Power distribution network power supply reliability evaluation method considering distributed power supply

Abstract

The invention relates to a power distribution network power supply reliability evaluation method considering a distributed power supply, and the method specifically comprises the steps: recognizing a weak link of a power distribution network according to the power failure information of an active power distribution network, and building a distributed power supply output and load uncertainty model according to the data of the weak link; establishing a power supply reliability model according to the distributed power supply output and load uncertainty model; dividing a power distribution network minimum area according to the power supply reliability model, dividing an island based on the divided power distribution network minimum area, and reducing a load to ensure stable operation of the island; establishing an active power distribution network reliability evaluation model based on sequential Monte Carlo simulation according to the divided islands; and calculating the annual average outage rate and the annual average outage time of a load point by using the reliability evaluation model of the active power distribution network, and calculating the power supply reliability index of the system to evaluate the power supply reliability of the power distribution network. And theoretical support is provided for planning, construction and operation after the distributed power supply is connected to the power distribution system.

Description

Technical Field

[0001] The invention relates to a distribution network power supply reliability simulation evaluation method, belonging to the technical field of power system planning and operation analysis. Background Art

[0002] Under the background of "dual carbon", the distribution network faces new challenges of massive distributed energy access and large-scale development of multiple loads. The access of distributed power sources to the distribution system will have a great impact on the reliability, safety and stability of the distribution system, and will change the single power network structure and unidirectional power transmission mode of the traditional distribution system, affecting the flow calculation, the design and setting of relay protection, etc. After the distributed power source is connected to the distribution system, when a fault occurs on the feeder, the distribution system may have an island operation mode containing distributed power sources, so that the load point behind the faulty feeder may not be powered off. The output power of distributed power sources has the characteristics of randomness, intermittency, and uncertainty. After it is connected to the distribution system, the distribution system is transformed from a single power network to a multi-power network, and the flow is no longer unidirectional. The emergence of bidirectional flow increases the difficulty of research on the reliability of the distribution system, and the reliability assessment model and algorithm of the distribution system taking into account distributed power sources will change.

[0003] Accurately evaluating the reliability of the distribution system taking into account distributed power sources can provide a basis for ensuring reliable and continuous power supply of the system, and provide theoretical support for the planning, construction and operation of distributed power sources after they are connected to the distribution system. This is of great significance to the development of new energy. At present, there is no reliability assessment method for the distribution system taking into account distributed power sources. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides an evaluation method for accurately evaluating the reliability of a power distribution system taking into account distributed power sources.

[0005] In order to achieve the above-mentioned purpose, the technical solution proposed by the present invention is: a method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources, specifically:

[0006] Identify weak links in the distribution network based on the power outage information data of the active distribution network and form weak link statistics;

[0007] Establish the uncertainty model of distributed generation output and load based on the weak link statistical data;

[0008] Establish a power supply reliability model based on the output and load uncertainty model of distributed generation;

[0009] Delineate the minimum grid area based on the power supply reliability model;

[0010] Based on the smallest area of ​​the distribution network and the output and load of the distributed power sources, the islands are divided and the loads are reduced to ensure the stable operation of the islands;

[0011] According to the divided islands, a reliability assessment model of active distribution network based on sequential Monte Carlo simulation is established;

[0012] The annual average outage rate and annual average outage time of load points are calculated using the active distribution network reliability assessment model;

[0013] The power supply reliability index of the system is calculated based on the annual average outage rate and the annual average outage time to evaluate the power supply reliability of the distribution network.

[0014] The further design of the above technical solution is: using the active distribution network reliability assessment model to calculate the annual average outage rate and annual average outage time of the load point, which specifically includes the following steps:

[0015] 1) Use the distributed power output and load uncertainty model to simulate the average output power Pk(t) of N distributed power sources at each hour in a year, where k represents the kth distributed power source and N represents the total number of distributed power sources; establish an uncertainty model L(t) for load fluctuation over time;

[0016] 2) Input the failure rate of each component in the distribution network λ i , Fault repair time γ i ; Input the load size, number of users and other parameters of each load point;

[0017] 3) Initialize simulation time SUMTTF = 0, set simulation duration T, and set system state counter q = 0;

[0018] 4) For each component i in the system, i = 1, 2, ..., n, a number Ei uniformly distributed in the interval [0, 1] is randomly generated. According to the input component failure rate and component state sampling model, the mean trouble-free working time TTFi of each component is calculated:

