A method and device for evaluating the operation risk of a distribution network containing distributed power sources

Abstract

The present invention provides a method and device for assessing the operating risk of a distribution network containing distributed power sources. The method first calculates the failure probability of distribution lines under different weather types and the failure probability of distributed power sources under different weather types and operating lifespans; then, based on the failure probability of distribution lines and distributed power sources, the operating state of the distribution network is simulated through a pre-constructed distributed power source output model; finally, the operating risk of the distribution network is assessed based on the simulation results. The present invention takes into account the types of distributed power sources and improves the accuracy of the operating risk assessment of the distribution network. The present invention calculates the failure probability of each photovoltaic power generation system, wind turbine and gas turbine in the distribution lines and distributed power sources under different weather types and operating lifespans, taking into account comprehensively, and can accurately reflect the failure probability of distribution lines and distributed power sources, thereby ensuring the accuracy of the operating risk assessment of the distribution network.

Description

Technical Field

[0001] The present invention relates to a distributed power supply access optimization technology, and in particular to a method and device for evaluating the operation risk of a distribution network containing distributed power supplies. Background Art

[0002] The intermittent and uncontrollable characteristics of distributed power generation have introduced new problems for the safe operation of the distribution network. The traditional distribution network is a single-power radial network. Under normal operating conditions, the voltage gradually decreases along the feeder flow direction. When the distributed power source is connected, the transmission power of the power source on the feeder to the load direction is reduced, and the voltage drop of the distribution line is reduced, which may cause the voltage of some nodes to increase or even exceed the voltage deviation. The voltage deviation is related to the access location, access capacity and power type of the distributed power source. When the access point is at the end of the feeder and the output of the distributed power source is large, if the distribution line is lightly loaded, the voltage at the access point of the distributed power source will be significantly increased, and the voltage at this point will exceed the upper limit.

[0003] In a radial distribution network without distributed power generation, the flow flows from the upper power source to each branch load along the feeder. The flow direction of the flow is certain, and the load on the distribution line is relatively light. After large-scale distributed power generation is connected, power is supplied to the surrounding loads first, the flow at the interface between the distribution network and the main network will decrease, and the flow of the distribution line around the distributed power generation will increase. When the entire distribution network is in a light load state and the output of the distributed power generation is large, a large amount of surplus power will flow from the installation point of the distributed power generation along the feeder to the first section of the feeder, forming a reverse flow. In severe cases, it is very likely to cause overload of the distribution line or transformer, and it is easy to cause damage to the transformer.

[0004] In the existing technology, the operational risk assessment of distribution networks mainly focuses on modeling the probability of failure and the severity of the consequences of failures of distribution equipment, such as the risk assessment index system of distribution lines, transformers, circuit breakers, and tie switches. There are few studies on the operational risk assessment of distribution networks containing distributed power sources, and distributed power sources are usually considered as a general unit, resulting in poor accuracy in the operational risk assessment of distribution networks. Summary of the invention

[0005] In order to overcome the shortcomings of poor accuracy in operation risk assessment of distribution networks in the above-mentioned prior art, the present invention provides a method and device for evaluating operation risk of distribution networks containing distributed power sources, which first calculate the failure probability of distribution lines under different weather types and the failure probability of distributed power sources under different weather types and operation lifespans; then, according to the failure probabilities of distribution lines and distributed power sources, the operation state of the distribution network is simulated through a pre-constructed distributed power source output model; finally, the operation risk of the distribution network is evaluated according to the simulation results, the type of distributed power sources is taken into consideration, and the accuracy of the operation risk assessment of the distribution network is improved.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical scheme:

[0007] In one aspect, the present invention provides a method for assessing the operation risk of a distribution network containing distributed power sources, comprising:

[0008] Calculate the failure probability of distribution lines under different weather types and the failure probability of distributed generation under different weather types and operating lifespans;

[0009] According to the failure probability of the distribution lines and the distributed power sources, the operation state of the distribution network is simulated by using a pre-built distributed power source output model;

[0010] The operation risk of the distribution network is evaluated based on the simulation results.

[0011] Weather types include normal weather and severe weather;

[0012] The failure probability of distribution lines under different weather types is calculated as follows:

[0013]

[0014]

[0015] Among them, λ Ln represents the failure probability of the distribution line under normal weather conditions, λ Ls represents the failure probability of distribution lines under severe weather conditions, λ l represents the average failure probability of the distribution line, r represents the proportion of bad weather days within a set time, R represents the proportion of normal weather days within a set time, and F represents the probability of distribution line failure in bad weather.

[0016] The weather type includes normal weather and severe weather, the operating life includes a running-in period and a wear-out period, and the type of distributed power source includes one or more of a photovoltaic power generation system, a wind turbine and a gas turbine;

[0017] The failure probability of photovoltaic power generation system under different weather types and operating life is calculated as follows:

[0018]

[0019]

[0020]

[0021]

[0022] Among them, λ PVn1represents the failure probability of the photovoltaic power generation system in normal weather and during the running-in period; PVn2 represents the failure probability of the photovoltaic power generation system in normal weather and wear period; PVs1 represents the failure probability of the photovoltaic power generation system during bad weather and the running-in period; PVs2 represents the failure probability of the photovoltaic power generation system during severe weather and wear period; λ pv represents the failure probability of a single photovoltaic inverter group in the photovoltaic power generation system; k represents the number of photovoltaic inverter groups in the photovoltaic power generation system; k RI Indicates the failure rate coefficient during the running-in period, k W represents the wear-out failure coefficient, and K0 represents the maximum value of the impact factor, t RI represents the number of days of the running-in period of the photovoltaic power generation system, t represents the working age of the photovoltaic power generation system, and t W Indicates the number of days of wear period of photovoltaic power generation system, t L represents the life of the photovoltaic power generation system, α represents the wear coefficient of the photovoltaic power generation system; R represents the proportion of normal weather days within the set time, and F represents the probability of failure of the distribution line in severe weather;

[0023] The failure probability of the fan under different weather types and operating life is calculated as follows:

[0024]

[0025]

[0026]

[0027]

[0028] Among them, λ Wn1 represents the failure probability of the fan in normal weather and during the running-in period, λ Wn2 represents the failure probability of the fan in normal weather and wear period, λ Ws1 represents the failure probability of the fan in bad weather and during the running-in period, λ Ws2 represents the failure probability of the fan during bad weather and wear period, λ w represents the failure probability of the wind rotor, λ g represents the failure probability of the generator, λ e Represents the failure probability of the power electronic converter.

