Power distribution network overvoltage probability risk assessment method based on voltage sensitivity analysis

Abstract

The invention discloses a power distribution network overvoltage probability risk assessment method based on voltage sensitivity analysis, and relates to the technical field of power distribution network overvoltage probability risk assessment. Constructing a voltage sensitivity model of the node voltage relative to the distributed power supply and the load injection power; constructing a probability model of node voltage fluctuation by using the voltage sensitivity matrix, and assuming that the output and load of the distributed power supply and the prediction error obey multivariate normal distribution; and constructing a normal distribution expression of node voltage deviation in combination with the current node operation voltage and the system reference voltage, calculating probability distribution of node voltage out-of-limit, and realizing overvoltage probability risk assessment of each node in the power distribution network according to the node voltage out-of-limit probability. The method has better effects in the aspects of evaluation efficiency, model precision and engineering suitability.

Description

Technical Field

[0001] The present invention relates to the technical field of overvoltage probability risk assessment of distribution networks, and specifically to a method for overvoltage probability risk assessment of distribution networks based on voltage sensitivity analysis. Background Art

[0002] With the continuous increase in the penetration rate of renewable energy distributed generations (DGs) in distribution networks (DNs), the randomness of distribution network operation has increased, which has increased the difficulty and complexity of operation control and posed challenges to the safe operation of distribution networks. Especially the strong fluctuation characteristics of distributed generation output cause voltage fluctuation problems in distribution networks, affecting the normal operation of distribution networks and reducing the economic benefits and operation efficiency of distributed generations. Therefore, it is necessary to quantify the impact risk of uncertainty sources on the voltage operation of distribution networks, provide decision-making support for distribution network supervision, and improve the ability of distribution networks to cope with the operation risks brought by uncertainty sources. At present, the research on overvoltage risk caused by the uncertainty of power sources and loads mainly adopts methods based on stochastic power flow calculation and voltage sensitivity analysis. The method of using stochastic power flow to calculate overvoltage risk is mainly based on Monte Carlo simulation (MCS) and its evolution and improvement of probability analysis methods. Such methods have high accuracy, but with the increase in the number of samples, the calculation efficiency is low. Currently, there is an analysis method based on voltage sensitivity that calculates the Jacobian voltage sensitivity matrix through power flow, and then maps the probability modeling of uncertainty sources to the probability distribution of voltage. The overvoltage model established by this method depends on the Jacobian matrix of power flow calculation, and the calculation complexity is low. The Jacobian matrix is obtained through numerical calculation, and its elements cannot reflect the relationship between node power and node voltage from an analytical perspective. In addition, the voltage analysis method based on the Jacobian voltage sensitivity matrix can only reflect the impact of the change in single-node power on node voltage, and cannot demonstrate the impact of all uncertainty sources in the distribution network on node voltage. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: the existing voltage risk assessment methods rely on Monte Carlo simulation and have low calculation efficiency; the sensitivity model based on the Jacobian matrix lacks physical analytical expression ability and cannot describe the node voltage probability distribution under multi-source disturbances; and how to construct a general method for quantifying and assessing the probability of voltage violation in distribution networks without relying on a large number of power flow iterations.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis, including constructing a voltage relationship model between nodes in a radial distribution network based on DistFlow power flow, and constructing a voltage sensitivity model of the node voltage with respect to the injection power of distributed power sources and loads; using the voltage sensitivity matrix to construct a probability model of node voltage fluctuations, assuming the output of distributed power sources and loads, and predicting that the errors obey a multivariate normal distribution; combining the current node operating voltage and the system reference voltage, constructing a normal distribution expression of the node voltage deviation, calculating the probability distribution of node voltage over-limit, and realizing the overvoltage probability risk assessment of each node in the distribution network according to the node voltage over-limit probability.

[0006] As a preferred embodiment of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to the present invention, wherein: constructing the voltage relationship model between nodes in the radial distribution network includes that the voltage of each node is obtained by linearly combining the resistance and reactance parameters of the upstream branch and the active power and reactive power injected into the node, and the combination relationship is superimposed branch by branch based on the topological structure to form a voltage sensitivity coefficient matrix representing the mutual influence between nodes.