[0019]

[0020] 5) Find the minimum MTTF among all components’ TTFi and determine the number t of the faulty component;

[0021] 6) Randomly generate two numbers Ei uniformly distributed in the interval [0,1] for the faulty component t, calculate the average repair time TTRt and switch switching time TTSt of the faulty component according to the input component repair rate, increase TTF and max(TTS,TTR) in the simulation years, and increase the state count q by 1;

[0022] 7) Calculate the average load L of load point i from the beginning of the fault to the end of the faultave (i), the expression is as follows:

[0023]

[0024] Where, t s and t e Respectively indicate the time when the fault starts and ends;

[0025] 8) According to the location of the faulty component t, determine the load points affected by the fault, mark the load points where the main substation can continue to supply power and the load points that can be transferred through the tie line and distributed power sources; accumulate the total load L that can be transferred through distributed power sources T , which is expressed as follows:

[0026]

[0027] In the formula, m represents the total number of load points contained in the island;

[0028] 9) Sample and generate the normal operation and shutdown status of each distributed power source from the beginning of the fault to the end of the fault repair, and calculate the minimum output power of the kth distributed power source within TTRt Calculate the total output power within the island:

[0029]

[0030] 10) According to the feeder partition of the distribution system, the feeder association matrix Bk(i,j) is determined by using the divided islands; the total load L in the island is compared T and the total output power PT of the distributed generation, combined with the information provided by the correlation matrix, to locate the load points that can be supplied by the distributed generation;

[0031] 11) Determine the power outage time of each load point according to the distribution network structure and operation modeγ t(i) , which is expressed as follows:

[0032] γ t (i) = α*TTS t +(1-α)*TTR t

[0033] If the load point can be restored to power by the main substation, distributed power source or interconnection line, α=1, otherwise α=0.

[0034] 12) Calculate the power shortage ENSt(i) at load point i caused by the faulty component t:

[0035] ENSt(i)=L ave (i)*y t (i)(3.15)

[0036] 13) If the current simulation time is less than the set simulation time, return to step 4), otherwise continue to the next step;

[0037] 14) Calculate the power outage duration γ(i) and power shortage ENS(i) of load point i during the entire simulation time:

[0038]

[0039] Calculate the annual average outage rate λ of load point i (i) , annual average downtime γ a(i) And the power shortage value AENS(i):

[0040]

[0041] The weak links include weak grids, weak units and weak lines.

[0042] According to the power outage information data of the active distribution network, the average power outage time, the average number of power outages per household and the power supply reliability rate are calculated, and the weak links of the distribution network are identified according to the average power outage time, the average number of power outages per household and the power supply reliability rate.

[0043] The basis for judging the weak line is: when the number of households on a line exceeds 150 or the number of power outages due to a fault is greater than 5 times, the line will be identified as a weak line.

[0044] The distributed power output model includes the output of photovoltaic cells and the output of wind power generation;

[0045] The output of photovoltaic cells is:

[0046]

[0047] Where, I is the light intensity; I max represents the maximum value of I; α and β are the two parameters of Beta distribution; Γ(.) is the gamma function;

[0048] Wind power output is:

[0049]

[0050] Where PWT.N is the rated power of wind power generation; vci, vr, and vco are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; a and b are fitting parameters determined by the power curve, and k and c are shape parameters and scale parameters, respectively.

[0051] The load uncertainty model is:

[0052]

[0053]

[0054] Where PLi and QLi represent the active load and reactive load of node i, respectively; μpi and μqi represent the expected active load and reactive load of node i, respectively; σ2pi and σ2qi represent the variance of active load and reactive load of node i, respectively.

[0055] The power supply reliability model includes a reliability model of distribution network components and a distributed power supply reliability model.

[0056] The reliability model of the distributed power source is:

[0057]

[0058] Where P 1DG , P 0DG They represent the probability of DG normal working condition and faulty working condition, λ DG , μ DG They represent the failure rate and repair of DG respectively.

[0059] The division rule of the minimum power distribution area is that the area isolated by the manual switch is the minimum power distribution area.

[0060] The beneficial effects of the present invention are:

[0061] The present invention conducts weak link mining and analysis based on historical power outage data statistics, and highlights weak grids, weak units, and weak lines to provide a reference for the planning direction; finally, the Monte Carlo algorithm is used to carry out theoretical simulation calculations of the power supply reliability of the distribution network, to achieve reliability analysis of the planning project scheme, intuitively compare the reliability improvement effect after planning, and assist in supporting the planning project review.

