Probabilistic Risk Assessment Method for Power Systems Considering the Uncertainty of New Energy

Abstract

The present invention discloses a probabilistic risk assessment method for a power system considering the uncertainty of new energy, which relates to the technical field of risk assessment and includes the following steps: constructing a cross-regional graph model of the power system, analyzing the cross-regional power flow changes, and obtaining a first change value; interfering with the power system with a first interference signal 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 the equipment N-1 security check 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, solving the problem that the traditional method cannot accurately quantify the comprehensive risk caused by new energy fluctuations.

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 power systems considering the uncertainty of new energy. Background Art

[0002] With the transformation of the global energy structure towards low-carbon and clean energy, the penetration rate of new energy (such as wind power, photovoltaic power, etc.) in the power system is continuously increasing. However, new energy has significant randomness, intermittency, and uncertainty, and its output is greatly affected by meteorological conditions, bringing many challenges to the stable operation of the power grid.

[0003] For example, a method for assessing the security risk of a power system considering the uncertainty of new energy disclosed in the invention patent announcement with the publication number CN112036718B discloses a method for assessing the security risk of a power system considering the uncertainty of new energy. For multiple possible scenarios generated by sampling the uncertain variables of new energy, scenario clustering is performed based on the distance of the uncertain variables weighted by the security and stability impact factors of new energy power plants and stations, which can largely avoid the possibility of "risk leakage" caused by missing small-probability high-risk scenarios and other key scenarios; for each class of scenario subsets, the maximum and minimum operating scenarios are determined respectively according to the magnitude of the impact of new energy uncertain variables on security and stability, and the maximum and minimum values of the security and stability operation risks can be accurately evaluated. The present invention realizes the online assessment of the security and stability operation risk of a power system considering the uncertainty of new energy prediction, and can meet the requirements of calculation speed and accuracy.

[0004] For example, a method and system for assessing the operation risk of a distribution transformer based on the correlation between wind power and load disclosed in the invention patent announcement with the publication number CN116050838B. With the continuous increase in the penetration rate of new energy in the new power system, the uncertainty of wind power output and its correlation with the load have become increasingly prominent, and the traditional deterministic transformer risk assessment can no longer meet the existing requirements. The present invention discloses a joint probability prediction method for the operation risk of a transformer considering the correlation between wind power and load. Based on the Copula function and the Susa model, indicators such as the probability of insulation deterioration are calculated, and the Monte Carlo method is used to calculate the values of each risk indicator and evaluate the operation risk of the transformer. The present invention introduces the uncertain factors of wind power and load fluctuations brought by the access of new energy on the basis of traditional equipment assessment, and considers the correlation between regional wind power and load, greatly improving the accuracy of the assessment of the operation risk level of transformers in the new power system.

[0005] In the above disclosed technical solutions, there are at least the following technical problems:

[0006] In traditional methods, the correlation analysis based on the fixed sensitivity direction ignores the polarity reversal of the path sensitivity in the wind-solar complementary scenario, and the static N-1 check is difficult to capture low-probability - high-loss cross-region cascading over-limit events, resulting in the inability to accurately quantify the comprehensive risk caused by new energy fluctuations. To address the above problems, the present invention proposes a solution. Summary of the Invention

[0007] To overcome the above defects of the prior art, an embodiment of the present invention provides a probabilistic risk assessment method for a power system considering the uncertainty of new energy. By analyzing the power flow changes, the interference paths are screened, and the over-limit risk probability is evaluated to solve the problem that the traditional method cannot accurately quantify the comprehensive risk caused by new energy fluctuations.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A probabilistic risk assessment method for a power system considering the uncertainty of new energy includes the following steps: constructing a cross-region graph model of the power system, analyzing the cross-region power flow changes to obtain a first change value; interfering with the power system with a first interference signal to obtain a set of interference paths, and screening the set of interference paths according to the cross-region graph model of the power system combined with the N-1 security check of equipment to obtain a first screened path set; screening the first screened path set according to the first change value to obtain a cross-region over-limit line set; analyzing the risk probability of the cross-region over-limit line set and outputting the probabilistic risk assessment level.

[0010] In a preferred embodiment, the constructing a cross-region graph model of the power system, analyzing the cross-region power flow changes to obtain a first change value is specifically: dividing the power system into several sub-regions and constructing a cross-region graph model of the power system; constructing a power flow sensitivity matrix under a preset new energy scenario set; based on the power flow sensitivity matrix, calculating the tie-line power flow change value in each new energy scenario; taking the maximum absolute value of the tie-line power flow change values in all new energy scenarios as the first change value.

