Micro-grid reliability evaluation method, device, equipment, medium and product

Abstract

The invention discloses a micro-grid reliability evaluation method, device and equipment, a medium and a product. The method comprises the following steps: acquiring sample data; according to the pseudo observation value matrix corresponding to the sample data, determining a vine Copular function; performing fitting processing on inverse distribution of the sample data on the basis of the vine Copular function to obtain a plurality of groups of prediction arrays; inputting grid parameters of the microgrid and the plurality of groups of prediction arrays into a source load layout model to obtain a plurality of groups of source load active power and a plurality of groups of bus installed capacity, the source load layout model comprising a target function and constraint conditions; and determining a reliability evaluation value of the micro-grid according to the grid frame parameters of the micro-grid, the multi-group source load active power and the multi-group bus installed capacity, and through the technical scheme of the invention, reliability evaluation of the micro-grid in the to-be-researched area is realized under the condition that actual data of the to-be-researched area cannot be acquired.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of active power distribution network, and in particular to a reliability assessment method, device, equipment, medium and product of a microgrid. Background Art

[0002] We have now entered the era of green and low-carbon energy, and the massive development of distributed power sources has provided key support for the future distribution system to shift to an active system.

[0003] However, the output of distributed energy is affected by natural factors such as weather, environment and geographical location, and it is often difficult to obtain actual data in the area to be studied. The inherent randomness and intermittency of distributed power sources also make it difficult to synergize the output characteristics of the power source with the load characteristics of the area to be evaluated.

[0004] Therefore, there is an urgent need for a reliability assessment method for the microgrid in the study area when the actual data of the study area cannot be obtained, so as to plan and construct the active distribution network in the study area according to the reliability assessment results of the microgrid in the study area. Summary of the invention

[0005] Embodiments of the present invention provide a microgrid reliability assessment method, device, equipment, medium and product to implement reliability assessment of a microgrid in a study area when actual data of the study area cannot be obtained.

[0006] According to one aspect of the present invention, a reliability assessment method for a microgrid is provided, comprising:

[0007] Acquire sample data, wherein the sample data includes: historical photovoltaic irradiance, historical wind speed, and historical load active power of the area to be studied to which the microgrid belongs;

[0008] Determine a copula function according to a pseudo observation value matrix corresponding to the sample data;

[0009] Based on the vine copula function, the inverse distribution of the sample data is fitted to obtain multiple sets of prediction arrays;

[0010] Inputting the grid parameters of the microgrid and the multiple sets of prediction arrays into the source-load layout model to obtain multiple sets of source-load active power and multiple sets of bus installed capacity, wherein the source-load layout model includes an objective function and constraint conditions;

[0011] The reliability evaluation value of the microgrid is determined according to the grid parameters of the microgrid, multiple groups of source and load active powers and multiple groups of bus installed capacities.

[0012] According to another aspect of the present invention, a reliability assessment device for a microgrid is provided, the reliability assessment device for a microgrid comprising:

[0013] A sample data acquisition module is used to acquire sample data, wherein the sample data includes: historical photovoltaic irradiance, historical wind speed and historical load active power of the area to be studied to which the microgrid belongs;

[0014] A vine copular function determination module, used to determine a vine copular function according to a pseudo observation value matrix corresponding to the sample data;

[0015] A module for determining multiple prediction arrays, used for fitting the inverse distribution of the sample data based on the vine commonal function to obtain multiple prediction arrays;

[0016] An active power and installed capacity determination module, used for inputting the grid parameters of the microgrid and the multiple sets of prediction arrays into a source-load layout model to obtain multiple sets of source-load active power and multiple sets of bus installed capacity, wherein the source-load layout model includes an objective function and constraint conditions;

[0017] The reliability evaluation value determination module of the microgrid is used to determine the reliability evaluation value of the microgrid according to the grid parameters of the microgrid, multiple groups of source and load active power and multiple groups of bus installed capacity.

[0018] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the microgrid reliability assessment method described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the microgrid reliability assessment method described in any embodiment of the present invention when executed.

[0023] According to another aspect of the present invention, a computer program product is provided. When the computer program is executed by a processor, the computer program implements the reliability assessment method for a microgrid as described in any one of the embodiments of the present invention.