[0062] The evaluation method of the present invention takes distributed power sources into account and has high evaluation accuracy. It can provide a basis for ensuring reliable and continuous power supply of the system and provide theoretical support for the planning, construction and operation of distributed power sources after they are connected to the power distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a flow chart of the present invention;

[0064] Figure 2 It is a two-state model of the component;

[0065] Figure 3 It is a schematic diagram of the working / shutdown process of the component;

[0066] Figure 4 This is an example diagram of the minimum power distribution area;

[0067] Figure 5 Schematic diagram of regional island division of distribution network. DETAILED DESCRIPTION

[0068] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0069] Embodiment 1

[0070] like Figure 1 As shown, a method for weak link mining and reliability simulation evaluation of power supply reliability of a distribution network in this embodiment mainly includes:

[0071] Step 1: Obtain the power outage information data model of the active distribution network, classify and screen the acquired data, import the corresponding data set into the database, and establish a reliability analysis data model;

[0072] Step 2: Establish a power supply reliability statistical method for the current status of active distribution network power supply reliability based on the reliability analysis data model;

[0073] Step 3: According to the power supply reliability statistics method of the active distribution network power supply reliability status, a method for mining weak links in the current power supply reliability status of the active distribution network is established. Specifically:

[0074] According to the dimensions of full caliber, fault, plan, etc., key data such as the number of households, duration of power outages, number of power outages, number of users with power outages, and number of connected users during the annual power outages of lines in various cities are comprehensively collected. Based on these data, core indicators such as the average power outage time per household, the average number of power outages per household, and the power supply reliability rate of each line are further calculated. According to the hierarchical relationship between the line and the power supply unit, power supply grid, district and county, and city, the above indicators are first summarized and then calculated at the level of different regional units, so as to obtain statistical data such as the average power outage time per household, the average number of power outages per household, and the power supply reliability rate at the provincial, municipal, district and county, grid, unit, etc., and establish a method for mining the weak links of the current status of power supply reliability of the active distribution network. The weak links include weak grids, weak units, and weak lines.

[0075] Step 4: Based on the obtained reliability status statistics, combined with the standards uniformly promoted by the State Grid, in-depth analysis and judgment of weak grids and weak units are carried out. Mainly based on the key indicator of average power outage time per household. For Class A+ power supply areas, the average power outage time per household should be strictly controlled within 0.0876 hours; for Class A power supply areas, this indicator should be less than 0.876 hours; the average power outage time per household in Class B power supply areas should not exceed 3 hours; Class C power supply areas should be controlled within 12 hours; and for Class D power supply areas, the upper limit of the average power outage time per household is 24 hours. In terms of identifying weak lines, the two important indicators of the number of households during power outages and the number of power outages are used as the basis for judgment. When the number of households during a power outage on a certain line exceeds 150 or the number of power outages due to a fault is greater than 5 times, the line will be identified as a weak line, and a deduction model for the active distribution network power supply reliability assessment method is established based on the weak link mining method of the active distribution network power supply reliability status.

[0076] This embodiment analyzes the power supply reliability of the distribution network from shallow to deep. First, the current status analysis of the power supply reliability of the distribution network is carried out. Based on the statistical analysis of historical power outage data, the power supply reliability statistical analysis of six levels, namely province, city, district and county, grid, unit and line, is realized.

[0077] Step 5: This step is to achieve the optimization of high reliability solutions. Quantitative indicators will be used for objective and accurate evaluation. Through these indicators, the performance of the planning scheme in terms of reliability can be comprehensively analyzed, and then the optimal distribution network planning scheme can be selected to ensure the stable and efficient operation of the distribution network. The specific steps include the following:

[0078] A. Based on the deduction model of the active distribution network power supply reliability assessment method, establish a distributed power generation output and load uncertainty modeling;

[0079] The distributed power generation output model is:

[0080] Photovoltaic power generation is generally affected by light intensity, humidity, and temperature, among which light intensity is the dominant factor. The distribution of light intensity is usually used to predict the output of photovoltaic cells. Light intensity is often described by Beta distribution, and the expression is as follows:

[0081]

[0082] Where, I is the light intensity; I max represents the maximum value of I; α and β are the two parameters of Beta distribution; Γ(.) is the gamma function.