[0029] The failure probability of gas turbines under different weather types and operating life is calculated as follows:

[0030] λ GTn1 =k RI λ gt

[0031] λ GTn2 =k W λ gt

[0032] λ GTs1 =k RI λ gt

[0033] λ GTs1 =k W λ gt

[0034] Among them, λ GTn1 represents the failure probability of the gas turbine in normal weather and during the running-in period, λ GTn2 represents the failure probability of the gas turbine in normal weather and wear period, λ GTs1 represents the failure probability of the gas turbine in bad weather and during the running-in period, λ GTs1 represents the failure probability of the gas turbine during severe weather and wear period, λ gt Represents the average failure probability of a gas turbine.

[0035] According to the failure probability of the distribution line and the distributed power source, the operation state of the distribution network is simulated by using a pre-built distributed power source output model, including:

[0036] The Monte Carlo method is used to sample the weather type, the distribution line operation state, the distributed power supply operation state and the distributed power supply output to obtain the sampling results; the distribution line operation state and the distributed power supply operation state both include a normal state and a fault state;

[0037] According to the failure probabilities of the distribution lines and distributed power sources, the failure probabilities of the distribution lines and distributed power sources in the sampling results are corrected; the failure probability of the distribution lines in the sampling results is determined according to the number of distribution lines in a faulty state and the total number of distribution lines, and the failure probability of the distributed power sources in the sampling results is determined according to the number of such distributed power sources in a faulty state and the total number of such distributed power sources;

[0038] According to the sampling results after correcting the fault probability and the pre-built distributed power output model, the operation status of the distribution network is simulated by the power system simulation software to obtain the simulation results.

[0039] The distributed power output model includes one or more of a photovoltaic power generation system output model, a wind turbine output model and a gas turbine output model;

[0040] The photovoltaic power generation system output model is constructed as follows:

[0041] P M =x·A·η

[0042] Among them, P M represents the output of the photovoltaic power generation system, A represents the area of ​​the solar cell array composed of solar cell modules, η represents the photoelectric conversion efficiency of the solar cell array, and x represents the light intensity;

[0043] The fan output model is constructed as follows:

[0044]

[0045] Where, v represents wind speed, P W (v) represents the output of the fan when the wind speed is v, P R Indicates the rated output of the fan, v R Indicates rated wind speed, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed;

[0046] The gas turbine output model is constructed as follows:

[0047]

[0048] Among them, P GT represents the output of the gas turbine; V represents the output voltage of the distributed power generation constant power model; V E represents the grid-end voltage of the distributed generation constant power model; X represents the connection reactance between the distributed generation and the distribution grid; m represents the control parameter, and δ represents the power angle.

[0049] The simulation results include power of the distribution line and bus node voltage;

[0050] The step of evaluating the operation risk of the distribution network according to the simulation results includes:

[0051] Determine the overload risk threshold of the distribution network according to the overload value of the transmission line, and determine the voltage over-limit risk threshold of the distribution network according to the voltage over-limit value of the distribution network;

[0052] If the power of the distribution line is less than the overload risk threshold of the distribution network and the bus node voltage is less than the voltage over-limit risk threshold, there is no operation risk in the distribution network; otherwise, the distribution network is at operation risk.

[0053] The overload risk threshold and voltage over-limit risk threshold of the distribution network are determined as follows:

[0054]

[0055]

[0056] Among them, R OD represents the overload risk threshold of the distribution network, RLV Indicates the voltage over-limit risk threshold of the distribution network; S ODl (C / E) represents the severity of overload of the transmission line, and L OD Indicates the overload value of the transmission line, L OD =L-0.8, L represents the proportion of the current flowing through the distribution line to its rated current; S LVb (C / E) indicates the severity of the voltage exceeding the limit in the distribution network, and L LV Indicates that the distribution network voltage exceeds the limit, L LV =V-1.05, V represents the node voltage; i represents the fault state number of the distribution line, NF represents the total number of fault states of the distribution line, l represents the number of the overloaded distribution line, NF l Indicates the total number of overloaded distribution lines, b indicates the bus node number where the voltage exceeds the limit, NF b represents the total number of busbar nodes with voltage exceeding the limit, and p(i) represents the probability of the i-th fault state of the distribution line.

[0057] In another aspect, the present invention provides a distribution network operation risk assessment device including a distributed power source, comprising:

[0058] A calculation module, used to calculate the failure probability of distribution lines under different weather types and the failure probability of distributed power sources under different weather types and operating lifespans;

[0059] A simulation module, used to simulate the operation status of the distribution network through a pre-built distributed power output model according to the failure probability of the distribution line and the distributed power source;

[0060] The evaluation module is used to evaluate the operation risk of the distribution network based on the simulation results.

[0061] Weather types include normal weather and severe weather;

[0062] The calculation module comprises:

[0063] The first calculation unit is used to calculate the failure probability of the distribution line under different weather types according to the following formula:

[0064]

[0065]

[0066] Among them, λ Ln represents the failure probability of the distribution line under normal weather conditions, λ Ls represents the failure probability of distribution lines under severe weather conditions, λ lrepresents the average failure probability of the distribution line, r represents the proportion of bad weather days within a set time, R represents the proportion of normal weather days within a set time, and F represents the probability of distribution line failure in bad weather.

[0067] The weather type includes normal weather and severe weather, the operating life includes a running-in period and a wear-out period; the type of distributed power source includes one or more of a photovoltaic power generation system, a wind turbine and a gas turbine;

[0068] The calculation module comprises:

[0069] The second calculation unit is used to calculate the failure probability of the photovoltaic power generation system under different weather types and operating life according to the following formula:

[0070]

[0071]

[0072]

[0073]

[0074] Among them, λ PVn1 represents the failure probability of the photovoltaic power generation system in normal weather and during the running-in period; PVn2 represents the failure probability of the photovoltaic power generation system in normal weather and wear period; PVs1 represents the failure probability of the photovoltaic power generation system during bad weather and the running-in period; PVs2 represents the failure probability of the photovoltaic power generation system during severe weather and wear period; λ pv represents the failure probability of a single photovoltaic inverter group in the photovoltaic power generation system; k represents the number of photovoltaic inverter groups in the photovoltaic power generation system; k RI Indicates the failure rate coefficient during the running-in period, k W represents the wear-out failure coefficient, and K0 represents the maximum value of the impact factor, t RI represents the number of days of the running-in period of the photovoltaic power generation system, t represents the working age of the photovoltaic power generation system, and t W Indicates the number of days of wear period of photovoltaic power generation system, t L represents the life of the photovoltaic power generation system, α represents the wear coefficient of the photovoltaic power generation system; R represents the proportion of normal weather days within the set time, and F represents the probability of failure of the distribution line in severe weather;