[0024] The embodiment of the present invention obtains sample data; determines a rattan copula function according to a pseudo observation value matrix corresponding to the sample data; performs fitting processing on the inverse distribution of the sample data based on the rattan copula function to obtain multiple sets of prediction arrays; inputs the grid parameters of the microgrid and the multiple sets of prediction arrays into a source-load layout model to obtain multiple sets of source-load active power and multiple sets of bus installed capacity, wherein the source-load layout model includes an objective function and constraints; determines a reliability evaluation value of the microgrid according to the grid parameters of the microgrid, the multiple sets of source-load active power and the multiple sets of bus installed capacity, so as to realize reliability evaluation of the microgrid in the study area when the actual data of the study area cannot be obtained.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a microgrid reliability assessment method in an embodiment of the present invention;

[0028] Figure 2 is a structural schematic diagram of a microgrid reliability assessment device in an embodiment of the present invention;

[0029] Figure 3 It is a structural schematic diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0033] Embodiment 1

[0034] Figure 1 A flowchart of a microgrid reliability assessment method provided in an embodiment of the present invention is provided. This embodiment is applicable to the case of microgrid reliability assessment. The method can be executed by a microgrid reliability assessment device in an embodiment of the present invention. The device can be implemented in software and / or hardware. Figure 1 As shown, the method specifically comprises the following steps:

[0035] S110, obtaining sample data.

[0036] In this embodiment, the sample data includes: historical photovoltaic irradiance, historical wind speed and historical load active power of the area to be studied to which the microgrid belongs.

[0037] In a specific example, the sample data is shown in Table 1:

[0038] Table 1

[0039]

[0040]

[0041] Microgrid (MG) is sometimes referred to as microgrid: it is a concept relative to the traditional large power grid. It refers to a network composed of multiple distributed power sources and their related loads in a certain topological structure, and is connected to the conventional power grid through static switches. Microgrid refers to a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, related loads and monitoring and protection devices. It is an autonomous system that can achieve self-control, protection and management. It can be connected to the external power grid or run in isolation. It is an important part of the smart grid.

[0042] S120, determining a common function according to a pseudo observation value matrix corresponding to the sample data.

[0043] In this embodiment, the method of determining the vine cooperative function according to the pseudo observation value matrix corresponding to the sample data can be: determining the pseudo observation value matrix according to the sample data, and determining the product of three two-dimensional cooperative functions, the expression and parameters of each two-dimensional cooperative function according to the pseudo observation value matrix and the sample data; determining the vine cooperative function according to the product of the three two-dimensional cooperative functions, the expression and parameters of each two-dimensional cooperative function.

[0044] Optionally, determining a common function according to a pseudo observation value matrix corresponding to the sample data includes:

[0045] The sample data is rank-changed to obtain a pseudo observation matrix.

[0046] In this embodiment, the sample data includes: historical photovoltaic irradiance, historical wind speed and historical load active power of the area to be studied in the microgrid. The sample data is a three-dimensional random variable, for example, X = (X1, X2, X3) T , where X1 is the historical photovoltaic irradiance, X2 is the historical wind speed, and X3 is the historical load active power.

[0047] In this embodiment, the method of performing rank change on the sample data to obtain the pseudo observation value matrix may be: normalizing the sample data to obtain normalized sample data, and performing rank change on the normalized sample data to obtain the pseudo observation value matrix. For example, the rank change may be performed on the sample data X to obtain the pseudo observation value matrix u=(u1,u2,u3) after rank change.

[0048] The pseudo observations of the pseudo observation matrix are converted into the product of three two-dimensional Copular functions.

[0049] In this embodiment, based on the pseudo observations u1, u2, u3, the pseudo observations are converted into the product of three two-dimensional Copular functions. For uniformly distributed u, the overall pseudo observations satisfy:

[0050] c 123 (u1,u2,u3)=c 12 (u1,u2)c 23 (u2,u3)·c 132 (u 12 ,u 23 );

[0051] c 123 (u1,u2,u3) is converted to c 12 (u1,u2),c 23 (u2,u3),c 132 (u 12 ,u 23 ) is the product of three two-dimensional Copular functions.

[0052] In this embodiment, the X data set is normalized to form a normalized data set Calculate the normalized data set Kendall coefficient, observe the calculated value of Kendall coefficient, and select the appropriate C vine or D vine Copula model.

[0053] The expressions and parameters of each two-dimensional Copular function are determined according to the sample data.

[0054] The rattan copula function is determined according to the product of the three two-dimensional copula functions, the expressions and parameters of each two-dimensional copula function.

[0055] In this embodiment, for the two-dimensional Copula function obtained by transformation, the maximum likelihood method is used to find the satisfactory Copula function analytical expression. For multiple possible Copula function analytical expressions, the most suitable fitting model is determined using criteria such as AIC and BIC. Finally, u = (u1, u2, u3) can be fitted to obtain the vine Copula function C. The vine Copula function C includes: vine array A, two-dimensional Copula model analytical expression matrix famhat and parameter matrix thetahat matching famhat.

[0056] In this embodiment, the expressions and parameters of each two-dimensional Copular function are shown in Table 2:

[0057] Table 2

[0058]

[0059] S130, based on the vine commonal function, fitting the inverse distribution of the sample data to obtain multiple sets of prediction arrays.

[0060] In this embodiment, each set of prediction arrays includes: predicted photovoltaic active power, predicted wind power active power and predicted load active power.

