Distribution network operation risk assessment methods, devices, media, equipment and products

Abstract

The present invention discloses a distribution network operation risk assessment method, device, medium, equipment and product, including the following steps: obtaining the measured value of the distribution network operation; predicting the distribution network operation state based on the unscented Kalman filter algorithm according to the measured value to obtain the state prediction value; inputting the state prediction value into a pre-constructed distribution network operation risk assessment model to obtain the distribution network operation risk assessment result; wherein the distribution network operation risk assessment model includes the distribution network overvoltage operation risk, the distribution network low voltage operation risk, the line overload operation risk, the line reverse overload operation risk and the renewable energy abandonment risk. The state prediction value obtained based on the unscented Kalman filter algorithm is used as the input of the distribution network operation risk assessment model, which improves the calculation efficiency, can effectively perform real-time operation risk assessment on the distribution network, provides a corresponding data basis for the risk control of the distribution network, and improves the safety of operation.

Description

Technical Field

[0001] The present invention relates to a distribution network operation risk assessment method, device, medium, equipment and product, and belongs to the technical field of power system steady-state analysis. Background Art

[0002] With the development of new power systems, the proportion of renewable energy in distribution networks continues to increase, and the amount of load access continues to increase. The structure and operation of distribution networks have become more complex, and the risks they face have become more severe. The shortcomings of traditional methods in real-time risk assessment make them unable to adapt well to the needs of new power systems.

[0003] Among the methods for risk assessment of power systems, simulation methods simulate the operating status of the system through a large number of sampling and comprehensively consider different operating conditions. Therefore, they are more suitable for complex large-scale power systems and are widely used in practice. However, this method relies on the probability distribution of uncertain factors in system operation. In particular, when there are many uncertain factors involved, it is difficult to quickly update the probability distribution of uncertain factors based on the latest information, and it is impossible to conduct real-time operation risk assessment of the distribution network in a timely and effective manner. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art, provide a distribution network operation risk assessment method, device, medium, equipment and product, use the state prediction value obtained based on the unscented Kalman filter algorithm as the input of the distribution network operation risk assessment model, improve the calculation efficiency, and effectively perform real-time operation risk assessment on the distribution network, provide a corresponding data basis for the risk control of the distribution network, and improve the safety of operation.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention discloses a distribution network operation risk assessment method, comprising the following steps:

[0007] Obtaining measured values ​​of distribution network operation;

[0008] According to the measured values, the operating state of the distribution network is predicted based on an unscented Kalman filter algorithm to obtain a state prediction value;

[0009] The state prediction value is input into a pre-built distribution network operation risk assessment model to obtain a distribution network operation risk assessment result; wherein the distribution network operation risk assessment model includes distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk.

[0010] Furthermore, the state prediction value includes the voltage of multiple nodes in any scenario, the active power of multiple lines in any scenario, various types of voltages and various types of power of multiple nodes, various types of power and electrical conductance of multiple lines;

[0011] The data processing steps of the distribution network operation risk assessment model are as follows:

[0012] Calculating the overvoltage operation risk and the undervoltage operation risk of the distribution network according to the voltages of the multiple nodes in any scenario;

[0013] According to the active power of the multiple lines in any scenario, the line overload operation risk and the line reverse overload operation risk are calculated;

[0014] According to the various types of voltages and various types of powers of the multiple nodes, and the various types of powers and electrical conductances of the multiple lines, the pre-constructed power abandonment optimization problem is solved by setting constraints to obtain the risk of renewable energy abandonment;

[0015] According to the distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk, the distribution network operation risk assessment result is calculated and the expression is as follows:

[0016]

[0017] In the formula, Indicates the results of the risk assessment of distribution network operation; The weight coefficient representing the overvoltage operation risk of the distribution network; Indicates the risk of overvoltage operation in the distribution network; The weight coefficient representing the risk of low voltage operation of the distribution network; Indicates the risk of low voltage operation of the distribution network; The weight coefficient indicating the risk of line overload operation; Indicates the risk of line overload operation; The weight coefficient indicating the risk of overload operation of the line; Indicates the risk of reverse overload operation of the line; The weight coefficient representing the risk of renewable energy curtailment; Indicates the risk of curtailment of renewable energy.

[0018] Furthermore, the expression of any risk weight coefficient is as follows:

[0019]

[0020] In the formula, Represents the weight coefficient of risk p, where p=1 represents the risk of overvoltage operation of the distribution network, p=2 represents the risk of undervoltage operation of the distribution network, p=3 represents the risk of line overload operation, p=4 represents the risk of reverse transmission overload operation, and p=5 represents the risk of renewable energy power abandonment; represents the weight combination coefficient for risk p; represents the objective weight set calculated by the entropy weight method for risk p; Represents the subjective weight set calculated by the AHP method for risk p.

[0021] Furthermore, the expression of the overvoltage operation risk of the distribution network is as follows:

[0022]

[0023] In the formula, Indicates the total number of nodes in the system; Represents the importance coefficient of the i-th node; represents the overvoltage operation risk of the i-th node, and the expression is as follows:

[0024]

[0025] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether the voltage exceeds the upper limit event at node i in scenario s; represents the voltage of node i in scenario s; The severity function of the voltage exceeding the upper limit risk of node i in scenario s is expressed as follows:

[0026]

[0027] In the formula, Indicates the upper limit of safe voltage; Represents a natural constant.

[0028] Furthermore, the expression of the low voltage operation risk of the distribution network is as follows:

[0029]

[0030] In the formula, Indicates the total number of nodes in the system; Represents the importance coefficient of the i-th node; represents the low voltage operation risk of the i-th node, and the expression is as follows:

[0031]

[0032] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether the voltage exceeds the lower limit event at node i in scenario s; represents the voltage of node i in scenario s; The severity function of the voltage risk exceeding the lower limit at node i in scenario s is expressed as follows:

[0033]

[0034] In the formula, Indicates the lower limit of safe voltage; Represents a natural constant.