[0011] In a preferred embodiment, the constructing a power flow sensitivity matrix under a preset new energy scenario set is specifically: constructing a probability distribution model based on the historical output data of new energy power stations; based on the probability distribution model, using a generative adversarial network to obtain a new energy scenario set and calculating the scenario probability density and the contribution weight of each new energy scenario; calculating the power flow sensitivity of the new energy nodes under each new energy scenario to obtain the sensitivity matrix of the new energy nodes in each new energy scenario; weighting and fusing the sensitivity matrix based on the new energy scenario probability density and the contribution weight of the new energy scenario to obtain the power flow sensitivity matrix.

[0012] In a preferred embodiment, the first interference signal is used to interfere with the power system to obtain an interference path set, specifically: the power system is interfered with by the first interference signal, and the power fluctuation data is collected; the power fluctuation amount of the node is calculated based on the power fluctuation data; the perturbation 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 perturbation value of each line in the power system is compared with a preset first threshold value to screen out the interference path set;

[0013] In a preferred embodiment, the interference path set is screened according to the power system cross-region graph model combined with the equipment N-1 security check to obtain a first screened path set, specifically: the interference path set is converted into an edge sequence set of the power system cross-region graph model; based on the depth-first algorithm, each path and all sub-paths corresponding to each path in the edge sequence set of the power system cross-region graph model are traversed; all sub-paths corresponding to each path are screened based on the equipment N-1 security check to obtain a first screened path set.

[0014] In a preferred embodiment, the screening of all sub-paths corresponding to each path based on the equipment N-1 security check to obtain a first screened path set is specifically: each edge in the current sub-path is disconnected in turn to simulate the triggering of an N-1 fault, and the topological connection state of the power system graph model is updated to obtain an updated power system cross-region graph model; the power flow distribution value of the updated power system cross-region graph model is calculated based on the power flow sensitivity matrix, and it is detected whether the power flow of the edge connecting different regions exceeds the limit; if the power flow of the cross-region edge is detected to exceed the limit, the edge is added to the first screened path set; all candidate over-limit lines are merged, and the first screened path set is obtained after removing the duplicate candidate over-limit lines.

[0015] In a preferred embodiment, the first screened path set is screened according to the first change value to obtain a cross-region over-limit line set, specifically: the power flow value of each first screened path in the first screened path set is calculated; based on the power flow sensitivity matrix, the sensitivity coefficient of each first screened path to the new energy output fluctuation is calculated; the sensitivity coefficient of each first screened path to the new energy output fluctuation is corrected according to the first change value to obtain the maximum additional power flow change amount; the power flow value of each first screened path is superimposed with the maximum additional power flow change amount to obtain the first maximum power flow value of the first screened path; the first maximum power flow value of the first screened path is compared and analyzed with a preset safe operation limit value to screen out the cross-region over-limit line set.

[0016] In a preferred embodiment, the risk probability of the analyzed cross - regional over - limit line set is calculated and the probability 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 power 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 power 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 probability risk average value and the standard deviation of all cross - regional over - limit lines in the power system are calculated; the ratio of the probability risk average value to the 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 a preset second threshold to construct the probability risk assessment level of the power system.

[0017] Technical effects and advantages of the power system probability risk assessment method considering new - energy uncertainty of the present invention:

[0018] 1. The power system of the present invention is accurately divided into multiple sub - regions and the power connection between different regions is visually presented in the form of a cross - regional graph model, thus improving the operability and calculation efficiency of power flow analysis. In addition, the generative adversarial network method based on the probability distribution of new - energy output is adopted, making the constructed new - energy scenario set more in line with the actual operation situation and enhancing the adaptability of power flow analysis. The finally calculated first change value fully considers the uncertainty of new - energy output, providing a reasonable reference benchmark for subsequent risk assessment. This step ensures a comprehensive assessment of the impact of new - energy output fluctuations on the power flow of the cross - regional power system, improving the scientificity and accuracy of risk analysis;