[0083] Wind power generation is mainly affected by wind speed changes. Wind speed generally obeys a two-parameter Weibull distribution, and its probability density function is:

[0084]

[0085] Where v is the wind speed, k and c are the shape parameter and scale parameter, respectively.

[0086] The relationship between wind power generation and wind speed is as follows:

[0087]

[0088] Where P WT.N is the rated power of wind power generation; v ci 、v r 、v co are the cut-in wind speed, rated wind speed and cut-out wind speed respectively; a and b are the fitting parameters determined by the power curve.

[0089]

[0090]

[0091] Then the probability density function of wind power is:

[0092]

[0093] The load uncertainty model is:

[0094] The load in the actual operation of the distribution network is volatile, which is generally described by normal distribution:

[0095]

[0096]

[0097] Where P Li and Q Li Respectively represent the active load and reactive load of node i; μ pi and μ qi They represent the expected active load and reactive load of node i respectively; σ 2pi and σ 2qi They represent the active load variance and reactive load variance of node i respectively. Based on the distributed power output and load uncertainty modeling, a power supply reliability model is established.

[0098] B. Based on the output and load uncertainty modeling of the distributed power source, a power supply reliability model is established; the power supply reliability model includes reliability models of components (lines, transformers, switches, etc.) in the distribution network and reliability models of distributed power sources;

[0099] The reliability of components in the distribution network is generally described by two state models. Components contain two states, the normal operation state (represented as 1) and the shutdown state caused by failure or maintenance (represented as 0). The failure rate λ of the component represents the probability that the component is fault-free from the start of operation to time t and fails after time t in a unit time, and the repair rate μ represents the probability that the component fails from the start of operation to time t and is repaired after time t in a unit time. The relationship is expressed as follows Figure 2 As shown:

[0100] The duration of a component in the normal "1" state is called the average continuous working time TTF (Time To Failure), and the duration of a component in the shutdown "0" state is called the average repair time TTR (Time To Repair). To simulate the alternating process of normal operation and repair of a component, Figure 3 It means that the average working time TTF and the average repair time TTR are generally considered to obey exponential distribution.

[0101] Distributed power reliability model:

[0102] The reliability model of distributed power generation is described by the probability of its normal state and fault state. The probability of normal working condition and fault working condition of DG (distributed power generation) is related to its failure rate and repair rate, which satisfies the following formula:

[0103]

[0104] Where P 1DG , P 0DG They represent the probability of DG normal working condition and faulty working condition, λ DG , μ DG They represent the failure rate and repair rate of DG respectively. Based on the power supply reliability model, a minimum area division model of the distribution network is established.

[0105] C. Based on the power supply reliability model, a minimum area division model of the distribution network is established;

[0106] When a fault occurs in the power distribution system, the circuit breaker on the feeder will automatically disconnect first, and the area bounded by the circuit breaker will be out of power. After the fault is located, the disconnector is operated to isolate the fault area, and then the tripped circuit breaker is closed to restore power to other areas.

[0107] Since the fault propagation and power supply restoration of the distribution network are both based on switches, the distribution system can be divided into different areas. This chapter uses automatic switches (circuit breakers, fuses) as boundaries to divide feeder zones, and then uses manual switches (disconnectors, section switches) as boundaries to divide them into several zones. The area isolated by manual switches is called the minimum distribution area. There are no switch elements in the same minimum distribution area, and the load points in the area are the same in terms of reliability indicators. Therefore, the failure of each component in the same area has the same impact on the reliability of the entire system.

[0108] After a fault occurs, the time it takes to restore power to the loads in the faulty area is the time it takes to repair the faulty component. The loads upstream of the fault point can be isolated by manually operating the switch to quickly restore power. If the downstream of the fault point can be supplied by a backup power source, the interconnection switch can be operated to restore power. Otherwise, it is necessary to wait until the faulty component is repaired before restoring power.

[0109] like Figure 4 As shown, F1, F2, and F3 are feeder partitions with automatic switches as boundaries, wherein F1 includes minimum distribution areas Z1, Z2, Z3, and Z4. Based on the distribution network minimum area partition model, an island partition method and a load reduction strategy model are established.