[0035] Furthermore, the line overload risk The expression is as follows:

[0036]

[0037] In the formula, Indicates the total number of branches in the distribution network; It represents the active power overload operation risk of line m, and the expression is as follows:

[0038]

[0039] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether line j is overloaded in scenario s; represents the active power of line m in scenario s; It represents the severity function of line overload of line m in scenario s, and its expression is as follows:

[0040]

[0041] In the formula, Indicates the rated capacity of line m.

[0042] Furthermore, the expression of the line reverse overload operation risk is as follows:

[0043]

[0044] In the formula, Indicates the total number of branches in the distribution network; Indicates the risk of overload operation due to active power reverse transmission of line m, and the expression is as follows:

[0045]

[0046] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether line m is overloaded in scenario s; represents the active power of line m in scenario s; The severity function of the line reverse overload of line m in scenario s is expressed as follows:

[0047]

[0048] In the formula, Indicates the rated capacity of line m.

[0049] Furthermore, the objective function of the power curtailment optimization problem is expressed as follows:

[0050]

[0051] In the formula, represents the objective function of the power curtailment optimization problem; Indicates the amount of power wasted; Indicates the extent to which the voltage exceeds the specified limit; Indicates the degree to which the tidal current exceeds the specified limit; It indicates the regulation coefficient, which ensures that the voltage and power flow can be controlled within the specified limits through power abandonment operation;

[0052] The constraints of the power curtailment optimization problem include power flow constraints, node power balance constraints and node injection power constraints;

[0053] The expression of the power flow constraint is as follows:

[0054]

[0055]

[0056] In the formula, represents the active power flowing from node i to node j; represents the reactive power flowing from node i to node j; represents the voltage of node i; represents the voltage at node j; represents the conductance of the branch from node i to node j; represents the susceptance on the branch from node i to node j; represents the voltage phase difference between node i and node j; represents the active power loss flowing from node i to node j; represents the earth susceptance of the line connecting nodes i and j; represents the reactive power loss flowing from node i to node j;

[0057] The expression of the node power balance constraint is as follows:

[0058]

[0059]

[0060] In the formula, represents the renewable energy output power at node i; represents the amount of power abandoned at node i; represents the load active power at node i; represents the injected power at node i; represents the reactive power of the load at node i; represents the injected power at node i;

[0061] The expression of the node injection power constraint is as follows:

[0062]

[0063]

[0064] In the formula, represents the real part of the element in the i-th row and j-th column of the admittance matrix; represents the imaginary part of the element in the i-th row and j-th column of the admittance matrix; Indicates the total number of nodes; Represents the set of all lines.

[0065] Furthermore, the unscented Kalman filter algorithm comprises the following steps:

[0066] Get the measurement value of this round , the initial or last updated state estimate and the covariance matrix ;

[0067] According to the state estimated value and the covariance matrix , perform untraceable transformation, and obtain the Sigma point set ;

[0068] For the Sigma point set Any Sigma point in Based on the historical state, the exponential smoothing method is used to predict the state of the next moment and obtain the corresponding state prediction value , and then get the mean of the state prediction value and the covariance matrix of the state prediction value ;

[0069] For the Sigma point set The predicted value corresponding to any Sigma point in , perform prediction calculation based on the measurement function and obtain the corresponding measurement prediction value , and then get the mean of the measured predicted value ;

[0070] According to the measured predicted value and the mean of the measured predicted values , by calculating the measurement prediction covariance matrix and the cross-covariance matrix , and the Kalman gain is ;

[0071] The mean of the predicted values ​​according to the state , Kalman gain , measured value and the mean of the measured predicted values , and get the updated state estimate ;

[0072] According to the covariance matrix of the state prediction value , Kalman gain and the measurement-prediction covariance matrix , and get the updated covariance matrix .

[0073] Furthermore, the Sigma point set Any Sigma point in The expression is as follows:

[0074]

[0075] In the formula, Represents the Sigma point set The y-th Sigma point in ; Represents the state estimate obtained by the k-1th iteration update; Indicates the dimension of state quantity; Representation Matrix The yth column of represents the covariance matrix obtained by the k-1th iteration update; represents the scaling parameter, , represents the proportional correction factor, It is used to ensure that the matrix Positive semidefinite free parameters.

[0076] Furthermore, the state prediction value The expression is as follows:

[0077]

[0078] In the formula, Represents the state prediction value of the yth Sigma point at the kth iteration; Represents the Sigma point set The y-th Sigma point in ; represents the state prediction function;

[0079] The mean of the state prediction values The expression is as follows:

[0080]

[0081] In the formula, represents the mean of the state prediction values ​​of the kth iteration; Represents the mean weight of the sigma point set; Indicates the dimension of state quantity;

[0082] The covariance matrix of the state prediction value The expression is as follows:

[0083]

[0084] In the formula, Represents the covariance matrix of the state prediction value of the kth iteration; represents the process noise covariance matrix; Represents the covariance matrix weights of the sigma point set.

[0085] Furthermore, the measured predicted value The expression is as follows:

[0086]

[0087] In the formula, represents the measured predicted value of the yth Sigma point at the kth iteration; represents the measurement function;

[0088] The mean of the measured predicted values The expression is as follows:

[0089]

[0090] In the formula, represents the mean of the measured prediction values ​​of the kth iteration; Represents the mean weight of the sigma point set; Represents the dimension of the state quantity.

[0091] Furthermore, the measurement prediction covariance matrix The expression is as follows:

[0092]

[0093] In the formula, represents the measurement prediction covariance matrix of the kth iteration; Represents the covariance matrix weight of the sigma point set; represents the measured predicted value of the yth Sigma point at the kth iteration; represents the mean of the measured prediction values ​​of the kth iteration; represents the measurement noise covariance matrix; Indicates the dimension of state quantity;

[0094] The cross-covariance matrix The expression is as follows:

[0095]

[0096] In the formula, represents the cross-covariance matrix of the kth iteration; Indicates the state prediction value corresponding to the yth sigma point of the kth iteration; represents the mean of the state prediction values ​​of the kth iteration;

[0097] The Kalman gain The expression is as follows:

[0098]

[0099] In the formula, represents the Kalman gain of the kth iteration.

[0100] Furthermore, the updated state estimate The expression is as follows:

[0101]

[0102] In the formula, represents the mean of the state prediction values ​​of the kth iteration; represents the Kalman gain of the kth iteration; represents the measurement value of the kth iteration; represents the mean of the measured prediction values ​​at the kth iteration.