[0061] S140, inputting the grid parameters of the microgrid and the multiple sets of prediction arrays into a source-load layout model to obtain multiple sets of source-load active powers and multiple sets of bus installed capacities.

[0062] In this embodiment, the source-load layout model includes an objective function and constraint conditions.

[0063] In this embodiment, the investment and income of power source investment, power source operation and maintenance, external power purchase, external power sales, etc. are all included in the objective function. The objective function is used to characterize the minimum cost.

[0064] Optionally, the objective function is:

[0065] F = MIN(f1+f2+f3-f4-f5);

[0066] f1=Q1S solar +Q2S wind ;

[0067] f2=δ1Q1S solar +δ2Q2S wind

[0068]

[0069] f5=∑P price Load(i)

[0070] Among them, δ1 is the proportion of photovoltaic operation and maintenance costs, δ2 is the proportion of wind power operation and maintenance costs, f1 is the sum of photovoltaic construction costs and wind power construction costs, f2 is the sum of photovoltaic operation and maintenance costs and wind power operation and maintenance costs, f3 is the cost of purchasing electricity from the external power grid, f4 is the positive indicator of selling electricity to the external power grid, f5 is the positive indicator of selling electricity to users, Q1 is the cost per installed capacity of photovoltaic power, Q2 is the cost per installed capacity of wind power, S solar is the total installed capacity of photovoltaic power generation, S wind is the total installed capacity of wind power, P price_in (i) is the unit price of electricity purchased from the external power grid corresponding to the i-th prediction array, is the first preset variable, P Grid_in (i) is the purchased external grid active power corresponding to the i-th prediction array, P price_out (i) is the on-grid electricity price corresponding to the i-th group of prediction arrays, is the second preset variable, P Grid_out (i) is the active power of the external power grid corresponding to the i-th prediction array, P price is the microgrid electricity selling price, and Load(i) is the predicted load active power in the i-th prediction array.

[0071] It should be noted that the positive indicator for selling electricity to external power grids may be the revenue from selling electricity to external power grids, and the positive indicator for selling electricity to users may be the revenue from selling electricity to users.

[0072] is a set of 0 and 1 variables, and only one of them is zero at the same time.

[0073] Optionally, the grid parameters of the microgrid include: the maximum installed photovoltaic capacity of each bus, the maximum installed wind power capacity of each bus, the maximum transmission power of the line between the microgrid and the external power grid, a preset photovoltaic power abandonment rate index, a preset wind power abandonment rate index, a carbon emission factor of indirect carbon emission, the resistance between any two busbars, the reactance between any two busbars, the grid voltage of the microgrid, the line resistance, the minimum transmission power of the line, the maximum transmission power of the line, the generator and bus association matrix, and the line and bus node association matrix;

[0074] The constraints include: at least one of capacity constraints, power supply constraints, power abandonment rate constraints, carbon emission constraints, power flow and network loss constraints, full network power balance constraints and Kirchhoff's law constraints.

[0075] In this embodiment, in the generator and bus association matrix, the rows represent the bus numbers, the columns represent the unit numbers, and the elements in the matrix are 1, indicating that the current row (bus) is connected to the column (unit number); in the line and bus node association matrix, the rows represent the bus numbers, the columns represent the grid line numbers, and each column in the matrix has elements 1 and -1, indicating that the flow direction is assumed to flow from the bus corresponding to 1 to the bus corresponding to -1.

[0076] In this embodiment, the grid parameters of the microgrid are shown in Table 3-Table 8:

[0077] Table 3

[0078]

[0079]

[0080] Table 4

[0081]

[0082] Table 5 (Unit: MW)

[0083]

[0084]

[0085] Table 6

[0086]

[0087]

[0088] Table 7

[0089]

[0090]

[0091] Table 8

[0092] type Emission factors Electricity emission factors 4.2tCO2 / 104kWh Heat emission factor 0.06tCO2 / GJ

[0093] Optionally, capacity constraints include:

[0094] 0≤S Gen_solar (i)≤S Gen_solar_max ;

[0095] 0≤S Gen_Wind (i)≤S Gen_Wind_max ;

[0096] Among them, S Gen_solar (i) is the photovoltaic installed capacity of each bus corresponding to the i-th group of predicted arrays, S Gen_Wind (i) is the wind power installed capacity of each bus corresponding to the i-th group of prediction arrays, S Gen_solar_max is the maximum installed capacity of photovoltaic power generation for each bus, S Gen_Wind_max is the maximum installed capacity of wind power of each bus.

[0097] In this embodiment, the capacity constraint mainly refers to the relationship between the capacity of various units within the microgrid and the maximum installed capacity. The maximum installed capacity is generally set according to actual conditions.

