A method and device for evaluating operation efficiency applied to a multi-terminal flexible HVDC power grid

Abstract

The application discloses a kind of applied to the operation efficiency evaluation method and device of multi-terminal flexible DC power grid, method includes: based on the topological characteristics of multi-terminal flexible DC power grid and constructs layered evaluation index model;Model contains several for representing the first-level index of power grid operation efficiency, and each first-level index contains at least one for representing the second-level index of bottom operation scheme;Using analytic hierarchy process calculates the first-level weight value of each first-level index;According to the operation data of power grid, the data set corresponding to each second-level index is generated and data matrix is constructed;Using entropy weight method calculates the second-level weight value of each second-level index;Based on the first-level weight value and second-level weight value, the comprehensive weight value of each second-level index is generated, the average value of bottom scheme operation corresponding to each second-level index in preset time is obtained, according to the comprehensive weight value and bottom scheme operation average value, the operation efficiency evaluation result of power grid is generated.The application can improve the accuracy of multi-terminal flexible DC power grid evaluation.

Description

Technical Field

[0001] This invention relates to the field of power grid assessment technology, and in particular to a method and apparatus for assessing the operational efficiency of multi-terminal flexible DC power grids. Background Technology

[0002] Multi-terminal flexible DC grids refer to power grids composed of three or more converter stations connected in a certain way. While retaining the advantages of two-terminal flexible DC grids, they also possess unique advantages. When one converter station is decommissioned, the multi-terminal flexible DC grid can ensure power balance among the other converter stations under power coordination control. Compared to traditional HVDC grids, the overall advantage of multi-terminal flexible DC grids lies in their flexible operation modes. Converter stations can operate as both rectifier stations and inverter stations without commutation failure issues, resulting in more stable operation. Due to the complexity of the equipment, topology, and model calculations involved in multi-terminal flexible DC grids, large-scale integration requires a comprehensive analysis and evaluation of their operational capabilities as a crucial technical support for grid development.

[0003] Existing methods for evaluating power grid operational efficiency generally assess the comprehensive operational efficiency of flexible DC power grids by determining the weights of evaluation indicators, including feasibility, safety, economy, coordination, and environmental friendliness. However, existing evaluation indicators are only applicable to traditional power grid structures and cannot reflect the equipment nature and topological characteristics of multi-terminal flexible DC power grids. They also have weak correlation with multi-terminal flexible DC power grids and cannot reflect their actual operational efficiency. Furthermore, assessments of the construction and operational levels of multi-terminal flexible DC power grids often rely directly on analysis of their original operational data reports. The vast amount of data cannot accurately reflect the actual operational status of multi-terminal flexible DC power grids, thus hindering the accuracy of power grid operational efficiency assessments. Summary of the Invention

[0004] This application provides a method and apparatus for evaluating the operational performance of multi-terminal flexible DC power grids, which improves the accuracy of operational evaluation of multi-terminal flexible DC power grids.

[0005] Firstly, this application provides a method for evaluating the operational efficiency of multi-terminal flexible DC power grids, including:

[0006] A hierarchical evaluation index model is constructed based on the topological characteristics of a multi-terminal flexible DC grid. The hierarchical evaluation index model includes several primary indicators for characterizing the operational efficiency of the multi-terminal flexible DC grid, and each primary indicator includes at least one secondary indicator for characterizing the underlying operational scheme.

[0007] The first-level weight value of each first-level indicator is calculated using the analytic hierarchy process (AHP).

[0008] Data sets corresponding to each secondary indicator are generated based on the operation data of the multi-terminal flexible DC grid, and all data sets are integrated to construct a data matrix; wherein, the data sets contain a preset number of underlying scheme operation values;

[0009] The entropy weight method is used to calculate the secondary weight value of each secondary indicator;

[0010] Based on the primary weight value and the secondary weight value, a comprehensive weight value is generated for each secondary indicator. The average operating value of the underlying scheme corresponding to each secondary indicator within a preset time period is obtained. Based on the comprehensive weight value and the average operating value of the underlying scheme, the operating efficiency evaluation result of the multi-terminal flexible DC grid is generated.

[0011] This application provides a method for evaluating the operational efficiency of multi-terminal flexible DC power grids. Based on the topological characteristics of the multi-terminal flexible DC power grid, a hierarchical evaluation index model is constructed. Several primary indicators characterizing the operational efficiency of the multi-terminal flexible DC power grid are set in the model to replace traditional evaluation indicators such as feasibility and safety, which are commonly used in traditional power grid structures. Furthermore, at least one secondary indicator characterizing a single underlying operational scheme of the multi-terminal flexible DC power grid is set under each primary indicator. This further emphasizes the differences in topological structure and structural characteristics between the multi-terminal flexible DC power grid and traditional power grids in actual operation, enhancing the correlation between the evaluation indicators and the actual operational status of the multi-terminal flexible DC power grid, thereby improving the accuracy of the operational efficiency evaluation. Different weighting methods are adopted according to the different characteristics of the primary and secondary indicators. When collecting operational data of the multi-terminal flexible DC power grid, corresponding data can be collected based on the secondary indicators, eliminating the need to process all operational data of the power grid and reducing the data processing pressure on power grid evaluation. Finally, by combining the comprehensive weight values ​​of each secondary indicator with the average operating value of each underlying scheme, we can not only obtain the actual operating conditions of each underlying scheme of the multi-terminal flexible power grid, but also achieve a comprehensive operational efficiency assessment of the power grid and generate comprehensive operational efficiency assessment results, which has guiding significance for the construction and development of multi-terminal flexible DC power grids.

[0012] In one implementation, the hierarchical evaluation index model includes several primary indicators and several secondary indicators, specifically:

[0013] The primary indicators include transmission efficiency, reliability, security, economic cost-effectiveness, investment efficiency, and the proportion of clean energy. Transmission efficiency includes secondary indicators such as network loss rate, converter valve loss percentage, and grid transmission efficiency. Reliability includes secondary indicators such as converter valve stability margin, system reliability, mean time between failures (MTBF), and equipment availability. Security includes secondary indicators such as fault ride-through capability and frequency stability. Economic cost-effectiveness includes the secondary indicator of investment cost rate. Investment efficiency includes the secondary indicator of investment efficiency. The proportion of clean energy includes the secondary indicator of renewable energy penetration rate.

[0014] In one implementation, the step of calculating the first-level weight value of each first-level indicator using the analytic hierarchy process specifically includes:

[0015] Obtain the relative importance rating results between every two primary indicators, and construct a first judgment matrix based on the relative importance rating results;

[0016] Normalize each column element of the first judgment matrix to generate the second judgment matrix;

[0017] The elements of each row of the second judgment matrix are summed to generate the sum of the elements of each row;

[0018] Each accumulated result is normalized to generate a first-level weight value for each first-level indicator.

[0019] In one implementation, after generating the first judgment matrix, a consistency check is also performed on the first judgment matrix, specifically including:

[0020] Construct a consistency index and consistency ratio for the first judgment matrix, with the following expressions:

[0021]

[0022] Where CI is the consistency index; λ max is the largest eigenvalue of the first judgment matrix; n is the order of the first judgment matrix;

[0023]

[0024] Wherein, CR is the consistency ratio; CI is the consistency index; and RI is the random consistency index.

[0025] When the consistency ratio is less than a preset threshold, the first judgment matrix is ​​determined to have passed the consistency test, and the first judgment matrix is ​​normalized.

[0026] Otherwise, the relative importance rating results are reassigned and the first allocation matrix is ​​reconstructed until the consistency check is passed.

[0027] In one implementation, the step of generating a dataset corresponding to each secondary indicator based on the operational data of the multi-terminal flexible DC power grid, and integrating all datasets to generate a data matrix, specifically includes:

[0028] Obtain the operation data of the multi-terminal flexible DC grid, and generate a preset number of underlying scheme operation values ​​according to the calculation formula of each secondary index;

[0029] The formula for calculating the network loss rate is:

[0030]

[0031] Where N is the network loss rate between two ends of the multi-terminal flexible DC grid; U1 is the voltage at the transmitting end, I1 is the current at the transmitting end; U2 is the voltage at the receiving end, and I2 is the current at the receiving end.

[0032] The formula for calculating the percentage of loss of the converter valve is as follows:

[0033]

[0034] Among them, K C.V. P represents the percentage of losses in the converter valve. C.V. S represents the total loss of the converter valve. C.V. P is the rated capacity of the converter valve; OS·I For IGBT on-state losses; P ON.I For IGBT turn-on losses; P OFF.I For IGBT turn-off losses; P OS.D For diode conduction loss; P ON.D For diode turn-on loss; P OFF.D This refers to the diode turn-off loss.

[0035] The formula for calculating the power grid transmission efficiency is as follows:

[0036]

[0037] Where η is the power grid transmission efficiency; Q1 is the power generation; and Q2 is the electricity consumed by end users.

