Power distribution network reliability evaluation method and device considering multi-time scale multi-load

Abstract

The application discloses a power distribution network reliability evaluation method and device considering multiple time scales and multiple loads, and the method comprises the following steps: obtaining basic data of a power distribution network; analyzing and obtaining a failure rate of each element in the power distribution network in each time scale; combining the failure rate of each element and the basic data of the power distribution network, obtaining quarterly reliability data of each load point through sampling simulation; giving a time weight of each load point in each quarter to obtain annual reliability data of each load point; and evaluating the reliability of the power distribution network based on the annual reliability data of each load point. The scheme of the application considers multiple time scales, multiple load types and other factors, and can obtain more realistic characteristic evaluation data in the power distribution network containing SOP.

Description

Technical Field

[0001] This invention belongs to the field of distribution network reliability evaluation technology, specifically involving a method and apparatus for evaluating the reliability of distribution networks considering multiple time scales and multiple loads. Background Technology

[0002] Currently, distributed power sources, such as wind and solar power, are being widely integrated into distribution networks, significantly impacting their reliability. Flexible soft switches (SOPs), a novel type of power electronic device, typically employ a back-to-back converter structure. They enable rapid and accurate power flow control and have been widely adopted in distribution networks, replacing tie switches.

[0003] The presence of Standard Operating Procedures (SOPs), distributed generation, and different types of loads (load points) can all impact the reliability of the distribution network, thus necessitating a reliability assessment.

[0004] Patent CN112330117A discloses a method and apparatus for assessing the reliability of power supply in a distribution network during the planning year. The method obtains characteristic parameters of a target area in the current year and the planning year, inputs these parameters into a pre-set power supply reliability assessment model, and outputs the average outage time assessment values ​​for users in the target area during the current year and the planning year. Based on the average outage time assessment values ​​for users in the current year and the actual average outage time for users, a reliability correction coefficient is calculated. Based on the reliability correction coefficient and the average outage time assessment values ​​for users in the planning year, a corrected value for the average outage time for users in the target area during the planning year is calculated. Based on the corrected value for the average outage time for users, the power supply reliability result for the target area in the planning year is obtained. This method addresses the technical problem of existing power supply reliability assessment methods that rely solely on historical statistical results for analysis and evaluation, resulting in low accuracy and difficulty in meeting high power supply reliability requirements.

[0005] However, existing technologies like the one described above assess the reliability of distribution networks primarily on an annual basis, and component failure rates are also measured on an annual basis, without considering the differences in failure rates of the same components in different quarters or in terms of reliability requirements.

[0006] Considering the impact of Standard Operating Procedures (SOPs) and distributed generation access, and taking into account different load types at different time scales, it is of great significance to conduct a comprehensive reliability assessment of the distribution network.

[0007] Therefore, how to develop a distribution network reliability assessment technology that includes SOPs and distributed power source access, covering multiple time scales and multiple load types, in order to obtain more realistic distribution network characteristic assessment data is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a method and apparatus for reliability assessment of distribution networks considering multiple time scales and loads. The method acquires basic data of the distribution network, analyzes and obtains the failure rate of each component in the distribution network at each time scale, and, based on the component failure rate, obtains quarterly reliability data for each load point through sampling simulation. It then assigns the time weight of each load point in each quarter to obtain the annual reliability data for each load point. Based on the annual reliability data of each load point, the reliability of the distribution network is assessed. This invention's solution considers multiple time scales and multiple load types, enabling the acquisition of more realistic characteristic assessment data in distribution networks containing standard operating procedures (SOPs).

[0009] In a first aspect, the present invention provides a method for reliability assessment of a distribution network that takes into account multiple time scales and multiple loads, specifically including the following steps:

[0010] Acquire basic data of the distribution network, including topology, component parameters, load parameters, SOP information, distributed generation information, and operating time data;

[0011] The failure rates of each component in the distribution network at each time scale were analyzed and obtained, where each time scale is a quarter.

[0012] By combining the failure rates of each component and the basic data of the distribution network, quarterly reliability data for each load point is obtained through sampling simulation.

[0013] Given the time weight of each load point in each quarter, we can obtain the annual reliability data for each load point.

[0014] The reliability of the distribution network is assessed based on the annual reliability data of each load point.

[0015] Furthermore, the SOP information includes the SOP access location information in the distribution network, the distributed power source information includes the distributed power source access location information in the distribution network, and the operation time data includes component fault repair time data and the time data required for switch operation.

