A comprehensive evaluation method and system for active balance power distribution network suitable for planning stage

Abstract

A kind of active balance power distribution network comprehensive evaluation method and system suitable for planning stage, including obtaining parameter, the step of constructing power distribution network comprehensive evaluation system, the step of obtaining secondary index value by evaluation, the step of calculating primary index score and comprehensive score, power distribution network comprehensive evaluation system is built in the system, including six primary indexes of power distribution network comprehensive evaluation system, each primary index includes multiple secondary indexes, the data source for calculating secondary index includes planning data determined in power distribution network planning stage, borrowed data of similar power distribution network corresponding data and predicted data obtained by using conventional prediction method, the evaluation operation of power distribution network can be completed in power distribution network planning stage by the three types of data.The design not only can complete power distribution network evaluation operation in planning stage, effectively expand the scope of application of evaluation method, but also can obtain accurate self-healing rate index in the planning stage without power distribution network actual operation data, effectively improve evaluation accuracy.

Description

Technical Field

[0001] This invention relates to a comprehensive evaluation method for distribution networks, and more particularly to a comprehensive evaluation method and system for actively balancing distribution networks applicable to the planning stage. Background Technology

[0002] The new active power distribution network is a next-generation power system based on new energy sources, driven by innovation, and utilizing digitalization and intelligence as key means. It promotes the integration and comprehensive allocation of power flow, information flow, and value flow across all stages of power production, transmission, consumption, and storage, creating a green, low-carbon, safe, controllable, economically efficient, flexible, open, and digitally empowered power system. Its technical characteristics can be described from multiple dimensions, including the power source side, grid side, user side, and the overall system.

[0003] The core technology of existing distribution network evaluation methods lies in the quantitative analysis of the actual operating status of the distribution network. Typical technical solutions include: reliability statistical analysis methods based on historical fault records, voltage deviation and harmonic content calculation methods based on measured data, and equipment load rate assessment methods based on information collected by SCADA systems. These methods require the distribution network to be already built and operational, and necessitate the acquisition of data such as fault outage records, measured load curves, and equipment operating status generated during actual operation.

[0004] Although this comprehensive evaluation method for distribution networks can accurately assess the condition of the distribution network, it still has the following drawbacks:

[0005] 1. During the distribution network planning stage, the power grid has not yet been built, and the above-mentioned operational data are lacking, which makes it impossible to implement the existing evaluation methods in the planning stage, and the scope of application is relatively small.

[0006] 2. There are many uncertainties in the planning stage. Conventional technical means can only simplify the assumptions, which makes the evaluation results unable to reflect the robustness and adaptability of the planning scheme and has poor accuracy.

[0007] The information disclosed in this background section is intended only to enhance understanding of the overall background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of existing technologies, such as limited applicability and poor accuracy, and to provide a comprehensive evaluation method and system for actively balanced distribution networks that has a wider applicability and higher accuracy, suitable for the planning stage.

[0009] To achieve the above objectives, the technical solution of the present invention is:

[0010] A comprehensive evaluation method for actively balanced distribution networks applicable to the planning stage, the evaluation method comprising the following steps:

[0011] S1. Obtain distribution network parameters and construct a comprehensive evaluation system for the distribution network in the planning stage. The comprehensive evaluation system includes multiple primary indicators, and each primary indicator includes multiple secondary indicators. Construct a prediction algorithm for each primary indicator and its subordinate secondary indicators. Based on the prediction algorithm, estimate the distribution network according to the comprehensive evaluation system to obtain the evaluation value of each secondary indicator.

[0012] S2. The evaluation values ​​of each secondary indicator are converted into secondary indicator scores through standardization methods;

[0013] S3. Assign values ​​to the weights of each secondary indicator based on the hybrid judgment matrix and the consistency self-correction algorithm, and obtain the primary indicator score and the comprehensive score of the distribution network by combining the secondary indicator weights and secondary indicator scores.

[0014] In S1, the primary indicators include self-healing rate, safety, reliability, economy, flexibility, and carbon contribution.

[0015] The secondary indicators of the self-healing rate index include the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial overhead feeders, the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial cables, and the self-healing rate of feeder faults considering the restoration of distributed power supply islands.

[0016] The secondary indicators of the safety index include the comprehensive voltage qualification rate, line overload rate, distribution transformer overload rate, capacity-to-load ratio, and capacity-to-generation ratio.