[0007] As a preferred embodiment of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to the present invention, wherein: each coefficient in the voltage sensitivity coefficient matrix is represented by the ratio between the resistance and reactance of the branch in the path from the node to the power source node and the system reference voltage, the active power sensitivity coefficient is proportional to the path resistance, and the reactive power sensitivity coefficient is proportional to the path reactance.

[0008] As a preferred embodiment of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to the present invention, wherein: constructing the voltage sensitivity model of the node voltage with respect to the injection power of distributed power sources and loads includes using multidimensional vector modeling, each dimension corresponding to the prediction error of a node, the error vector obeying a multivariate normal distribution, the mean vector and covariance matrix of the errors being obtained by statistical analysis of historical time series samples, and any element in the covariance matrix being composed of the covariance of the errors between related nodes.

[0009] As a preferred embodiment of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to the present invention, wherein: the prediction error obeying a multivariate normal distribution includes that the active power and reactive power respectively correspond to a group of sensitivity vectors, the prediction error is from a multivariate normal distribution, obtained from the statistical law of linear transformation, and the node voltage deviation obeys a normal distribution.

[0010] As a preferred embodiment of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to the present invention, wherein: the node voltage deviation consists of three parts, namely, the difference between the dispatching predicted voltage and the system reference voltage, the voltage change caused by the prediction error of distributed power sources, and the voltage change caused by the prediction error of loads.

[0011] As a preferred embodiment of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to the present invention, wherein: the evaluation of the overvoltage probability risk of each node in the distribution network according to the node voltage overlimit probability is obtained by calculating the complementary value of the cumulative probability function of the normal distribution of the node voltage deviation at the voltage upper limit. After normalizing the predetermined voltage upper limit value with the expected value and standard deviation of the node deviation, it is substituted into the standard normal distribution function for probability calculation, and the obtained probability value is used as the risk index of the node voltage overlimit.

[0012] Another object of the present invention is to provide a system for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis, which can establish a voltage sensitivity analysis model based on the DistFlow power flow equation, and solves the problem that the current voltage sensitivity analysis method based on the Jacobian matrix only relies on numerical differentiation and lacks a clear physical meaning.

[0013] As a preferred embodiment of the system for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to the present invention, wherein: it includes a voltage sensitivity modeling template, a fluctuation probability modeling template, and a risk assessment module; the voltage sensitivity modeling template is used to construct a voltage relationship model between nodes in a radial distribution network based on the DistFlow power flow, and construct a voltage sensitivity model of the node voltage with respect to the injection power of distributed power sources and loads; the fluctuation probability modeling template is used to construct a probability model of the node voltage fluctuation by using the voltage sensitivity matrix, assuming that the output of distributed power sources and loads and the prediction error follow a multivariate normal distribution; the risk assessment module is used to combine the current node operating voltage and the system reference voltage, construct a normal distribution expression of the node voltage deviation, calculate the probability distribution of the node voltage overlimit, and evaluate the overvoltage probability risk of each node in the distribution network according to the node voltage overlimit probability.

[0014] A computer device includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis.

[0015] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps of the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis.

[0016] Advantages of the present invention: The method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis provided by the present invention constructs a voltage sensitivity matrix based on network physical parameters and power injection data, and can establish an analytical expression of voltage change without relying on numerical differentiation or iteration. By expressing the voltage of each node as a linear combination of upstream branch parameters and injection power, and superimposing branch by branch based on the topological structure, a complete node voltage sensitivity coefficient matrix is formed, realizing the expansion of the sensitivity expression from local single-point analysis to the influence relationship of multiple nodes in the whole network, improving the analytical generality of sensitivity analysis and the path correlation control ability, and adapting to the sensitivity customization of different distribution network structures. By defining the voltage sensitivity coefficient as the ratio of the branch resistance and reactance in the path from the node to the power source to the system reference voltage, and distinguishing active sensitivity and reactive sensitivity, a clear division and parametric control of the physical source of node sensitivity are achieved. The present invention has achieved better effects in terms of evaluation efficiency, model accuracy and engineering adaptability. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0018] Figure 1 It is the overall flowchart of a method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis provided by the first embodiment of the present invention.