[0038] The formula for calculating the stability margin of the converter valve is as follows:

[0039]

[0040] Among them, K SM For the stability margin of the converter valve; P xλ represents the stability margin of the x-th type of device; y represents the number of device types; λ represents the stability margin of the x-th type of device. x Let y be the failure rate of the x-th type of device; x Let x be the number of devices of type x;

[0041] The formula for calculating the system reliability is:

[0042]

[0043] Among them, R(y) o y1 is the system reliability; y2 is the number of system components; y3 is the number of normally functioning components; R0 is the reliability of a single component; the reliability of a single component R0 = e -λt λ is the failure rate of the component (assumed to be a constant); t is the usage time of the component;

[0044] The formula for calculating the fault-free operating time ratio is:

[0045]

[0046] Where τ is the fault-free operating time ratio; T is the total operating time of the multi-terminal flexible DC grid within the preset time period; T fault The total fault time of a multi-terminal flexible DC power grid within a preset time period;

[0047] The formula for calculating the availability of the equipment is as follows:

[0048]

[0049] Among them, K ar X represents equipment availability; X represents the number of equipment types; s represents the s-th type of equipment; α s λ is the available depreciation factor for the lifespan of the s-th type of equipment. o For equipment failure rate; t s The usage time of the equipment;

[0050] The formula for calculating the fault ride-through capability is:

[0051]

[0052]

[0053]

[0054] Among them, K FRTC For fault-tolerant capability; K FRTc1 K represents the locking sensitivity. FRTc2 For fault suppression and recovery capability; t1 is the time of fault occurrence; t 21 t is the converter lockout time; 22The moment when the short-circuit current reaches its maximum under no converter lockout; t 31 The time when the system resumes normal operation after the converter is locked out; t 32 This is the time it takes for the system to resume normal operation when there is no converter lockout.

[0055] The formula for calculating the frequency stability is:

[0056]

[0057]

[0058] Among them, K f For frequency stability; Δf is the overall deviation of the multi-terminal flexible DC grid; f N U represents the rated frequency of the multi-terminal flexible DC power grid; U represents the number of power grid frequency data extracted according to a preset time interval; fu represents the u-th frequency data.

[0059] The formula for calculating the investment cost rate is as follows:

[0060]

[0061] Among them; K cost The investment cost rate; C in For investment and construction costs; C ma Management costs for project construction and operation; C op The costs of project construction and operation maintenance; P busi Revenue; P sub Government financial subsidies;

[0062] The formula for calculating the investment efficiency is as follows:

[0063]

[0064] Where, η e For investment efficiency; P per C represents the net revenue generated annually after the power grid commences operation. total This refers to the total investment amount of the project;

[0065] The formula for calculating the renewable energy penetration rate is as follows:

[0066]

[0067] Where β is the renewable energy penetration rate; Q re The amount of electricity connected to a multi-terminal flexible DC grid from renewable energy sources; Q total The total amount of electricity connected to the multi-terminal flexible DC grid;

[0068] The datasets for each of the secondary indicators are generated based on the underlying scheme's running values, and all datasets are integrated to construct a data matrix with each dataset as a row element.

[0069] In one implementation, the calculation of the secondary weight value for each secondary indicator using the entropy weight method specifically includes:

[0070] Based on the data matrix, the weight of each underlying scheme's operating value relative to each secondary indicator is calculated, and its expression is as follows:

[0071]

[0072] Among them, pb c Let a be the weight of the b-th secondary indicator relative to the c-th underlying solution's operating value. bc Let be the value of the b-th secondary indicator with respect to the c-th underlying scheme; m is the number of underlying scheme operation values;

[0073] Using the running value of each underlying solution as an indicator variable, the entropy of the running value of each underlying solution is calculated, and its expression is as follows:

[0074]

[0075] Among them, l c Let pb be the entropy of the c-th underlying scheme's running value. c The weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value;

[0076] The coefficient of variation of the running values ​​of each underlying scheme is calculated based on the entropy of the running values ​​of each underlying scheme. The expression is as follows:

[0077] g c =1-l c c = 1, 2, ..., m;

[0078] Among them, g c Let l be the coefficient of variation of the running value of the c-th underlying scheme. c The entropy of the c-th underlying scheme's running value;

[0079] The initial weights of the operating values ​​of each underlying scheme are calculated based on the coefficient of variation of the operating values ​​of each underlying scheme. The expression is as follows:

[0080]

[0081] Where, ω c The initial weight values ​​for the c-th underlying scheme's running values;

[0082] Based on the initial weight value of each underlying solution's operating value, the secondary weight value of each secondary indicator is calculated, and its expression is as follows:

[0083]

[0084] Among them, s b Let ω be the secondary weight value of the b-th secondary indicator. c Let p be the initial weight value of the c-th underlying scheme's running value. bc This represents the weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value.

[0085] In one implementation, the step of generating a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtaining the average operating value of the underlying scheme for each secondary indicator within a preset time period, and generating an operational efficiency evaluation result for the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme, specifically includes:

[0086] The secondary weight values ​​of each secondary indicator and the primary weight values ​​of the corresponding primary indicator are integrated to generate the comprehensive weight value of each secondary indicator;

[0087] Within a preset time period, obtain several underlying scheme running values ​​for each secondary indicator and calculate the average running value of the underlying schemes;

[0088] Based on the comprehensive weight value and the average operating value of the underlying scheme, the operational efficiency evaluation result of the multi-terminal flexible DC grid is generated.

[0089] Secondly, this application also provides an operational performance evaluation device for multi-terminal flexible DC power grids, including a model building module, a first weighting module, a data integration module, a second weighting module, and an operational evaluation module;

[0090] The model building module constructs a hierarchical evaluation index model based on the topological characteristics of the multi-terminal flexible DC grid. The hierarchical evaluation index model includes several primary indicators for characterizing the operational efficiency of the multi-terminal flexible DC grid, and each primary indicator includes at least one secondary indicator for characterizing the underlying operational scheme.

[0091] The first weighting module uses the analytic hierarchy process (AHP) to calculate the first-level weight value for each first-level indicator;

[0092] The data integration module generates a dataset corresponding to each secondary indicator based on the operation data of the multi-terminal flexible DC power grid, and integrates all datasets to construct a data matrix; wherein, the dataset contains a preset number of underlying scheme operation values;

[0093] The second weighting module uses the entropy weighting method to calculate the secondary weight value of each secondary indicator;

[0094] The operation evaluation module generates a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtains the average operating value of the underlying scheme for each secondary indicator within a preset time period, and generates the operation efficiency evaluation result of the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme.

[0095] This application provides a method for evaluating the operational efficiency of multi-terminal flexible DC power grids. Based on the topological characteristics of the multi-terminal flexible DC power grid, a hierarchical evaluation index model is constructed. Several primary indicators characterizing the operational efficiency of the multi-terminal flexible DC power grid are set in the model to replace traditional evaluation indicators such as feasibility and safety, which are commonly used in traditional power grid structures. Furthermore, at least one secondary indicator characterizing a single underlying operational scheme of the multi-terminal flexible DC power grid is set under each primary indicator. This further emphasizes the differences in topological structure and structural characteristics between the multi-terminal flexible DC power grid and traditional power grids in actual operation, enhancing the correlation between the evaluation indicators and the actual operational status of the multi-terminal flexible DC power grid, thereby improving the accuracy of the operational efficiency evaluation. Different weighting methods are adopted according to the different characteristics of the primary and secondary indicators. When collecting operational data of the multi-terminal flexible DC power grid, corresponding data can be collected based on the secondary indicators, eliminating the need to process all operational data of the power grid and reducing the data processing pressure on power grid evaluation. Finally, by combining the comprehensive weight values ​​of each secondary indicator with the average operating value of each underlying scheme, we can not only obtain the actual operating conditions of each underlying scheme of the multi-terminal flexible power grid, but also achieve a comprehensive operational efficiency assessment of the power grid and generate comprehensive operational efficiency assessment results, which has guiding significance for the construction and development of multi-terminal flexible DC power grids.

[0096] In one implementation, the hierarchical evaluation index model includes several primary indicators and several secondary indicators, specifically:

[0097] The primary indicators include transmission efficiency, reliability, security, economic cost-effectiveness, investment efficiency, and the proportion of clean energy. Transmission efficiency includes secondary indicators such as network loss rate, converter valve loss percentage, and grid transmission efficiency. Reliability includes secondary indicators such as converter valve stability margin, system reliability, mean time between failures (MTBF), and equipment availability. Security includes secondary indicators such as fault ride-through capability and frequency stability. Economic cost-effectiveness includes the secondary indicator of investment cost rate. Investment efficiency includes the secondary indicator of investment efficiency. The proportion of clean energy includes the secondary indicator of renewable energy penetration rate.

[0098] In one implementation, the first weighting module uses the analytic hierarchy process (AHP) to calculate the first-level weight value of each first-level indicator, specifically including:

[0099] Obtain the relative importance rating results between every two primary indicators, and construct a first judgment matrix based on the relative importance rating results;

[0100] Normalize each column element of the first judgment matrix to generate the second judgment matrix;

[0101] The elements of each row of the second judgment matrix are summed to generate the sum of the elements of each row;

[0102] Each accumulated result is normalized to generate a first-level weight value for each first-level indicator.