[0075] The third calculation unit is used to calculate the failure probability of the fan under different weather types and operating life according to the following formula:

[0076]

[0077]

[0078]

[0079]

[0080] Among them, λ Wn1 represents the failure probability of the fan in normal weather and during the running-in period, λ Wn2 represents the failure probability of the fan in normal weather and wear period, λ Ws1 represents the failure probability of the fan in bad weather and during the running-in period, λ Ws2 represents the failure probability of the fan during severe weather and wear period, λ w represents the failure probability of the wind rotor, λ g represents the failure probability of the generator, λ e represents the failure probability of the power electronic converter;

[0081] The fourth calculation unit is used to calculate the failure probability of the gas turbine under different weather types and operating life according to the following formula:

[0082] λ GTn1 =k RI λ gt

[0083] λ GTn2 =k W λ gt

[0084] λ GTs1 =k RI λ gt

[0085] λ GTs1 =k W λ gt

[0086] Among them, λ GTn1 represents the failure probability of the gas turbine in normal weather and during the running-in period, λ GTn2 represents the failure probability of the gas turbine in normal weather and wear period, λ GTs1 represents the failure probability of the gas turbine in bad weather and during the running-in period, λ GTs1 represents the failure probability of the gas turbine during severe weather and wear period, λ gt Represents the average failure probability of a gas turbine.

[0087] The simulation modules include:

[0088] A sampling unit is used to sample weather type, distribution line operation status, distributed power supply operation status and distributed power supply output by using Monte Carlo method to obtain sampling results; the distribution line operation status and distributed power supply operation status both include normal status and fault status;

[0089] A correction unit, used to correct the failure probability of the distribution line and the distributed power source in the sampling result according to the failure probability of the distribution line and the distributed power source; the failure probability of the distribution line in the sampling result is determined according to the number of distribution lines in a faulty state and the total number of distribution lines, and the failure probability of the distributed power source in the sampling result is determined according to the number of such distributed power sources in a faulty state and the total number of such distributed power sources;

[0090] The simulation unit is used to simulate the operation state of the distribution network through the power system simulation software according to the sampling result after the correction of the fault probability and the pre-built distributed power output model to obtain the simulation result.

[0091] The simulation modules include:

[0092] A modeling unit, used to establish a distributed power output model, including one or more of a photovoltaic power generation system output model, a wind turbine output model, and a gas turbine output model;

[0093] The photovoltaic power generation system output model is constructed as follows:

[0094] P M =x·A·η

[0095] Among them, P M represents the output of the photovoltaic power generation system, A represents the area of ​​the solar cell array composed of solar cell modules, η represents the photoelectric conversion efficiency of the solar cell array, and x represents the light intensity;

[0096] The fan output model is constructed as follows:

[0097]

[0098] Where, v represents wind speed, P W (v) represents the output of the fan when the wind speed is v, P R Indicates the rated output of the fan, v R Indicates rated wind speed, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed;

[0099] The gas turbine output model is constructed as follows:

[0100]

[0101] Among them, P GT represents the output of the gas turbine; V represents the output voltage of the distributed power generation constant power model; V E represents the grid-end voltage of the distributed generation constant power model; X represents the connection reactance between the distributed generation and the distribution grid; m represents the control parameter, and δ represents the power angle.

[0102] The simulation results include power of the distribution line and bus node voltage;

[0103] The evaluation module includes:

[0104] A determination unit, configured to determine an overload risk threshold of the distribution network according to an overload value of the transmission line, and to determine a voltage over-limit risk threshold of the distribution network according to an over-limit value of the distribution network voltage;

[0105] The evaluation unit is used to determine that if the power of the distribution line is less than the overload risk threshold of the distribution network and the bus node voltage is less than the voltage over-limit risk threshold, then there is no operation risk in the distribution network; otherwise, the distribution network is at operation risk.

[0106] The determining unit is specifically used for:

[0107] The overload risk threshold and voltage over-limit risk threshold of the distribution network are determined according to the following formula:

[0108]

[0109]

[0110] Among them, R OD represents the overload risk threshold of the distribution network, R LV Indicates the voltage over-limit risk threshold of the distribution network; S ODl (C / E) represents the severity of overload of the transmission line, and L OD Indicates the overload value of the transmission line, L OD =L-0.8, L represents the proportion of the current flowing through the distribution line to its rated current; S LVb (C / E) indicates the severity of the voltage over-limit in the distribution network, and L LV Indicates that the distribution network voltage exceeds the limit, L LV =V-1.05, V represents the node voltage; i represents the fault state number of the distribution line, NF represents the total number of fault states of the distribution line, l represents the number of the overloaded distribution line, NF l Indicates the total number of overloaded distribution lines, b indicates the bus node number where the voltage exceeds the limit, NF b represents the total number of busbar nodes with voltage exceeding the limit, and p(i) represents the probability of the i-th fault state of the distribution line.

[0111] Compared with the closest prior art, the technical solution provided by the present invention has the following beneficial effects:

[0112] In the method for assessing the operating risk of a distribution network containing distributed power sources provided by the present invention, the failure probability of the distribution line under different weather types and the failure probability of the distributed power source under different weather types and operating life are first calculated; then, according to the failure probability of the distribution line and the distributed power source, the operating state of the distribution network is simulated through a pre-constructed distributed power source output model; finally, the operating risk of the distribution network is assessed according to the simulation results. The present invention fully considers the type of distributed power source, the weather type and the operating life of the distributed power source, and improves the accuracy of the operating risk assessment of the distribution network;

[0113] The distribution network operation risk assessment device containing distributed power sources provided by the present invention comprises a calculation module, a simulation module and an assessment module. The calculation module is used to calculate the failure probability of the distribution line under different weather types and the failure probability of the distributed power source under different weather types and operating lifespans; the simulation module is used to calculate the failure probability of the distribution line and the distributed power source, and simulates the operating state of the distribution network through a pre-built distributed power source output model; the assessment module is used to assess the operating risk of the distribution network according to the simulation results. The present invention fully considers the type of distributed power source, the weather type and the operating lifespan of the distributed power source, and improves the accuracy of the operation risk assessment of the distribution network;

[0114] The present invention also determines the risk threshold of the distribution network from the two aspects of overload and voltage over-limit. Specifically, the overload risk threshold of the distribution network is determined by the overload severity of the distribution network, and the voltage over-limit risk threshold of the distribution network is determined by the voltage over-limit severity obtained in the distribution network. The power and bus node voltage of the distribution line obtained by simulation are compared with the overload risk threshold and the voltage over-limit risk threshold of the distribution network, respectively, to ensure the accuracy of the risk assessment of the distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 is a flow chart of a method for risk assessment of distribution network operation containing distributed power sources in Embodiment 1 of the present invention;

