A critical load importance assessment method for different power outage scenarios

Abstract

The present invention provides a critical load importance evaluation method for different power outage scenarios, including the following processes: establishing a critical load importance index evaluation model based on known power grid critical loads; calculating the influence weights of various evaluation indicators on load importance in the critical load importance evaluation index model through a hierarchical analysis method; calculating the quantitative indicators of economic losses and social impacts of critical loads based on the critical load importance index evaluation model; weighted summing the influence weights of various price indicators of critical loads on load importance, as well as the quantitative indicators of economic losses and social impacts, to obtain the importance of critical loads. The method provided by the present invention can form a refined and rapid evaluation of the importance of critical loads for different power outage scenarios, and provide a basis for emergency decision-making in the power sector.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid load assessment, and in particular to a method for assessing the importance of critical loads in different power outage scenarios. Background Art

[0002] As an important basic industry of the national economy, the power industry will cause factories to stop production, government functions to be damaged, and urban infrastructure to fail after a major power outage, causing serious economic losses and social impacts on the entire society. In addition, sudden power outages in hospitals and high-risk industrial parks may also cause serious consequences such as personal injury and environmental pollution.

[0003] When a flood disaster occurs, if the drainage pump loses power, it may cause urban flooding, further expanding the losses and social impact caused by the flood. Therefore, it needs to be considered when making decisions on power protection and restoration. This type of load that plays a key role in certain disaster scenarios is defined as a critical load in this patent. However, there is currently no method to evaluate the importance of such critical loads. Summary of the invention

[0004] An embodiment of the present invention provides a critical load importance assessment method for different power outage scenarios, which is used to solve the technical problems existing in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme.

[0006] A critical load importance assessment method for different power outage scenarios, including:

[0007] According to the known key loads of the power grid, a key load importance index evaluation model is established;

[0008] The influence weight of each evaluation index on the load importance in the key load importance evaluation index model is calculated by the hierarchical analysis method;

[0009] Based on the critical load importance index evaluation model, the quantitative indicators of economic losses and social impacts of critical loads are calculated;

[0010] The importance of the critical load is obtained by taking the weighted sum of the influence weights of various price indicators of the critical load on the load importance, as well as the quantitative indicators of economic losses and social impacts;

[0011] The importance of critical loads is used for load restoration in case of grid failure.

[0012] Preferably, the critical load importance index evaluation model has an overall target layer; the overall target layer includes a first level criterion layer and a second level criterion layer, the first level criterion layer is the economic loss layer, and the second level criterion layer is the social impact layer; the economic loss layer includes a first level criterion layer and a second level criterion layer, the first level criterion layer is the direct economic loss layer, and the second level criterion layer is the indirect economic loss layer; the social impact layer has a third level criterion layer, a fourth level criterion layer, a fifth level criterion layer and a sixth level criterion layer, the third level criterion layer is the public emergency function restriction layer, the fourth level criterion layer is the environmental pollution layer, the fifth level criterion layer is the urban infrastructure failure layer, and the sixth level criterion layer is other adverse impact layer; the direct economic loss layer, the indirect economic loss layer, the public emergency function restriction layer, the environmental pollution layer, the urban infrastructure failure layer and the other adverse impact layer respectively have one or more indicator layers.

[0013] Preferably, the weights of the impact of various evaluation indicators on the load importance in the key load importance evaluation indicator model calculated by the hierarchical analysis method include:

[0014] According to the importance of the criterion layer and / or the overall target layer to which the indicator layer belongs, the indicator layer is compared and assigned values, and a judgment matrix for a certain layer in the critical load importance evaluation indicator model is established;

[0015] Based on the judgment matrix, the maximum eigenvalue and the corresponding eigenvector of each element of the judgment matrix are calculated by arithmetic mean method;

[0016] Based on the maximum eigenvalue root and corresponding eigenvector of each element of the judgment matrix, the consistency check of the judgment matrix is ​​performed.