[0016] Furthermore, the SOP information also includes the SOP output curve, the distributed power information also includes the distributed power output curve, and the load parameters include the load curves for each load type in each quarter.

[0017] Furthermore, the failure rate of each component in the distribution network at each time scale is analyzed and obtained. The specific analysis and calculation formula is as follows:

[0018]

[0019] in, Let SY be the failure rate of component k in quarter j. jFor the total number of quarters j, Let k be the number of repairable failures of component k in the i-th quarter j. This is the difference between the total time and the non-mandatory downtime in quarter j.

[0020] Furthermore, by combining the failure rates of each component with the basic data of the distribution network, quarterly reliability data for each load point is obtained through sampling simulation. This process includes the following steps:

[0021] The initial simulation clock and simulation duration are set according to the different quarter lengths;

[0022] Repeat the following process: Based on the failure rate of each component, give the fault-free uptime of each component;

[0023] Find the component corresponding to the minimum value of the fault-free operation time, set this component as the first component, and calculate the repair time of the first component;

[0024] Based on the pre-built island partitioning model, the initial quarterly reliability data for each load point corresponding to the first element is obtained;

[0025] Iterate through the fault-free uptime of the first element, update the initial simulation clock until the simulation duration is reached, obtain the quarterly reliability data of the first element for each load point, and then obtain the element corresponding to the minimum fault-free uptime from the remaining elements until the processing of all elements is completed.

[0026] The quarterly reliability data for load points includes the load point failure rate and the average quarterly outage time for load point failures, calculated using the following formula:

[0027]

[0028]

[0029] Let L be the failure rate at load point j in quarter. Let L be the total number of failures at load point L in quarter j, and SC be the total number of failures at load point L in quarter j. j The simulation time is for quarter j. Let L be the average outage time per quarter due to faults at load point j. The total outage time for load point L in the quarter is denoted as _j_.

[0030] Furthermore, an island partitioning model is pre-constructed, specifically including the following steps:

[0031] Based on the SOP access location information, determine the type of the ports at both ends of the SOP access, and classify the types of the ports at both ends of the SOP access as distributed power source and load point, respectively.

[0032] Based on the topology, component parameters, load parameters, SOP access location information, SOP output curve, distributed power source access location information, and distributed power source output curve, an islanding model is constructed.

[0033] Among the types of ports at both ends of the SOP connection, one port has a positive output and the other port has a negative output. The port type with a positive output is equivalent to a distributed power source, and the port type with a negative output is equivalent to a load point.

[0034] Furthermore, based on the failure rate of each component, the mean time between failures (MTBF) for each component is given, and the specific calculation formula is as follows:

[0035]

[0036] in, Let k be the fault-free uptime of component k in quarter j. Let denot be the failure rate of component k in quarter j, and let ζ be a random number uniformly distributed in the interval [0,1].

[0037] Furthermore, the time weight of each load point in each quarter is given to obtain the annual reliability data for each load point. This involves the following steps:

[0038] By combining load parameters and reliability assessment evaluation factors, the time weight of each load point in each quarter is obtained;

[0039] The quarterly reliability data are summed to obtain the annual reliability data for each load point.

[0040] The process involves combining load parameters and reliability assessment factors to obtain the time weight of each load point in each quarter. The specific steps are as follows:

[0041] Analyze the load parameters for each quarter to obtain the subjective weight of each load point in each quarter;

[0042] Determine the evaluation factors for reliability assessment, and obtain the objective weight of each load point in each quarter based on the impact of load parameters on the evaluation factors;

[0043] Combining the subjective and objective weights of each load point in each quarter, a weight matrix for each load point in each quarter is obtained, specifically represented as follows:

[0044]

[0045] Where B is the weight matrix of a certain load point in each quarter, ω1 is the proportion coefficient of objective weight in the weight, ω2 is the proportion coefficient of subjective weight in the weight, and b 1-1，1 b is the objective weight coefficient of the first evaluation factor at a certain load point in the first quarter.1-1，i Let b be the objective weight coefficient of the i-th evaluation factor at a certain load point in the first quarter. 4-1，1 b is the objective weight coefficient of the first evaluation factor at a certain load point in the fourth quarter. 4-1，i Let b be the objective weight coefficient of the i-th evaluation factor at a certain load point in the fourth quarter. 1-2，1 b is the subjective weight coefficient of the first evaluation factor at a certain load point in the first quarter. 1-2，i Let b be the subjective weight coefficient of the i-th evaluation factor at a certain load point in the first quarter. 4-2，1 b is the subjective weight coefficient of the first evaluation factor at a certain load point in the fourth quarter. 4-2，i The subjective weight coefficient of the i-th evaluation factor at a certain load point in the fourth quarter;