[0098] Power supply constraints include:

[0099] P Gen_solar_xn (i) ≥ 0;

[0100] P Gen_solar_qd (i) ≥ 0;

[0101] P Gen_wind_xn (i) ≥ 0;

[0102] P Gen_wind_qd (i) ≥ 0;

[0103] P Gen_solar_xn (i)+P Gen_solar_qd(i) = P solar (i);

[0104] P Gen_wind_xn (i)+P Gen_wind_qd (i) = P wind (i);

[0105] 0≤P Grid_in (i)≤P Line_max ;

[0106] -P Line_max ≤P Grid_out (i)≤0;

[0107] Among them, P Gen_solar_xn (i) is the current PV active power corresponding to the i-th predicted array, P Gen_wind_xn is the current wind power active power corresponding to the i-th group of prediction array, P Gen_solar_qd (i) is the abandoned PV active power corresponding to the i-th predicted array, P Gen_wind_qd (i) is the abandoned wind power active power corresponding to the i-th group of prediction arrays, P Grid_in (i) is the power input from the external grid to the microgrid corresponding to the i-th prediction array, P Grid_out (i) is the power output from the microgrid corresponding to the i-th prediction array to the external grid, P Line_max is the maximum transmission power of the line between the microgrid and the external grid.

[0108] In this embodiment, the power supply power constraint is mainly set as the linkage relationship between the output of each type of unit and the output characteristics of each unit. The power input from the external power grid corresponding to the i-th group of prediction arrays to the microgrid = predicted load active power - (predicted photovoltaic active power + photovoltaic power abandonment power + predicted wind power active power + wind power abandonment power); the power output from the microgrid corresponding to the i-th group of prediction arrays to the external power grid = (predicted photovoltaic active power + photovoltaic power abandonment power + predicted wind power active power + wind power abandonment power) - predicted load active power.

[0109] The constraints on the curtailment rate include:

[0110] ∑P Gen_solar_qd (i)≤α∑S Gen_solar (i)C solar_character (i);

[0111] ∑P Gen_wind_qd (i)≤β∑S Gen_wind (i)C wind_character (i);

[0112] Among them, α is the preset photovoltaic power abandonment rate index, β is the preset wind power abandonment rate index, C solar_character(i) is the predicted PV active power in the i-th prediction array, C wind_character (i) is the predicted wind power active power in the i-th prediction array.

[0113] In this embodiment, in order to ensure that the electricity generated by distributed photovoltaic power is used locally as much as possible and to avoid abandonment of light, the abandonment rate constraint is considered.

[0114] Carbon emission constraints include:

[0115] ωP Grid_in (i)≤γωLoad(i);

[0116] Among them, ω is the carbon emission factor of indirect carbon emission, γ is the preset ratio, and Load(i) is the predicted load active power in the i-th prediction array.

[0117] In this embodiment, the carbon emission is determined only by the power input from the external power grid, and the original carbon emission with carbon emission lower than the specified ratio is considered. The external transmission of renewable energy power cannot offset the carbon emission.

[0118] The power flow and network loss constraints include:

[0119]

[0120] P ab_min ≤P ab (i)≤P ab_max ;

[0121] Among them, P ab (i) is the active power of the line between bus a and bus b corresponding to the i-th prediction array, θ a (i) is the phase angle of bus a corresponding to the i-th prediction array, θ b (i) is the phase angle of bus bar b corresponding to the i-th prediction array, R ab is the resistance between busbar a and busbar b, X ab is the reactance between busbar a and busbar b, P cost_net_ab (i) is the network loss between bus a and bus b corresponding to the i-th prediction array, U is the grid voltage of the microgrid, P ab_min is the minimum transmission power of the line between busbar a and busbar b, P ab_max is the maximum transmission power of the line between bus a and bus b.

[0122] In this embodiment, in terms of power flow constraints, active power expressions are constructed by setting optimization variables based on input grid parameters, while satisfying branch power flow constraints and bus node power balance. ab (i) can be calculated based on the installed capacity of busbar a and busbar b.

[0123] The power balance constraints of the entire network include:

[0124]

[0125]

[0126] in, is the first preset variable, is the second preset variable, ∑P cost_net (i) is the total network loss of all lines corresponding to the i-th group of prediction arrays, and n is the total number of lines;

[0127] In this embodiment, the power of the entire network is balanced at the same time, p1 and p2 are both 0 and 1 variables, and only one of them is zero at the same time.

[0128] Kirchhoff's law constraints include:

[0129] |G|P Bus (i)-P load_Bus (i)-|B|P cost_net (i)=|B|P ab (i);

[0130] Where |G| is the generator and bus association matrix, the rows represent the bus numbers, and the columns represent the unit numbers; P Bus (i) is the sum of the photovoltaic and wind power input active powers of each bus corresponding to the i-th group of prediction arrays; P load_Bus (i) is the load active power of each bus corresponding to the i-th group of prediction arrays; |B| is the line and bus node association matrix, the row represents the bus number, the column represents the grid line number, P cost_net (i) is the network loss of each line corresponding to the i-th group of prediction arrays.

