of interference paths.

5. The method for probabilistic risk assessment of a power system taking into account the uncertainty of new energy sources according to claim 4 is characterized in that: The interference path set is screened according to the cross-regional graph model of the power system combined with the safety check of the device N-1 to obtain the first screened path set, which is specifically: The interference path set is converted into a set of edge sequences of a cross-regional graph model of the power system; Based on the depth-first algorithm, each path and all sub-paths corresponding to each path in the edge sequence set of the cross-regional graph model of the power system are traversed; All sub-paths corresponding to each path are screened based on the security check of device N-1 to obtain a first screened path set.

6. The method for probabilistic risk assessment of a power system taking into account the uncertainty of new energy sources according to claim 5 is characterized in that: The method filters all sub-paths corresponding to each path based on the security check of device N-1 to obtain a first filtered path set, which is specifically: Disconnect each edge in the current subpath in turn, simulate triggering N-1 faults, and update the topological connection state of the power system graph model to obtain an updated cross-regional graph model of the power system; Based on the power flow sensitivity matrix, the power flow distribution value of the updated cross-regional graph model of the power system is calculated to detect whether the edges connecting different regions have power flow exceeding the limit; If it is detected that the power flow of the cross-region edge exceeds the limit, the cross-region edge is added to the first screening path set; All candidate crossing-limit routes are merged and repeated candidate crossing-limit routes are eliminated to obtain the first screening path set.

7. The method for probabilistic risk assessment of a power system taking into account the uncertainty of new energy sources according to claim 6 is characterized in that: The first filtering path set is filtered according to the first change value to obtain the cross-regional limit-crossing route set, specifically: Calculating the power flow value of each first screening path in the first screening path set; Based on the power flow sensitivity matrix, the sensitivity coefficient of each first screening path to the fluctuation of renewable energy output is calculated; Correcting the sensitivity coefficient of each first screening path to the fluctuation of the new energy output according to the first change value to obtain the maximum additional power flow change; The power flow value of each first screening path is superimposed with the maximum additional power flow change to obtain the first maximum power flow value of the first screening path; The first maximum power flow value of the first screening path is compared and analyzed with the preset safe operation limit to screen out the cross-regional over-limit line set.

8. The method for probabilistic risk assessment of a power system taking into account the uncertainty of new energy sources according to claim 7 is characterized in that: The risk probability of the cross-regional over-limit line set is analyzed and the probability risk assessment level is output, specifically: Obtain the frequency of each cross-regional over-limit line triggering over-limit under the new energy scenario set and calculate the over-limit probability of each cross-regional over-limit line; Calculate the power flow exceeding the limit for each cross-regional over-limit line under the new energy scenario set, and calculate the expected severity of each cross-regional over-limit line based on the power flow exceeding the limit; Multiply the crossing probability and expected severity of each cross-region crossing route to obtain the risk probability value of each cross-region crossing route; Calculate the mean and standard deviation of the probability risk of all cross-regional over-limit lines in the power system; The ratio of the mean value and standard deviation of probability risk is taken as the comprehensive risk value of the power system; The comprehensive risk value of the power system is compared and analyzed with the preset second threshold to construct a probabilistic risk assessment level of the power system.

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