[0046] The distribution coefficient matrix for each load point across different evaluation factors in each quarter is determined as follows:

[0047]

[0048] Where A is the distribution coefficient matrix among different evaluation factors in each quarter at a certain load point, and a 1，1 Let a be the allocation coefficient of the first evaluation factor in the first quarter at a certain load point. 1，i Let a be the allocation coefficient of the i-th evaluation factor in the first quarter at a certain load point. 4，1 Let a be the allocation coefficient of the first evaluation factor in the fourth quarter at a certain load point. 4，i The allocation coefficient for the i-th evaluation factor in the fourth quarter at a certain load point;

[0049] The weight matrix of each load point in each quarter and the distribution coefficient matrix of each load point among different evaluation factors in each quarter are analyzed and calculated to obtain the time weight of each load point in each quarter.

[0050] Furthermore, the quarterly reliability data are summed to obtain the annual reliability data for each load point. The specific calculation formula is as follows:

[0051]

[0052]

[0053] Where, λ L The annual failure rate at load point L. Let L be the time weight of the j-th quarter. For the failure rate of load point j in quarter L, t L The average annual outage time for load point L is the fault mean time. The average outage time for load point L in quarterly is denoted as L.

[0054] Secondly, the present invention also provides a distribution network reliability assessment device that considers multiple time scales and multiple loads, employing the above-described distribution network reliability assessment method that considers multiple time scales and multiple loads, including:

[0055] The data acquisition module obtains basic data of the power distribution network, including topology, component parameters, load parameters, SOP information, distributed power source information, and operation time data.

[0056] The analysis and evaluation module analyzes and obtains the failure rate of each component in the distribution network at each time scale, where each time scale is a quarter. Combining the failure rate of each component with the basic data of the distribution network, the module obtains the quarterly reliability data of each load point through sampling simulation, gives the time weight of each load point in each quarter, and obtains the annual reliability data of each load point. Based on the annual reliability data of each load point, the reliability of the distribution network is evaluated.

[0057] The present invention provides a method and apparatus for reliability assessment of distribution networks considering multiple time scales and multiple loads, which has at least the following beneficial effects:

[0058] The solution of this invention takes into account the different failure rates of different types of components in different quarters, as well as the different reliability requirements of different types of loads in different quarters. It also covers factors such as multiple time scales and multiple load types, and can obtain more realistic characteristic evaluation data in distribution networks containing SOPs. Attached Figure Description

[0059] Figure 1 A flowchart illustrating the distribution network reliability assessment method considering multiple time scales and multiple loads provided by this invention;

[0060] Figure 2 A schematic diagram of the process for obtaining quarterly reliability data for each load point according to one embodiment of the present invention;

[0061] Figure 3 A schematic diagram of the process for obtaining annual reliability data for each load point according to a certain embodiment of the present invention;

[0062] Figure 4 A schematic diagram of the structure of the distribution network reliability assessment device considering multiple time scales and multiple loads provided by the present invention. Detailed Implementation

[0063] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0064] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0065] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0066] like Figure 1 As shown, a method for reliability assessment of distribution networks considering multiple time scales and multiple loads specifically includes the following steps:

[0067] Acquire basic data of the distribution network, including topology, component parameters, load parameters, SOP information, distributed generation information, and operating time data;

[0068] Analyze and obtain the failure rate of each component in the power distribution network for each quarter;

[0069] By combining the failure rates of each component and the basic data of the distribution network, quarterly reliability data for each load point is obtained through sampling simulation.

[0070] Given the time weight of each load point in each quarter, we can obtain the annual reliability data for each load point.

[0071] The reliability of the distribution network is assessed based on the annual reliability data of each load point.

[0072] SOP information includes SOP access location information in the distribution network; distributed power source information includes distributed power source access location information in the distribution network; and operation time data includes fault component repair time data and switch operation time data.

[0073] SOP information also includes SOP output curves, distributed power information also includes distributed power output curves, and load parameters include load curves for each load type in each quarter.

[0074] In the basic data of the distribution network, the topology is used to determine the loads that will be affected after a component failure, the component parameters are used to determine the failure time of the component, the load parameters are mainly used to calculate the power loss, the access location information of distributed generation and SOP in the distribution network is used to determine the islanding, thereby judging the loads that can be restored, the repair time data of the faulty component indicates the duration of the fault, and the time data required for switching operation is the failure time of the loads that can be restored through switching operation.