[0019] Figure 2 It is a typical radial DN topology diagram of a method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis provided by the first embodiment of the present invention.

[0020] Figure 3 It is the framework diagram of a method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis provided by the first embodiment of the present invention. Detailed Embodiments

[0021] To make the above objects, features and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all 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, refer to Figure 1, which is an embodiment of the present invention, provides a method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis, including:

[0023] S1: Based on the DistFlow power flow, construct a voltage relationship model between nodes in a radial distribution network, and construct a voltage sensitivity model of node voltage with respect to the injection power of distributed power sources and loads.

[0024] Furthermore, constructing a voltage relationship model between nodes in a radial distribution network includes that the voltage of each node is obtained by linearly combining the resistance and reactance parameters of the upstream branch and the active power and reactive power injected into the node. The combination relationship is superimposed branch by branch based on the topological structure to form a voltage sensitivity coefficient matrix representing the mutual influence between nodes.

[0025] Each coefficient in the voltage sensitivity coefficient matrix is represented by the ratio between the resistance and reactance of the branch in the path from the node to the power source node and the system base voltage. The active power sensitivity coefficient is proportional to the path resistance, and the reactive power sensitivity coefficient is proportional to the path reactance.

[0026] Constructing a voltage sensitivity model of node voltage with respect to the injection power of distributed power sources and loads includes using multi-dimensional vector modeling. Each dimension corresponds to the prediction error of a node. The error vector follows a multivariate normal distribution. The mean vector and covariance matrix of the errors are obtained by statistical analysis of historical time series samples. Any element in the covariance matrix is composed of the covariance of the errors between related nodes.

[0027] It should be noted that for a designed radial distribution network with n nodes, V0 is the voltage of the slack node. l ij represents the branch between nodes i and j, and its impedance value is r ij +jx ij . ε i and ε j respectively represent the sets of branches from nodes i and j to the power source node. If the direction of injecting active and reactive power into the node is defined as the positive direction of power, and the direction of power flowing out of the power node is defined as the positive topological reference direction. Then, the voltage relationship between nodes i and j based on the DistFlow power flow model can be described as:

[0028]

[0029] where P i and Q i are the active power and reactive power injected into node i, and Ω jrepresents the set of nodes downstream of node j. Considering that the voltage amplitude of each node in DN is close to V0, it can be assumed that the voltage of each node is approximately equal to V0. By generalizing (1) to all branches, the voltage sensitivity analysis model of the node voltage can be obtained:

[0030]

[0031] where R j,k and X j,k are the resistance and reactance at the intersection of the paths from node j and node k to the power node, respectively, and are the active power and reactive power injected into the DG connected to node k, respectively, and and are the active power and reactive power consumed by the load of node k respectively. From formula (2), we can see that It can be regarded as the active power P of node k k Voltage V at node j j The sensitivity of It can be regarded as the active power Q of node k k Voltage V at node j j Assuming that the power of the system node fluctuates, the voltage of node j after the corresponding change is recorded as V' j Therefore, the resulting voltage deviation is:

[0032]

[0033] in and are the active and reactive power fluctuations generated by the DG connected to node k, and and is the load active and reactive power fluctuation at node k. DG only injects active power into DN. If the load power factor remains unchanged, the active power fluctuation between DG and load can be considered to be the main cause of node voltage fluctuation. (3) can be further simplified as:

[0034]

[0035] Where λ = tanθ.

[0036] S2: The voltage sensitivity matrix is used to construct a probability model of node voltage fluctuation, assuming that the output and load of distributed generation are followed by a multivariate normal distribution.

[0037] Furthermore, the node voltage deviation consists of three parts, namely the difference between the dispatching predicted voltage and the system reference voltage, the voltage change caused by the distributed generation (DG) prediction error, and the voltage change caused by the load prediction error.