[0131] In this embodiment, the generator and bus association matrix, the element in the matrix is ​​1, which means that the current row (node) is connected to the column (unit number). The line and bus node association matrix, each column in the matrix has elements 1 and -1, which means that the flow direction is assumed to flow from the node corresponding to 1 to the node corresponding to -1.

[0132] The source-load layout model is constructed based on the objective function and constraints.

[0133] In this embodiment, the source-load layout model includes: an objective function and constraints. The constraints include: at least one of capacity constraints, power constraints, power abandonment rate constraints, carbon emission constraints, power flow and network loss constraints, full network power balance constraints, and Kirchhoff's law constraints.

[0134] In this embodiment, each group of source-load active power includes: current photovoltaic active power, current wind power active power and current load active power.

[0135] In this embodiment, based on the fitted vine common function C, the fitting specifies N groups of prediction arrays, and the prediction arrays can be three-dimensional simulation pseudo observation value data groups.

[0136] For sample data X = (X1, X2, X3) T Find the inverse of the distribution:

[0137]

[0138] Perform cubic spline interpolation fitting according to the inverse distribution and substitute into the specified N groups of three-dimensional simulation pseudo observation data sets Obtain N sets of three-dimensional model fitting sample data:

[0139]

[0140] Above Contains fitted photovoltaic irradiance Wind speed With load And the data satisfy the dependency relationship between sample data. Wind speed Substituting the relationship expression between irradiance and new energy output, we can get N groups of prediction arrays.

[0141] Optionally, the grid parameters of the microgrid include: a grid state matrix of the microgrid;

[0142] According to the grid parameters of the microgrid, multiple groups of source and load active powers and multiple groups of bus installed capacities, a reliability evaluation value of the microgrid is determined, including:

[0143] Determine the lost load and the total load according to the grid state matrix of the microgrid, multiple groups of source-load active powers, and multiple groups of bus installed capacities;

[0144] The ratio of the lost load to the total load is determined as a reliability evaluation value of the microgrid.

[0145] In this embodiment, the grid parameters of the microgrid also include: data of multiple buses and data of lines connected to each bus.

[0146] In this embodiment, the grid state matrix of the microgrid may be obtained by: determining the grid state matrix of the microgrid according to component failure rate parameters of the grid of the microgrid.

[0147] In a specific example, according to the grid parameters of the microgrid, multiple groups of source-load active powers and multiple groups of bus installed capacities, determining the reliability evaluation value of the microgrid includes the following steps:

[0148] Step 1: Based on the output of the source-load layout model, form the source-end layout configuration S of each bus Gen_solar (i) S Gen_Wind (i) and Load(i).

[0149] Step 2: Based on the component failure rate parameters of the microgrid, Monte Carlo sampling is used to generate a random number matrix and compare it with the line failure rate. If it is less than the failure rate, the line is considered to be faulty, and M groups of grid state matrices are obtained.

[0150] Step 3: Search the grid state matrix one by one. When a fault occurs in a row of the grid state matrix, based on the current faulty grid, based on the installed capacity of each bus and the active power of N groups of sources and loads, determine whether the load will be lost.

[0151] Step 4: First determine whether the faulty grid is a branch. If it is a branch, directly cut off the load; if it is not a branch, find the tie switch according to the forward direction of the DC power flow calculation result under normal operation, close the tie switch, disconnect the switches of the two sections of the faulty line, and reconstruct the grid;

[0152] Step 5: If an island is formed after the grid is reconstructed, the island operation is allowed; after the grid is reconstructed, the DC power flow of the new grid is calculated to determine whether there is an overloaded line;

[0153] Step 6: If there is an overloaded line, arrange the sum of the loads of each user on the faulty feeder, select the load closest to the load to be removed and remove it, and record the removed load value;

[0154] Step 7: After the calculation of the grid state matrix of this row is completed, go to (step 3), search for the abnormal situation of the next row grid fault, and repeat (steps 4 to 6);

[0155] Step 8: After the specified M groups of grid state matrices are traversed, the sum of the removed loads (lost loads) is counted, and the ratio of the lost load to the total load is determined as the reliability evaluation value of the microgrid.

[0156] The embodiments of the present invention, on the one hand, overcome the problem of not considering the coordinated layout with the power grid in the current planning and construction of distributed power sources, and on the other hand, overcome the problem of insufficient coordination and weak correlation between distributed power source data and load data in the model. It is helpful to the coordinated development of source-grid-load on the distribution network side under the background of "dual carbon".

[0157] S150, determining a reliability assessment value of the microgrid according to the grid parameters of the microgrid, multiple groups of source and load active powers, and multiple groups of bus installed capacities.

[0158] In this embodiment, the reliability evaluation value of the microgrid may be: a reliability index of the microgrid. For example, the ratio of the lost load to the total load may be determined as the reliability evaluation value of the microgrid.