[0017] Preferably, based on the judgment matrix, calculating the maximum eigenvalue and the corresponding eigenvector of each element of the judgment matrix by arithmetic mean method includes:

[0018] Pass-through

[0019]

[0020] Normalize the elements of the judgment matrix by column; where n is the matrix order, x ij is the element value of the i-th row and j-th column of the judgment matrix, and k is the row index value of the judgment matrix;

[0021] Pass-through

[0022]

[0023] Add up each row element of the normalized judgment matrix;

[0024] Pass-through

[0025]

[0026] Set the vector M=(M1,M2,…,M n ) T Normalize to obtain the weight of the judgment matrix;

[0027] Pass-through

[0028]

[0029] Calculate the maximum eigenvalue of the judgment matrix; where the vector W = (W1, W1, …, W n ) T is the influence weight of each evaluation index on the load importance in the key load importance evaluation index model, and the matrix X is the judgment matrix.

[0030] Preferably, based on the maximum eigenvalue root and the corresponding eigenvector of each element of the judgment matrix, performing consistency check on the judgment matrix includes:

[0031] Pass-through

[0032]

[0033] Calculate the one-time index CI of the judgment matrix;

[0034] Pass-through

[0035]

[0036] Calculate the consistency ratio CR of the judgment matrix; where RI is the consistency test value;

[0037] Determine whether the value of the consistency ratio CR is less than 0.1. If so, end the consistency check process. Otherwise, repeat the steps of establishing the judgment matrix, calculating the maximum eigenvalue and the corresponding eigenvector of each element of the judgment matrix, and the consistency check.

[0038] Preferably, based on the critical load importance index evaluation model, the quantitative indicators of economic losses and social impacts of critical loads are calculated and obtained, including:

[0039] The economic losses of key loads include direct economic losses and indirect economic losses; direct economic losses include economic losses of users and power supply departments, and indirect economic losses include loss of production personnel value, loss of production materials delay and economic losses of casualties;

[0040] The value of economic losses of the power supply department is calculated by

[0041] E2=SQ (7)

[0042] Calculated; where S is the user's electricity price; Q is the user's loss load;

[0043] The value of production personnel loss is calculated by

[0044]

[0045] Calculated; where W is the average annual salary of employees; T is the average annual effective working time of employees, and N is the total number of employees of the user;

[0046] The value of the delay loss of production materials is calculated by

[0047]

[0048] Calculated; where m is the number of transport modes, n i is the sum of the number of transport channels under the i-th transport mode, L ij is the length of the jth transport channel under transportation mode i, W ij is the transportation volume of the jth transportation channel under transportation mode i, and E0 is the income of the transportation enterprise per ton-kilometer;

[0049] The value of economic losses from casualties is calculated by

[0050]

[0051] Calculated; where μ is the attenuation coefficient, μ = 1 for fatalities, μ = 0.5 for injuries, G is the GDP per capita, T is the average loss time, T = 21 years according to the statutory length of service, N i is the number of casualties in the i-th age group, β i is the mental loss coefficient of the i-th age group.

[0052] Preferably, the weights of the impact of various price indicators of the key load on the load importance, as well as the quantitative indicators of economic losses and social impacts are weighted and summed to obtain the importance of the key load, including:

[0053] Pass-through

[0054]

[0055] The importance of the critical load is calculated; where I is the quantitative value of the load importance; p i is the influence weight of the third-level index on the load importance.

[0056] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the present invention provides a method for evaluating the importance of critical loads for different power outage scenarios. According to the known critical loads under natural disasters and the important loads specified in the national standard, a load importance evaluation index system is established. Then, the analytic hierarchy process is used to calculate the influence weight of each indicator on the load importance, and reasonable quantification rules are proposed for each indicator, thereby forming a refined and rapid evaluation of the importance of critical loads for different power outage scenarios, providing a basis for emergency decision-making of the power department.

[0057] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0059] Figure 1 A processing flow chart of a critical load importance assessment method for different power outage scenarios provided by the present invention;

[0060] Figure 2 A schematic diagram of a key load importance index evaluation model of a key load importance evaluation method for different power outage scenarios provided by the present invention. DETAILED DESCRIPTION

[0061] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0062] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0063] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0064] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0065] The present invention provides a method for the precise and rapid assessment of the importance of critical loads in different power outage scenarios, studies the impact of power outages on critical loads, constructs a multi-dimensional load importance evaluation index system and evaluation method that takes into account the economic losses and social impacts of load power outages, and provides a basis for the power sector's emergency decision-making on power supply protection and restoration.