[0110] D. Based on the distribution network minimum area division model, establish an island division method and a load reduction strategy model;

[0111] When an active distribution network fails, distributed power sources as backup power sources will form an island phenomenon, shortening the power outage time and scope in the area. Due to the structure of the distribution network and the output limitations of distributed power sources, it is necessary to compare and analyze the output and load of distributed power sources to achieve island division.

[0112] After a fault occurs, due to changes in the system structure, the load may lose connection with the main substation. Even if it is still connected to the distributed power source, the insufficient power supply capacity will cause the load to lack power. It is necessary to cut off part of the load according to the load reduction strategy to ensure stable operation within the island system. The load reduction strategy first needs to determine the scope of power transfer through distributed power sources, and then determine the power supply order according to the priority of the load.

[0113] Assuming that the substation capacity is sufficient and the DG capacity is insufficient, the association matrix Bk(i, j) indicates whether the i-th smallest distribution area can be connected to the k-th DG and the power supply order after the connection is maintained when the j-th smallest distribution area fails. The element value in Bk(i, j) varies in the range of [0, N-1], where N represents the total number of areas. The element value 0 represents no connection with the power supply, and other integer values ​​represent the order of power supply recovery.

[0114] by Figure 5 The six minimum distribution area networks in the example illustrate how the correlation matrix is ​​established.

[0115]

[0116] B1(4,2)=3 means that when a fault occurs in the minimum distribution area 2, the minimum distribution area 4 is the third to resume power supply, behind the minimum distribution areas 6 and 5 and in front of the minimum distribution area 3. Based on the island partitioning method and the load reduction strategy model, an active distribution network reliability assessment model based on sequential Monte Carlo simulation is established.

[0117] E. Based on the islanding partitioning method and load reduction strategy model, an active distribution network reliability assessment model based on sequential Monte Carlo simulation is established;

[0118] The use of active distribution network reliability assessment model to assess the reliability of distribution network power supply includes the following steps:

[0119] 1) Use the model established in this chapter to simulate the average output power P of N distributed power sources in each hour of a year k(t) , k represents the kth distributed power source, N represents the total number of distributed power sources; establish the uncertainty model L(t) of load fluctuation over time;

[0120] 2) Input the failure rate of each component (circuit, transformer, switch, etc.) i , Fault repair time γ i ; Input the load size, number of users and other parameters of each load point;

[0121] 3) Initialize simulation time SUMTTF = 0, set simulation duration T, and set system state counter q = 0;

[0122] 4) For each component i (i = 1, 2, ..., n) in the system, a number Ei uniformly distributed in the interval [0, 1] is randomly generated. According to the input component failure rate and component state sampling model, the mean trouble-free working time TTFi of each component is calculated:

[0123]

[0124] 5) Find the minimum MTTF among all components’ TTFi and determine the number t of the faulty component;

[0125] 6) Randomly generate two numbers Ei uniformly distributed in the interval [0,1] for the faulty component t, calculate the average repair time TTRt and switch switching time TTSt of the faulty component according to the input component repair rate, increase TTF and max(TTS,TTR) in the simulation years, and increase the state count q by 1;

[0126] 7) Calculate the average load L of load point i from the beginning of the fault to the end of the fault ave (i), the expression is as follows:

[0127]

[0128] Where, t s and t e They represent the time when the fault starts and ends respectively.

[0129] 8) According to the location of the faulty component t, determine the load points affected by the fault, mark the load points where the main substation can continue to supply power and the load points that can be transferred through the interconnection line and distributed power sources. The post-fault power supply recovery strategy adopted in this chapter is: give priority to the formation of island power supply by distributed power sources. If the distributed power sources cannot supply power or the power supply is insufficient, the remaining load is considered to be transferred through the interconnection line. Accumulate the total load L that can be transferred through the distributed power sources T , which is expressed as follows:

[0130]

[0131] Where m represents the total number of load points contained in the island.

[0132] 9) Sample and generate the normal operation and shutdown status of each distributed power source from the beginning of the fault to the end of the fault repair, and calculate the minimum output power of the kth distributed power source within TTRt Calculate the total output power within the island:

[0133]

[0134] 10) According to the feeder partitioning of the distribution system, the regional island division method is adopted to determine the feeder association matrix Bk(i,j); by comparing the total load LT in the island and the total output power PT of the distributed power source, combined with the information provided by the association matrix, the load point that can be transferred by the distributed power source is located;

[0135] 11) Determine the power outage time of each load point according to the distribution network structure and operation modeγ t (i) is expressed as follows:

[0136] γ t (i) = α*TTS t +(1-α)*TTR t

[0137] If the load point can be restored to power by the main substation, distributed power source or interconnection line, α=1, otherwise α=0.