[0159] In this embodiment, the grid parameters of the microgrid and the multiple groups of prediction arrays are input into the source-load layout model to obtain the installed capacity, current photovoltaic active power, current wind power active power, and current load active power corresponding to each bus when the network loss is minimized and the benefit is maximized (the cost is minimized); the reliability index is determined based on the grid parameters, current photovoltaic active power, current wind power active power, and current load active power.

[0160] In the embodiment of the present invention, the dependency relationship between the renewable resource endowment and the load of the area to be studied is constructed by using the rattan-communicable theory, and N-dimensional sample data based on the dependency relationship is obtained; after the resource endowment is converted into actual output, the optimization model is used to calculate the optimal layout of the source and load under the steady state and the output level of each node after the layout; and based on this, a specified number of data groups of each node to be calculated are formed, and an N×M-dimensional system state matrix is ​​obtained by combining the M-dimensional model of the failure rate of the grid components obtained in advance. If the system state is abnormal, a reasonable closing operation of the tie line is arranged according to the line and grid structure of a certain fault, and the flow is calculated. Whether the load needs to be cut is judged based on whether it is overloaded, and finally the reliability index of the microgrid is statistically calculated to obtain the reliability index of the microgrid under the resource endowment and load development conditions of the region.

[0161] In a specific example, the output results of the source-load layout model are shown in Table 9:

[0162] Table 9

[0163]

[0164]

[0165] The reliability indicators are shown in Table 10:

[0166] Table 10

[0167] Reliability (Average System Interruption Frequency ASIFI) Reliability (Average Supply Availability Index ASAI) 0.0674％ 99.9326％

[0168] The embodiment of the present invention starts with using the rank correlation coefficient to describe the correlation of random variables, takes the three-dimensional variables of wind speed, irradiance, and load as input, constructs a multi-dimensional variable correlation Vine-Copular model based on the Vine-Copular function, and forms sampling data that conforms to the above multi-dimensional variable dependency structure. Then, based on the correspondence between wind speed, irradiance, wind power, and photovoltaic output, a source-load layout model based on the multi-dimensional variable dependency structure is constructed. In terms of constraints, the DC current is used to consider the current and the network loss is calculated based on this. At the same time, the network loss is included in the constraints of power balance and bus node balance, so that the network loss is automatically considered when the optimization model is calculated; finally, based on this model and the supporting examples, the impact on the configuration and layout of the wind, light, and storage microgrid when the boundary conditions of the example power grid change is analyzed and studied, and the reliability of the microgrid is evaluated based on this layout.

[0169] The technical solution of this embodiment is to establish an objective function; set constraints for the objective function; construct a source-load layout model according to the objective function and the constraints; obtain sample data, wherein the sample data includes: historical photovoltaic irradiance, historical wind speed and historical load active power of the area to be studied to which the microgrid belongs; determine the Vine Copula function according to the pseudo observation value matrix corresponding to the sample data; based on the Vine Copula function, fit the inverse distribution of the sample data to obtain multiple groups of prediction arrays, each group of prediction arrays includes: predicted photovoltaic active power, predicted wind power active power and predicted load active power; input the grid parameters of the microgrid and the multiple groups of prediction arrays into the source-load layout model to obtain the current photovoltaic active power, the current wind power active power and the current load active power; determine the reliability evaluation value of the microgrid according to the grid parameters of the microgrid, the current photovoltaic active power, the current wind power active power and the current load active power, so as to realize the reliability evaluation of the microgrid in the area to be studied when the actual data of the area to be studied cannot be obtained.

[0170] Embodiment 2

[0171] Figure 2 This is a schematic diagram of the structure of a microgrid reliability assessment device provided by an embodiment of the present invention. This embodiment is applicable to the situation of microgrid reliability assessment. The device can be implemented in software and / or hardware. The device can be integrated in any device that provides microgrid reliability assessment function, such as Figure 2 As shown, the reliability assessment device of the microgrid specifically includes: a sample data acquisition module 210, a universal function determination module 220, a multiple prediction array determination module 230, an active power and installed capacity determination module 240 and a microgrid reliability assessment value determination module 250.

[0172] The sample data acquisition module is used to acquire sample data, wherein the sample data includes: historical photovoltaic irradiance, historical wind speed and historical load active power of the area to be studied to which the microgrid belongs;

[0173] A vine copular function determination module, used to determine a vine copular function according to a pseudo observation value matrix corresponding to the sample data;

[0174] A module for determining multiple prediction arrays, used for fitting the inverse distribution of the sample data based on the vine commonal function to obtain multiple prediction arrays;

[0175] An active power and installed capacity determination module, used for inputting the grid parameters of the microgrid and the multiple sets of prediction arrays into a source-load layout model to obtain multiple sets of source-load active power and multiple sets of bus installed capacity, wherein the source-load layout model includes an objective function and constraint conditions;

[0176] The reliability evaluation value determination module of the microgrid is used to determine the reliability evaluation value of the microgrid according to the grid parameters of the microgrid, multiple groups of source and load active power and multiple groups of bus installed capacity.