[0103] In one implementation, after generating the first judgment matrix, a consistency check is also performed on the first judgment matrix, specifically including:

[0104] Construct a consistency index and consistency ratio for the first judgment matrix, with the following expressions:

[0105]

[0106] Where CI is the consistency index; λ max is the largest eigenvalue of the first judgment matrix; n is the order of the first judgment matrix;

[0107]

[0108] Wherein, CR is the consistency ratio; CI is the consistency index; and RI is the random consistency index.

[0109] When the consistency ratio is less than a preset threshold, the first judgment matrix is ​​determined to have passed the consistency test, and the first judgment matrix is ​​normalized.

[0110] Otherwise, the relative importance rating results are reassigned and the first allocation matrix is ​​reconstructed until the consistency check is passed.

[0111] In one implementation, the data integration module generates a dataset corresponding to each secondary indicator based on the operational data of the multi-terminal flexible DC power grid, and integrates all datasets to generate a data matrix, specifically including:

[0112] Obtain the operation data of the multi-terminal flexible DC grid, and generate a preset number of underlying scheme operation values ​​according to the calculation formula of each secondary index;

[0113] The formula for calculating the network loss rate is:

[0114]

[0115] Where N is the network loss rate between two ends of the multi-terminal flexible DC grid; U1 is the voltage at the transmitting end, I1 is the current at the transmitting end; U2 is the voltage at the receiving end, I2 is the current at the receiving end;

[0116] The formula for calculating the percentage of loss of the converter valve is as follows:

[0117]

[0118] Among them, K C.V . represents the percentage of losses in the converter valve; P C.V. S represents the total loss of the converter valve. C.V . represents the rated capacity of the converter valve; P OS·I For IGBT on-state losses; P ON.I For IGBT turn-on losses; P OFF.I For IGBT turn-off losses; P OS.D For diode conduction loss; P ON.D For diode turn-on loss; P OFF.D This refers to the diode turn-off loss.

[0119] The formula for calculating the power grid transmission efficiency is as follows:

[0120]

[0121] Where η is the power grid transmission efficiency; Q1 is the power generation; and Q2 is the electricity consumed by end users.

[0122] The formula for calculating the stability margin of the converter valve is as follows:

[0123]

[0124] Among them, K SM For the stability margin of the converter valve; P x λ represents the stability margin of the x-th type of device; y represents the number of device types; λ represents the stability margin of the x-th type of device. x Let y be the failure rate of the x-th type of device; x Let x be the number of devices of type x;

[0125] The formula for calculating the system reliability is:

[0126]

[0127] Among them, R(y) o y1 is the system reliability; y2 is the number of system components; y3 is the number of normally functioning components; R0 is the reliability of a single component; the reliability of a single component R0 = e -λt λ is the failure rate of the component (assumed to be a constant); t is the usage time of the component;

[0128] The formula for calculating the fault-free operating time ratio is:

[0129]

[0130] Where τ is the fault-free operating time ratio; T is the total operating time of the multi-terminal flexible DC grid within the preset time period; T fault The total fault time of a multi-terminal flexible DC power grid within a preset time period;

[0131] The formula for calculating the availability of the equipment is as follows:

[0132]

[0133] Among them, K ar X represents equipment availability; X represents the number of equipment types; s represents the s-th type of equipment; α s λ is the available depreciation factor for the lifespan of the s-th type of equipment. o For equipment failure rate; t s The usage time of the equipment;

[0134] The formula for calculating the fault ride-through capability is:

[0135]

[0136]

[0137]

[0138] Among them, K FRTC For fault-tolerant capability; K FRTc1 K represents the locking sensitivity. FRTc2 For fault suppression and recovery capability; t1 is the time of fault occurrence; t 21 t is the converter lockout time; 22 The moment when the short-circuit current reaches its maximum under no converter lockout; t 31 The time when the system resumes normal operation after the converter is locked out; t 32 This is the time it takes for the system to resume normal operation when there is no converter lockout.

[0139] The formula for calculating the frequency stability is:

[0140]

[0141]

[0142] Among them, K f For frequency stability; Δf is the comprehensive deviation of the multi-terminal flexible DC grid; fN is the rated frequency of the multi-terminal flexible DC grid; U is the number of grid frequency data extracted according to the preset time interval; fu This is the u-th frequency data;

[0143] The formula for calculating the investment cost rate is as follows:

[0144]

[0145] Among them; K cost The investment cost rate; C in For investment and construction costs; C ma Management costs for project construction and operation; C op The costs of project construction and operation maintenance; P busi Revenue; P sub Government financial subsidies;

[0146] The formula for calculating investment efficiency is as follows:

[0147]

[0148] Where, η e For investment efficiency; P per C represents the net revenue generated annually after the power grid commences operation. total This refers to the total investment amount of the project;

[0149] The formula for calculating the renewable energy penetration rate is as follows:

[0150]

[0151] Where β is the renewable energy penetration rate; Q re The amount of electricity connected to a multi-terminal flexible DC grid from renewable energy sources; Q total The total amount of electricity connected to the multi-terminal flexible DC grid;

[0152] The datasets for each of the secondary indicators are generated based on the underlying scheme's running values, and all datasets are integrated to construct a data matrix with each dataset as a row element.

[0153] In one implementation, the second weighting module uses the entropy weighting method to calculate the secondary weight value of each secondary indicator, specifically including:

[0154] Based on the data matrix, the weight of each underlying scheme's operating value relative to each secondary indicator is calculated, and its expression is as follows:

[0155]

[0156] Where, p bc Let a be the weight of the b-th secondary indicator relative to the c-th underlying solution's operating value. bcLet be the value of the b-th secondary indicator with respect to the c-th underlying scheme; m is the number of underlying scheme operation values;

[0157] Using the running value of each underlying solution as an indicator variable, the entropy of the running value of each underlying solution is calculated, and its expression is as follows:

[0158]

[0159] Among them, l c Let pb be the entropy of the c-th underlying scheme's running value. c The weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value;

[0160] The coefficient of variation of the running values ​​of each underlying scheme is calculated based on the entropy of the running values ​​of each underlying scheme. The expression is as follows:

[0161] g c =1-l c c = 1, 2, ..., m;

[0162] Among them, g c Let l be the coefficient of variation of the running value of the c-th underlying scheme. c The entropy of the c-th underlying solution's running value:

[0163] The initial weights of the operating values ​​of each underlying scheme are calculated based on the coefficient of variation of the operating values ​​of each underlying scheme. The expression is as follows:

[0164]

[0165] Where, ω c The initial weight values ​​for the c-th underlying scheme's running values;

[0166] Based on the initial weight value of each underlying solution's operating value, the secondary weight value of each secondary indicator is calculated, and its expression is as follows:

[0167]

[0168] Among them, s b Let ω be the secondary weight value of the b-th secondary indicator. c Let p be the initial weight value of the c-th underlying scheme's running value. bc This represents the weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value.

[0169] In one approach, the Xi'an method, the operation evaluation module generates a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtains the average operating value of the underlying scheme for each secondary indicator within a preset time period, and generates an operation efficiency evaluation result for the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme, specifically including:

[0170] The secondary weight values ​​of each secondary indicator and the primary weight values ​​of the corresponding primary indicator are integrated to generate the comprehensive weight value of each secondary indicator;

[0171] Within a preset time period, obtain several underlying scheme running values ​​for each secondary indicator and calculate the average running value of the underlying schemes;

[0172] Based on the comprehensive weight value and the average operating value of the underlying scheme, the operational efficiency evaluation result of the multi-terminal flexible DC grid is generated.

[0173] Thirdly, this application also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the above-described method for evaluating the operational efficiency of a multi-terminal flexible DC power grid.

[0174] Fourthly, this application also provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described method for evaluating the operational performance of a multi-terminal flexible DC power grid. Attached Figure Description

[0175] Figure 1 This is a flowchart illustrating a method for evaluating the operational efficiency of a multi-terminal flexible DC power grid, as provided in an embodiment of the present invention.

[0176] Figure 2 This is a module structure diagram of an operational efficiency evaluation device for a multi-terminal flexible DC power grid provided in an embodiment of the present invention. Detailed Implementation

[0177] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0178] The terms "first" and "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0179] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0180] Example 1

[0181] See Figure 1 , Figure 1 This is a flowchart illustrating a method for evaluating the operational efficiency of a multi-terminal flexible DC power grid, as provided in an embodiment of the present invention. The embodiment of the present invention provides a method for evaluating the operational efficiency of a multi-terminal flexible DC power grid, including steps 101 to 105, each step of which is detailed below:

[0182] Step 101: Construct a hierarchical evaluation index model based on the topological characteristics of the multi-terminal flexible DC grid; wherein, the hierarchical evaluation index model includes several primary indicators for characterizing the operating efficiency of the multi-terminal flexible DC grid, and each primary indicator includes at least one secondary indicator for characterizing the underlying operating scheme.