[0019] 2. By calculating the sensitivity coefficient of the power flow value and the impact of new - energy fluctuations, the present invention ensures a quantitative assessment of the impact of new - energy uncertainty on the power flow distribution of the power grid. Through the correction calculation of the maximum additional power flow change amount, the selected over - limit lines are more in line with the actual operation 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 with the safe operation limit value, providing a scientific basis for subsequent risk probability analysis. Finally, this step improves the accuracy of over - limit line screening, ensures the safety and stability of the power system operation, and improves the reliability of risk assessment. Brief Description of the Drawings

[0020] Figure 1 It is a schematic flow chart of the power system probability risk assessment method considering new - energy uncertainty of the present invention. Detailed Embodiment

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1 Figure 1 A probabilistic risk assessment method for a power system considering the uncertainty of new energy in the present invention is given, including the following steps:

[0023] S1. Construct a cross-regional graph model of the power system, analyze the cross-regional power flow changes, and obtain the first change value;

[0024] In this example, constructing a cross-regional graph model of the power system and analyzing the cross-regional power flow changes to obtain the first change value are specifically as follows:

[0025] Divide the power system into several sub-regions and construct a cross-regional graph model of the power system;

[0026] Construct a power flow sensitivity matrix under a preset new energy scenario set;

[0027] Based on the power flow sensitivity matrix, calculate the tie-line power flow change value in each new energy scenario;

[0028] Take the maximum absolute value of the tie-line power flow change values in all new energy scenarios as the first change value.

[0029] In this example, constructing a power flow sensitivity matrix under a preset new energy scenario set is specifically as follows:

[0030] Construct a probability distribution model based on the historical output data of new energy power stations;

[0031] Based on the probability distribution model, use a generative adversarial network to obtain a new energy scenario set and calculate the scenario probability density and contribution weight of each new energy scenario;

[0032] Calculate the power flow sensitivity of the new energy nodes under each new energy scenario to obtain the sensitivity matrix of the new energy nodes in each new energy scenario;

[0033] Weight and fuse the sensitivity matrix based on the new energy scenario probability density and the contribution weight of the new energy scenario to obtain a power flow sensitivity matrix.

[0034] Among them, weighting and fusing the sensitivity matrix based on the new energy scenario probability density and the contribution weight of the new energy scenario to obtain a power flow sensitivity matrix, the specific calculation formula is as follows:

[0035] The calculation formula for the contribution weight of the new energy scenario is as follows:

[0036]

[0037] The calculation formula for the power flow sensitivity matrix is as follows:

[0038]

[0039] Where, is the m-th new energy scenario, is the contribution weight of the m-th new energy scenario, is the probability density value of the m-th new energy field, is the probability density value of the j-th new energy field, is the total number of new energy scenarios in the new energy scenario group, is the sensitivity matrix, is the power flow sensitivity matrix.

[0040] 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 ability 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, which reasonably simplifies the complexity of the system and at the same time ensures that the power interconnection relationship between regions is clearly expressed, contributing to the calculation efficiency and accuracy of subsequent power flow analysis. Second, a power flow sensitivity matrix is constructed under the preset new energy scenario set, fully considering the power flow distribution characteristics under different new energy output conditions, and using sensitivity analysis to quantify the impact of new energy fluctuations on the power system power flow, thus providing accurate basic data for further risk assessment. In addition, based on the power flow sensitivity matrix, the power flow change value of the tie line in each new energy scenario is calculated, so that the power flow fluctuations under different scenarios are quantitatively presented, providing a more targeted assessment method for grid operation. Finally, the maximum absolute value of the power flow change values of the tie lines in all new energy scenarios is used as the first change value, effectively capturing the most extreme impact of new energy uncertainty on the grid power flow, thus ensuring the rigor and safety of subsequent risk assessment. Through this method, grid operators can more accurately identify high-risk areas, formulate targeted control strategies in advance, reduce the operation risks brought by new energy access, and ensure the stable operation of the grid.

[0041] In addition, a probability distribution model is constructed based on the historical output data of new energy power stations, enabling the quantification of the volatility of new energy output, 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 traditional random sampling methods, GAN can more realistically simulate the complex changes in new energy output, improve the representativeness and diversity of scenarios, and avoid potential bias problems in 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, enabling the precise characterization of the impact of new energy fluctuations on the power grid flow and providing fine-grained data support for subsequent security assessments. Finally, by weighted fusion of the sensitivity matrix based on the probability density and contribution weight of new energy scenarios, a 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 the power grid flow, provides more reliable data support for power grid operators, helps optimize dispatching strategies, reduces the uncertainty risks brought by new energy access, and enhances the stability and security of the power system.