[0075] The establishment of the above basic data for the power distribution network provides a data basis and reference for the subsequent calculation of reliability indicators.

[0076] The failure rates of various components in the distribution network at different time scales are analyzed and obtained. The specific calculation formula is as follows:

[0077]

[0078] in, Let SY be the failure rate of component k in quarter j. j For the total number of quarters j, Let k be the number of repairable failures of component k in the i-th quarter j. This is the difference between the total time and the non-mandatory downtime in quarter j.

[0079] The above formula yields the failure rate of all components in each quarter. Based on the obtained failure rate of each component and the islanding model in subsequent steps, quarterly reliability data for each component at its corresponding load point is provided.

[0080] In the basic data of the distribution network, the output curves of distributed generation mainly consider the output curves of wind power and photovoltaic power, while also taking into account the uncertainty of distributed generation output. Specifically, when considering wind power output, the output curve of the wind turbine can be obtained through the following process:

[0081]

[0082] The formulas for calculating x1, x2, and x3 are as follows:

[0083]

[0084]

[0085]

[0086] P1 is the output power of the wind turbine, P r V is the rated power of the wind turbine. ci To cut off the wind speed, V r For the rated wind speed, V co To cut off the wind speed, V T Let T be the real-time wind speed.

[0087] When generating photovoltaic power, the photovoltaic output curve can be obtained through the following process:

[0088]

[0089] η=η0[1-γ(T t -T0)]

[0090] Where P2 is the output power of the photovoltaic unit, A0 is the area of ​​the photovoltaic cell, η is the conversion efficiency of the photovoltaic cell, η0 is the conversion efficiency of the photovoltaic cell at the reference temperature, γ is the temperature coefficient of the photovoltaic cell, and T t T0 is the ambient temperature, and T0 is the reference temperature of 298K.

[0091] In the basic data of the distribution network, the SOP output curve is calculated using the particle swarm optimization algorithm. The specific steps are as follows:

[0092] Initialize the output of the two ports connected to SOP at each time point as the initial value for each particle in the population;

[0093] Based on the SOP operation and maintenance costs, the power costs of real-time exchange between the distribution network and the external main grid, the distribution network losses, and the distribution network voltage deviation, an objective function is established, and the calculation formula is as follows:

[0094]

[0095] In the above formula, minJ is the objective function, λ1, λ2, and λ3 represent the weights of the three objectives, and their sum is 1, ω t For the real-time electricity price of the power grid, P 1,t Let ω be the active power flowing into the distribution network from the node connected to the external main grid at time t, where T is the operation optimization period. SOP The SOP operation and maintenance cost is defined as the cost per operation optimization cycle, where M is the number of SOPs connected, and N is the number of SOPs connected. SOP,i Let N be the number of SOP access ports for the i-th device, typically 2 or N. node U represents the number of nodes in the distribution network. i,t Let P be the voltage magnitude of the i-th node at time t. loss,t Let t be the network loss of the distribution network at time t;

[0096] Calculate the fitness function value of each particle based on the objective function;

[0097] Based on the fitness function values ​​of each particle, the individual optimal value and the global optimal value of each particle are obtained. The output of each particle, i.e., SOP, is then iteratively updated using the following formula:

[0098] v i ′=v i +c1×rand()×[pbest i -(x i +v i )]+c2×rand()×[gbest-(x i +v i )]

[0099] Among them, v i 'For v i The velocity after iteration, v i It is the velocity of the i-th particle, c1 and c2 are learning factors, rand() generates a random number between (0,1), and pbest i It is the individual optimal value of the i-th particle, x i is the position of the i-th particle, i.e., the SOP output represented by the i-th particle, and gbest is the global optimal value of all particles.

[0100] Determine if the set number of iterations has been reached. If not, return to the step of establishing the objective function. If the objective function has been reached, the optimization ends and the SOP output is directly output.

[0101] The curves for different types of loads in different quarters are represented by typical daily load curves for different types of loads in different quarters.