[0183] Step 102: Calculate the first-level weight value of each first-level indicator using the Analytic Hierarchy Process (AHP);

[0184] Step 103: Generate a dataset corresponding to each secondary indicator based on the operation data of the multi-terminal flexible DC grid, and integrate all datasets to construct a data matrix; wherein, the dataset contains a preset number of underlying scheme operation values;

[0185] Step 104: Calculate the secondary weight value for each secondary indicator using the entropy weight method;

[0186] Step 105: Generate a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtain the average operating value of the underlying scheme for each secondary indicator within a preset time period, and generate the operational efficiency evaluation result of the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme.

[0187] In one embodiment, the hierarchical evaluation index model includes several primary indicators and several secondary indicators. Specifically, the primary indicators include transmission efficiency, reliability, security, economic cost-effectiveness, investment efficiency, and clean energy share. Transmission efficiency includes secondary indicators such as network loss rate, converter valve loss percentage, and grid transmission efficiency; reliability includes secondary indicators such as converter valve stability margin, system reliability, mean time between failures (MTBF), and equipment availability; security includes secondary indicators such as fault ride-through capability and frequency stability; economic cost-effectiveness includes secondary indicator such as investment cost rate; investment efficiency includes secondary indicator such as investment efficiency; and clean energy share includes secondary indicator such as renewable energy penetration rate. In this embodiment of the invention, based on the topological characteristics of a multi-terminal flexible DC grid and combined with the requirements of existing evaluation indicators, an evaluation index model that conforms to the actual operation of a multi-terminal flexible DC grid is constructed.

[0188] In the primary indicators, transmission efficiency is used to consider the network losses, converter valve losses, and overall efficiency of multi-terminal flexible DC grids. Based on the topological characteristics and actual operational structural features of multi-terminal flexible DC grids, secondary indicators are set under transmission efficiency: network loss rate, converter valve loss percentage, and grid transmission efficiency. The network loss rate, also known as the line loss rate, represents the percentage of electricity lost in the power grid relative to the total electricity supplied to the grid, and is a crucial indicator for assessing grid transmission efficiency. The converter valve loss percentage is used because flexible DC converter stations convert AC to DC through converter valves to enable normal power transmission in the DC network; therefore, the transmission efficiency of the flexible DC grid is assessed through the percentage of converter valve losses. Grid transmission efficiency is assessed by the ratio of power generation to end-user electricity consumption.

[0189] Reliability is one of the most important indicators for evaluating the quality of a power system, ensuring its normal operation and improving its quality. Based on power system operation calculation methods, this invention sets up secondary indicators under reliability: converter valve stability margin, system reliability, mean time between failures (MTBF), and equipment availability. The converter valve stability margin uses the probability multiplication principle to calculate the probability of normally functioning devices in a multi-terminal flexible DC grid, avoiding a decrease in objectivity and accuracy in assessing the actual operational efficiency of the grid due to analyzing only converter valve losses. System reliability reflects the stability of the grid, which is related to the reliability of system components. Establishing system reliability allows for the assessment of the reliability of the flexible DC grid. The MTBF reflects the normal operating capability of the DC grid. Equipment availability reflects the degree of equipment availability; a higher availability indicates greater reliability, while a lower availability indicates lower reliability. Equipment availability is related to the equipment's lifespan and characteristics.

[0190] In the primary indicator, safety considers the fault ride-through and frequency fluctuation issues inherent in multi-terminal flexible DC grids. In this embodiment of the invention, secondary indicators of fault ride-through capability and frequency stability are set under the safety framework. The flexible DC grid achieves self-clearing of system faults by blocking the converter. Considering the delay time from fault occurrence to converter blocking and the subsequent fault clearing and system recovery time, fault ride-through capability also includes two major evaluation indicators: blocking sensitivity and fault suppression and recovery capability. The multi-terminal flexible DC grid includes AC and DC components; for the AC component, its frequency stability needs to be considered.

[0191] In the primary indicator of economic cost-effectiveness, the construction and operation and maintenance of flexible DC power grids are considered. In this embodiment of the invention, a secondary indicator of investment cost rate is set in the economic cost-effectiveness. The investment cost rate, an economic indicator, can intuitively reflect the profitability of DC power grid projects.

[0192] The primary indicator, investment efficiency, proposes a method for calculating investment efficiency based on the static investment payback period. This avoids analyzing the economics of flexible DC grids solely through the investment cost rate at the cost-benefit level, and prevents the accuracy of actual operational assessments of flexible DC grids from being reduced by a single economic evaluation indicator. In this embodiment of the invention, a secondary indicator, investment efficiency, is set under investment efficiency.

[0193] The primary indicator, the proportion of clean energy, takes into account the current need for modern flexible DC grids to have a good proportion of clean energy in order to meet the "dual carbon" targets and promote the use of renewable energy sources such as wind and solar power. In this invention, a secondary indicator, the penetration rate of renewable energy, is set under the proportion of clean energy.

[0194] In one embodiment, the step of calculating the first-level weight value of each first-level indicator using the analytic hierarchy process (AHP) specifically includes: obtaining the relative importance calibration results between every two first-level indicators; constructing a first judgment matrix based on the relative importance calibration results; normalizing the elements of each column of the first judgment matrix to generate a second judgment matrix; summing the elements of each row of the second judgment matrix to generate a summed result for each row; and normalizing each summed result to generate the first-level weight value of each first-level indicator. In this embodiment of the invention, the first-level indicators are determined as C = {c1, c2, c3, ..., c...} n We invited m1 experts in this field to assess the relative importance of each pair of primary indicators by considering various factors and using Santy's 1-9 scale method. Based on the results of the relative importance assessment, we constructed the first judgment matrix A1, whose expression is as follows:

[0195]

[0196] Among them, a ijUsed to represent the first-level indicator c i Compared to the first-level indicator c j The relative importance results are represented by numbers 1-9 in the matrix; n is the order of the first judgment matrix. It should be noted that 1-9 represent the degree of importance; a higher degree of importance indicates a higher priority and greater weight. Furthermore, the elements in matrix A1 should satisfy a... ij ×a ji =1, that is, a ij and a ji They are reciprocals of each other, such as when a ij When the value is 3, a ji It should be 1 / 3, and the same applies to other cases. If a ij A value of 1 indicates that the two indicators are of equal importance, meaning that a is equal to 1 in this case. ij In this context, i = j represents the same indicator. Preferably, in this embodiment of the invention, the first judgment matrix constructed based on each primary indicator is as follows:

[0197]

[0198] After generating the first judgment matrix, it is necessary to perform hierarchical sorting of the matrix using the sum-product method. First, the elements of each column of the first judgment matrix are normalized to generate the second judgment matrix. The normalization calculation formula is as follows:

[0199]

[0200] in, The normalized relative importance rating result is given, where k is the element in the i-th row, and the value of k ranges from (1, n). Preferably, in this embodiment of the invention, the elements in each column of the first judgment matrix are normalized as follows:

[0201]

[0202] The summation is performed on each element of the normalized matrix to generate the summation result for each row. The summation calculation formula is as follows:

[0203]

[0204] in, This represents the accumulated result of the elements in the i-th row. Preferably, in this embodiment of the invention, the accumulated result of the elements in each row of the second judgment matrix is ​​calculated as follows:

[0205]

[0206] The sum of the elements in each row is normalized. The normalization formula is as follows:

[0207]

[0208] Where, ω i The vector obtained by assigning weight coefficients to the corresponding primary indicators, i.e., primary weight values, is the feature vector. Preferably, in this embodiment of the invention, the calculation result after normalization of each row of elements is as follows:

[0209]

[0210] In one embodiment, after generating the first judgment matrix, a consistency check is also required on the first judgment matrix, specifically including: constructing a consistency index and a consistency ratio for the first judgment matrix, the expression of which is:

[0211]

[0212] Where CI is the consistency index; λ max is the largest eigenvalue of the first judgment matrix; n is the order of the first judgment matrix, and in this embodiment of the invention, n = 6;

[0213]

[0214] Wherein, CR is the consistency ratio; CI is the consistency index; and RI is the random consistency index. According to the average random consistency index table of 1000 repeated calculations of the first-order judgment matrix (derived by Gong Musen and Xu Shubo), when the order of the first judgment matrix is ​​6, the random consistency index RI = 1.26.

[0215] When the consistency ratio is less than a preset threshold, the first judgment matrix is ​​determined to have passed the consistency test, and normalization is performed on the first judgment matrix; otherwise, the relative importance labeling result is reassigned and the first allocation matrix is ​​reconstructed until the consistency test is passed. Preferably, in this embodiment of the invention, when CR < 0.1, the consistency of the first judgment matrix is ​​determined to be qualified; otherwise, the first judgment matrix needs to be revised until consistency is passed. Only after consistency is passed can normalization be performed to calculate the first-level weight value ω of each first-level indicator. i .

[0216] In one embodiment, the step of generating a dataset corresponding to each secondary indicator based on the operational data of the multi-terminal flexible DC grid, and integrating all datasets to generate a data matrix, specifically includes:

[0217] Obtain the operation data of the multi-terminal flexible DC grid, and generate a preset number of underlying scheme operation values ​​according to the calculation formula of each secondary index;

[0218] The formula for calculating the network loss rate is:

[0219]

[0220] Where N is the network loss rate between two ends of the multi-terminal flexible DC grid; U1 is the voltage at the transmitting end, I1 is the current at the transmitting end; U2 is the voltage at the receiving end, and I2 is the current at the receiving end.