[0038] It should be noted that due to the high intermittency and random volatility of DG and load demand, there are certain errors between the predicted DG and load demand powers and the actual powers. It can be considered that the voltage fluctuations are caused by the power prediction errors of distributed generation and the load demand errors. Assume that the prediction errors of DG output and load demand follow a multivariate normal distribution, and the prediction errors of DG output and load demand follow a multivariate normal distribution:

[0039]

[0040] where E g and E c respectively represent the prediction errors of DG and load, μ g and μ c , Σ g and Σ c respectively represent the expected vector and covariance matrix of the prediction errors of DG and load. The formula for the covariance matrix of the prediction error is:

[0041]

[0042] where Var(·) and Cov(·) represent the variance and covariance calculation functions, and respectively represent the prediction errors of DG and load at node i. If the sample size of the prediction errors of DG and load is T, that is:

[0043]

[0044] where, and represent the k-th prediction error samples of DG and load. Then, the elements of the covariance matrix can be calculated by the following equation:

[0045]

[0046] According to the combination of linear multivariate normal distributions still follows a normal distribution:

[0047]

[0048] In the formula and They respectively represent the active voltage sensitivity matrix and the reactive voltage sensitivity matrix of node j. Assuming that the prediction errors of DG and load are independently distributed, (4) still follows a normal distribution. Then, the probability function of the node voltage fluctuation is as follows:

[0049]

[0050] S3: Combine the current operating voltage of the node with the system reference voltage, construct a normal distribution expression of the node voltage deviation, calculate the probability distribution of the node voltage exceeding the limit, and realize the overvoltage probability risk assessment of each node in the distribution network according to the node voltage exceeding the limit probability.

[0051] Furthermore, realizing the overvoltage probability risk assessment of each node in the distribution network according to the node voltage exceeding the limit probability includes obtaining it by calculating the complementary value of the cumulative probability function of the normal distribution of the node voltage deviation at the voltage upper limit. After standardizing the predetermined voltage upper limit value with the expected value and standard deviation of the node deviation, substituting it into the standard normal distribution function for probability calculation, and using the obtained probability value as the risk index of the node voltage exceeding the limit.

[0052] It should be noted that taking the prediction data of DG and load as the deterministic data for the operation and dispatching of DN, the voltage of node j obtained is denoted as V j,p , and the voltage of node j during the fluctuation of DG and load is denoted as V j,f . Therefore, the voltage deviation of node j can be expressed as:

[0053]

[0054] Among them, taking the prediction data of DG and load as the deterministic data for the operation and dispatching of DN, the voltage of node j obtained is denoted as V j,p , and the voltage of node j during the fluctuation of DG and load is denoted as V j,f . In the formula, V ref is the system reference voltage, taking 1.0 p.u. By combining (14) and (12), there is:

[0055]

[0056] It can be seen from (15) that the overvoltage is also related to the current voltage operation level. Then, the overvoltage risk is evaluated according to the probability distribution of the voltage deviation.

[0057] Embodiment 2 is the second embodiment of the present invention. The difference from the previous embodiment is:

[0058] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0059] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a predefined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0060] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber device, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways if necessary, and then storing it in a computer memory.

[0061] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0062] Embodiment 3 is the third embodiment of the present invention. This embodiment provides a system for overvoltage probability risk assessment of a distribution network based on voltage sensitivity analysis, including a voltage sensitivity modeling template, a fluctuation probability modeling template, and a risk assessment module.

[0063] Among them, the voltage sensitivity modeling template is used to construct a voltage relationship model between nodes in a radial distribution network based on DistFlow power flow, and construct a voltage sensitivity model of node voltage with respect to the injection power of distributed power sources and loads; the fluctuation probability modeling template is used to construct a probability model of node voltage fluctuation by using a voltage sensitivity matrix, assuming the output of distributed power sources and loads, and predicting that the error follows a multivariate normal distribution; the risk assessment module is used to combine the current node operating voltage and the system reference voltage, construct a normal distribution expression of the node voltage deviation, calculate the probability distribution of node voltage over-limit, and realize the overvoltage probability risk assessment of each node in the distribution network according to the node voltage over-limit probability.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for overvoltage probability risk assessment of a distribution network based on voltage sensitivity analysis, characterized in that, Including: Based on the DistFlow power flow, a voltage relationship model between nodes in a radial distribution network is constructed, and a voltage sensitivity model of node voltage with respect to the injection power of distributed power sources and loads is constructed; Using the voltage sensitivity matrix, a probability model of node voltage fluctuation is constructed, assuming the output of distributed power sources and loads, and the prediction errors follow a multivariate normal distribution; Combined with the current node operating voltage and the system reference voltage, a normal distribution expression of the node voltage deviation is constructed, the probability distribution of node voltage crossing the limit is calculated, and the overvoltage probability risk assessment of each node in the distribution network is realized according to the node voltage crossing the limit probability.