[0102] See Figure 2 As shown, by combining the failure rates of each component and the basic data of the distribution network, quarterly reliability data for each load point is obtained through sampling simulation. The specific steps include the following:

[0103] The initial simulation clock and simulation duration are set according to the different quarter lengths;

[0104] Repeat the following process: Based on the failure rate of each component, give the fault-free uptime of each component;

[0105] Find the component corresponding to the minimum value of the fault-free operation time, set this component as the first component, and calculate the repair time of the first component;

[0106] Based on the pre-built island partitioning model, the initial quarterly reliability data for each load point corresponding to the first element is obtained;

[0107] Iterate through the fault-free uptime of the first element, update the initial simulation clock until the simulation duration is reached, obtain the quarterly reliability data of the first element for each load point, and then obtain the element corresponding to the minimum fault-free uptime from the remaining elements until the processing of all elements is completed.

[0108] The quarterly reliability data for load points includes the load point failure rate and the average quarterly outage time for load point failures, calculated using the following formula:

[0109]

[0110]

[0111] Let L be the failure rate at load point j in quarter. Let L be the total number of failures at load point L in quarter j, and SC be the total number of failures at load point L in quarter j. j The simulation time is for quarter j. Let L be the average outage time per quarter due to faults at load point j. The total outage time for load point L in the quarter is denoted as _j_.

[0112] The main objective of distribution network islanding schemes is to restore as many lost loads as possible after a fault occurs in a distribution network component. Therefore, the objective function for islanding is:

[0113]

[0114] Where f is the objective function for islanding, N is the set of all load points in the distribution network, t = start represents the fault start time, t = end represents the fault end time, and δ L This indicates whether power has been restored to load point L. Its value is 1 if power has been restored, and 0 if power has not been restored. Let be the active power at load point L at time t.

[0115] In addition to the objective function of island partitioning, the following constraints also need to be satisfied:

[0116] Firstly, the power distribution network needs to satisfy power flow constraints during normal operation or fault conditions, specifically as follows:

[0117]

[0118] Among them, P L1 (t), Q L1 (t) represent the active power and reactive power of load point L1 at time t, respectively. L1 (t), U L2 (t) represents the voltage amplitudes at load points L1 and L2 at time t, respectively, and θ L1L2(t) represents the phase difference between load points L1 and L2 at time t, G L1L2 B L1L2 These are the conductance and susceptance between load point L1 and load point L2, respectively.

[0119] Secondly, the distribution network needs to meet radial operation constraints during normal operation or fault conditions, specifically as follows:

[0120] β L1L2 +β L2L1 =α L1L2 L1L2∈B BR

[0121]

[0122]

[0123] α L1L2 β L1L2 β L2L1 ∈{0,1}

[0124] Where, β L1L2 β L2L1 Let β represent the relationship between load points L1 and L2. If load point L1 is a child node of load point L2, then β L1L2 If load point L2 is not a child node of load point L1, then β is 1. L1L2 α is 0 L1L2 This represents the connection status of branches L1 and L2. Its value is 1 when there is a branch connection between load point L1 and load point L2, and 0 when there is no branch connection. BR N is the set of branches in the distribution network. node Let N be the set of load points in the distribution network. node,slack It is the set of balanced load points in the distribution network.

[0125] Third, when dividing a distribution network into islands, power balance constraints within the islands must be met, specifically expressed as follows:

[0126]

[0127] Where, N i Let P represent the i-th isolated island. DGNi P represents the active power output of the distributed power source within the i-th island, L represents the L-th load point within the i-th island, and P represents the active power output of the distributed power source within the i-th island. L This represents the active power at the Lth load point.

[0128] Since reactive power is not suitable for long-distance transmission, local compensation is used. Therefore, when dividing islands, the reactive power output of distributed power sources is not considered, and only the active power balance is considered.

[0129] Fourth, during the operation of the distribution network, it is necessary to consider the voltage constraints at the load points, specifically as follows:

[0130]

[0131] Among them, V L The voltage at load point L, and These are the minimum and maximum values ​​of the voltage at the load point L, respectively.

[0132] Based on the output curves of distributed generation sources, the output curves of SOPs (Start of Production) and the load conditions in the distribution network, an islanding model is pre-constructed to determine the islanding scheme of the distribution network. This includes the following steps:

[0133] Based on the SOP access location information, determine the type of the ports at both ends of the SOP access, and classify the types of the ports at both ends of the SOP access as distributed power source and load point, respectively.

[0134] Based on the topology, component parameters, load parameters, SOP access location information, SOP output curve, distributed power source access location information, and distributed power source output curve, an islanding model is constructed.

[0135] Among the types of ports at both ends of the SOP connection, one port has a positive output and the other port has a negative output. The port type with a positive output is equivalent to a distributed power source, and the port type with a negative output is equivalent to a load point.