[0066] See also Figure 1 The present invention provides a critical load importance assessment method for different power outage scenarios, comprising the following steps:

[0067] According to the known key loads of the power grid, a key load importance index evaluation model is established;

[0068] The influence weight of each evaluation index on the load importance in the key load importance evaluation index model is calculated by the hierarchical analysis method;

[0069] Based on the critical load importance index evaluation model, the quantitative indicators of economic losses and social impacts of critical loads are calculated;

[0070] The importance of the critical load is obtained by taking the weighted sum of the influence weights of various price indicators of the critical load on the load importance, as well as the quantitative indicators of economic losses and social impacts.

[0071] The importance of the critical load can be used for load restoration in case of power grid failure, especially hierarchical restoration according to the importance of the load.

[0072] In the preferred embodiment provided by the present invention, the key load importance index evaluation model is established based on the known key loads under different natural disasters and the important loads specified in the national standard. Figure 2 As shown. In this model, the load importance A is the total target layer, which includes the first and second first-level criterion layers, the first and second first-level criterion layers are the economic loss layer B1, and the second and second first-level criterion layers are the social impact layer B2; the economic loss layer includes the first and second second-level criterion layers, the first and second second-level criterion layers are the direct economic loss layer C1, and the second and second second-level criterion layers are the indirect economic loss layer C2; the social impact layer has the third and second second-level criterion layers, the fourth and second second-level criterion layers, the fifth and second second-level criterion layers, and the sixth and second second-level criterion layers, the third and second second-level criterion layers are the public emergency function limited layer C3, the fourth and second second-level criterion layers are the environmental pollution layer C4, the fifth and second second-level criterion layers are the urban infrastructure failure layer C5, and the sixth and second second-level criterion layers are other adverse impact layers C6; the direct economic loss layer C1, the indirect economic loss layer C2, the public emergency function limited layer C3, the environmental pollution layer C4, the urban infrastructure failure layer C5, and the other adverse impact layer C6 have one or more indicator layers respectively. As shown in the figure, the direct economic loss layer C1 includes the user economic loss layer D1 and the power supply department economic loss layer D2; the indirect economic loss layer C2 includes the production personnel value loss layer D3, the production materials delay loss layer D4, and the casualties economic loss layer D5; the public emergency function first layer C3 includes the only work obstruction layer D6, the rescue work obstruction layer D7, and the subsequent reconstruction work obstruction layer D8; the environmental pollution layer C4 includes the air pollution layer D9, the water pollution layer D10, and the soil pollution layer D11; the urban infrastructure failure layer C5 includes the water supply and drainage facilities damage layer D12, the heating facilities damage layer D13, the gas supply facilities damage D14, the communication facilities damage layer D15, and the transportation facilities damage layer D16; the other adverse impact layer C6 includes the casualties layer D17 and the bad public opinion and complaint layer D18. In this embodiment, the hierarchical relationship between the indicator layer, the two-level criterion layer, and the overall target layer is based on Figure 2 settings.

[0073] Furthermore, in a preferred embodiment provided by the present invention, the second step specifically includes:

[0074] According to the importance of the criterion layer and / or the overall target layer to which the indicator layer belongs, the indicator layer is compared and assigned values, and a judgment matrix for a certain layer in the critical load importance evaluation indicator model is established;

[0075] Based on the judgment matrix, the maximum eigenvalue and the corresponding eigenvector of each element of the judgment matrix are calculated by arithmetic mean method;

[0076] Based on the maximum eigenvalue root and corresponding eigenvector of each element of the judgment matrix, the consistency check of the judgment matrix is ​​performed.

[0077] The process of constructing the load importance evaluation judgment matrix is ​​to compare the importance of a certain level of indicators to the upper level indicators based on engineering practice and empirical data, and use a scale of 1 to 9 to assign values ​​to construct the judgment matrix. The assignment principle is shown in Table 1. The empirical data can be existing data, such as data from relevant experts / authorities.