2. The method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to claim 1, characterized in that: The construction of the voltage relationship model between nodes in the radial distribution network includes that the voltage of each node is obtained by the linear combination of the resistance and reactance parameters of the upstream branch and the active power and reactive power injected at the node. The combination relationship is superimposed branch by branch based on the topological structure to form a voltage sensitivity coefficient matrix representing the mutual influence between nodes.

3. The method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to claim 2, characterized in that: Each coefficient in the voltage sensitivity coefficient matrix is represented by the ratio between the resistance and reactance of the branch in the path from the node to the power source node and the system reference voltage. The active power sensitivity coefficient is proportional to the path resistance, and the reactive power sensitivity coefficient is proportional to the path reactance.

4. The method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to claim 3, characterized in that: The construction of the voltage sensitivity model of node voltage with respect to the injection power of distributed power sources and loads includes using multi-dimensional vector modeling, where each dimension corresponds to the prediction error of a node. The error vector follows a multivariate normal distribution, and the mean vector and covariance matrix of the errors are obtained by statistical analysis of historical time series samples. Any element in the covariance matrix is composed of the covariance between the errors of related nodes.

5. The method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to claim 4, wherein: The prediction errors following a multivariate normal distribution include that the active power and reactive power respectively correspond to a group of sensitivity vectors. The prediction errors are from a multivariate normal distribution, obtained from the statistical law of linear transformation, and the node voltage deviation follows a normal distribution.

6. The overvoltage probability risk assessment method for a distribution network based on voltage sensitivity analysis according to claim 5, wherein: The node voltage deviation includes that the voltage deviation of the node is composed of three parts, namely the difference between the dispatching predicted voltage and the system reference voltage, the voltage change caused by the prediction error of the distributed power source, and the voltage change caused by the prediction error of the load.

7. The method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to claim 6, characterized in that: The realization of the overvoltage probability risk assessment of each node in the distribution network according to the node voltage crossing the limit probability includes obtaining it by calculating the complementary value of the cumulative probability function of the normal distribution of the node voltage deviation at the voltage upper limit. After normalizing the predetermined voltage upper limit value with the expected value and standard deviation of the node deviation, substituting it into the standard normal distribution function for probability calculation, and using the obtained probability value as the risk index of the node voltage crossing the limit.

8. A system adopting the method for evaluating the overvoltage probability risk of a distribution network based on voltage sensitivity analysis according to any one of claims 1 to 7, characterized in that: Including a voltage sensitivity modeling template, a fluctuation probability modeling template, and a risk assessment module; The voltage sensitivity modeling template is used to construct a voltage relationship model between nodes in a radial distribution network based on the DistFlow power flow, and construct a voltage sensitivity model of node voltage with respect to the injection power of distributed power sources and loads; The fluctuation probability modeling template is used to construct a probability model of node voltage fluctuation using the voltage sensitivity matrix, assuming the output of distributed power sources and loads, and the prediction errors follow a multivariate normal distribution; The risk assessment module is used to construct a normal distribution expression of the node voltage deviation by combining the operating voltage of the current node with the system reference voltage, calculate the probability distribution of the node voltage exceeding the limit, and realize the over-voltage probability risk assessment of each node in the distribution network according to the probability of the node voltage exceeding the limit.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it realizes the steps of the method for assessing the over-voltage probability risk of a distribution network based on voltage sensitivity analysis according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it realizes the steps of the method for assessing the over-voltage probability risk of a distribution network based on voltage sensitivity analysis according to any one of claims 1 to 7.

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