[0103] Furthermore, the updated covariance matrix The expression is as follows:

[0104]

[0105] In the formula, Represents the covariance matrix of the state prediction value of the kth iteration; represents the Kalman gain of the kth iteration; represents the covariance matrix of the measurement prediction value at the kth iteration.

[0106] In a second aspect, the present invention discloses a distribution network operation risk assessment device, comprising:

[0107] A data acquisition module is used to obtain the measured values ​​of the distribution network operation;

[0108] A data prediction module is used to predict the operation state of the distribution network based on the measured value and the unscented Kalman filter algorithm to obtain a state prediction value;

[0109] The risk assessment module is used to input the state prediction value into a pre-built distribution network operation risk assessment model to obtain a distribution network operation risk assessment result; wherein the distribution network operation risk assessment model includes distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk.

[0110] Furthermore, the expression of the distribution network operation risk assessment model is as follows:

[0111]

[0112] In the formula, represents the distribution network operation risk assessment model; The weight coefficient representing the overvoltage operation risk of the distribution network; Indicates the risk of overvoltage operation in the distribution network; The weight coefficient representing the risk of low voltage operation of the distribution network; Indicates the risk of low voltage operation of the distribution network; The weight coefficient indicating the risk of line overload operation; Indicates the risk of line overload operation; The weight coefficient indicating the risk of overload operation of the line; Indicates the risk of reverse overload operation of the line; The weight coefficient representing the risk of renewable energy curtailment; Indicates the risk of curtailment of renewable energy.

[0113] In a third aspect, the present invention discloses a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the distribution network operation risk assessment method described in the first aspect are implemented.

[0114] In a fourth aspect, the present invention discloses a computer device, comprising:

[0115] Memory, for storing computer programs / instructions;

[0116] A processor is used to execute the computer program / instructions to implement the steps of the distribution network operation risk assessment method described in the first aspect.

[0117] In a fifth aspect, the present invention discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the distribution network operation risk assessment method described in the first aspect.

[0118] Compared with the prior art, the present invention has the following beneficial effects:

[0119] The present invention uses the state prediction value obtained based on the unscented Kalman filtering algorithm as the input of the distribution network operation risk assessment model, which can make full use of the state prediction value while avoiding the difficulty of independently updating the probability information, and can significantly improve the calculation efficiency, thereby effectively performing real-time operation risk assessment on the distribution network, providing a corresponding data basis for the risk control of the distribution network, and improving the safety of operation.

[0120] The distribution network operation risk assessment model of the present invention integrates multiple risk indicators such as the overvoltage operation risk of the distribution network, the low voltage operation risk of the distribution network, the overload operation risk of the line, the overload operation risk of the line reverse transmission, and the risk of renewable energy abandonment from the perspective of node voltage, line flow and renewable energy utilization, and assigns appropriate weights to each risk indicator to form a risk indicator system, combining subjective and objective information to achieve effective perception of the comprehensive risk of the distribution network. The weight determination method adopted can appropriately determine the risk indicator weight according to the specific risk situation faced by the distribution network. When the penetration rate of renewable energy is high, the overall risk of the distribution network can be reduced by appropriately abandoning power and adjusting the operation mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1 is a flow chart of the distribution network operation risk assessment method provided in Example 1;

[0122] Figure 2 It is a flow chart of the distribution network operation risk assessment method provided in Example 1. DETAILED DESCRIPTION

[0123] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0124] Example 1

[0125] This embodiment 1 provides a distribution network operation risk assessment method, including the following steps:

[0126] Obtaining measured values ​​of distribution network operation;

[0127] According to the measured values, the distribution network operation state is predicted based on the unscented Kalman filter algorithm to obtain the state prediction value;

[0128] The state prediction value is input into the pre-built distribution network operation risk assessment model to obtain the distribution network operation risk assessment result; wherein, the distribution network operation risk assessment model includes the distribution network overvoltage operation risk, the distribution network low voltage operation risk, the line overload operation risk, the line reverse overload operation risk and the renewable energy power abandonment risk.

[0129] The technical concept of the present invention is: using the state prediction value obtained based on the unscented Kalman filter algorithm as the input of the distribution network operation risk assessment model, which can make full use of the state prediction value and avoid the difficulty of independently updating the probability information, and can significantly improve the calculation efficiency, so as to effectively perform real-time operation risk assessment on the distribution network, provide a corresponding data basis for the risk control of the distribution network, and improve the safety of operation.

[0130] like Figure 1 and Figure 2 As shown, the process steps of the present invention are as follows:

[0131] Step 1: Obtain the measured values ​​of the distribution network operation.

[0132] Step 2: According to the measured value, the distribution network operation state is predicted based on the unscented Kalman filter algorithm to obtain the state prediction value.

[0133] like Figure 2 As shown, the specific steps include:

[0134] 2.1、Get the measurement value of this round , the initial or last updated state estimate and the covariance matrix .

[0135] 2.2. Estimated value based on state and the covariance matrix , perform untraceable transformation, and obtain the Sigma point set .

[0136] Sigma Point Set Any Sigma point in The expression is as follows:

[0137]

[0138] In the formula, Represents the Sigma point set The y-th Sigma point in ; Represents the state estimate obtained by the k-1th iteration update; Indicates the dimension of state quantity; Representation Matrix The yth column of represents the covariance matrix obtained by the k-1th iteration update; represents the scaling parameter, , represents the proportional correction factor, It is used to ensure that the matrix Positive semidefinite free parameters.

[0139] 2.3. For Sigma point set Any Sigma point in Based on the historical state, the exponential smoothing method is used to predict the state of the next moment and obtain the corresponding state prediction value , and then get the mean of the state prediction value and the covariance matrix of the state prediction value .

[0140] State prediction value The expression is as follows:

[0141]

[0142] In the formula, Represents the state prediction value of the yth Sigma point at the kth iteration; Represents the Sigma point set The y-th Sigma point in ; represents a state prediction function. The state prediction function in this embodiment adopts the state transfer equation in Kalman filtering.