[0078] Table 1 Quantitative regulations for comparison between indicators

[0079]

[0080] Thus, we can obtain the judgment matrix AB between the total target layer A and the criterion layer B of the load importance evaluation index system, the judgment matrices B1-C and B2-C between the indicators of the criterion layer B and the criterion layer C, and the judgment matrices C1-D, C2-D, C3-D, C4-D, C5-D, and C6-D between the indicators of the criterion layer C and the indicator layer D. The values ​​and elements of each judgment matrix are shown in Tables 2 to 10.

[0081] Table 2A-B Judgment Matrix Assignment Table

[0082]

[0083] Table 3B1-C Judgment Matrix Assignment Table

[0084]

[0085] Table 4B2-C Judgment Matrix Assignment Table

[0086]

[0087]

[0088] Table 5C1-D Judgment Matrix Assignment Table

[0089]

[0090] Table 6C2-D judgment matrix assignment table

[0091]

[0092] Table 7C3-D judgment matrix assignment table

[0093]

[0094] Table 8C4-D Judgment Matrix Assignment Table

[0095]

[0096] Table 9C5-D Judgment Matrix Assignment Table

[0097]

[0098] Table 10C6-D Judgment Matrix Assignment Table

[0099]

[0100]

[0101] Then the arithmetic mean method is used to calculate the maximum eigenvalue and the corresponding eigenvector. The calculation steps are as follows:

[0102] Pass-through

[0103]

[0104] Normalize each element of the judgment matrix by column; where n is the matrix order;

[0105] Pass-through

[0106]

[0107] Adding each row element of the normalized judgment matrix;

[0108] Pass-through

[0109]

[0110] Set the vector M=(M1,M1,…,M n ) T Normalization is performed to obtain the weight of the judgment matrix; the vector M is an intermediate variable;

[0111] Pass-through

[0112]

[0113] Calculate the maximum eigenvalue of the judgment matrix; where the vector W = (W1, W1, ..., W n ) TThat is, it is the influence weight of each evaluation index on the load importance in the key load importance evaluation index model, and the matrix X is the judgment matrix.

[0114] According to equations (1)-(3), the eigenvectors W1, W2, W3, W4, W5, W6, W7, W8, and W9 of each judgment matrix can be obtained.

[0115] W1=(0.1,0.9) T ;

[0116] W2=(0.5,0.5) T ;

[0117] W3=(0.351,0.189,0.351,0.109) T ;

[0118] W4=(0.5,0.5) T ;

[0119] W5=(0.333,0.333,0.333) T ;

[0120] W6=(0.581,0.309,0.11) T ;

[0121] W7=(0.333,0.333,0.333) T ;

[0122] W8=(0.352,0.091,0.146,0.059,0.352) T ;

[0123] W9=(0.9,0.1) T ;

[0124] According to formula (4), the maximum eigenvalues ​​of each judgment matrix are 2, 2, 4.01, 2, 3, 3.004, 3, 5.062, 2 respectively.

[0125] In order to prevent logical contradictions between evaluation indicators, it is also necessary to perform consistency check on the judgment matrix. The test steps are as follows:

[0126] Calculate the one-time index CI of the judgment matrix;

[0127]

[0128] Calculate the random consistency index RI;

[0129] For a fixed order n, by constructing a positive reciprocal matrix, the matrix elements are randomly selected from 1 to 9 and their reciprocals, and a sufficiently large sample is taken to calculate the average value k of the maximum eigenvalue of the matrix. The calculation formula is: RI = (kn) / (n-1);

[0130] According to the order of the judgment matrix, the random consistency index RI value is obtained by looking up Table 11;

[0131] Table 11 RI values ​​for consistency test

[0132]

[0133] The table shows RI values ​​of 1 to 6 orders by way of example. RI values ​​of 1 to 15 orders can all be calculated and tabulated using the above method.

[0134] Calculate the consistency ratio CR of the judgment matrix.

[0135]

[0136] If CR<0.1, it is considered that the inconsistency of A is within the allowable range, and its normalized eigenvector can be used as the weight vector; otherwise, the judgment matrix needs to be reconstructed.

[0137] The consistency check is performed on the judgment matrix of each layer in the key load importance evaluation index model in turn, and the results are summarized, for example, the calculation results are recorded in Table 12.