[0221] Considering that the total loss of the converter valve includes the losses of the insulated gate bipolar transistor (IGBT) and the diode losses, including conduction losses and switching losses, in this embodiment of the invention, the formula for calculating the percentage of the converter valve loss is as follows:

[0222]

[0223] Among them, K C.V. P represents the percentage of losses in the converter valve. C.V. S represents the total loss of the converter valve. C.V . represents the rated capacity of the converter valve; P OS·I For IGBT on-state losses; P ON.I For IGBT turn-on losses; P OFF·I For IGBT turn-off losses; P OS.D For diode conduction loss; P ON.D For diode turn-on loss; P OFF.D This represents the diode turn-off loss.

[0224] The formula for calculating the power grid transmission efficiency is as follows:

[0225]

[0226] Where η is the power grid transmission efficiency; Q1 is the power generation; and Q2 is the electricity consumed by end users.

[0227] For a given device x, the probability of it working normally is P. x If a system has y types of devices, then according to the probability multiplication principle, the probability of the system operating normally should be the product of the probabilities of each type of device operating normally. Considering that the number of devices in each type is greater than or equal to 1, and that the failure rate of device x is known to be λ... x Then the probability of it working normally can be calculated from... In summary, the formula for calculating the stability margin of the converter valve is as follows:

[0228]

[0229] Among them, K SM For the stability margin of the converter valve; P x λ represents the stability margin of the x-th type of device; y represents the number of device types; λ represents the stability margin of the x-th type of device. x Let y be the failure rate of the x-th type of device; xLet x be the number of devices of type x.

[0230] If the number of system components is Y, then at least y of the Y components must be... o When all components are functioning normally, that is, when the number of normally functioning components is greater than or equal to y. o If the system operates normally, it will function correctly; otherwise, it will not. The formula for calculating the system's reliability is:

[0231]

[0232] Among them, R(y) o y1 is the system reliability; y2 is the number of system components; y3 is the number of normally functioning components; R0 is the reliability of a single component; the reliability of a single component R0 = e -λt λ is the failure rate of the component (assumed to be a constant); t is the usage time of the component;

[0233] The formula for calculating the fault-free operating time ratio is:

[0234]

[0235] Where τ is the fault-free operating time ratio; T is the total operating time of the multi-terminal flexible DC grid within the preset time period; T fault This represents the total fault time of the multi-terminal flexible DC power grid within a preset time period. Preferably, taking one month of operating time as an example, the fault-free operating time ratio can be expressed by the following formula: The time unit for Tfault is min.

[0236] The formula for calculating the availability of the equipment is as follows:

[0237]

[0238] Among them, K ar X represents equipment availability; X represents the number of equipment types; s represents the s-th type of equipment. Preferably, in a multi-terminal flexible DC power grid, the equipment types include converter valves, transmission lines, transformers, reactors, filters, and other equipment; α s λ is the available depreciation factor for the lifespan of the s-th type of equipment. o For equipment failure rate; t s This represents the equipment's usage time.

[0239] The formula for calculating the fault ride-through capability is as follows:

[0240]

[0241]

[0242]

[0243] Among them, K FRTc For fault-tolerant capability; K FRTc1 K represents the locking sensitivity. FRTC2 For fault suppression and recovery capability; t1 is the time of fault occurrence; t 21 t is the converter lockout time; 22 The moment when the short-circuit current reaches its maximum under no converter lockout; t 31 The time when the system resumes normal operation after the converter is locked out; t 32 This is the time it takes for the system to return to normal operation when there is no converter lockout.

[0244] The formula for calculating the frequency stability is:

[0245]

[0246]

[0247] Among them, K f For frequency stability; Δf is the overall deviation of the multi-terminal flexible DC grid; f N The rated frequency of the multi-terminal flexible DC power grid is 50Hz; U is the number of power grid frequency data extracted according to a preset time interval; f u This is the u-th frequency data;

[0248] The formula for calculating the investment cost rate is as follows:

[0249]

[0250] Among them; K cost The investment cost rate; C in For investment and construction costs; C ma Management costs for project construction and operation; C op The costs of project construction and operation maintenance; P busi Revenue; P sub It is a government financial subsidy.

[0251] The formula for calculating the investment efficiency is as follows:

[0252]

[0253] Where, η e For investment efficiency; P per C represents the net revenue generated annually after the power grid commences operation. total This represents the total investment amount of the project.

[0254] The formula for calculating the renewable energy penetration rate is as follows:

[0255]

[0256] Where β is the renewable energy penetration rate; Q re The amount of electricity connected to a multi-terminal flexible DC grid from renewable energy sources; Q total This refers to the total amount of electricity connected to a multi-terminal flexible DC grid.

[0257] Obtain the operational data of the multi-terminal flexible DC grid to be evaluated, and generate m underlying scheme operational values ​​for each secondary index according to the calculation formula of each secondary index. Use the m underlying scheme operational values ​​as a dataset of a secondary index, integrate the datasets of 12 secondary indices, and construct a data matrix A2 = (a bc ) 12×m The data matrix A2 can be expressed using the following data formula:

[0258]

[0259] Among them, a bc (b = 1, 2, ..., 12; c = 1, 2, ..., m) represents the value of the b-th secondary indicator with respect to the c-th underlying scheme's operating value.

[0260] In one embodiment, the calculation of the secondary weight value of each secondary indicator using the entropy weight method specifically includes:

[0261] Based on the data matrix, the weight of each underlying scheme's operating value relative to each secondary indicator is calculated, and its expression is as follows:

[0262]

[0263] Where, p bc Let a be the weight of the b-th secondary indicator relative to the c-th underlying solution's operating value. bc Let be the value of the b-th secondary indicator with respect to the c-th underlying scheme; m is the number of underlying scheme operation values;

[0264] Using the running value of each underlying solution as an indicator variable, the entropy of the running value of each underlying solution is calculated, and its expression is as follows:

[0265]

[0266] Among them, l c Let p be the entropy of the c-th underlying solution's running value. bc The weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value;

[0267] The coefficient of variation of the running values ​​of each underlying scheme is calculated based on the entropy of the running values ​​of each underlying scheme. The expression is as follows:

[0268] gc =1-l c c = 1, 2, ..., m;

[0269] Among them, g c Let l be the coefficient of variation of the running value of the c-th underlying scheme. c The entropy of the c-th underlying scheme's running value;

[0270] The initial weights of the operating values ​​of each underlying scheme are calculated based on the coefficient of variation of the operating values ​​of each underlying scheme. The expression is as follows:

[0271]

[0272] Where, ω c The initial weight values ​​for the c-th underlying scheme's running values;

[0273] Based on the initial weight value of each underlying solution's operating value, the secondary weight value of each secondary indicator is calculated, and its expression is as follows:

[0274]

[0275] Among them, s b Let ω be the secondary weight value of the b-th secondary indicator. c Let p be the initial weight value of the c-th underlying scheme's running value. bc Let s be the weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value. Preferably, s... b It can also be understood as the comprehensive evaluation value of the b-th secondary indicator, with the c-th underlying scheme operation value as the indicator variable.

[0276] In one embodiment, the step of generating a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtaining the average operating value of the underlying scheme corresponding to each secondary indicator within a preset time, and generating an operational efficiency evaluation result of the multi-terminal flexible DC power grid based on the comprehensive weight value and the average operating value of the underlying scheme, specifically includes: integrating the secondary weight value of each secondary indicator and the primary weight value of the corresponding primary indicator to generate a comprehensive weight value for each secondary indicator; obtaining several underlying scheme operating values ​​for each secondary indicator within a preset time and calculating the average operating value of the underlying scheme; and generating an operational efficiency evaluation result of the multi-terminal flexible DC power grid based on the comprehensive weight value and the average operating value of the underlying scheme. In this embodiment of the invention, the primary weight value W of each primary indicator is integrated. i And the secondary weight value S of the secondary indicators c as follows:

[0277] W i =ω i i = 1, 2, ..., 6;

[0278] S c =s b , b=c=1, 2,...,12;

[0279] Based on the integrated primary and secondary weight values, a comprehensive weight value μ is generated for each secondary indicator. b as follows:

[0280]

[0281]

[0282]

[0283] μ b =W4, b=c=10;

[0284] μ b =W5, b=c=11;

[0285] μ b =W6, b=c=12;

[0286] Take several sets of data within a preset time period t′, and obtain the arithmetic mean of the data sets corresponding to the 12 secondary indicators according to the calculation formulas. By combining the comprehensive weight values ​​of each secondary indicator, the operational efficiency assessment results of the multi-terminal flexible DC power grid are generated, and the assessment results are expressed as follows:

[0287]

[0288] Wherein, δ represents the operational efficiency evaluation structure of a multi-terminal flexible DC grid. μ is the arithmetic mean. b This is the comprehensive weight value of the secondary indicators.