[0116] Figure 2 This is a schematic diagram of the IEEE 33-node distribution network in Embodiment 2 of the present invention;

[0117] Figure 3 This is a schematic diagram of the impact of the type of distributed power source on the overload risk of distribution lines in Example 2 of the present invention;

[0118] Figure 4 Schematic diagram of the impact of the type of distributed power source on the voltage over-limit risk of the distribution network in Embodiment 2 of the present invention;

[0119] Figure 5Schematic diagram of the impact of the capacity distribution of distributed power sources on the overload risk of distribution lines in Embodiment 2 of the present invention;

[0120] Figure 6 It is a schematic diagram of the impact of the capacity distribution of distributed power sources on the voltage over-limit risk of the distribution network in Example 2 of the present invention. DETAILED DESCRIPTION

[0121] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0122] Example 1

[0123] Embodiment 1 of the present invention provides a method for evaluating the operation risk of a distribution network containing distributed power sources, and its flow chart is as follows: Figure 1 As shown, the specific process is as follows:

[0124] S101: Calculate the failure probability of distribution lines under different weather types and the failure probability of distributed generation under different weather types and operating lifespans;

[0125] S102: According to the failure probability of the distribution lines and the distributed power sources, the operation state of the distribution network is simulated by using a pre-built distributed power source output model;

[0126] S103: Evaluate the operation risk of the distribution network according to the simulation results obtained in S102.

[0127] The weather type in the above S101 includes normal weather and severe weather, the operating life includes a running-in period and a wear-out period, and the type of distributed power source includes one or more of a photovoltaic power generation system, a wind turbine and a gas turbine;

[0128] In the embodiment of the present invention, the failure probability of each of the photovoltaic power generation system, wind turbine and gas turbine in the distribution line and distributed power source under different weather types and operating life is calculated, and comprehensive consideration is given to accurately reflect the failure probability of the distribution line and distributed power source, thereby ensuring the accuracy of the distribution network operation risk assessment. The specific steps are as follows:

[0129] The failure probability of distribution lines under different weather types is calculated as follows:

[0130]

[0131]

[0132] Among them, λ Ln represents the failure probability of the distribution line under normal weather conditions, λ Ls represents the failure probability of distribution lines under severe weather conditions, λ lrepresents the average failure probability of the distribution line, r represents the proportion of bad weather days within a set time, R represents the proportion of normal weather days within a set time, and F represents the probability of distribution line failure in bad weather.

[0133] 1) The failure probability of photovoltaic power generation system under different weather types and operating life is calculated as follows:

[0134]

[0135]

[0136]

[0137]

[0138] Among them, λ PVn1 represents the failure probability of the photovoltaic power generation system in normal weather and during the running-in period; PVn2 represents the failure probability of the photovoltaic power generation system in normal weather and wear period; PVs1 represents the failure probability of the photovoltaic power generation system during bad weather and the running-in period; PVs2 represents the failure probability of the photovoltaic power generation system during severe weather and wear period; λ pv represents the failure probability of a single photovoltaic inverter group in the photovoltaic power generation system; k represents the number of photovoltaic inverter groups in the photovoltaic power generation system; k RI Indicates the failure rate coefficient during the running-in period, k W represents the wear-out failure coefficient, and K0 represents the maximum value of the impact factor, t RI represents the number of days of the running-in period of the photovoltaic power generation system, t represents the working age of the photovoltaic power generation system, and t W Indicates the number of days of wear period of photovoltaic power generation system, t L represents the life of the photovoltaic power generation system, and α represents the wear coefficient of the photovoltaic power generation system.

[0139] 2) The failure probability of the fan under different weather types and operating life is calculated as follows:

[0140]

[0141]

[0142]

[0143]

[0144] Among them, λ Wn1 represents the failure probability of the fan in normal weather and during the running-in period, λ Wn2represents the failure probability of the fan in normal weather and wear period, λ Ws1 represents the failure probability of the fan in bad weather and during the running-in period, λ Ws2 represents the failure probability of the fan during bad weather and wear period, λ w represents the failure probability of the wind rotor, λ g represents the failure probability of the generator, λ e Represents the failure probability of the power electronic converter.

[0145] 3) The failure probability of the gas turbine under different weather types and operating life is calculated as follows:

[0146] λ GTn1 =k RI λ gt

[0147] λ GTn2 =k W λ gt

[0148] λ GTs1 =k RI λ gt

[0149] λ GTs1 =k W λ gt

[0150] Among them, λ GTn1 represents the failure probability of the gas turbine in normal weather and during the running-in period, λ GTn2 represents the failure probability of the gas turbine in normal weather and wear period, λ GTs1 represents the failure probability of the gas turbine in bad weather and during the running-in period, λ GTs1 represents the failure probability of the gas turbine during severe weather and wear period, λ gt Represents the average failure probability of a gas turbine.

[0151] In the above S102, according to the failure probability of the distribution line and the distributed power source, the operation state of the distribution network is simulated through the pre-built distributed power source output model, and the specific process is as follows:

[0152] 1) Using the Monte Carlo method to sample weather type, distribution line operation status, distributed power supply operation status and distributed power supply output to obtain sampling results; the distribution line operation status and distributed power supply operation status both include normal status and fault status;

[0153] 2) According to the failure probabilities of the distribution lines and distributed power sources, the failure probabilities of the distribution lines and distributed power sources in the sampling results are corrected; the failure probability of the distribution lines in the sampling results is determined according to the number of distribution lines in a faulty state and the total number of distribution lines, and the failure probability of the distributed power sources in the sampling results is determined according to the number of such distributed power sources in a faulty state and the total number of such distributed power sources;

[0154] 3) According to the sampling results after correcting the fault probability and the pre-built distributed power output model, the operation status of the distribution network is simulated through the power system simulation software to obtain the simulation results.