[0138] Table 12 Judgment matrix consistency test calculation table

[0139]

[0140] According to Table 12, all judgment matrices meet consistency, and their eigenvectors are the weights of each indicator on the previous level indicator. The influence of each level indicator on the load importance can be obtained by multiplying the weights of each level indicator in turn. The 18 level 3 indicators are calculated in turn, and the calculation results are recorded in Table 13.

[0141] Table 13 Comprehensive weights of various load importance indicators

[0142]

[0143] Furthermore, in the preferred embodiment provided by the present invention, the economic losses of critical loads include direct economic losses and indirect economic losses; direct economic losses include economic losses of users and economic losses of power supply departments, and indirect economic losses include loss of value of production personnel, loss of production materials delays and economic losses due to casualties.

[0144] The economic loss of the power supply department refers to the loss of electricity charges caused by power outage. The economic loss of the power supply department caused by a power outage of a user for 1 hour is calculated as follows:

[0145] E2=SQ (7)

[0146] In the formula, S is the user's electricity price; Q is the user's loss load.

[0147] There is currently a lack of systematic theoretical methods for calculating indirect economic losses. In this embodiment, indirect economic losses are defined as delay losses caused by power outages for users, including loss of value of production personnel, loss of production materials delay, and economic losses caused by casualties.

[0148] The loss of production personnel value refers to the loss of labor value caused by the production personnel stopping work due to power outage. The calculation formula for the loss of production personnel value per unit time of a certain user is as follows:

[0149]

[0150] In the formula, W is the average annual salary of employees; T is the average annual effective working time of employees, generally T = 2000h; N is the total number of employees of the user.

[0151] The loss of production materials delay refers to the loss of transportation company income caused by traffic jams or even suspension of operation due to power outages. The calculation formula for the loss of production materials delay per unit time of a certain user is as follows:

[0152]

[0153] Where m is the number of transport modes; n i is the sum of the number of transport channels under the i-th transport mode; L ij is the length of the jth transport channel under transportation mode i; W ij is the transport volume of the jth transport channel under transport mode i; E0 is the income of the transport enterprise per ton-kilometer.

[0154] Economic losses due to casualties mainly include the loss of social labor value caused by the casualties’ termination of life or loss of working ability due to injury, as well as the mental loss caused to their family members. The calculation formula is as follows:

[0155]

[0156] Where, μ is the attenuation coefficient, μ = 1 for death, μ = 0.5 for injury; G is the GDP per capita; T is the average loss time, T = 21 years according to the statutory length of service; N i is the number of casualties in the i-th age group; β i is the mental loss coefficient of the i-th age group. The values ​​are shown in Table 14.

[0157] Table 14 Mental damage allocation coefficient

[0158]

[0159] In the evaluation of load importance, the social impact caused by power outages is extremely important. However, the impact indicators such as government emergency functions, urban infrastructure failures, and casualties cannot be directly quantified and calculated through data. Therefore, in this embodiment, based on expert experience data, the degree of the adverse consequences of various social impacts is combined to give a quantitative value S i The quantization rules are shown in Table 15.

[0160] Table 15 Rules for quantifying social impact

[0161]

[0162] According to the quantitative rules, the importance of a certain load can be estimated by simply adding up the three-level indicators according to the weights according to the impact of power outages on users. However, considering the different probabilities and severity of social impacts caused by different loads, the quantitative values ​​of the corresponding indicators should also be appropriately changed. For example: for the third-level indicator D17, in flood disasters, power outages in hospitals and water pumps may cause casualties, but the probability of casualties in hospitals is greater than that in water pumps, so the corresponding quantitative values ​​will also be different. In order to make the final evaluation results more scientific and reasonable, this paper introduces the adjustment factor λ i (λ i ≥0,λ i =0 means that the possibility of the load power outage causing the i-th social impact is 0), that is, according to different actual situations, the quantitative values ​​of the three-level indicators in the quantitative rules are adjusted.

[0163] Therefore, the quantitative calculation formula of load importance is as follows:

[0164]

[0165] Where I is the quantitative value of load importance; p i is the influence weight of the third-level index on the load importance.

[0166] The present invention also provides an embodiment for showing an example of executing the method provided by the present invention to perform evaluation, specifically, the method provided by the present invention is used to evaluate water pumps and water purification equipment under flood disasters to verify the effectiveness of the method.