[0017] The secondary indicators of the reliability index include the line N-1 throughput rate, power supply reliability rate, and absorption reliability rate;

[0018] The secondary indicators of the economic indicators include the comprehensive line loss rate, the increase in power supply per unit investment, the increase in load per unit investment, the average load rate of the line, the average load rate of the distribution transformer, the new distributed capacity per unit investment, and the increase in distributed power generation per unit investment.

[0019] The secondary indicators of the flexibility index include the proportion of adjustable and controllable load, the proportion of adjustable and controllable power supply, the proportion of load that can be transferred, the proportion of users that can be disconnected from the grid, the coverage rate of primary and secondary integrated equipment, and the coverage rate of DC transformation of distribution network.

[0020] The secondary indicators of the carbon contribution index include carbon emissions per kilowatt-hour, terminal electrification rate, demand response carbon contribution rate, and distributed power penetration rate.

[0021] The distribution network parameters include planning data, borrowed data, and forecast data. The planning data is the data determined during the distribution network planning stage. The borrowed data is the data of the same type of feeder in the distribution network of the same region. The forecast data is the data obtained by using conventional forecasting methods.

[0022] In S1, the calculation of the self-healing rate index in the prediction algorithm includes first determining the type of each feeder in the distribution network, and then calculating the self-healing rate of the corresponding feeder using different formulas according to the feeder type.

[0023] The self-healing rate of the overhead feeder with connection includes:

[0024] ;

[0025] In the above formula, For overhead feeder lines The topological self-healing rate, For line fault outage rate, For switch failure downtime rate, For distribution transformer outage rate, For feeder Total length, For feeder The number of three remote segments, For feeder The number of users;

[0026] The line fault outage rate Switch failure downtime and transformer outage rate This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0027] The self-healing rate of the radial overhead feeder includes:

[0028] ;

[0029] In the above formula, Radial overhead feeder Topological self-healing rate;

[0030] The self-healing rate of the connecting cable includes:

[0031] ;

[0032] In the above formula, There is a connecting cable. The topological self-healing rate, This refers to the total length of the cable branch lines. This represents the average number of switches within a single ring main unit.

[0033] The self-healing rate of the radial cable includes:

[0034] ;

[0035] The feeder fault self-healing rate considering the islanding restoration of distributed power sources includes:

[0036] ;

[0037] In the above formula, To consider the self-healing rate of feeder faults in distributed generation islanded power restoration, For feeder i, the fault self-healing rate;

[0038] The feeder Fault self-healing rate include:

[0039] ;

[0040] In the above formula, The success rate of the self-healing action of feeder i. This represents the self-healing recovery ratio of the medium-voltage fault section of feeder i. The gain coefficient for DG synergistic self-healing;

[0041] The success rate of the self-healing action of feeder i and the self-healing recovery ratio of the medium-voltage fault section of feeder i This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0042] The DG cooperative self-healing gain coefficient include:

[0043] ;

[0044] In the above formula, For feeder The number of users supported by DG in China For feeder The number of users that can be recovered through DG islands in the event of a failure. To comprehensively consider the DG type, DG islanding operation / power transfer, and the voltage stability capability of DG nodes in islanding mode, the correction coefficient is set to 0-1 and can be obtained through regression analysis of historical data.

[0045] The S3 includes: The hybrid judgment matrix includes:

[0046] Secondary indicators under the same primary indicator are labeled C11, C12, ..., Cmn, where m is the primary indicator number and n is the secondary indicator number. Each secondary indicator is compared pairwise using the 1-9 scale of the Analytic Hierarchy Process (AHP) to generate a subjective judgment matrix. The subjective judgment matrix It is an n×n positively reciprocal matrix;

[0047] By collecting the distribution network parameters corresponding to each secondary indicator, and after standardization, the entropy weight method is used to transform them into an objective judgment matrix that conforms to the analytic hierarchy process (AHP) scaling. The objective judgment matrix These are positively reciprocal matrices, including:

[0048] ;

[0049] In the above formula, For comparison The second-level indicators and the first The weights obtained from the secondary indicators For the first Each secondary indicator For the first One secondary indicator;

[0050] Subjective judgment matrix and objective judgment matrix The initial mixing judgment matrix is ​​generated by fusion. ,include:

[0051] ;

[0052] In the above formula, This is the fusion coefficient, used to adjust the relative weights of subjective and objective information. ∈[0,1], =1 indicates that the traditional analytic hierarchy process is used completely. =0 indicates that it relies entirely on objective data, and α can be preset according to the application scenario;