[0155] The distributed power output model in the above S102 includes one or more of a photovoltaic power generation system output model, a wind turbine output model, and a gas turbine output model;

[0156] 1) The output model of photovoltaic power generation system is constructed as follows:

[0157] P M =x·A·η

[0158] Among them, P M represents the output of the photovoltaic power generation system, A represents the area of ​​the solar cell array composed of solar cell modules, η represents the photoelectric conversion efficiency of the solar cell array, and x represents the light intensity;

[0159] 2) The fan output model is constructed as follows:

[0160]

[0161] Where, v represents wind speed, P W (v) represents the output of the fan when the wind speed is v, P R Indicates the rated output of the fan, v R Indicates rated wind speed, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed;

[0162] 3) The gas turbine output model is constructed as follows:

[0163]

[0164] Among them, P GT represents the output of the gas turbine; V represents the output voltage of the distributed power generation constant power model; V E represents the grid-end voltage of the distributed generation constant power model; X represents the connection reactance between the distributed generation and the distribution grid; m represents the control parameter, and δ represents the power angle.

[0165] In the above S102, the operating state of the distribution network is simulated according to the failure probabilities of the distribution lines and distributed power sources under different weather types and operating lifespans, the sampling results and the pre-built distributed power source output model. Specifically, the operating state of the distribution network is simulated according to the failure probabilities of the distribution lines and distributed power sources under different weather types and operating lifespans, the sampling results and the pre-built distributed power source output model, and the power system simulation software is used to obtain the simulation results, which include the power of the distribution lines and the bus node voltage.

[0166] In the above S103, the simulation results include the power of the distribution line and the bus node voltage. In the embodiment of the present invention, the risk threshold of the distribution network is determined from the two aspects of overload and voltage over-limit, and the power of the distribution line and the bus node voltage obtained by simulation are compared with the overload risk threshold and the voltage over-limit risk threshold of the distribution network respectively to ensure the accuracy of the distribution network operation risk assessment. The specific process is as follows:

[0167] 1) Determine the overload risk threshold of the distribution network according to the overload value of the transmission line, and determine the voltage over-limit risk threshold of the distribution network according to the voltage over-limit value of the distribution network;

[0168] 2) If the power of the distribution line is less than the overload risk threshold of the distribution network and the bus node voltage is less than the voltage over-limit risk threshold, there is no operation risk in the distribution network; otherwise, the distribution network is at operation risk.

[0169] In the above 1), the overload risk threshold and voltage over-limit risk threshold of the distribution network are determined as follows:

[0170]

[0171]

[0172] Among them, R OD represents the overload risk threshold of the distribution network, R LV Indicates the voltage over-limit risk threshold of the distribution network; S ODl (C / E) represents the severity of overload of the transmission line, and L OD Indicates the overload value of the transmission line, L OD =L-0.8, L represents the proportion of the current flowing through the distribution line to its rated current; S LVb (C / E) indicates the severity of the voltage over-limit in the distribution network, and L LV Indicates that the distribution network voltage exceeds the limit, L LV =V-1.05, V represents the node voltage; i represents the fault state number of the distribution line, NF represents the total number of fault states of the distribution line, l represents the number of the overloaded distribution line, NF lIndicates the total number of overloaded distribution lines, b indicates the bus node number where the voltage exceeds the limit, NF b represents the total number of busbar nodes with voltage exceeding the limit, and p(i) represents the probability of the i-th fault state of the distribution line.

[0173] Based on the same inventive concept as the above-mentioned distribution network operation risk assessment method containing distributed power sources, embodiment 1 of the present invention also provides a distribution network operation risk assessment device containing distributed power sources, including a calculation module, a simulation module and an assessment module. The functions of these three modules are described in detail below:

[0174] The calculation module is used to calculate the failure probability of the distribution line under different weather types and the failure probability of the distributed power source under different weather types and operating life;

[0175] The simulation module is used to simulate the operation status of the distribution network through a pre-built distributed power output model according to the failure probability of the distribution lines and distributed power sources;

[0176] The evaluation module is used to evaluate the operation risk of the distribution network based on the simulation results.

[0177] Weather types include normal weather and severe weather.

[0178] The above-mentioned calculation module includes:

[0179] The first calculation unit is used to calculate the failure probability of the distribution line under different weather types according to the following formula:

[0180]

[0181]

[0182] Among them, λ Ln represents the failure probability of the distribution line under normal weather conditions, λ Ls represents the failure probability of distribution lines under severe weather conditions, λ l represents the average failure probability of the distribution line, r represents the proportion of bad weather days within a set time, R represents the proportion of normal weather days within a set time, and F represents the probability of distribution line failure in bad weather.

[0183] The types of the above-mentioned distributed power sources include one or more of photovoltaic power generation systems, wind turbines and gas turbines;

[0184] Weather types include normal weather and severe weather, and operating life includes a running-in period and a wear period.

[0185] The above-mentioned calculation modules include:

[0186] 1) The second calculation unit is used to calculate the failure probability of the photovoltaic power generation system under different weather types and operating life according to the following formula:

[0187]

[0188]

[0189]

[0190]

[0191] Among them, λ PVn1 represents the failure probability of the photovoltaic power generation system in normal weather and during the running-in period; PVn2 represents the failure probability of the photovoltaic power generation system in normal weather and wear period; PVs1 represents the failure probability of the photovoltaic power generation system during bad weather and the running-in period; PVs2 represents the failure probability of the photovoltaic power generation system during severe weather and wear period; λ pv represents the failure probability of a single photovoltaic inverter group in the photovoltaic power generation system; k represents the number of photovoltaic inverter groups in the photovoltaic power generation system; k RI Indicates the failure rate coefficient during the running-in period, k W represents the wear-out failure coefficient, and K0 represents the maximum value of the impact factor, t RI represents the number of days of the running-in period of the photovoltaic power generation system, t represents the working age of the photovoltaic power generation system, and t W Indicates the number of days of wear period of photovoltaic power generation system, t L represents the life of the photovoltaic power generation system, and α represents the wear coefficient of the photovoltaic power generation system.

[0192] 2) The third calculation unit is used to calculate the failure probability of the fan under different weather types and operating life according to the following formula:

[0193]

[0194]

[0195]

[0196]

[0197] Among them, λ Wn1 represents the failure probability of the fan in normal weather and during the running-in period, λ Wn2 represents the failure probability of the fan in normal weather and wear period, λ Ws1 represents the failure probability of the fan in bad weather and during the running-in period, λ Ws2 represents the failure probability of the fan during bad weather and wear period, λw represents the failure probability of the wind rotor, λ g represents the failure probability of the generator, λ e Represents the failure probability of the power electronic converter.

[0198] 3) The fourth calculation unit is used to calculate the failure probability of the gas turbine under different weather types and operating life according to the following formula:

[0199] λ GTn1 =k RI λ gt

[0200] λ GTn2 =k W λ gt

[0201] λ GTs1 =k RI λ gt

[0202] λ GTs1 =k W λ gt

[0203] Among them, λ GTn1 represents the failure probability of the gas turbine in normal weather and during the running-in period, λ GTn2 represents the failure probability of the gas turbine in normal weather and wear period, λ GTs1 represents the failure probability of the gas turbine in bad weather and during the running-in period, λ GTs1 represents the failure probability of the gas turbine during severe weather and wear period, λ gt Represents the average failure probability of a gas turbine.