[0143] The mean of the state prediction values The expression is as follows:

[0144]

[0145] In the formula, represents the mean of the state prediction values ​​of the kth iteration; Represents the mean weight of the sigma point set, and the expression is as follows:

[0146]

[0147] In the formula, Indicates the dimension of state quantity; Represents the scaling parameter.

[0148] Covariance matrix of state predictions The expression is as follows:

[0149]

[0150] In the formula, Represents the covariance matrix of the state prediction value of the kth iteration; represents the process noise covariance matrix; Represents the covariance matrix weight of the sigma point set, and the expression is as follows:

[0151]

[0152] In the formula, Indicates the dimension of state quantity; Represents the scaling parameter; Indicates the sampling point distribution state control parameter; represents a non-negative weight coefficient.

[0153] 2.4. For Sigma point set The state prediction value corresponding to any Sigma point in , perform prediction calculation based on the measurement function and obtain the corresponding measurement prediction value , and then get the mean of the measured predicted value .

[0154] Measuring predicted values The expression is as follows:

[0155]

[0156] In the formula, represents the measured predicted value of the yth Sigma point at the kth iteration; represents a measurement function. The measurement function in this embodiment adopts the observation equation in Kalman filtering.

[0157] Measuring the mean of the predicted values The expression is as follows:

[0158]

[0159] In the formula, represents the mean of the measured prediction values ​​of the kth iteration; Represents the mean weight of the sigma point set.

[0160] 2.5. Predicted values ​​based on measurements and the mean of the measured predicted values , by calculating the measurement prediction covariance matrix and the cross-covariance matrix , and the Kalman gain is .

[0161] Measurement prediction covariance matrix The expression is as follows:

[0162]

[0163] In the formula, represents the measurement prediction covariance matrix of the kth iteration; Represents the covariance matrix weight of the sigma point set; represents the measured predicted value of the yth Sigma point at the kth iteration; represents the mean of the measured prediction values ​​of the kth iteration; represents the measurement noise covariance matrix; Indicates the dimension of state quantity;

[0164] Cross-covariance matrix The expression is as follows:

[0165]

[0166] In the formula, represents the cross-covariance matrix of the kth iteration; Represents the state prediction value of the yth Sigma point at the kth iteration; represents the mean of the state prediction values ​​of the kth iteration;

[0167] Kalman Gain The expression is as follows:

[0168]

[0169] In the formula, represents the Kalman gain of the kth iteration.

[0170] 2.6. Mean of the predicted value based on the state , Kalman gain , measured value and the mean of the measured predicted values , and get the updated state estimate .

[0171] Updated state estimate The expression is as follows:

[0172]

[0173] In the formula, represents the mean of the state prediction values ​​of the kth iteration; represents the Kalman gain of the kth iteration; represents the measurement value of the kth iteration; represents the mean of the measured prediction values ​​at the kth iteration.

[0174] 2.7. Covariance matrix based on state prediction value , Kalman gain and the measurement-prediction covariance matrix , and get the updated covariance matrix .

[0175] Updated covariance matrix The expression is as follows:

[0176]

[0177] In the formula, Represents the covariance matrix of the state prediction value of the kth iteration; represents the Kalman gain of the kth iteration; represents the measurement prediction covariance matrix of the kth iteration.

[0178] The distribution network state estimation based on the unscented Kalman filter structurally has a prediction stage and completes the sampling of the system operation state. In addition to being combined with the measurement information of the next moment to realize the distribution network operation state estimation, these prediction information can also be used for early perception and evaluation of operation risks before the next moment. Using the output of the distribution network state estimation based on the unscented Kalman filter in the prediction stage as the input of the risk assessment model can make full use of the state estimation results, avoid the difficulty of independently updating the probability information, and significantly improve the calculation efficiency, thereby effectively conducting real-time operation risk assessment of the distribution network, providing a corresponding data basis for the risk control of the distribution network, and improving the safety of operation.

[0179] Step 3: Input the state prediction value into the pre-built distribution network operation risk assessment model to obtain the distribution network operation risk assessment result.

[0180] The state prediction values ​​include the voltage of multiple nodes in any scenario, the active power of multiple lines in any scenario, various types of voltage and various types of power of multiple nodes, various types of power and electrical conductance of multiple lines;

[0181] The data processing steps of the distribution network operation risk assessment model are as follows:

[0182] According to the voltage of multiple nodes in any scenario, the overvoltage operation risk and the undervoltage operation risk of the distribution network are calculated;

[0183] According to the active power of multiple lines in any scenario, the line overload operation risk and line reverse overload operation risk are calculated;

[0184] According to the various types of voltages and powers of multiple nodes, as well as the various types of powers and conductance admittance of multiple lines, the pre-constructed power abandonment optimization problem is solved by setting constraints to obtain the risk of renewable energy abandonment.

[0185] The distribution network operation risk assessment results are calculated based on the distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk.

[0186] 3.1. The distribution network operation risk assessment model of this embodiment establishes a set of comprehensive new distribution network risk assessment system indicators from the perspectives of node voltage, line flow and renewable energy utilization, including distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk.

[0187] The various operational risk indicators of the distribution network reflect the operational risks in different aspects. In order to evaluate the overall risk of the system, it is necessary to assign appropriate weights to each risk indicator to form a risk indicator system. The analytic hierarchy process determines the indicator weights from a subjective level based on past knowledge, and the entropy weight method determines the indicator weights from an objective level by analyzing the amount of information contained in the data. After obtaining the subjective and objective weights, they are integrated to obtain the final risk weight coefficient, where the expression of any risk weight coefficient is as follows:

[0188]

[0189] In the formula, Represents the weight coefficient of risk p, where p=1 represents the risk of overvoltage operation of the distribution network, p=2 represents the risk of undervoltage operation of the distribution network, p=3 represents the risk of line overload operation, p=4 represents the risk of reverse transmission overload operation, and p=5 represents the risk of renewable energy power abandonment;

[0190] represents the weight combination coefficient for risk p; represents the objective weight set calculated by the entropy weight method for risk p; Represents the subjective weight set calculated by the AHP method for risk p.