[0053] The consistency self-correction algorithm includes:

[0054] Calculate the initial mixed judgment matrix Maximum eigenvalue And calculate the consistency index. :

[0055] ;

[0056] Based on the order of the matrix According to the preset Parameter table lookup for corresponding credential random consistency index ;

[0057] According to the consistency index and the random consistency index of vouchers Calculate the random consistency index :

[0058] ;

[0059] In the above formula, if If so, the judgment matrix is ​​considered to meet the consistency requirement;

[0060] After the judgment matrix passes the consistency check, the weight of the judgment matrix is ​​calculated, and its weight vector is equal to the eigenvector corresponding to the largest eigenvalue of the judgment matrix.

[0061] If it fails, then the current matrix... corresponding feature vector Add the features in the same row and normalize them to obtain the normalized feature vectors, and then construct the comparison matrix. and make According to fixed step size direction The corresponding Gradually get closer, Each step closer to verifying consistency metrics This continues until the matrix meets the consistency requirements.

[0062] A comprehensive evaluation system for actively balanced distribution networks suitable for the planning stage includes: an evaluation system construction module, an indicator scoring module, and a comprehensive scoring module;

[0063] The evaluation system construction module is used to: obtain distribution network parameters, construct a comprehensive evaluation system for the distribution network in the planning stage, the comprehensive evaluation system for the distribution network includes multiple primary indicators, each of the primary indicators includes multiple secondary indicators, construct a prediction algorithm for each primary indicator and its subordinate secondary indicators, and make a prediction of the distribution network based on the prediction algorithm and the comprehensive evaluation system for the distribution network to obtain the evaluation value of each secondary indicator.

[0064] The indicator scoring module is used to: convert the evaluation values ​​of each secondary indicator into secondary indicator scores using a standardized method;

[0065] The comprehensive scoring module is used to: assign weights to each secondary indicator based on the hybrid judgment matrix and the consistency self-correction algorithm, and combine the secondary indicator weights and secondary indicator scores to obtain the primary indicator score and the comprehensive score of the distribution network.

[0066] In the evaluation system construction module, the primary indicators include self-healing rate, safety, reliability, economy, flexibility, and carbon contribution.

[0067] The secondary indicators of the self-healing rate index include the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial overhead feeders, the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial cables, and the self-healing rate of feeder faults considering the restoration of distributed power supply islands.

[0068] The secondary indicators of the safety index include the comprehensive voltage qualification rate, line overload rate, distribution transformer overload rate, capacity-to-load ratio, and capacity-to-generation ratio.

[0069] The secondary indicators of the reliability index include the line N-1 throughput rate, power supply reliability rate, and absorption reliability rate;

[0070] The secondary indicators of the economic indicators include the comprehensive line loss rate, the increase in power supply per unit investment, the increase in load per unit investment, the average load rate of the line, the average load rate of the distribution transformer, the new distributed capacity per unit investment, and the increase in distributed power generation per unit investment.

[0071] The secondary indicators of the flexibility index include the proportion of adjustable and controllable load, the proportion of adjustable and controllable power supply, the proportion of load that can be transferred, the proportion of users that can be disconnected from the grid, the coverage rate of primary and secondary integrated equipment, and the coverage rate of DC transformation of distribution network.

[0072] The secondary indicators of the carbon contribution index include carbon emissions per kilowatt-hour, terminal electrification rate, demand response carbon contribution rate, and distributed power penetration rate.

[0073] The distribution network parameters include planning data, borrowed data, and forecast data. The planning data is the data determined during the distribution network planning stage. The borrowed data is the data of the same type of feeder in the distribution network of the same region. The forecast data is the data obtained by using conventional forecasting methods.

[0074] In the evaluation system construction module, the calculation of the self-healing rate index in the prediction algorithm includes first determining the type of each feeder in the distribution network, and then calculating the self-healing rate of the corresponding feeder using different formulas according to the feeder type.

[0075] The self-healing rate of the overhead feeder with connection includes:

[0076] ;

[0077] In the above formula, For overhead feeder lines The topological self-healing rate, For line fault outage rate, For switch failure downtime rate, For distribution transformer outage rate, For feeder Total length, For feeder The number of three remote segments, For feeder The number of users;

[0078] The line fault outage rate Switch failure downtime and transformer outage rate This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0079] The self-healing rate of the radial overhead feeder includes:

[0080] ;

[0081] In the above formula, Radial overhead feeder Topological self-healing rate;

[0082] The self-healing rate of the connecting cable includes:

[0083] ;

[0084] In the above formula, There is a connecting cable. The topological self-healing rate, This refers to the total length of the cable branch lines. This represents the average number of switches within a single ring main unit.