[0042] S2, interfere with the power system with a first interference signal to obtain a set of interference paths, and screen the set of interference paths according to the cross-regional graph model of the power system combined with the equipment N-1 security check to obtain a first screened path set;

[0043] In this example, interfering with the power system with a first interference signal to obtain a set of interference paths is specifically as follows:

[0044] Interfere with the power system with a first interference signal and collect power fluctuation data;

[0045] Calculate the power fluctuation amount of the node based on the power fluctuation data;

[0046] Calculate the perturbation value of each line in the power system based on the power fluctuation amount of the node and the power flow sensitivity matrix;

[0047] Compare the perturbation value of each line in the power system with a preset first threshold to screen out the set of interference paths.

[0048] Among them, calculating the perturbation value of each line in the power system based on the power fluctuation amount of the node and the power flow sensitivity matrix, the specific calculation formula is as follows:

[0049]

[0050] Among them, is the perturbation value of line l in the power system, is the power sensitivity of line l in the power flow sensitivity matrix to node i, is the power fluctuation amount of the node, is the total number of nodes in the power system.

[0051] It should be noted that by applying the first interference signal to the power system and collecting the power fluctuation data in real time, the dynamic response characteristics of the power grid under external disturbances can be comprehensively captured, providing real operating data support for subsequent risk analysis. Secondly, based on the collected power fluctuation data, the power fluctuation amounts of each node are calculated, so as to quantify the sensitivity of different nodes to external disturbances, enabling key regions and vulnerable nodes to be effectively identified. In addition, combined with the power flow sensitivity matrix, the disturbance values of each line in the power system are further calculated, enabling the impact of new energy fluctuations or external disturbances on the system power flow to be accurately evaluated, ensuring the comprehensiveness of risk analysis. Finally, the calculated disturbance values are compared and screened with the preset first threshold to ensure that only the interference paths that may truly affect the safe operation of the power grid are identified, avoiding misjudgment caused by data noise or small fluctuations, and improving the reliability and practicability of the screening results. This method can not only effectively identify the key lines greatly affected by the uncertainty of new energy, but also provide more targeted risk prevention and control measures for power dispatching personnel, improve the safety and stability of the system, reduce the adverse impact of new energy access on the operation of the power grid, and thus ensure the reliability and operation efficiency of the power system.

[0052] In this example, according to the power system cross-regional graph model combined with the equipment N-1 security check, the interference path set is screened to obtain the first screened path set, specifically:

[0053] Convert the interference path set into a set of edge sequences of the power system cross-regional graph model;

[0054] Based on the depth-first algorithm, traverse each path and all sub-paths corresponding to each path in the set of edge sequences of the power system cross-regional graph model;

[0055] Screen all sub-paths corresponding to each path based on the equipment N-1 security check to obtain the first screened path set.

[0056] It should be noted that converting the interference path set into a set of edge sequences of the power system's cross-regional graph model enables the intuitive expression of the complex power grid topology 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 search (DFS) algorithm, traverse the set of edge sequences in the graph model to ensure that all possible paths and their corresponding sub-paths are fully searched, avoiding the omission of potential risk paths due to insufficient screening scope. DFS has a high search efficiency and can quickly lock in the critical paths, reducing the computational complexity. In addition, all sub-paths corresponding to each path need to undergo N-1 security verification, that is, sequentially disconnect a single device (such as a transmission line or transformer) in the system and simulate the change in the system power flow after its failure to ensure that the remaining network can still maintain stable operation. This process can effectively screen out the paths that may lead to the unsafe operation of the power grid under N-1 fault conditions, thereby obtaining a first screening path set with stronger risk directivity. This method not only improves the accuracy of power system security verification, avoids unnecessary interference path interventions, but also helps power dispatchers accurately identify high-risk areas, providing strong data support for the optimal operation and fault prevention of the power grid, and ultimately enhancing the stability and security of the power system.