[0289] As an optimized embodiment of the present invention, a comprehensive operation level evaluation standard for multi-terminal flexible DC power grids can be pre-set, and the comprehensive operation level of the multi-terminal flexible DC power grid can be determined based on the generated operation efficiency evaluation results. Preferably, if the operation efficiency evaluation index is [0, 0.7), the comprehensive operation level of the power grid is determined to be unqualified; if the operation efficiency evaluation index is [0.7, 0.8), the comprehensive operation level of the power grid is determined to be low; if the operation efficiency evaluation index is [0.8, 0.9), the comprehensive operation level of the power grid is determined to be medium; and if the operation efficiency evaluation index is [0.9, 1.0), the comprehensive operation level of the power grid is determined to be high.

[0290] In this embodiment of the invention, an operational performance evaluation device for a multi-terminal flexible DC grid is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-described operational performance evaluation method for a multi-terminal flexible DC grid.

[0291] In this embodiment of the invention, a computer-readable storage medium is also provided, which includes a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the above-described method for evaluating the operational efficiency of a multi-terminal flexible DC power grid.

[0292] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in an operational performance evaluation device applied to a multi-terminal flexible DC power grid.

[0293] The operational performance evaluation device for multi-terminal flexible DC power grids can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, a processor, memory, and display. Those skilled in the art will understand that the above components are merely examples of operational performance evaluation devices for multi-terminal flexible DC power grids and do not constitute a limitation on such devices. The device may include more or fewer components than described above, or a combination of certain components, or different components. For example, the operational performance evaluation device for multi-terminal flexible DC power grids may also include input / output devices, network access devices, buses, etc.

[0294] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the operational performance evaluation equipment applied to the multi-terminal flexible DC power grid, connecting all parts of the equipment through various interfaces and lines.

[0295] The memory can be used to store the computer programs and / or modules. The processor, by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the operational efficiency evaluation device applied to the multi-terminal flexible DC grid. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, text conversion function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0296] The module integrated into the performance evaluation equipment for multi-terminal flexible DC power grids, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content of the computer-readable medium may be appropriately added to or subtracted from the content according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals. This can be understood and implemented by those skilled in the art without inventive effort.

[0297] This invention provides a method for evaluating the operational efficiency of multi-terminal flexible DC grids. Based on the topological characteristics of the multi-terminal flexible DC grid, a hierarchical evaluation index model is constructed. This model sets several primary indicators to characterize the operational efficiency of the multi-terminal flexible DC grid, replacing traditional evaluation indicators such as feasibility and safety commonly used in traditional power grid structures. Furthermore, under each primary indicator, at least one secondary indicator is set to characterize a specific underlying operational scheme of the multi-terminal flexible DC grid. This further emphasizes the differences in topological structure and structural characteristics between the multi-terminal flexible DC grid and traditional power grids in actual operation, enhancing the correlation between the evaluation indicators and the actual operational status of the multi-terminal flexible DC grid, thereby improving the accuracy of the operational efficiency evaluation. Different weighting methods are adopted according to the different characteristics of the primary and secondary indicators. When collecting operational data of the multi-terminal flexible DC grid, corresponding data can be collected based on the secondary indicators, eliminating the need to process all operational data of the power grid and reducing the data processing pressure on power grid evaluation. Finally, by combining the comprehensive weight values ​​of each secondary indicator with the average operating value of each underlying scheme, we can not only obtain the actual operating conditions of each underlying scheme of the multi-terminal flexible power grid, but also achieve a comprehensive operational efficiency assessment of the power grid and generate comprehensive operational efficiency assessment results, which has guiding significance for the construction and development of multi-terminal flexible DC power grids.

[0298] Example 2

[0299] See Figure 2 , Figure 2 This is a module structure diagram of an operational performance evaluation device for a multi-terminal flexible DC power grid provided by an embodiment of the present invention. The embodiment of the present invention provides an operational performance evaluation device for a multi-terminal flexible DC power grid, including a model building module 201, a first weighting module 202, a data integration module 203, a second weighting module 204, and an operational evaluation module 205;

[0300] The model building module 201 constructs a hierarchical evaluation index model based on the topological characteristics of the multi-terminal flexible DC grid; wherein, the hierarchical evaluation index model includes several primary indicators for characterizing the operating efficiency of the multi-terminal flexible DC grid, and each primary indicator includes at least one secondary indicator for characterizing the underlying operating scheme.

[0301] The first weighting module 202 uses the analytic hierarchy process (AHP) to calculate the first-level weight value of each first-level indicator;

[0302] The data integration module 203 generates a dataset corresponding to each secondary indicator based on the operation data of the multi-terminal flexible DC power grid, and integrates all datasets to construct a data matrix; wherein, the dataset contains a preset number of underlying scheme operation values;

[0303] The second weighting module 204 uses the entropy weighting method to calculate the secondary weight value of each secondary indicator;

[0304] The operation evaluation module 205 generates a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtains the average value of the underlying scheme operation for each secondary indicator within a preset time, and generates the operation efficiency evaluation result of the multi-terminal flexible DC grid based on the comprehensive weight value and the average value of the underlying scheme operation.

[0305] In one embodiment, the hierarchical evaluation index model includes several primary indicators and several secondary indicators. Specifically, the primary indicators include transmission efficiency, reliability, security, economic cost-effectiveness, investment efficiency, and clean energy share. Transmission efficiency includes secondary indicators such as network loss rate, converter valve loss percentage, and grid transmission efficiency. Reliability includes secondary indicators such as converter valve stability margin, system reliability, mean time between failures (MTBF), and equipment availability. Security includes secondary indicators such as fault ride-through capability and frequency stability. Economic cost-effectiveness includes secondary indicator such as investment cost rate. Investment efficiency includes secondary indicator such as investment efficiency. Clean energy share includes secondary indicator such as renewable energy penetration rate.

[0306] In one embodiment, the first weighting module 202 uses the analytic hierarchy process (AHP) to calculate the first-level weight value of each first-level indicator, specifically including: obtaining the relative importance calibration results between every two first-level indicators; constructing a first judgment matrix based on the relative importance calibration results; normalizing each column element of the first judgment matrix to generate a second judgment matrix; summing each row element of the second judgment matrix to generate a summed result for each row element; and normalizing each summed result to generate the first-level weight value of each first-level indicator.

[0307] In one embodiment, after generating the first judgment matrix, a consistency check is also required on the first judgment matrix, specifically including: constructing a consistency index and a consistency ratio for the first judgment matrix, the expression of which is:

[0308]

[0309] Where CI is the consistency index; λ max is the largest eigenvalue of the first judgment matrix; n is the order of the first judgment matrix;

[0310]

[0311] Wherein, CR is the consistency ratio; CI is the consistency index; and RI is the random consistency index.

[0312] When the consistency ratio is less than a preset threshold, the first judgment matrix is ​​determined to have passed the consistency test, and the first judgment matrix is ​​normalized.

[0313] Otherwise, the relative importance rating results are reassigned and the first allocation matrix is ​​reconstructed until the consistency check is passed.

[0314] In one embodiment, the data integration module 203 generates a dataset corresponding to each secondary indicator based on the operation data of the multi-terminal flexible DC power grid, and integrates all datasets to generate a data matrix, specifically including:

[0315] Obtain the operation data of the multi-terminal flexible DC grid, and generate a preset number of underlying scheme operation values ​​according to the calculation formula of each secondary index;

[0316] The formula for calculating the network loss rate is:

[0317]

[0318] Where N is the network loss rate between two ends of the multi-terminal flexible DC grid; U1 is the voltage at the transmitting end, I1 is the current at the transmitting end; U2 is the voltage at the receiving end, I2 is the current at the receiving end;

[0319] The formula for calculating the percentage of loss of the converter valve is as follows:

[0320]

[0321] Among them, K C.V. P represents the percentage of losses in the converter valve. C.V. S represents the total loss of the converter valve. C.V. P is the rated capacity of the converter valve; OS·I For IGBT on-state losses; P ON.I For IGBT turn-on losses; P OFF.I For IGBT turn-off losses; P OS.D For diode conduction loss; P ON.D For diode turn-on loss; P OFF.D This refers to the diode turn-off loss.

[0322] The formula for calculating the power grid transmission efficiency is as follows:

[0323]

[0324] Where η is the power grid transmission efficiency; Q1 is the power generation; and Q2 is the electricity consumed by end users.