[0191] The expression of the distribution network operation risk assessment result is as follows:

[0192]

[0193] In the formula, Indicates the results of the risk assessment of distribution network operation; The weight coefficient representing the overvoltage operation risk of the distribution network; Indicates the risk of overvoltage operation in the distribution network; The weight coefficient representing the risk of low voltage operation of the distribution network; Indicates the risk of low voltage operation of the distribution network; The weight coefficient indicating the risk of line overload operation; Indicates the risk of line overload operation; The weight coefficient indicating the risk of overload operation of the line; Indicates the risk of reverse overload operation of the line; The weight coefficient representing the risk of renewable energy curtailment; Indicates the risk of curtailment of renewable energy.

[0194] in, .

[0195] 3.2. Based on the voltages of multiple nodes in any scenario, that is, the voltage of node i in scenario s , the overvoltage operation risk of the distribution network is calculated.

[0196] The expression of overvoltage operation risk of distribution network is as follows:

[0197]

[0198] In the formula, Indicates the risk of overvoltage operation in the distribution network; Indicates the total number of nodes in the system; Represents the importance coefficient of the i-th node; It represents the overvoltage operation risk of the i-th node, reflecting the risk that the voltage of each node in the distribution network exceeds the safe voltage range. The expression is as follows:

[0199]

[0200] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether node i has an overvoltage risk in scenario s; represents the voltage of node i in scenario s; The severity function of the voltage exceeding the upper limit risk of node i in scenario s is expressed as follows:

[0201]

[0202] In the formula, Indicates the upper limit of safe voltage; represents a natural constant. .

[0203] The importance coefficient of the i-th node , considering the power characteristics of the node and the network characteristics, the expression is as follows:

[0204]

[0205] In the formula, Indicates the number of branches connected to the i-th node; represents the active power of the i-th node; Represents the sum of active power of all nodes.

[0206] 3.3. Based on the voltages of multiple nodes in any scenario, that is, the voltage of node i in scenario s , the low voltage operation risk of the distribution network is calculated.

[0207] The expression of low voltage operation risk of distribution network is as follows:

[0208]

[0209] In the formula, Indicates the risk of low voltage operation of the distribution network; Indicates the total number of nodes in the system; Represents the importance coefficient of the i-th node; It represents the low voltage operation risk of the i-th node, reflecting the risk that the node voltage is lower than the voltage lower limit. The expression is as follows:

[0210]

[0211] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether node i has low voltage risk in scenario s; represents the voltage of node i in scenario s; The severity function of the voltage risk exceeding the lower limit at node i in scenario s is expressed as follows:

[0212]

[0213] In the formula, Indicates the lower limit of safe voltage; Represents a natural constant.

[0214] In this embodiment .

[0215] 3.4. Based on the active power of multiple lines in any scenario, that is, the active power of line m in scenario s , and calculate the line overload operation risk.

[0216] Risk of line overload operation is the cumulative sum of the overload operation risks of each line in the distribution network, and the expression is as follows:

[0217]

[0218] In the formula, Indicates the total number of branches in the distribution network; It represents the active power overload operation risk of line m, reflecting the risk that the active power carried on the line exceeds the maximum limit. The expression is as follows:

[0219]

[0220] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether line j is overloaded in scenario s; represents the active power of line m in scenario s; It represents the severity function of line overload of line m in scenario s, and its expression is as follows:

[0221]

[0222] In the formula, Indicates the rated capacity of line m.

[0223] 3.5. Based on the active power of multiple lines in any scenario, that is, the active power of line m in scenario s , the risk of line reverse overload operation is calculated.

[0224] The reverse transmission overload operation risk of the line is the cumulative sum of the reverse transmission overload operation risks of each line in the distribution network, and the expression is as follows:

[0225]

[0226] In the formula, Indicates the total number of branches in the distribution network; Indicates the risk of overload operation due to active power reverse transmission of line m.

[0227] When the output of renewable power is large, the distribution network will have reverse power flow, and the large surplus power will be fed back from the installation point of the photovoltaic power source along the feeder to the head end of the distribution network. The line reverse overload risk index reflects the risk that the reverse active power carried on the line exceeds the maximum limit. The risk of node voltage increase caused by incomplete absorption has been included in the above-mentioned node overvoltage operation risk. Therefore, only the reverse overload risk caused by its impact on the power flow is considered here. Therefore, the active power reverse overload operation risk of line m is The expression is as follows:

[0228]

[0229] In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether line m is overloaded in scenario s; represents the active power of line m in scenario s; The severity function of the line reverse overload of line m in scenario s is expressed as follows:

[0230]

[0231] In the formula, Indicates the rated capacity of line m.

[0232] 3.6. According to the various types of voltages and powers of multiple nodes, as well as the various types of powers and conductance admittances of multiple branches, the pre-constructed power abandonment optimization problem is solved by setting constraints to obtain the risk of renewable energy abandonment.

[0233] When the output of renewable energy connected to the distribution network cannot be fully absorbed, the impact can be limited by curtailing power, but this will result in a low utilization rate of renewable energy. By constructing an optimization problem, the amount of renewable energy curtailment required to avoid serious voltage and power flow risks is calculated, and then the risk of renewable energy curtailment is calculated.

[0234] 3.6.1. The objective function of the power curtailment optimization problem is expressed as follows:

[0235]

[0236] In the formula, represents the objective function of the power curtailment optimization problem; Indicates the amount of power wasted; Indicates the regulation coefficient, which ensures that the voltage and power flow can be controlled within the specified limit through power abandonment operation ; Indicates the degree to which the voltage exceeds the specified limit; Indicates the degree to which the flow exceeds the specified limit. The expression is as follows:

[0237]

[0238]

[0239] In the formula, represents the voltage of the i-th node in the s-th operation scenario; represents the active power flowing through line m in the sth operation scenario; Indicates per unit value.

[0240] The constraints of the power curtailment optimization problem include power flow constraints, node power balance constraints and node injection power constraints.