[0136] Based on the failure rate of each component, the fault-free operating time of each component is given, and the specific calculation formula is as follows:

[0137]

[0138] in, Let k be the fault-free uptime of component k in quarter j. Let denot be the failure rate of component k in quarter j, and let ζ be a random number uniformly distributed in the interval [0,1].

[0139] See Figure 3 As shown, the time weight of each load point in each quarter is given, and the annual reliability data of each load point is obtained. The specific steps include the following:

[0140] By combining load parameters and reliability assessment evaluation factors, the time weight of each load point in each quarter is obtained;

[0141] The quarterly reliability data are summed to obtain the annual reliability data for each load point.

[0142] The process involves combining load parameters and reliability assessment factors to obtain the time weight of each load point in each quarter. The specific steps are as follows:

[0143] Analyze the load parameters for each quarter to obtain the subjective weight of each load point in each quarter;

[0144] Determine the evaluation factors for reliability assessment, and obtain the objective weight of each load point in each quarter based on the impact of load parameters on the evaluation factors;

[0145] Combining the subjective and objective weights of each load point in each quarter, a weight matrix for each load point in each quarter is obtained, specifically represented as follows:

[0146]

[0147] Where B is the weight matrix of a certain load point in each quarter, ω1 is the proportion coefficient of objective weight in the weight, ω2 is the proportion coefficient of subjective weight in the weight, and b 1-1，1 b is the objective weight coefficient of the first evaluation factor at a certain load point in the first quarter. 1-1，i Let b be the objective weight coefficient of the i-th evaluation factor at a certain load point in the first quarter. 4-1，1 b is the objective weight coefficient of the first evaluation factor at a certain load point in the fourth quarter. 4-1，i Let b be the objective weight coefficient of the i-th evaluation factor at a certain load point in the fourth quarter. 1-2，1 b is the subjective weight coefficient of the first evaluation factor at a certain load point in the first quarter. 1-2，i Let b be the subjective weight coefficient of the i-th evaluation factor at a certain load point in the first quarter. 4-2，1 b is the subjective weight coefficient of the first evaluation factor at a certain load point in the fourth quarter. 4-2，i The subjective weight coefficient of the i-th evaluation factor at a certain load point in the fourth quarter;

[0148] By normalizing the initial subjective weight values, the subjective weight of this type of load in each quarter can be obtained. Then, by taking into account the actual objective situation and the load demand of different types of load in different quarters, the objective weight is determined. The subjective and objective weights are then weighted together to obtain the final weight.

[0149] The distribution coefficient matrix for each load point across different evaluation factors in each quarter is determined as follows:

[0150]

[0151] Where A is the distribution coefficient matrix among different evaluation factors in each quarter at a certain load point, and a 1，1Let a be the allocation coefficient of the first evaluation factor in the first quarter at a certain load point. 1，i Let a be the allocation coefficient of the i-th evaluation factor in the first quarter at a certain load point. 4，1 Let a be the allocation coefficient of the first evaluation factor in the fourth quarter at a certain load point. 4，i The allocation coefficient for the i-th evaluation factor in the fourth quarter at a certain load point;

[0152] The weight matrix of each load point in each quarter and the distribution coefficient matrix of each load point among different evaluation factors in each quarter are analyzed and calculated to obtain the time weight of each load point in each quarter.

[0153] The quarterly reliability data are summed to obtain the annual reliability data for each load point. The specific calculation formula is as follows:

[0154]

[0155]

[0156] Where, λ L The annual failure rate at load point L. Let L be the time weight of the j-th quarter. For the failure rate of load point j in quarter L, t L The average annual outage time for load point L is the fault mean time. The average outage time for load point L in quarterly is denoted as L.

[0157] The reliability data of the distribution network is evaluated based on the annual reliability data of each load point. The overall distribution network reliability data includes the system average outage frequency (SAIFI), the system average outage duration (SAIDI), the system power supply reliability (ASAI), and the system expected power shortage (EENS). The specific calculation formulas are as follows:

[0158]

[0159]

[0160]

[0161] EENS=∑L L t L

[0162] In the above formula, C L Where L is the number of users at load point L, SC is the set annual simulation time, and L is the number of users at load point L. L It is the average load at load point L.

[0163] Since the annual reliability index of load points takes into account the different reliability requirements of the same type of load in different quarters when calculating the annual reliability index, reliability assessment after improving the reliability of the distribution network can better take into account the characteristics of different types of loads and be more in line with the actual situation.