[0325] The formula for calculating the stability margin of the converter valve is as follows:

[0326] Among them, K SM For the stability margin of the converter valve; P x λ represents the stability margin of the x-th type of device; y represents the number of device types; λ represents the stability margin of the x-th type of device. x Let y be the failure rate of the x-th type of device; x Let x be the number of devices of type x;

[0327] The formula for calculating the system reliability is:

[0328]

[0329] Among them, R(y) o y1 is the system reliability; y2 is the number of system components; y3 is the number of normally functioning components; R0 is the reliability of a single component; the reliability of a single component R0 = e -λt λ is the failure rate of the component (assumed to be a constant); t is the usage time of the component;

[0330] The formula for calculating the fault-free operating time ratio is:

[0331]

[0332] Where τ is the fault-free operating time ratio; T is the total operating time of the multi-terminal flexible DC grid within the preset time period; T fault The total fault time of a multi-terminal flexible DC power grid within a preset time period;

[0333] The formula for calculating the availability of the equipment is as follows:

[0334]

[0335] Among them, K ar X represents equipment availability; X represents the number of equipment types; s represents the s-th type of equipment; α represents the equipment availability rate. s λ is the available depreciation factor for the lifespan of the s-th type of equipment. o For equipment failure rate; t s The usage time of the equipment;

[0336] The formula for calculating the fault ride-through capability is as follows:

[0337]

[0338]

[0339]

[0340] Among them, K FRTC For fault-tolerant capability; K FRTc1 K represents the locking sensitivity. FRTc2 For fault suppression and recovery capability; t1 is the time of fault occurrence; t 21 t is the converter lockout time; 22 The moment when the short-circuit current reaches its maximum under no converter lockout; t 31 The time when the system resumes normal operation after the converter is locked out; t 32 This is the time it takes for the system to resume normal operation when there is no converter lockout.

[0341] The formula for calculating the frequency stability is:

[0342]

[0343]

[0344] Among them, K f Δf is the frequency stability; fN is the comprehensive deviation of the multi-terminal flexible DC grid; U is the rated frequency of the multi-terminal flexible DC grid; fu is the number of grid frequency data extracted according to the preset time interval; fu is the u-th frequency data.

[0345] The formula for calculating the investment cost rate is as follows:

[0346]

[0347] Among them; K cost The investment cost rate; C in For investment and construction costs; C ma Management costs for project construction and operation; C op The costs of project construction and operation maintenance; P busi Revenue; P sub Government financial subsidies;

[0348] The formula for calculating the investment efficiency is as follows:

[0349]

[0350] Where, η e For investment efficiency; P per C represents the net revenue generated annually after the power grid commences operation. total This refers to the total investment amount of the project;

[0351] The formula for calculating the renewable energy penetration rate is as follows:

[0352]

[0353] Where β is the renewable energy penetration rate; Q re The amount of electricity connected to a multi-terminal flexible DC grid from renewable energy sources; Q total The total amount of electricity connected to the multi-terminal flexible DC grid;

[0354] The datasets for each of the secondary indicators are generated based on the underlying scheme's running values, and all datasets are integrated to construct a data matrix with each dataset as a row element.

[0355] In one embodiment, the second weighting module 204 calculates the secondary weight value of each secondary indicator using the entropy weight method, specifically including:

[0356] Based on the data matrix, the weight of each underlying scheme's operating value relative to each secondary indicator is calculated, and its expression is as follows:

[0357]

[0358] Where, p bc Let a be the weight of the b-th secondary indicator relative to the c-th underlying solution's operating value. bc Let be the value of the b-th secondary indicator with respect to the c-th underlying scheme; m is the number of underlying scheme operation values;

[0359] Using the running value of each underlying solution as an indicator variable, the entropy of the running value of each underlying solution is calculated, and its expression is as follows:

[0360]

[0361] Among them, l c Let pb be the entropy of the c-th underlying scheme's running value. c The weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value;

[0362] The coefficient of variation of the running values ​​of each underlying scheme is calculated based on the entropy of the running values ​​of each underlying scheme. The expression is as follows:

[0363] g c =1-lc, c=1, 2,...,m;

[0364] Among them, g c Let l be the coefficient of variation of the running value of the c-th underlying scheme. c The entropy of the c-th underlying solution's running value:

[0365] The initial weights of the operating values ​​of each underlying scheme are calculated based on the coefficient of variation of the operating values ​​of each underlying scheme. The expression is as follows:

[0366]

[0367] Where, ω c The initial weight values ​​for the c-th underlying scheme's running values;

[0368] Based on the initial weight value of each underlying solution's operating value, the secondary weight value of each secondary indicator is calculated, and its expression is as follows:

[0369]

[0370] Among them, s b Let ω be the secondary weight value of the b-th secondary indicator. c Let p be the initial weight value of the c-th underlying scheme's running value. bc This represents the weight of the b-th secondary indicator relative to the c-th underlying scheme's operating value.

[0371] In one embodiment, the operation evaluation module 205 generates a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtains the average operating value of the underlying scheme corresponding to each secondary indicator within a preset time, and generates an operation efficiency evaluation result of the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme, specifically including:

[0372] The secondary weight values ​​of each secondary indicator and the primary weight values ​​of the corresponding primary indicator are integrated to generate the comprehensive weight value of each secondary indicator;

[0373] Within a preset time period, obtain several underlying scheme running values ​​for each secondary indicator and calculate the average running value of the underlying schemes;

[0374] Based on the comprehensive weight value and the average operating value of the underlying scheme, the operational efficiency evaluation result of the multi-terminal flexible DC grid is generated.

[0375] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0376] This invention provides an operational performance evaluation device for multi-terminal flexible DC grids. Based on the topological characteristics of the multi-terminal flexible DC grid, a hierarchical evaluation index model is constructed. The model sets several primary indicators to characterize the operational performance of the multi-terminal flexible DC grid, replacing traditional evaluation indicators such as feasibility and safety commonly used in traditional power grid structures. Furthermore, under each primary indicator, at least one secondary indicator is set to characterize a specific underlying operational scheme of the multi-terminal flexible DC grid. This further emphasizes the differences in topological structure and structural characteristics between the multi-terminal flexible DC grid and traditional power grids in actual operation, enhancing the correlation between the evaluation indicators and the actual operational status of the multi-terminal flexible DC grid, thereby improving the accuracy of the operational performance evaluation. Different weighting methods are adopted according to the different characteristics of the primary and secondary indicators. When collecting operational data of the multi-terminal flexible DC grid, corresponding data can be collected based on the secondary indicators, eliminating the need to process all operational data of the power grid and reducing the data processing pressure for power grid evaluation. Finally, by combining the comprehensive weight values ​​of each secondary indicator with the average operating value of each underlying scheme, we can not only obtain the actual operating conditions of each underlying scheme of the multi-terminal flexible power grid, but also achieve a comprehensive operational efficiency assessment of the power grid and generate comprehensive operational efficiency assessment results, which has guiding significance for the construction and development of multi-terminal flexible DC power grids.

[0377] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the operational efficiency of a multi-terminal flexible DC power grid, characterized in that, include: A hierarchical evaluation index model is constructed based on the topological characteristics of a multi-terminal flexible DC grid. The hierarchical evaluation index model includes several primary indicators for characterizing the operational efficiency of the multi-terminal flexible DC grid, and each primary indicator includes at least one secondary indicator for characterizing the underlying operational scheme. The first-level weight value of each first-level indicator is calculated using the analytic hierarchy process (AHP). Data sets corresponding to each secondary indicator are generated based on the operation data of the multi-terminal flexible DC grid, and all data sets are integrated to construct a data matrix; wherein, the data sets contain a preset number of underlying scheme operation values; The entropy weight method is used to calculate the secondary weight value of each secondary indicator; Based on the primary weight value and the secondary weight value, a comprehensive weight value for each secondary indicator is generated. The average operating value of the underlying scheme corresponding to each secondary indicator within a preset time period is obtained. Based on the comprehensive weight value and the average operating value of the underlying scheme, the operating efficiency evaluation result of the multi-terminal flexible DC grid is generated. The primary indicators include transmission efficiency, reliability, safety, economic cost-effectiveness, investment efficiency, and the proportion of clean energy. The calculation of the first-level weight value for each first-level indicator using the analytic hierarchy process specifically includes: Obtain the relative importance rating results between every two primary indicators, and construct a first judgment matrix based on the relative importance rating results; Normalize each column element of the first judgment matrix to generate the second judgment matrix; The elements of each row of the second judgment matrix are summed to generate the sum of the elements of each row; Each of the accumulated results is normalized to generate a first-level weight value for each of the first-level indicators; The hierarchical evaluation index model includes several primary indicators and several secondary indicators, specifically: The transmission efficiency includes secondary indicators such as network loss rate, converter valve loss percentage, and grid transmission efficiency; the reliability includes secondary indicators such as converter valve stability margin, system reliability, mean time between failures (MTBF), and equipment availability; the security includes secondary indicators such as fault ride-through capability and frequency stability; the economic cost-effectiveness includes secondary indicator such as investment cost rate; the investment efficiency includes secondary indicator such as investment efficiency; and the clean energy share includes secondary indicator such as renewable energy penetration rate. The process of generating a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtaining the average operating value of the underlying scheme for each secondary indicator within a preset time period, and generating an operational efficiency evaluation result for the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme, specifically includes: The secondary weight values ​​of each secondary indicator and the primary weight values ​​of the corresponding primary indicator are integrated to generate the comprehensive weight value of each secondary indicator; Within a preset time period, obtain several underlying scheme running values ​​for each secondary indicator and calculate the average running value of the underlying schemes; Based on the comprehensive weight value and the average operating value of the underlying scheme, the operational efficiency evaluation result of the multi-terminal flexible DC grid is generated.