[0241] 3.6.2、The expression of power flow constraint is as follows:

[0242]

[0243]

[0244] In the formula, represents the active power flowing from node i to node j; represents the reactive power flowing from node i to node j; represents the voltage of node i; represents the voltage at node j; represents the conductance of the branch from node i to node j; represents the susceptance on the branch from node i to node j; represents the voltage phase difference between node i and node j; represents the active power loss flowing from node i to node j; represents the earth susceptance of the line connecting nodes i and j; represents the reactive power loss flowing from node i to node j;

[0245] , Approximate expression: The cosine function of the phase angle difference uses the quadratic Taylor expansion , and based on the small voltage phase angle difference, the higher-order terms are ignored; in addition, based on the node voltage amplitude being close to 1p.u., an approximate value is used for the product of the voltage phase angle difference and the amplitude ; Then, the square of the voltage phase angle difference and the square of the amplitude difference are respectively expanded by the first-order Taylor and the higher-order terms are ignored, and the approximate expression of the network loss can be obtained:

[0246]

[0247]

[0248] 3.6.3. The expression of node power balance constraint is as follows:

[0249]

[0250]

[0251] In the formula, represents the renewable energy output power at node i; represents the amount of power abandoned at node i; represents the load active power at node i; represents the injected power at node i; represents the reactive power of the load at node i; represents the injected power at node i;

[0252] 3.6.4. The expression of node injection power constraint is as follows:

[0253]

[0254]

[0255] In the formula, represents the real part of the element in the i-th row and j-th column of the admittance matrix; represents the imaginary part of the element in the i-th row and j-th column of the admittance matrix; represents the total number of nodes; K represents the set of all lines.

[0256] In summary, the present invention firstly realizes the effective perception of the comprehensive risk of the distribution network based on the risk indicators of low voltage, overvoltage, line overload, reverse transmission line overload, and renewable energy power abandonment, combined with subjective and objective information. The adopted weight determination method can appropriately determine the risk indicator weight according to the specific risk situation faced by the distribution network. When the penetration rate of renewable energy is high, the overall risk of the distribution network can be reduced by appropriately adjusting the operation mode through power abandonment.

[0257] Secondly, the present invention uses the output of the distribution network state estimation based on the unscented Kalman filter in the prediction stage as the input of the risk assessment model, which can make full use of the state estimation results, avoid the difficulty of independently updating the probability information, and significantly improve the calculation efficiency, thereby effectively performing real-time operation risk assessment on the distribution network, providing a corresponding data basis for the risk control of the distribution network, and improving the safety of operation.

[0258] Example 2

[0259] This embodiment 2 provides a distribution network operation risk assessment device, including:

[0260] A data acquisition module is used to obtain the measured values ​​of the distribution network operation;

[0261] A data prediction module is used to predict the operation state of the distribution network based on the measured value and the unscented Kalman filter algorithm to obtain a state prediction value;

[0262] The risk assessment module is used to input the state prediction value into a pre-built distribution network operation risk assessment model to obtain the distribution network operation risk assessment result; wherein the distribution network operation risk assessment model includes the distribution network overvoltage operation risk, the distribution network low voltage operation risk, the line overload operation risk, the line reverse overload operation risk and the renewable energy power abandonment risk.

[0263] Furthermore, the expression of the distribution network operation risk assessment model is as follows:

[0264]

[0265] In the formula, represents the distribution network operation risk assessment model; The weight coefficient representing the overvoltage operation risk of the distribution network; Indicates the risk of overvoltage operation in the distribution network; The weight coefficient representing the risk of low voltage operation of the distribution network; Indicates the risk of low voltage operation of the distribution network; The weight coefficient indicating the risk of line overload operation; Indicates the risk of line overload operation; The weight coefficient indicating the risk of overload operation of the line; Indicates the risk of reverse overload operation of the line; The weight coefficient representing the risk of renewable energy curtailment; Indicates the risk of curtailment of renewable energy.

[0266] Example 3

[0267] This embodiment 3 provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the distribution network operation risk assessment method of embodiment 1 are implemented.

[0268] Example 4

[0269] This embodiment 4 provides a computer device, including:

[0270] Memory, for storing computer programs / instructions;

[0271] A processor is used to execute computer programs / instructions to implement the steps of the distribution network operation risk assessment method of Example 1.

[0272] Example 5

[0273] This embodiment 5 provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the distribution network operation risk assessment method of embodiment 1.

[0274] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0275] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0276] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0277] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0278] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Distribution network operation risk assessment method, which is characterized by: The steps include: Obtaining measured values ​​of distribution network operation; According to the measured values, the operating state of the distribution network is predicted based on an unscented Kalman filter algorithm to obtain a state prediction value; The state prediction value is input into a pre-built distribution network operation risk assessment model to obtain a distribution network operation risk assessment result; wherein the distribution network operation risk assessment model includes distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk; The state prediction values ​​include the voltages of multiple nodes in any scenario, the active powers of multiple lines in any scenario, various types of voltages and various types of powers of multiple nodes, and various types of powers and electrical conductance of multiple lines; The data processing steps of the distribution network operation risk assessment model are as follows: Calculating the overvoltage operation risk and the undervoltage operation risk of the distribution network according to the voltages of the multiple nodes in any scenario; According to the active power of the multiple lines in any scenario, the line overload operation risk and the line reverse overload operation risk are calculated; According to the various types of voltages and various types of powers of the multiple nodes, and the various types of powers and electrical conductances of the multiple lines, the pre-constructed power abandonment optimization problem is solved by setting constraints to obtain the risk of renewable energy abandonment; According to the distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk, the distribution network operation risk assessment result is calculated and the expression is as follows: ； In the formula, Indicates the results of the distribution network operation risk assessment; The weight coefficient representing the overvoltage operation risk of the distribution network; Indicates the risk of overvoltage operation in the distribution network; The weight coefficient representing the risk of low voltage operation of the distribution network; Indicates the risk of low voltage operation of the distribution network; The weight coefficient indicating the risk of line overload operation; Indicates the risk of line overload operation; The weight coefficient indicating the risk of overload operation of the line; Indicates the risk of reverse overload operation of the line; The weight coefficient representing the risk of renewable energy curtailment; Indicates the risk of curtailment of renewable energy; The expression of the line reverse overload operation risk is as follows: ； In the formula, Indicates the total number of branches in the distribution network; Indicates the risk of overload operation due to active power reverse transmission of line m, and the expression is as follows: ； In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether line m is overloaded in scenario s; represents the active power of line m in scenario s; The severity function of the line reverse overload of line m in scenario s is expressed as follows: ； In the formula, Indicates the rated capacity of line m; The objective function of the power curtailment optimization problem is expressed as follows: ； In the formula, represents the objective function of the power curtailment optimization problem; Indicates the amount of power wasted; Indicates the extent to which the voltage exceeds the specified limit; Indicates the degree to which the tidal current exceeds the specified limit; It indicates the regulation coefficient, which ensures that the voltage and power flow can be controlled within the specified limits through power abandonment operation; The constraints of the power curtailment optimization problem include power flow constraints, node power balance constraints and node injection power constraints; The expression of the power flow constraint is as follows: ； ； In the formula, represents the active power flowing from node i to node j; represents the reactive power flowing from node i to node j; represents the voltage of node i; represents the voltage at node j; represents the conductance of the branch from node i to node j; represents the susceptance on the branch from node i to node j; represents the voltage phase difference between node i and node j; represents the active power loss flowing from node i to node j; represents the earth susceptance of the line connecting nodes i and j; represents the reactive power loss flowing from node i to node j; The expression of the node power balance constraint is as follows: ； ； In the formula, represents the renewable energy output power at node i; represents the amount of power abandoned at node i; represents the load active power at node i; represents the injected power at node i; represents the reactive power of the load at node i; represents the injected power at node i; The expression of the node injection power constraint is as follows: ； ； In the formula, represents the real part of the element in the i-th row and j-th column of the admittance matrix; represents the imaginary part of the element in the i-th row and j-th column of the admittance matrix; Indicates the total number of nodes; Represents the set of all lines.