[0164] like Figure 4 As shown, a distribution network reliability assessment device considering multiple time scales and multiple loads is presented, employing the aforementioned distribution network reliability assessment method considering multiple time scales and multiple loads, including:

[0165] The data acquisition module obtains basic data of the power distribution network, including topology, component parameters, load parameters, SOP information, distributed power source information, and operation time data.

[0166] The analysis and evaluation module analyzes and obtains the failure rate of each component in the distribution network at each time scale, where each time scale is a quarter. Combining the failure rate of each component with the basic data of the distribution network, the module obtains the quarterly reliability data of each load point through sampling simulation, gives the time weight of each load point in each quarter, and obtains the annual reliability data of each load point. Based on the annual reliability data of each load point, the reliability of the distribution network is evaluated.

[0167] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for reliability evaluation of a power distribution network considering multi-time scale multi-load, characterized in that, Specifically comprising the following steps: Obtaining basic data of the power distribution network, wherein the basic data comprises a topological structure, element parameters, load parameters, SOP information, distributed power information and operation time data, wherein the SOP information comprises SOP access position information in the power distribution network and an SOP output curve, the SOP output curve is calculated by a particle swarm algorithm, and specific steps are as follows: Initializing the output of the two end ports of the SOP access at each time as initial values of each particle in the population; The target function is established according to the operation and maintenance cost of the SOP, the power cost of real-time exchange between the distribution network and the external main network, the network loss of the distribution network and the voltage deviation of the distribution network, and the calculation formula is as follows: In the formula, min J is the objective function, λ1, λ2, λ3 represent the weights of three objectives respectively, ω t is the real-time electricity price of the power grid, P 1,t is the active power flowing into the distribution network from the node connected to the external main grid at time t, T is the operation optimization period, ω SOP is the SOP operation maintenance cost per operation optimization period, M is the number of SOPs, N SOP,i is the number of the i-th SOP access port, N node is the number of nodes of the distribution network, U i,t is the voltage amplitude of the i-th node at time t, P loss,t is the network loss of the distribution network at time t; Calculating the fitness function value of each particle according to the objective function; According to the fitness function value of each particle, the individual optimal value and the global optimal value are obtained, and each particle is iteratively updated; It is judged whether the set iteration number has been reached, if not, the step of establishing the objective function is returned, if yes, the optimization is ended, and the SOP output is directly outputted; Analyzing and obtaining the failure rate of each element in the power distribution network at each time scale, wherein each time scale is each quarter; According to different quarter lengths, an initial simulation clock and a simulation length are respectively set; Repeating the following process: based on the failure rate of each element, the failure-free operation time of each element is given; Obtaining the element corresponding to the minimum value in the failure-free operation time, setting the element as a first element, and calculating the repair time of the first element; According to the pre-constructed island division model, the initial quarterly reliability data of each load point corresponding to the first element is obtained, wherein the pre-constructed island division model specifically comprises the following steps: According to the SOP access position information, the type of the two end ports of the SOP access is determined, and the type of the two end ports of the SOP access is respectively equivalent to a distributed power and a load point; Based on the topological structure, the element parameters, the load parameters, the access position information of the SOP, the output curve of the SOP, the access position information of the distributed power, and the output curve of the distributed power, the island division model is constructed; Among the types of the two end ports of the SOP access, one port outputs positively and the other outputs negatively, the type of the port outputting positively is equivalent to the distributed power, and the type of the port outputting negatively is equivalent to the load point; Iterating the failure-free operation time of the first element, updating the initial simulation clock, until the simulation length is reached, the quarterly reliability data of each load point corresponding to the first element is obtained, and then the element corresponding to the minimum value in the failure-free operation time is obtained from the remaining elements, until the processing of all elements is completed; Giving the time weight of each load point in each quarter, the annual reliability data of each load point is obtained; Based on the annual reliability data of each load point, the reliability of the power distribution network is evaluated.

2. The method of claim 1, wherein the method further comprises: The distributed power information comprises distributed power access position information in the power distribution network, and the operation time data comprises element failure repair time data and time data required for switch operation.

3. The method of claim 2, wherein the method further comprises: The distributed power information further comprises a distributed power output curve, and the load parameters comprise load curves of each load point in each quarter.

4. The method of claim 3, wherein the method further comprises: The failure rate of each element in the power distribution network at each time scale is analyzed and obtained, and the specific analysis calculation formula is as follows: wherein, is the failure rate of the k component in the j quarter, SY j is the total number of the j quarter, is the number of repairable failures of the k component in the i j quarter, is the difference between the total time and the non-forced outage time in the j quarter.