2. The method for evaluating the operational efficiency of a multi-terminal flexible DC power grid as described in claim 1, characterized in that, After generating the first judgment matrix, a consistency check needs to be performed on the first judgment matrix, specifically including: Construct a consistency index and consistency ratio for the first judgment matrix, with the following expressions: ； in, As a consistency indicator; The largest eigenvalue of the first judgment matrix; Let be the order of the first judgment matrix; ； in, The consistency ratio; As a consistency indicator; It is a random consistency indicator; When the consistency ratio is less than a preset threshold, the first judgment matrix is ​​determined to have passed the consistency test, and the first judgment matrix is ​​normalized. Otherwise, the relative importance rating results are reassigned and the first allocation matrix is ​​reconstructed until the consistency check is passed.

3. The method for evaluating the operational efficiency of a multi-terminal flexible DC power grid as described in claim 1, characterized in that, The process of generating a dataset corresponding to each secondary indicator based on the operational data of the multi-terminal flexible DC power grid, and integrating all datasets to generate a data matrix, specifically includes: Obtain the operation data of the multi-terminal flexible DC grid, and generate a preset number of underlying scheme operation values ​​according to the calculation formula of each secondary index; The formula for calculating the network loss rate is: ； in, The network loss rate between two ends of a multi-terminal flexible DC grid; The voltage at the transmitting end. The current at the transmitting end; The voltage at the receiving end. This refers to the current at the receiving end; The formula for calculating the percentage of loss of the converter valve is as follows: ； in, This represents the percentage of losses in the converter valve. This represents the total loss of the converter valve; This refers to the rated capacity of the converter valve; For IGBT on-state losses; For IGBT turn-on losses; For IGBT turn-off losses; This refers to the diode's conduction loss. This refers to the diode's turn-on loss. This refers to the diode turn-off loss. The formula for calculating the power grid transmission efficiency is as follows: ； in, For power grid transmission efficiency; For electricity generation; Electricity consumption at the terminal; The formula for calculating the stability margin of the converter valve is as follows: ； in, For the stability margin of the converter valve; For the first Stability margin of similar devices; Number of device types; For the first Failure rate of such devices; For the first The number of devices of each type; The formula for calculating the system reliability is: ； in, For system reliability; This represents the number of system components. y0 represents the number of normally functioning components; For the reliability of a single component; the reliability of a single component , The failure rate of the component; This represents the usage time of the component; The formula for calculating the fault-free operating time ratio is: ； in; The ratio of trouble-free operating time; The total operating time of the multi-terminal flexible DC grid within a preset time period; The total fault time of a multi-terminal flexible DC power grid within a preset time period; The formula for calculating the availability of the equipment is as follows: ； in, For equipment availability; Number of device types; For the first Class of equipment; For the first The lifespan of this type of equipment can be measured using a depreciation factor; For equipment failure rate; The equipment's usage time; The formula for calculating the fault ride-through capability is: ； ； ； in, For fault-tolerant capability; This refers to the locking sensitivity. For fault suppression and recovery capabilities; The time when the fault occurred; This is the converter lockout time; This is the moment when the short-circuit current reaches its maximum when there is no converter lockout. This is the moment when the system resumes normal operation after the converter is locked out. This is the time it takes for the system to resume normal operation when there is no converter lockout. The formula for calculating the frequency stability is: ； ； in, For frequency stability; This refers to the overall deviation of a multi-terminal flexible DC power grid. The rated frequency for a multi-terminal flexible DC power grid; This represents the number of power grid frequency data points extracted based on a preset time interval. For the first One frequency data; The formula for calculating the investment cost rate is as follows: in; The investment cost rate; For investment and construction costs; Management costs for project construction and operation; The costs of project construction and operation / maintenance; Revenue; Government financial subsidies; The formula for calculating the investment efficiency is as follows: ； in, For investment efficiency; This refers to the net revenue generated annually after the power grid is put into operation. This refers to the total investment amount of the project; The formula for calculating the renewable energy penetration rate is as follows: ； in, For renewable energy penetration rate; Electricity connected to multi-terminal flexible DC grids for renewable energy sources; The total amount of electricity connected to the multi-terminal flexible DC grid; The datasets for each of the secondary indicators are generated based on the underlying scheme's running values, and all datasets are integrated to construct a data matrix with each dataset as a row element.

4. The method for evaluating the operational efficiency of a multi-terminal flexible DC power grid as described in claim 1, characterized in that, The calculation of the secondary weight value for each secondary indicator using the entropy weight method specifically includes: Based on the data matrix, the weight of each underlying scheme's operating value relative to each secondary indicator is calculated, and its expression is as follows: ； in, For the first The second-level indicator is relative to the first The proportion of the underlying solution's running values No. The second-level indicator regarding the first The values ​​that can be obtained from the operation of each underlying solution; This represents the number of values ​​in the underlying solution. Using the running value of each underlying solution as an indicator variable, the entropy of the running value of each underlying solution is calculated, and its expression is as follows: ； in, For the first The entropy of the underlying solution's operating value. For the first The second-level indicator regarding the first The proportion of the operating values ​​of each underlying solution; The coefficient of variation of the running values ​​of each underlying scheme is calculated based on the entropy of the running values ​​of each underlying scheme. The expression is as follows: ； in, For the first The coefficient of variation of the underlying scheme's running values For the first The entropy of the underlying solution's operating value; The initial weights of the operating values ​​of each underlying scheme are calculated based on the coefficient of variation of the operating values ​​of each underlying scheme. The expression is as follows: ； in, For the first The initial weight values ​​of each underlying scheme's running values; Based on the initial weight value of each underlying solution's operating value, the secondary weight value of each secondary indicator is calculated, and its expression is as follows: ； in, For the first The secondary weight values ​​of each secondary indicator. For the first The initial weight values ​​of each underlying solution's running values. For the first The second-level indicator regarding the first The proportion of the underlying solution's running value.

5. A device for evaluating the operational efficiency of a multi-terminal flexible DC power grid, characterized in that, It includes a model building module, a first weight module, a data integration module, a second weight module, and an operation evaluation module; The model building module constructs a hierarchical evaluation index model based on the topological characteristics of the multi-terminal flexible DC grid. The hierarchical evaluation index model includes several primary indicators for characterizing the operational efficiency of the multi-terminal flexible DC grid, and each primary indicator includes at least one secondary indicator for characterizing the underlying operational scheme. The first weighting module uses the analytic hierarchy process (AHP) to calculate the first-level weight value for each first-level indicator; The data integration module generates a dataset corresponding to each secondary indicator based on the operation data of the multi-terminal flexible DC power grid, and integrates all datasets to construct a data matrix; wherein, the dataset contains a preset number of underlying scheme operation values; The second weighting module uses the entropy weighting method to calculate the secondary weight value of each secondary indicator; The operation evaluation module generates a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtains the average operating value of the underlying scheme for each secondary indicator within a preset time, and generates the operation efficiency evaluation result of the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme. The primary indicators include transmission efficiency, reliability, safety, economic cost-effectiveness, investment efficiency, and the proportion of clean energy. The calculation of the first-level weight value for each first-level indicator using the analytic hierarchy process specifically includes: Obtain the relative importance rating results between every two primary indicators, and construct a first judgment matrix based on the relative importance rating results; Normalize each column element of the first judgment matrix to generate the second judgment matrix; The elements of each row of the second judgment matrix are summed to generate the sum of the elements of each row; Each of the accumulated results is normalized to generate a first-level weight value for each of the first-level indicators; The hierarchical evaluation index model includes several primary indicators and several secondary indicators, specifically: The transmission efficiency includes secondary indicators such as network loss rate, converter valve loss percentage, and grid transmission efficiency; the reliability includes secondary indicators such as converter valve stability margin, system reliability, mean time between failures (MTBF), and equipment availability; the security includes secondary indicators such as fault ride-through capability and frequency stability; the economic cost-effectiveness includes secondary indicator such as investment cost rate; the investment efficiency includes secondary indicator such as investment efficiency; and the clean energy share includes secondary indicator such as renewable energy penetration rate. The process of generating a comprehensive weight value for each secondary indicator based on the primary weight value and the secondary weight value, obtaining the average operating value of the underlying scheme for each secondary indicator within a preset time period, and generating an operational efficiency evaluation result for the multi-terminal flexible DC grid based on the comprehensive weight value and the average operating value of the underlying scheme, specifically includes: The secondary weight values ​​of each secondary indicator and the primary weight values ​​of the corresponding primary indicator are integrated to generate the comprehensive weight value of each secondary indicator; Within a preset time period, obtain several underlying scheme running values ​​for each secondary indicator and calculate the average running value of the underlying schemes; Based on the comprehensive weight value and the average operating value of the underlying scheme, the operational efficiency evaluation result of the multi-terminal flexible DC grid is generated.

6. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the operational performance evaluation method for multi-terminal flexible DC grids as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the operational performance evaluation method for multi-terminal flexible DC power grids as described in any one of claims 1 to 4.

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