2. The distribution network operation risk assessment method according to claim 1 is characterized in that: The expression of any risk weight coefficient is as follows: ； In the formula, Represents the weight coefficient of risk p, where p=1 represents the risk of overvoltage operation of the distribution network, p=2 represents the risk of undervoltage operation of the distribution network, p=3 represents the risk of line overload operation, p=4 represents the risk of reverse transmission overload operation, and p=5 represents the risk of renewable energy power abandonment; represents the weight combination coefficient for risk p; represents the objective weight set calculated by the entropy weight method for risk p; Represents the subjective weight set calculated by the AHP method for risk p.

3. The distribution network operation risk assessment method according to claim 1 is characterized in that: The expression of the overvoltage operation risk of the distribution network is as follows: ； In the formula, Indicates the total number of nodes in the system; Represents the importance coefficient of the i-th node; represents the overvoltage operation risk of the i-th node, and the expression is as follows: ； In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether the voltage exceeds the upper limit event at node i in scenario s; represents the voltage of node i in scenario s; The severity function of the voltage exceeding the upper limit risk of node i in scenario s is expressed as follows: ； In the formula, Indicates the upper limit of safe voltage; Represents a natural constant.

4. The distribution network operation risk assessment method according to claim 1 is characterized in that: The expression of the low voltage operation risk of the distribution network is as follows: ； In the formula, Indicates the total number of nodes in the system; Represents the importance coefficient of the i-th node; represents the low voltage operation risk of the i-th node, and the expression is as follows: ； In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether the voltage exceeds the lower limit event at node i in scenario s; represents the voltage of node i in scenario s; The severity function of the voltage risk exceeding the lower limit at node i in scenario s is expressed as follows: ； In the formula, Indicates the lower limit of safe voltage; Represents a natural constant.

5. The distribution network operation risk assessment method according to claim 1 is characterized in that: The risk of overload operation of the line The expression is as follows: ； In the formula, Indicates the total number of branches in the distribution network; It represents the active power overload operation risk of line m, and the expression is as follows: ； In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether line j is overloaded in scenario s; represents the active power of line m in scenario s; It represents the severity function of line overload of line m in scenario s, and its expression is as follows: ； In the formula, Indicates the rated capacity of line m.

6. The distribution network operation risk assessment method according to claim 1 is characterized in that: The unscented Kalman filter algorithm comprises the following steps: Get the measurement value of this round , the initial or last updated state estimate and the covariance matrix ; According to the state estimated value and the covariance matrix , perform untraceable transformation, and obtain the Sigma point set ; For the Sigma point set Any Sigma point in Based on the historical state, the exponential smoothing method is used to predict the state of the next moment and obtain the corresponding state prediction value , and then get the mean of the state prediction value and the covariance matrix of the state prediction value ; For the Sigma point set The state prediction value corresponding to any Sigma point in , perform prediction calculation based on the measurement function and obtain the corresponding measurement prediction value , and then get the mean of the measured predicted value ; According to the measured predicted value and the mean of the measured predicted values , by calculating the measurement prediction covariance matrix and the cross-covariance matrix , and the Kalman gain is ; The mean of the predicted values ​​according to the state , Kalman gain , the measurement value of this round and the mean of the measured predicted values , and get the updated state estimate ; According to the covariance matrix of the state prediction value , Kalman gain and the measurement-prediction covariance matrix , and get the updated covariance matrix .

7. The distribution network operation risk assessment method according to claim 6 is characterized in that: The Sigma Point Set Any Sigma point in The expression is as follows: ； In the formula, Represents the Sigma point set The y-th Sigma point in ; Represents the state estimate obtained by the k-1th iteration update; Indicates the dimension of state quantity; Representation Matrix The yth column of represents the covariance matrix obtained by the k-1th iteration update; represents the scaling parameter, , represents the proportional correction factor, It is used to ensure that the matrix Positive semidefinite free parameters.

8. The distribution network operation risk assessment method according to claim 6 is characterized in that: The state prediction value The expression is as follows: ； In the formula, Represents the state prediction value of the yth Sigma point at the kth iteration; Represents the Sigma point set The y-th Sigma point in ; represents the state prediction function; The mean of the state prediction values The expression is as follows: ； In the formula, represents the mean of the state prediction values ​​of the kth iteration; Represents the mean weight of the sigma point set; Indicates the dimension of state quantity; The covariance matrix of the state prediction value The expression is as follows: ； In the formula, Represents the covariance matrix of the state prediction value of the kth iteration; represents the process noise covariance matrix; Represents the covariance matrix weights of the sigma point set.