[0204] The above simulation modules include:

[0205] A sampling unit is used to sample weather type, distribution line operation status, distributed power supply operation status and distributed power supply output by using Monte Carlo method to obtain sampling results; the distribution line operation status and distributed power supply operation status both include normal status and fault status;

[0206] A correction unit, used to correct the failure probability of the distribution line and the distributed power source in the sampling result according to the failure probability of the distribution line and the distributed power source; the failure probability of the distribution line in the sampling result is determined according to the number of distribution lines in a faulty state and the total number of distribution lines, and the failure probability of the distributed power source in the sampling result is determined according to the number of such distributed power sources in a faulty state and the total number of such distributed power sources;

[0207] The simulation unit is used to simulate the operation state of the distribution network through the power system simulation software according to the sampling result after the correction of the fault probability and the pre-built distributed power output model to obtain the simulation result.

[0208] The simulation module also includes a modeling unit, which is used to establish a distributed power output model, including one or more of a photovoltaic power generation system output model, a wind turbine output model, and a gas turbine output model;

[0209] The photovoltaic power generation system output model is constructed as follows:

[0210] P M =x·A·η

[0211] Among them, P M represents the output of the photovoltaic power generation system, A represents the area of ​​the solar cell array composed of solar cell modules, η represents the photoelectric conversion efficiency of the solar cell array, and x represents the light intensity;

[0212] The fan output model is constructed as follows:

[0213]

[0214] Where, v represents wind speed, P W (v) represents the output of the fan when the wind speed is v, P R Indicates the rated output of the fan, v R Indicates rated wind speed, v ci represents the cut-in wind speed, v co Indicates the cut-out wind speed;

[0215] The gas turbine output model is constructed as follows:

[0216]

[0217] Among them, P GT represents the output of the gas turbine; V represents the output voltage of the distributed power generation constant power model; V E represents the grid-end voltage of the distributed power generation constant power model; X represents the connection reactance between the distributed power generation and the distribution grid; m represents the control parameter, δ represents the power angle, and the output power of the distributed power generation is adjusted by controlling δ and m, and the output power of the distributed power generation is kept constant.

[0218] The simulation results include the power and bus node voltage of the distribution line. The above evaluation modules include:

[0219] 1) A determination unit, configured to determine an overload risk threshold of a distribution network according to an overload value of a transmission line, and to determine a voltage over-limit risk threshold of a distribution network according to a voltage over-limit value of the distribution network;

[0220] 2) An evaluation unit, which is used to determine that if the power of the distribution line is less than the overload risk threshold of the distribution network and the bus node voltage is less than the voltage over-limit risk threshold, the distribution network does not have an operation risk; otherwise, the distribution network is at an operation risk.

[0221] The determination unit determines the overload risk threshold and voltage over-limit risk threshold of the distribution network according to the following formula:

[0222]

[0223]

[0224] Among them, R OD represents the overload risk threshold of the distribution network, R LV Indicates the voltage over-limit risk threshold of the distribution network; S ODl (C / E) represents the severity of overload of the transmission line, and L OD Indicates the overload value of the transmission line, L OD =L-0.8, L represents the proportion of the current flowing through the distribution line to its rated current; S LVb (C / E) indicates the severity of the voltage over-limit in the distribution network, and L LV Indicates that the distribution network voltage exceeds the limit, L LV =V-1.05, V represents the node voltage; i represents the fault state number of the distribution line, NF represents the total number of fault states of the distribution line, l represents the number of the overloaded distribution line, NF l Indicates the total number of overloaded distribution lines, b indicates the bus node number where the voltage exceeds the limit, NF b represents the total number of busbar nodes with voltage exceeding the limit, and p(i) represents the probability of the i-th fault state of the distribution line.

[0225] Example 2

[0226] Embodiment 2 of the present invention provides a method for assessing the operation risk of a distribution network containing distributed power sources, which is applicable to scenarios with high penetration of distributed power sources, and is specifically divided into the following two situations:

[0227] (1) Impact of different types of distributed power sources on the power supply security of distribution networks:

[0228] Connect different types of distributed power sources to a single node, such as Figure 2In the IEEE 33-node distribution network shown in the figure, DG1, DG2, and DG3 represent distributed power sources, and 3 wind turbines, 3 photovoltaic power sources, and 3 micro gas turbines are connected to nodes 7, 17, and 30 respectively. The ratio of the rated output of each distributed power source is 2:5:3. The influence of distributed power sources on the distribution network when the penetration rate is 0.15, 0.25, 0.35, 0.45, and 0.6 is explored, and the following is obtained: Figure 3 The schematic diagram of the impact of distributed power generation types on the overload risk of distribution lines and the Figure 4 The diagram shows the impact of distributed generation types on the voltage over-limit risk of the distribution network.

[0229] (2) Impact of capacity distribution on distribution network under mixed access of multiple distributed generation sources:

[0230] like Figure 2 In the IEEE 33-node distribution network shown in the figure, a wind turbine is connected at node 7, a photovoltaic power source is connected at node 17, and a micro gas turbine is connected at node 30. The influence of distributed power sources on the system is explored when the penetration rates are 0.15, 0.25, 0.35, 0.45, and 0.6, respectively. The following results are obtained: Figure 5 The schematic diagram of the impact of distributed generation capacity distribution on the risk of overload of distribution lines and Figure 6 The schematic diagram shows the impact of distributed generation capacity distribution on the voltage over-limit risk of the distribution network.

[0231] For the convenience of description, the various parts of the above-mentioned device are divided into various modules or units according to their functions and described separately. Of course, when implementing the present application, the functions of each module or unit can be implemented in the same or multiple software or hardware.

[0232] 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 in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Ordinary technicians in the relevant field can still modify or make equivalent substitutions to the specific implementation methods of the present invention with reference to the above embodiments. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention are within the scope of protection of the claims of the present invention to be approved.