[0167] 1) Water pump

[0168] As a key infrastructure of my country's flood control projects, water pumps will cause long-term urban waterlogging if power is interrupted during flood disasters, causing serious paralysis of the transportation system, and will also affect rescue work and post-disaster urban reconstruction to a certain extent; in addition, urban waterlogging will also lead to combined sewer overflow pollution, which will have a great impact on urban sanitation and produce some adverse public opinion. Since the social impact caused by the power outage of water pumps is far greater than the economic loss, the economic loss part is not considered in the quantitative calculation.

[0169] The evaluation was conducted using seven three-level indicators: obstruction of rescue work D7, obstruction of post-disaster reconstruction work D8, water pollution D10, damage to water supply and drainage facilities D12, damage to transportation facilities D15, casualties D17, and negative public opinion and complaints D18. Assume that λ7 = 0.8, λ8 = 1, λ 10 =1.2,λ 12 =1.5,λ 15 =1.5,λ 17 =0.6,λ 18 =0.6 (the rest are 0). According to the influence weights of the indicators in Table 13 and the quantitative values ​​of the indicators in Table 15, the quantitative calculation of the importance of the water pump is as follows:

[0170]

[0171] 2) Water purification equipment

[0172] Water purification equipment is mainly responsible for the purification of water and sewage. Urban waterlogging accompanied by combined sewer overflow pollution will cause rapid bacterial proliferation. If the power supply of water purification equipment is interrupted, it will not be able to provide safe and reliable water sources for residents in time, which is likely to cause the spread of intestinal infectious diseases, endanger the personal safety of urban residents, and generate some negative public opinion. Similar to water pumps, the economic losses caused by power outages of water purification equipment can also be ignored.

[0173] The evaluation was conducted using four three-level indicators: water pollution D10, damage to water supply and drainage facilities D12, casualties D17, and negative public opinion and complaints D18. 10 =1.5,λ 12 =1.2,λ 17 =0.8,λ 18 =0.8 (the rest are 0). According to the influence weights of the indicators in Table 13 and the quantitative values ​​of the indicators in Table 15, the quantitative calculation of the importance of water purification equipment is as follows:

[0174]

[0175] The calculation results are listed in Table 16. According to the calculation results, when flood disasters occur, water pumps are more important than water purification equipment. Therefore, when the power department makes decisions, it can give priority to the power supply protection measures for water pumps.

[0176] Table 16 Quantified values ​​of key loads under flood disasters

[0177]

[0178]

[0179] In summary, the present invention provides a critical load importance assessment method for different power outage scenarios, including the following processes: establishing a critical load importance index evaluation model based on known power grid critical loads; calculating the influence weights of various evaluation indicators on load importance in the critical load importance evaluation index model through a hierarchical analysis method; calculating the quantitative indicators of economic losses and social impacts of critical loads based on the critical load importance index evaluation model; weighted summing the influence weights of various price indicators of critical loads on load importance, as well as the quantitative indicators of economic losses and social impacts, to obtain the importance of critical loads. The method provided by the present invention can form a refined and rapid assessment of the importance of critical loads for different power outage scenarios, and provide a basis for emergency decision-making in the power sector.