9. The distribution network operation risk assessment method according to claim 6 is characterized in that: The measured predicted value The expression is as follows: ； In the formula, represents the measured predicted value corresponding to the yth Sigma point at the kth iteration; represents the measurement function; The mean of the measured predicted values The expression is as follows: ； In the formula, represents the mean of the measured prediction values ​​of the kth iteration; Represents the mean weight of the sigma point set; Represents the dimension of the state quantity.

10. The distribution network operation risk assessment method according to claim 6 is characterized in that: The measurement prediction covariance matrix The expression is as follows: ； In the formula, represents the measurement prediction covariance matrix of the kth iteration; Represents the covariance matrix weight of the Sigma point set; represents the measured predicted value of the yth Sigma point at the kth iteration; represents the mean of the measured prediction values ​​of the kth iteration; represents the measurement noise covariance matrix; Indicates the dimension of state quantity; The cross-covariance matrix The expression is as follows: ； In the formula, represents the cross-covariance matrix of the kth iteration; Represents the state prediction value of the yth sigma point in the kth iteration; represents the mean of the state prediction values ​​of the kth iteration; The Kalman gain The expression is as follows: ； In the formula, represents the Kalman gain of the kth iteration.

11. The distribution network operation risk assessment method according to claim 6, characterized in that: The updated state estimate The expression is as follows: ； In the formula, represents the mean of the state prediction values ​​of the kth iteration; represents the Kalman gain of the kth iteration; represents the measurement value of the kth iteration; represents the mean of the measured prediction values ​​at the kth iteration.

12. The distribution network operation risk assessment method according to claim 6, characterized in that: The updated covariance matrix The expression is as follows: ； In the formula, Represents the covariance matrix of the state prediction value of the kth iteration; represents the Kalman gain of the kth iteration; represents the measurement prediction covariance matrix of the kth iteration.

13. A distribution network operation risk assessment device, characterized in that it includes: A data acquisition module is used to obtain the measured values ​​of the distribution network operation; A data prediction module is used to predict the operation state of the distribution network based on the measured value and the unscented Kalman filter algorithm to obtain a state prediction value; A risk assessment module is used to input the state prediction value into a pre-built distribution network operation risk assessment model to obtain a distribution network operation risk assessment result; wherein the distribution network operation risk assessment model includes distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk; The state prediction values ​​include the voltages of multiple nodes in any scenario, the active powers of multiple lines in any scenario, various types of voltages and various types of powers of multiple nodes, and various types of powers and electrical conductance of multiple lines; The data processing steps of the distribution network operation risk assessment model are as follows: Calculating the overvoltage operation risk and the undervoltage operation risk of the distribution network according to the voltages of the multiple nodes in any scenario; According to the active power of the multiple lines in any scenario, the line overload operation risk and the line reverse overload operation risk are calculated; According to the various types of voltages and various types of powers of the multiple nodes, and the various types of powers and electrical conductances of the multiple lines, the pre-constructed power abandonment optimization problem is solved by setting constraints to obtain the risk of renewable energy abandonment; According to the distribution network overvoltage operation risk, distribution network low voltage operation risk, line overload operation risk, line reverse overload operation risk and renewable energy power abandonment risk, the distribution network operation risk assessment result is calculated and the expression is as follows: ； In the formula, Indicates the results of the distribution network operation risk assessment; The weight coefficient representing the risk of overvoltage operation in the distribution network; Indicates the risk of overvoltage operation in the distribution network; The weight coefficient representing the risk of low voltage operation of the distribution network; Indicates the risk of low voltage operation in the distribution network; The weight coefficient indicating the risk of line overload operation; Indicates the risk of line overload operation; The weight coefficient indicating the risk of overload operation of the line; Indicates the risk of reverse overload operation of the line; The weight coefficient representing the risk of renewable energy curtailment; Indicates the risk of curtailment of renewable energy; The expression of the line reverse overload operation risk is as follows: ； In the formula, Indicates the total number of branches in the distribution network; It represents the risk of overload operation due to reverse active power transmission of line m, and the expression is as follows: ； In the formula, s represents the running scenario; D represents the set of running scenarios; represents the probability of scene s; Indicates whether line m is overloaded in scenario s; represents the active power of line m in scenario s; The severity function of the line reverse overload of line m in scenario s is expressed as follows: ； In the formula, Indicates the rated capacity of line m; The objective function of the power curtailment optimization problem is expressed as follows: ； In the formula, represents the objective function of the power curtailment optimization problem; Indicates the amount of power wasted; Indicates the extent to which the voltage exceeds the specified limit; Indicates the degree to which the tidal current exceeds the specified limit; It indicates the regulation coefficient, which ensures that the voltage and power flow can be controlled within the specified limits through power abandonment operation; The constraints of the power curtailment optimization problem include power flow constraints, node power balance constraints and node injection power constraints; The expression of the power flow constraint is as follows: ； ； In the formula, represents the active power flowing from node i to node j; represents the reactive power flowing from node i to node j; represents the voltage of node i; represents the voltage at node j; represents the conductance of the branch from node i to node j; represents the susceptance on the branch from node i to node j; represents the voltage phase difference between node i and node j; represents the active power loss flowing from node i to node j; represents the earth susceptance of the line connecting nodes i and j; represents the reactive power loss flowing from node i to node j; The expression of the node power balance constraint is as follows: ； ； In the formula, represents the renewable energy output power at node i; represents the amount of power abandoned at node i; represents the load active power at node i; represents the injected power at node i; represents the reactive power of the load at node i; represents the injected power at node i; The expression of the node injection power constraint is as follows: ； ； In the formula, represents the real part of the element in the i-th row and j-th column of the admittance matrix; represents the imaginary part of the element in the i-th row and j-th column of the admittance matrix; Indicates the total number of nodes; Represents the set of all lines.

14. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the distribution network operation risk assessment method described in any one of claims 1-12 are implemented.

15. A computer device, comprising: Memory, for storing computer programs / instructions; A processor, configured to execute the computer program / instructions to implement the steps of the distribution network operation risk assessment method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the distribution network operation risk assessment method described in any one of claims 1-12 are implemented.

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