[0057] S3. Screen the first screening path set according to the first change value to obtain a cross-regional over-limit line set;

[0058] In this example, screening the first screening path set according to the first change value to obtain a cross-regional over-limit line set is specifically as follows:

[0059] Calculate the power flow value of each first screening path in the first screening path set;

[0060] Based on the power flow sensitivity matrix, calculate the sensitivity coefficient of each first screening path to the new energy output fluctuation;

[0061] According to the first change value, correct the sensitivity coefficient of each first screening path to the new energy output fluctuation to obtain the maximum additional power flow change;

[0062] Superimpose the power flow value of each first screening path and the maximum additional power flow change to obtain the first maximum power flow value of the first screening path;

[0063] Compare and analyze the first maximum power flow value of the first screening path with the preset safe operation limit value to screen out the cross-regional over-limit line set.

[0064] 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 operating condition, providing benchmark data for the changes that may be caused by the fluctuations of new energy. Subsequently, the influence degree of the new energy output fluctuation on each screening path is calculated by using the power flow sensitivity matrix, avoiding the misjudgment problem caused by the traditional method ignoring the sensitivity of local nodes. Then, the sensitivity coefficient is corrected according to the first change value to quantify the worst case that may be caused by the new energy fluctuation, ensuring that the calculation results can cover extreme situations, thus effectively making up for the deficiencies of the traditional static assessment method in dealing with the randomness of new energy. Then, the power flow value is superimposed with the maximum additional power flow change amount to calculate the first maximum power flow value, evaluating the possible impact of new energy fluctuation from a dynamic perspective and avoiding the limitations brought by relying solely on the analysis of a single operating condition. Finally, by comparing and analyzing the first maximum power flow value with the safe operating limit, the high-risk cross-regional over-limit lines are accurately screened out, enabling the potential risks caused by new energy fluctuation to be quantitatively evaluated and overcoming the problems of the traditional method relying on empirical judgment and lacking 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 new energy grid connection strategy, and improves the safety and stability of the power system under the condition of high proportion of new energy access.

[0065] S4. Analyze the risk probability of the cross-regional over-limit line set and output the probability risk assessment level.

[0066] In this example, analyze the risk probability of the cross-regional over-limit line set and output the probability risk assessment level, specifically:

[0067] 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;

[0068] Calculate the power flow over-limit amount of 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 over-limit amount;

[0069] Multiply the over-limit probability and the expected severity of each cross-regional over-limit line to obtain the risk probability value of each cross-regional over-limit line;

[0070] Calculate the probability risk average value and standard deviation of all cross-regional over-limit lines in the power system;

[0071] Take the ratio of the probability risk average value and the standard deviation as the comprehensive risk value of the power system;

[0072] Compare and analyze the comprehensive risk value of the power system with the preset second threshold to construct the probability risk assessment level of the power system.

[0073] It should be noted that by systematically analyzing the risk probability of the cross-regional over-limit line set and outputting the probability risk assessment level, the accuracy of the comprehensive risk assessment of the power system caused by new energy fluctuations has been significantly improved, and the problem that the traditional method cannot accurately quantify the risks brought by new energy fluctuations has been solved. First, obtain the frequency of each cross-regional over-limit line triggering over-limit under the new energy scenario set, and calculate its over-limit probability. This process can dynamically reflect the risks of lines 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 uncertainties brought by new energy fluctuations. Then, by calculating the power flow over-limit amount of each cross-regional over-limit line and quantifying its expected severity based on this, a quantitative basis is provided for the risk magnitude that each line may cause, so as to ensure that the risk assessment results include not only the occurrence probability but also the possible impact degree, avoiding the one-sidedness of simple probability analysis.

[0074] Then, multiply the over-limit probability and the expected severity to obtain the risk probability value of each line, further refining the risk level of each line under different scenarios. By calculating the average value and standard deviation of the probability risks of all cross-regional over-limit lines and taking their ratio as the comprehensive risk value of the power system, the risk situation of the power system can be globally evaluated, providing a more comprehensive and reliable risk analysis result. Finally, through comparative analysis with a preset second threshold, a probability risk assessment level of the power system is constructed, effectively distinguishing high, medium, and low-risk regions, and providing accurate support for power grid dispatching and operation decision-making. The advantage of this method is that it comprehensively considers multiple risk factors brought by new energy uncertainties, 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 deficiencies of the traditional method in the risk assessment of new energy fluctuations.

[0075] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data and performing software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0076] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0077] Those of ordinary skill in the art will realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0078] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0079] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0080] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within 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: 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 change value of the interconnection line power flow 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; 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; 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; Compare and analyze the first maximum power flow value of the first screening path with the preset safe operation limit value to screen out the cross-regional over-limit line 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 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.

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 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.

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 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.

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 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.

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 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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