5. The method of claim 3, wherein the method further comprises: The quarterly reliability data of the load point comprises a load point failure rate and a load point quarterly failure average outage time, and the specific calculation formula is as follows: is the number of failures of the L load point in the jth quarter, is the total number of failures of the L load point in the jth quarter, SC j is the simulation time of the jth quarter, is the average outage time of the L load point in the jth quarter, is the total outage time of the L load point in the jth quarter.

6. The method of claim 5, wherein the method further comprises: Based on the failure rate of each element, the failure-free running time of each element is given, and the specific calculation formula is as follows: wherein, is the failure-free operating time of the k-th component in the j-th quarter, is the failure rate of the k-th component in the j-th quarter, ζ is a random number uniformly distributed over the interval [0,1].

7. The method of claim 3, wherein the method further comprises: The time weight of each load point in each quarter is given, and the annual reliability data of each load point is obtained, which includes the following steps: The time weight of each load point in each quarter is obtained by combining the load parameters and the evaluation factors of reliability evaluation. The annual reliability data of each load point is obtained by adding calculation of the quarterly reliability data. The time weight of each load point in each quarter is obtained by combining the load parameters and the evaluation factors of reliability evaluation, and the specific steps are as follows: The subjective weight of each load point in each quarter is obtained by analyzing the load parameters. The objective weight of each load point in each quarter is obtained based on the influence of load parameters on evaluation factors. The weight matrix of each load point in each quarter is obtained by combining the subjective weight and the objective weight of each load point in each quarter, which is specifically represented as: Wherein, B is the weight matrix of each quarter of a certain load point, ω1 is the proportion coefficient of objective weight in weight, ω2 is the proportion coefficient of subjective weight in weight, b 1-1，1 is the objective weight coefficient of the first evaluation factor in the first quarter under a certain load point, b 1-1，i is the objective weight coefficient of the i evaluation factor in the first quarter under a certain load point, b 4-1，1 is the objective weight coefficient of the first evaluation factor in the fourth quarter under a certain load point, b 4-1，i is the objective weight coefficient of the i evaluation factor in the fourth quarter under a certain load point, b 1-2，1 is the subjective weight coefficient of the first evaluation factor in the first quarter under a certain load point, b 1-2，i is the subjective weight coefficient of the i evaluation factor in the first quarter under a certain load point, b 4-2，1 is the subjective weight coefficient of the first evaluation factor in the fourth quarter under a certain load point, b 4-2，i is the subjective weight coefficient of the i evaluation factor in the fourth quarter under a certain load point; The distribution coefficient matrix between different evaluation factors of each load point in each quarter is determined, which is specifically represented as: wherein A is a distribution coefficient matrix of different evaluation factors in each quarter under a certain load point, a 1，1 a1 is a distribution coefficient of the first evaluation factor in the first quarter under a certain load point, 1， ai is a distribution coefficient of the i-th evaluation factor in the first quarter under a certain load point, 4，1 a41 is a distribution coefficient of the first evaluation factor in the fourth quarter under a certain load point, 4，i ai is a distribution coefficient of the i-th evaluation factor in the fourth quarter under a certain load point. The weight matrix of each load point in each quarter and the distribution coefficient matrix between different evaluation factors of each load point in each quarter are analyzed and calculated to obtain the time weight of each load point in each quarter.

8. The method of claim 7, wherein the method further comprises: The annual reliability data of each load point is obtained by adding calculation of the quarterly reliability data, and the specific calculation formula is as follows: where λ L is the annual failure rate for L load point, is the time weight for L load point j quarter, is the failure rate for L load point j quarter, t L is the annual average outage time for L load point, is the average outage time for L load point j quarter.

9. A power distribution network reliability evaluation device considering multi-time scale multi-load, characterized in that, The power distribution network reliability evaluation method considering multiple time scales and multiple loads is adopted, which includes: The acquisition module obtains the basic data of the power distribution network, wherein the basic data includes topology structure, element parameters, load parameters, SOP information, distributed power information and operation time data; The analysis and evaluation module analyzes and obtains the failure rate of each element in the power distribution network of each time scale, wherein each time scale is each quarter. Based on the failure rate of each element and the basic data of the power distribution network, the quarterly reliability data of each load point is obtained by sampling simulation. The time weight of each load point in each quarter is given, and the annual reliability data of each load point is obtained. Based on the annual reliability data of each load point, the reliability of the power distribution network is evaluated.

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