[0138] 12) Calculate the power shortage ENSt(i) at load point i caused by the faulty component t:

[0139] ENSt(i)=L ave (i)*y t (i)(3.15)

[0140] 13) If the current simulation time is less than the set simulation time, return to step 4), otherwise continue to the next step;

[0141] 14) Calculate the power outage duration γ(i) and power shortage ENS(i) of load point i during the entire simulation time:

[0142]

[0143]

[0144] Calculate the annual average outage rate λ of load point i (i) , annual average downtime γ a(i) 、Power shortage value AENS(i):

[0145]

[0146]

[0147]

[0148] 15) According to the formula, calculate the system's power supply reliability indicators SAIFI, SAIDI, CAIDI, ASAI, EENS, SAENS, etc., apply specific examples, get simulation results, and assist in power grid planning.

[0149] Let λi be the annual average outage rate of the load point (times / year), and the annual average outage rate index refers to the expected value of the number of power outages at the load point during the statistical period. Ui is the annual average outage time of the load point (hours / year), and the annual average outage time index refers to the expected value of the duration of power outages at the load point during the statistical period. Ni is the total number of users at the load point (households), then the SAIFI, SAIDI, CAIDI, ASAI, EENS, and SAENS indicators are as follows:

[0150] ① System average outage frequency index-SAIFI

[0151] The system average interruption frequency index (SAIFI) refers to the average number of power outages experienced by users powered by the system during the statistical period. It can be calculated by dividing the cumulative number of power outages experienced by users during the statistical period by the total number of users powered by the system.

[0152]

[0153] ② System average outage duration index-SAIDI

[0154] The system average interruption duration index (SAIDI) refers to the average duration of power outages suffered by each user supplied by the system during the statistical period. It can be calculated by dividing the total duration of power outages suffered by users during the statistical period by the total number of system users.

[0155]

[0156] ③User average power outage duration indicator-CAIDI

[0157] The customer average interruption duration index (CAIDI) refers to the average duration of power outages experienced by users who are supplied with power by the system during the statistical period. It can be calculated by dividing the total duration of power outages experienced by users during the statistical period by the total number of users who experienced power outages in that year.

[0158]

[0159] ④ Power supply reliability index-RS or ASAI

[0160] The power supply reliability index ASA1 (Average service availability index) refers to the ratio of the total power outage time experienced by users during the statistical period to the total power supply time required by users.

[0161]

[0162] ⑤ System power shortage indicator EENS

[0163] The system power shortage indicator EENS (Expected energy not supplied) refers to the expected value of the total power shortage of the system during the statistical time.

[0164] EENS=∑L i U i

[0165] Where L is the average load at load point i.

[0166] ⑥ System average power shortage index SAENS

[0167] The system average energy not supplied (SAENS) refers to the average power shortage value of users caused by power outages during the statistical period. It can be calculated by dividing the power shortage caused by power outages during the statistical period by the total number of users.

[0168]

[0169] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources, characterized in that: Identify weak links in the distribution network based on the power outage information data of the active distribution network and form weak link statistics; Establish the uncertainty model of distributed generation output and load based on the weak link statistical data; Establish a power supply reliability model based on the output and load uncertainty model of distributed generation; Delineate the minimum grid area based on the power supply reliability model; Based on the smallest area of ​​the distribution network and the output and load of the distributed power sources, the islands are divided and the loads are reduced to ensure the stable operation of the islands; According to the divided islands, a reliability assessment model of active distribution network based on sequential Monte Carlo simulation is established; The annual average outage rate and annual average outage time of load points are calculated using the active distribution network reliability assessment model; The power supply reliability index of the system is calculated based on the annual average outage rate and the annual average outage time to evaluate the power supply reliability of the distribution network.

2. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 1, characterized in that: The annual average outage rate and annual average outage time of the load point are calculated using the active distribution network reliability assessment model, which specifically includes the following steps: 1) Use the distributed power output and load uncertainty model to simulate the average output power Pk(t) of N distributed power sources at each hour in a year, where k represents the kth distributed power source and N represents the total number of distributed power sources; establish an uncertainty model L(t) for load fluctuation over time; 2) Input the failure rate of each component in the distribution network λ i , Fault repair time γ i ;enter Parameters such as load size and number of users at each load point; 3) Initialize simulation time SUMTTF = 0, set simulation duration T, and set system state counter q = 0; 4) For each component i in the system, i = 1, 2, ..., n, a number Ei uniformly distributed in the interval [0, 1] is randomly generated. According to the input component failure rate and component state sampling model, the mean trouble-free working time TTFi of each component is calculated: 5) Find the minimum MTTF among all components’ TTFi and determine the number t of the faulty component; 6) Randomly generate two numbers uniformly distributed in the interval [0,1] for the faulty component t, calculate the average repair time TTRt and switch switching time TTSt of the faulty component according to the input component repair rate, increase TTF and max(TTS,TTR) in the simulation years, and increase the state count q by 1; 7) Calculate the average load L of load point i from the beginning of the fault to the end of the fault ave (i), the expression is as follows: Where, t s and t e Respectively indicate the time when the fault starts and ends; 8) According to the location of the faulty component t, determine the load points affected by the fault, mark the load points where the main substation can continue to supply power and the load points that can be transferred through the tie line and distributed power sources; accumulate the total load L that can be transferred through distributed power sources T , which is expressed as follows: In the formula, m represents the total number of load points contained in the island; 9) Sample and generate the normal operation and shutdown status of each distributed power source from the beginning of the fault to the end of the fault repair, and calculate the minimum output power of the kth distributed power source within TTRt Calculate the total output power within the island: 10) According to the feeder partition of the distribution system, the feeder association matrix Bk(i,j) is determined by using the divided islands; the total load L in the island is compared T and the total output power PT of the distributed generation, combined with the information provided by the correlation matrix, to locate the load points that can be supplied by the distributed generation; 11) Determine the power outage time of each load point according to the distribution network structure and operation modeγ t(i) , which is expressed as follows: c t (i)=α*TTS t +(1-a)*TTR t If the load point can be restored to power by the main substation, distributed power source or interconnection line, α=1, otherwise α=0. 12) Calculate the power shortage ENSt(i) at load point i caused by the faulty component t: ENSt(i)=L ave (i)*y t (i)(3.15) 13) If the current simulation time is less than the set simulation time, return to step 4), otherwise continue to the next step; 14) Calculate the power outage duration γ(i) and power shortage ENS(i) of load point i during the entire simulation time: Calculate the annual average outage rate λ of load point i (i) , annual average downtime γ a(i) And the power shortage value AENS(i):

3. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 1, characterized in that: The weak links include weak grids, weak units and weak lines.

4. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 3 is characterized in that: The average power outage time and number of power outages per household and the power supply reliability rate are calculated based on the power outage information data of the active distribution network, and the weak links of the distribution network are identified based on the average power outage time and number of power outages per household and the power supply reliability rate.

5. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 4 is characterized in that: The basis for judging the weak line is: when the number of households on a line exceeds 150 or the number of power outages due to a fault is greater than 5 times, the line will be identified as a weak line.

6. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 1, characterized in that: The distributed power output model includes the output of photovoltaic cells and the output of wind power generation; The output of photovoltaic cells is: Where, I is the light intensity; I max represents the maximum value of I; α and β are the two parameters of Beta distribution; Γ(.) is the gamma function; Wind power output is: Where PWT.N is the rated power of wind power generation; vci, vr, and vco are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; a and b are fitting parameters determined by the power curve, and k and c are shape parameters and scale parameters, respectively.

7. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 6, characterized in that: The load uncertainty model is: Where PLi and QLi represent the active load and reactive load of node i, respectively; μpi and μqi represent the expected active load and reactive load of node i, respectively; σ2pi and σ2qi represent the variance of active load and reactive load of node i, respectively.

8. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 7, characterized in that: The power supply reliability model includes a reliability model of distribution network elements and a distributed power supply reliability model.

9. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 8, characterized in that: The reliability model of the distributed power source is: Where P 1DG , P 0DG They represent the probability of DG normal working condition and faulty working condition, λ DG , μ DG They represent the failure rate and repair of DG respectively.

10. The method for evaluating the reliability of power supply of a distribution network taking into account distributed power sources according to claim 9, characterized in that: The division rule of the minimum power distribution area is that the area isolated by the manual switch is the minimum power distribution area.

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