[0177] The above-mentioned product can execute the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0178] Embodiment 3

[0179] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0180] like Figure 3As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0181] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0182] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a reliability assessment method for a microgrid.

[0183] In some embodiments, the reliability assessment method of the microgrid may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the reliability assessment method of the microgrid described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the reliability assessment method of the microgrid in any other appropriate manner (e.g., by means of firmware).

[0184] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0185] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0186] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0187] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0188] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0189] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0190] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0191] An embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method for evaluating the reliability of a microgrid according to any embodiment of the present invention is implemented.

[0192] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0193] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A reliability assessment method for a microgrid, characterized in that: include: Acquire sample data, wherein the sample data includes: historical photovoltaic irradiance, historical wind speed, and historical load active power of the area to be studied to which the microgrid belongs; Determine a copula function according to a pseudo observation value matrix corresponding to the sample data; Based on the vine copula function, the inverse distribution of the sample data is fitted to obtain multiple sets of prediction arrays; Inputting the grid parameters of the microgrid and the multiple sets of prediction arrays into the source-load layout model to obtain multiple sets of source-load active power and multiple sets of bus installed capacity, wherein the source-load layout model includes an objective function and constraint conditions; The reliability evaluation value of the microgrid is determined according to the grid parameters of the microgrid, multiple groups of source and load active powers and multiple groups of bus installed capacities.

2. The method according to claim 1, characterized in that According to the pseudo observation value matrix corresponding to the sample data, a common function is determined, including: Performing a rank change on the sample data to obtain a pseudo observation value matrix; Converting the pseudo observations of the pseudo observation matrix into the product of three two-dimensional Copular functions; Determine expressions and parameters of each two-dimensional Copular function according to the sample data; The rattan copula function is determined according to the product of the three two-dimensional copula functions, the expressions and parameters of each two-dimensional copula function.

3. The method according to claim 1, characterized in that: The grid parameters of the microgrid include: a grid state matrix of the microgrid; According to the grid parameters of the microgrid, multiple groups of source and load active powers and multiple groups of bus installed capacities, a reliability evaluation value of the microgrid is determined, including: Determine the lost load and the total load according to the grid state matrix of the microgrid, multiple groups of source-load active powers, and multiple groups of bus installed capacities; The ratio of the lost load to the total load is determined as a reliability evaluation value of the microgrid.

4. The method according to claim 3, characterized in that The objective function is: F = MIN(f1+f2+f3-f4-f5); f1=Q1S solar +Q2S wind ; f2=δ1Q1S solar +δ2Q2S wind ; f5=∑P price Load(i); Among them, δ1 is the proportion of photovoltaic operation and maintenance costs, δ2 is the proportion of wind power operation and maintenance costs, f1 is the sum of photovoltaic construction costs and wind power construction costs, f2 is the sum of photovoltaic operation and maintenance costs and wind power operation and maintenance costs, f3 is the cost of purchasing electricity from the external power grid, f4 is the positive indicator of selling electricity to the external power grid, f5 is the positive indicator of selling electricity to users, Q1 is the cost per installed capacity of photovoltaic power, Q2 is the cost per installed capacity of wind power, S solar is the total installed capacity of photovoltaic power generation, S wind is the total installed capacity of wind power, P price_in (i) is the unit price of electricity purchased from the external power grid corresponding to the i-th prediction array, is the first preset variable, P Grid_in (i) is the purchased external grid active power corresponding to the i-th prediction array, P price_out (i) is the on-grid electricity price corresponding to the i-th group of prediction arrays, is the second preset variable, P Grid_out (i) is the active power of the external power grid corresponding to the i-th prediction array, P price is the microgrid electricity selling price, and Load(i) is the predicted load active power in the i-th prediction array.

5. The method according to claim 1, characterized in that The grid parameters of the microgrid include: the maximum installed photovoltaic capacity of each bus, the maximum installed wind power capacity of each bus, the maximum transmission power of the line between the microgrid and the external power grid, the preset photovoltaic power abandonment rate index, the preset wind power abandonment rate index, the carbon emission factor of indirect carbon emission, the resistance between any two busbars, the reactance between any two busbars, the grid voltage of the microgrid, the line resistance, the minimum transmission power of the line, the maximum transmission power of the line, the generator and bus association matrix, and the line and bus node association matrix; The constraints include: at least one of capacity constraints, power supply constraints, power abandonment rate constraints, carbon emission constraints, power flow and network loss constraints, full network power balance constraints and Kirchhoff's law constraints.