Claims

1. A method for assessing the operation risk of a distribution network containing distributed power sources, characterized in that: include: Calculate the failure probability of distribution lines under different weather types and the failure probability of distributed generation under different weather types and operating lifespans; According to the failure probability of the distribution lines and the distributed power sources, the operation state of the distribution network is simulated by using a pre-built distributed power source output model; Evaluate the operation risk of the distribution network based on the simulation results; Weather types include normal weather and severe weather; The failure probability of distribution lines under different weather types is calculated as follows: Among them, λ Ln represents the failure probability of the distribution line under normal weather conditions, λ Ls represents the failure probability of distribution lines under severe weather conditions, λ l represents the average failure probability of the distribution line, r represents the proportion of bad weather days within a set time, R represents the proportion of normal weather days within a set time, and F represents the probability of failure of the distribution line under bad weather; The operating life includes a running-in period and a wear period, and the type of the distributed power source includes one or more of a photovoltaic power generation system, a wind turbine and a gas turbine; The failure probability under different weather types and operating life is calculated as follows: Among them, λ PVn1 represents the failure probability of the photovoltaic power generation system in normal weather and during the running-in period; PVn2 represents the failure probability of the photovoltaic power generation system in normal weather and wear period; PVs1 represents the failure probability of the photovoltaic power generation system during bad weather and the running-in period; PVs2 represents the failure probability of the photovoltaic power generation system during severe weather and wear period; λ pv represents the failure probability of a single photovoltaic inverter group in the photovoltaic power generation system; k represents the number of photovoltaic inverter groups in the photovoltaic power generation system; k RI Indicates the failure rate coefficient during the running-in period, k W represents the wear-out failure coefficient, and K0 represents the maximum value of the impact factor, t RI represents the number of days of the running-in period of the photovoltaic power generation system, t represents the working age of the photovoltaic power generation system, and t W Indicates the number of days of wear period of photovoltaic power generation system, t L represents the life of the photovoltaic power generation system, α represents the wear coefficient of the photovoltaic power generation system; R represents the proportion of normal weather days within the set time, and F represents the probability of failure of the distribution line in severe weather; The types of distributed power sources include wind turbines. The failure probability under different weather types and operating life is calculated as follows: Among them, λ Wn1 represents the failure probability of the fan in normal weather and during the running-in period, λ Wn2 represents the failure probability of the fan in normal weather and wear period, λ Ws1 represents the failure probability of the fan in bad weather and during the running-in period, λ Ws2 represents the failure probability of the fan during severe weather and wear period, λ w represents the failure probability of the wind turbine, λ g represents the failure probability of the generator, λ e represents the failure probability of the power electronic converter; The failure probability of gas turbines under different weather types and operating life is calculated as follows: l GTn1 =k RI l gt l GTn2 =k W l gt l GTs1 =k RI l gt l GTs1 =k W l gt Among them, λ GTn1 represents the failure probability of the gas turbine in normal weather and during the running-in period, λ GTn2 represents the failure probability of the gas turbine in normal weather and wear period, λ GTs1 represents the failure probability of the gas turbine in bad weather and during the running-in period, λ GTs1 represents the failure probability of the gas turbine during severe weather and wear period, λ gt Represents the average failure probability of a gas turbine.

2. The method for risk assessment of distribution network operation containing distributed power sources according to claim 1, characterized in that: According to the failure probability of the distribution line and the distributed power source, the operation state of the distribution network is simulated by using a pre-built distributed power source output model, including: The Monte Carlo method is used to sample the weather type, the distribution line operation state, the distributed power supply operation state and the distributed power supply output to obtain the sampling results; the distribution line operation state and the distributed power supply operation state both include a normal state and a fault state; According to the failure probabilities of the distribution lines and distributed power sources, the failure probabilities of the distribution lines and distributed power sources in the sampling results are corrected; the failure probability of the distribution lines in the sampling results is determined according to the number of distribution lines in a faulty state and the total number of distribution lines, and the failure probability of the distributed power sources in the sampling results is determined according to the number of such distributed power sources in a faulty state and the total number of such distributed power sources; According to the sampling results after correcting the fault probability and the pre-built distributed power output model, the operation status of the distribution network is simulated by the power system simulation software to obtain the simulation results.

3. The method for risk assessment of distribution network operation containing distributed power sources according to claim 1 or 2, characterized in that: The distributed power output model includes one or more of a photovoltaic power generation system output model, a wind turbine output model and a gas turbine output model; The photovoltaic power generation system output model is constructed as follows: P M =x·A·η Among them, P M represents the output of the photovoltaic power generation system, A represents the area of ​​the solar cell array composed of solar cell modules, η represents the photoelectric conversion efficiency of the solar cell array, and x represents the light intensity; The gas turbine output model is constructed as follows: Among them, P GT represents the output of the gas turbine; V represents the output voltage of the distributed power generation constant power model; V E represents the grid-end voltage of the distributed generation constant power model; X represents the connection reactance between the distributed generation and the distribution grid; m represents the control parameter, and δ represents the power angle.

4. The method for risk assessment of distribution network operation containing distributed power sources according to claim 1, characterized in that: The simulation results include power of the distribution line and bus node voltage; The step of evaluating the operation risk of the distribution network according to the simulation results includes: Determine the overload risk threshold of the distribution network according to the overload value of the transmission line, and determine the voltage over-limit risk threshold of the distribution network according to the voltage over-limit value of the distribution network; If the power of the distribution line is less than the overload risk threshold of the distribution network and the bus node voltage is less than the voltage over-limit risk threshold, there is no operation risk in the distribution network; otherwise, there is an operation risk in the distribution network.

5. The method for risk assessment of distribution network operation containing distributed power sources according to claim 4, characterized in that: The overload risk threshold and voltage over-limit risk threshold of the distribution network are determined by the following formulas: Among them, R OD represents the overload risk threshold of the distribution network, R LV Indicates the voltage over-limit risk threshold of the distribution network; S ODl (C / E) represents the severity of overload of the transmission line, and L OD Indicates the overload value of the transmission line, L OD =L-0.8, L represents the proportion of the current flowing through the distribution line to its rated current; S LVb (C / E) indicates the severity of the voltage over-limit in the distribution network, and L LV Indicates that the distribution network voltage exceeds the limit, L LV =V-1.05, V represents the node voltage; i represents the fault state number of the distribution line, NF represents the total number of fault states of the distribution line, l represents the number of the overloaded distribution line, NF l Indicates the total number of overloaded distribution lines, b indicates the bus node number where the voltage exceeds the limit, NF b represents the total number of busbar nodes with voltage exceeding the limit, and p(i) represents the probability of the i-th fault state of the distribution line.