[0180] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0181] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0182] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0183] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A critical load importance assessment method for different power outage scenarios, characterized in that: include: According to the known key loads of the power grid, a key load importance index evaluation model is established; The critical load importance index evaluation model has an overall target layer; The overall target layer includes a first-level criterion layer and a second-level criterion layer, the first-level criterion layer is an economic loss layer, and the second-level criterion layer is a social impact layer; the economic loss layer includes a first-level criterion layer and a second-level criterion layer, the first-level criterion layer is a direct economic loss layer, and the second-level criterion layer is an indirect economic loss layer; the social impact layer has a third-level criterion layer, a fourth-level criterion layer, a fifth-level criterion layer and a sixth-level criterion layer, the third-level criterion layer is a public emergency function restriction layer, the fourth-level criterion layer is an environmental pollution layer, the fifth-level criterion layer is an urban infrastructure failure layer, and the sixth-level criterion layer is other adverse impact layer; the direct economic loss layer, the indirect economic loss layer, the public emergency function restriction layer, the environmental pollution layer, the urban infrastructure failure layer and the other adverse impact layer each have one or more indicator layers; The influence weight of each evaluation index on the load importance in the key load importance evaluation index model is calculated by the hierarchical analysis method; Specifically include: According to the importance of the criterion layer and / or the overall target layer to which the indicator layer belongs, the indicator layer is compared and assigned a value, and a judgment matrix for a certain layer in the critical load importance evaluation indicator model is established; Based on the judgment matrix, the maximum characteristic root and the corresponding characteristic vector of each element of the judgment matrix are calculated by arithmetic averaging method; including: Calculating the maximum eigenvalue and the corresponding eigenvector of each element of the judgment matrix by the arithmetic mean method includes: Pass-through Normalize the elements of the judgment matrix by column; where n is the matrix order, x ij is the element value of the i-th row and j-th column of the judgment matrix, and k is the row index value of the judgment matrix; Pass-through Adding each row element of the normalized judgment matrix; Pass-through Set the vector M=(M1,M2,…,M n ) T Normalizing to obtain the weight of the judgment matrix; Pass-through Calculate the maximum eigenvalue of the judgment matrix; where the vector W = (W1, W1, ..., W n ) T is the influence weight of each evaluation index on the load importance in the key load importance evaluation index model, and the matrix X is the judgment matrix; Based on the maximum eigenvalue root and the corresponding eigenvector of each element of the judgment matrix, the consistency check of the judgment matrix is ​​performed; including: Pass-through Calculating a one-time indicator CI of the judgment matrix; Pass-through Calculate the consistency ratio CR of the judgment matrix; where RI is the consistency test value; Determine whether the value of the consistency ratio CR is less than 0.

1. If so, end the consistency check process. Otherwise, repeat the steps of establishing a judgment matrix, calculating the maximum eigenvalue and the corresponding eigenvector of each element of the judgment matrix, and consistency check. Based on the critical load importance index evaluation model, the quantitative indicators of economic losses and social impacts of critical loads are calculated; specifically, the following are included: The economic losses of the critical loads include direct economic losses and indirect economic losses; the direct economic losses include economic losses of users and power supply departments, and the indirect economic losses include loss of production personnel value, loss of production materials delay and economic losses of casualties; The value of the economic loss of the power supply department is expressed by E2=SQ (7) Calculated; where S is the user's electricity price; Q is the user's loss load; The value of the production personnel's loss of value is expressed by Calculated; where W is the average annual salary of employees; T is the average annual effective working time of employees, and N is the total number of employees of the user; The value of the delay loss of production materials is expressed by Calculated; where m is the number of transport modes, n i is the sum of the number of transport channels under the i-th transport mode, L ij is the length of the jth transport channel under transportation mode i, W ij is the transportation volume of the jth transportation channel under transportation mode i, and E0 is the income of the transportation enterprise per ton-kilometer; The value of the economic loss of casualties is Calculated; where μ is the attenuation coefficient, μ = 1 for fatalities, μ = 0.5 for injuries, G is the GDP per capita, T is the average loss time, T = 21 years according to the statutory length of service, N i is the number of casualties in the i-th age group, β i is the mental loss coefficient for the i-th age group; The importance of the key load is obtained by taking the weighted sum of the influence weights of various price indicators of the key load on the load importance, as well as the quantitative indicators of economic losses and social impacts; specifically, the following are included: Pass-through The importance of the critical load is obtained by calculation; where I is the quantitative value of the load importance; p i is the influence weight of the third-level index on the load importance; i is the adjustment factor; S i It is the quantitative value of social impact; In the equation, parameter i represents any one of the economic losses of users, economic losses of power supply departments, value losses of production personnel, delay losses of production materials and economic losses of casualties; In the equation, the parameter i represents any one of the following: obstruction of command work, obstruction of rescue work, obstruction of post-disaster reconstruction work, air pollution, water pollution, soil pollution, damage to water supply and drainage facilities, damage to heating facilities, damage to gas supply facilities, damage to communication facilities, damage to transportation facilities, casualties, and negative public opinion and complaints; The importance of the critical load is used for load restoration in case of power grid failure.

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