6. The method according to claim 5, characterized in that Capacity constraints include: 0≤S Gen_solar (i)≤S Gen_solar_max ; 0≤S Gen_Wind (i)≤S Gen_Wind_max ; Among them, S Gen_solar (i) is the photovoltaic installed capacity of each bus corresponding to the i-th group of predicted arrays, S Gen_Wind (i) is the wind power installed capacity of each bus corresponding to the i-th group of prediction arrays, S Gen_solar_max is the maximum installed capacity of photovoltaic power generation for each bus, S Gen_Wind_max is the maximum installed capacity of wind power of each bus; Power supply constraints include: P Gen_solar_xn (i)≥0; P Gen_solar_qd (i)≥0; P Gen_wind_xn (i)≥0; P Gen_wind_qd (i)≥0; P Gen_solar_xn (i)+P Gen_solar_qd (i)=P solar (i); P Gen_wind_xn (i)+P Gen_wind_qd (i)=P wind (i); 0≤P Grid_in (i)≤P Line_max ; -P Line_max ≤P Grid_out (i)≤0; Among them, P Gen_solar_xn (i) is the current PV active power corresponding to the i-th predicted array, P Gen_wind_xn is the current wind power active power corresponding to the i-th group of prediction array, P Gen_solar_qd (i) is the abandoned PV active power corresponding to the i-th predicted array, P Gen_wind_qd (i) is the abandoned wind power active power corresponding to the i-th group of prediction arrays, P Grid_in (i) is the power input from the external grid to the microgrid corresponding to the i-th prediction array, P Grid_out (i) is the power output from the microgrid corresponding to the i-th prediction array to the external grid, P Line_max is the maximum transmission power of the line between the microgrid and the external grid; The constraints on the curtailment rate include: ∑P Gen_solar_qd (i)≤α∑S Gen_solar (i)C solar_character (i); ∑P Gen_wind_qd (i)≤β∑S Gen_wind (i)C wind_character (i); Among them, α is the preset photovoltaic power abandonment rate index, β is the preset wind power abandonment rate index, C solar_character (i) is the predicted PV active power in the i-th prediction array, C wind_character (i) is the predicted wind power active power in the i-th prediction array; Carbon emission constraints include: ωP Grid_in (i)≤γωLoad(i); Among them, ω is the carbon emission factor of indirect carbon emission, γ is the preset ratio, and Load(i) is the predicted load active power in the i-th prediction array; The power flow and network loss constraints include: P ab_min ≤P ab (i)≤P ab_max ; Among them, P ab (i) is the active power of the line between bus a and bus b corresponding to the i-th prediction array, θ a (i) is the phase angle of bus a corresponding to the i-th prediction array, θ b (i) is the phase angle of bus bar b corresponding to the i-th prediction array, R ab is the resistance between busbar a and busbar b, X ab is the reactance between busbar a and busbar b, P cost_net_ab (i) is the network loss between bus a and bus b corresponding to the i-th prediction array, U is the grid voltage of the microgrid, P ab_min is the minimum transmission power of the line between busbar a and busbar b, P ab_max is the maximum transmission power of the line between busbar a and busbar b; The power balance constraints of the entire network include: in, is the first preset variable, is the second preset variable, ∑P cost_net (i) is the total network loss of all lines corresponding to the i-th group of prediction arrays, and n is the total number of lines; Kirchhoff's law constraints include: |GP Bus (i)-P load_Bus (i)-BP cost_net (i)=|BP ab (i); Where |G| is the generator and bus association matrix, the rows represent the bus numbers, and the columns represent the unit numbers; P Bus (i) is the sum of the photovoltaic and wind power input active powers of each bus corresponding to the i-th group of prediction arrays; P load_Bus (i) is the load active power of each bus corresponding to the i-th group of prediction arrays; |B| is the line and bus node association matrix, the row represents the bus number, the column represents the grid line number, P cost_net (i) is the network loss of each line corresponding to the i-th group of prediction arrays.

7. A reliability assessment device for a microgrid, characterized in that: include: A sample data acquisition module is used to acquire sample data, wherein the sample data includes: historical photovoltaic irradiance, historical wind speed and historical load active power of the area to be studied to which the microgrid belongs; A vine copular function determination module, used to determine a vine copular function according to a pseudo observation value matrix corresponding to the sample data; A module for determining multiple prediction arrays, used for fitting the inverse distribution of the sample data based on the vine commonal function to obtain multiple prediction arrays; An active power and installed capacity determination module, used for inputting the grid parameters of the microgrid and the multiple sets of prediction arrays into a source-load layout model to obtain multiple sets of source-load active power and multiple sets of bus installed capacity, wherein the source-load layout model includes an objective function and constraint conditions; The reliability evaluation value determination module of the microgrid is used to determine the reliability evaluation value of the microgrid according to the grid parameters of the microgrid, multiple groups of source and load active power and multiple groups of bus installed capacity.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the reliability assessment method for a microgrid according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the reliability assessment method for a microgrid according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the reliability assessment method for a microgrid according to any one of claims 1 to 6.

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