6. A distribution network operation risk assessment device containing distributed power sources, characterized in that: include: A calculation module, used to calculate the failure probability of distribution lines under different weather types and the failure probability of distributed power sources under different weather types and operating lifespans; A simulation module, used to simulate the operation status of the distribution network through a pre-built distributed power output model according to the failure probability of the distribution line and the distributed power source; An evaluation module, used to evaluate the operation risk of the distribution network based on the simulation results; Said weather types include normal weather and severe weather; The calculation module comprises: The first calculation unit is used to calculate the failure probability of the distribution line under different weather types according to the following formula: Among them, λ Ln represents the failure probability of the distribution line under normal weather conditions, λ Ls represents the failure probability of distribution lines under severe weather conditions, λ l represents the average failure probability of the distribution line, r represents the proportion of bad weather days within a set time, R represents the proportion of normal weather days within a set time, and F represents the probability of failure of the distribution line under bad weather; The weather type includes normal weather and severe weather, the operating life includes a running-in period and a wear-out period, and the type of distributed power source includes one or more of a photovoltaic power generation system, a wind turbine and a gas turbine; The calculation module comprises: The second calculation unit is used to calculate the failure probability of the photovoltaic power generation system under different weather types and operating life according to the following formula: Among them, λ PVn1 represents the failure probability of the photovoltaic power generation system in normal weather and during the running-in period; PVn2 represents the failure probability of the photovoltaic power generation system in normal weather and wear period; PVs1 represents the failure probability of the photovoltaic power generation system during bad weather and the running-in period; PVs2 represents the failure probability of the photovoltaic power generation system during severe weather and wear period; λ pv represents the failure probability of a single photovoltaic inverter group in the photovoltaic power generation system; k represents the number of photovoltaic inverter groups in the photovoltaic power generation system; k RI Indicates the failure rate coefficient during the running-in period, k W represents the wear-out failure coefficient, and K0 represents the maximum value of the impact factor, t RI represents the number of days of the running-in period of the photovoltaic power generation system, t represents the working age of the photovoltaic power generation system, and t W Indicates the number of days of wear period of photovoltaic power generation system, t L represents the life of the photovoltaic power generation system, α represents the wear coefficient of the photovoltaic power generation system; R represents the proportion of normal weather days within the set time, and F represents the probability of failure of the distribution line in severe weather; The third calculation unit is used to calculate the failure probability of the fan under different weather types and operating life according to the following formula: Among them, λ Wn1 represents the failure probability of the fan in normal weather and during the running-in period, λ Wn2 represents the failure probability of the fan in normal weather and wear period, λ Ws1 represents the failure probability of the fan in bad weather and during the running-in period, λ Ws2 represents the failure probability of the fan during severe weather and wear period, λ w represents the failure probability of the wind turbine, λ g represents the failure probability of the generator, λ e represents the failure probability of the power electronic converter; The calculation module comprises: The fourth calculation unit is used to calculate the failure probability of the gas turbine under different weather types and operating life according to the following formula: l GTn1 =k RI l gt l GTn2 =k W l gt l GTs1 =k RI l gt l GTs1 =k W l gt Among them, λ GTn1 represents the failure probability of the gas turbine in normal weather and during the running-in period, λ GTn2 represents the failure probability of the gas turbine in normal weather and wear period, λ GTs1 represents the failure probability of the gas turbine in bad weather and during the running-in period, λ GTs1 represents the failure probability of the gas turbine during severe weather and wear period, λ gt Represents the average failure probability of a gas turbine.

7. The distribution network operation risk assessment device containing distributed power sources according to claim 6 is characterized in that: The simulation module comprises: A sampling unit is used to sample weather type, distribution line operation status, distributed power supply operation status and distributed power supply output by using Monte Carlo method to obtain sampling results; the distribution line operation status and distributed power supply operation status both include normal status and fault status; A correction unit, used to correct the failure probability of the distribution line and the distributed power source in the sampling result according to the failure probability of the distribution line and the distributed power source; the failure probability of the distribution line in the sampling result is determined according to the number of distribution lines in a faulty state and the total number of distribution lines, and the failure probability of the distributed power source in the sampling result is determined according to the number of such distributed power sources in a faulty state and the total number of such distributed power sources; The simulation unit is used to simulate the operation state of the distribution network through the power system simulation software according to the sampling result after the correction of the fault probability and the pre-built distributed power output model to obtain the simulation result.

8. The distribution network operation risk assessment device containing distributed power sources according to claim 6 or 7, characterized in that: The simulation module comprises: A modeling unit, used to establish a distributed power output model, including one or more of a photovoltaic power generation system output model, a wind turbine output model, and a gas turbine output model; The photovoltaic power generation system output model is constructed as follows: P M =x·A·η Among them, P M represents the output of the photovoltaic power generation system, A represents the area of ​​the solar cell array composed of solar cell modules, η represents the photoelectric conversion efficiency of the solar cell array, and x represents the light intensity; The gas turbine output model is constructed as follows: Among them, P GT represents the output of the gas turbine; V represents the output voltage of the distributed power generation constant power model; V E represents the grid-end voltage of the distributed generation constant power model; X represents the connection reactance between the distributed generation and the distribution grid; m represents the control parameter, and δ represents the power angle.

9. The distribution network operation risk assessment device containing distributed power sources according to claim 7, characterized in that: The simulation results include power of the distribution line and bus node voltage; The evaluation module includes: A determination unit, configured to determine an overload risk threshold of the distribution network according to an overload value of the transmission line, and to determine a voltage over-limit risk threshold of the distribution network according to an over-limit value of the distribution network voltage; The evaluation unit is used to determine that if the power of the distribution line is less than the overload risk threshold of the distribution network and the bus node voltage is less than the voltage over-limit risk threshold, then there is no operation risk in the distribution network; otherwise, there is an operation risk in the distribution network.

10. The distribution network operation risk assessment device containing distributed power sources according to claim 9, characterized in that: The determining unit is specifically used for: The overload risk threshold and voltage over-limit risk threshold of the distribution network are determined according to the following formula: Among them, R OD represents the overload risk threshold of the distribution network, R LV Indicates the voltage over-limit risk threshold of the distribution network; S ODl (C / E) represents the severity of overload of the transmission line, and L OD Indicates the overload value of the transmission line, L OD =L-0.8, L represents the proportion of the current flowing through the distribution line to its rated current; S LVb (C / E) indicates the severity of the voltage over-limit in the distribution network, and L LV Indicates that the distribution network voltage exceeds the limit, L LV =V-1.05, V represents the node voltage; i represents the fault state number of the distribution line, NF represents the total number of fault states of the distribution line, l represents the number of the overloaded distribution line, NF l Indicates the total number of overloaded distribution lines, b indicates the bus node number where the voltage exceeds the limit, NF b represents the total number of busbar nodes with voltage exceeding the limit, and p(i) represents the probability of the i-th fault state of the distribution line.

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