[0085] The self-healing rate of the radial cable includes:

[0086] ;

[0087] The feeder fault self-healing rate considering the islanding restoration of distributed power sources includes:

[0088] ;

[0089] In the above formula, To consider the self-healing rate of feeder faults in distributed generation islanded power restoration, For feeder i, the fault self-healing rate;

[0090] The fault self-healing rate of feeder i include:

[0091] ;

[0092] In the above formula, The success rate of the self-healing action of feeder i. This represents the self-healing recovery ratio of the medium-voltage fault section of feeder i. The gain coefficient for DG synergistic self-healing;

[0093] The success rate of the self-healing action of feeder i and the self-healing recovery ratio of the medium-voltage fault section of feeder i This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0094] The DG cooperative self-healing gain coefficient include:

[0095] ;

[0096] In the above formula, For feeder The number of users supported by DG in China For feeder The number of users that can be recovered through DG islands in the event of a failure. To comprehensively consider the DG type, DG islanding operation / power transfer, and the voltage stability capability of DG nodes in islanding mode, the correction coefficient is set to 0-1 and can be obtained through regression analysis of historical data.

[0097] In the comprehensive scoring module, the hybrid judgment matrix includes:

[0098] Secondary indicators under the same primary indicator are labeled C11, C12, ..., Cmn, where m is the primary indicator number and n is the secondary indicator number. Each secondary indicator is compared pairwise using the 1-9 scale of the Analytic Hierarchy Process (AHP) to generate a subjective judgment matrix. The subjective judgment matrix It is an n×n positively reciprocal matrix;

[0099] By collecting the distribution network parameters corresponding to each secondary indicator, and after standardization, the entropy weight method is used to transform them into an objective judgment matrix that conforms to the analytic hierarchy process (AHP) scaling. The objective judgment matrix These are positively reciprocal matrices, including:

[0100] ;

[0101] In the above formula, For comparison The second-level indicators and the first The weights obtained from the secondary indicators For the first Each secondary indicator For the first One secondary indicator;

[0102] Subjective judgment matrix and objective judgment matrix The initial mixing judgment matrix is ​​generated by fusion. ,include:

[0103] ;

[0104] In the above formula, This is the fusion coefficient, used to adjust the relative weights of subjective and objective information. ∈[0,1], =1 indicates that the traditional analytic hierarchy process is used completely. =0 indicates that it relies entirely on objective data, and α can be preset according to the application scenario;

[0105] The consistency self-correction algorithm includes:

[0106] Calculate the initial mixed judgment matrix Maximum eigenvalue And calculate the consistency index. :

[0107] ;

[0108] Based on the order of the matrix According to the preset Parameter table lookup for corresponding credential random consistency index ;

[0109] According to the consistency index and the random consistency index of vouchers Calculate the random consistency index :

[0110] ;

[0111] In the above formula, if If so, the judgment matrix is ​​considered to meet the consistency requirement;

[0112] After the judgment matrix passes the consistency check, the weight of the judgment matrix is ​​calculated, and its weight vector is equal to the eigenvector corresponding to the largest eigenvalue of the judgment matrix.

[0113] If it fails, then the current matrix... corresponding feature vector Add the features in the same row and normalize them to obtain the normalized feature vectors, and then construct the comparison matrix. and make According to fixed step size direction The corresponding Gradually get closer, Each step closer to verifying consistency metrics This continues until the matrix meets the consistency requirements.

[0114] An active balancing distribution network comprehensive evaluation device suitable for the planning stage includes a memory and a processor. The memory is used to store computer program code and transmit the computer program code to the processor.

[0115] The processor is configured to execute the aforementioned comprehensive evaluation method for active balancing distribution networks applicable to the planning stage, according to instructions in the computer program code.

[0116] A computer program product includes a computer program that is executed by a processor to perform the aforementioned comprehensive evaluation method for actively balancing distribution networks applicable to the planning phase.

[0117] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0118] 1. This invention provides a comprehensive evaluation method for actively balancing distribution networks applicable to the planning stage. It constructs a comprehensive evaluation system for distribution networks comprising six primary indicators. Each primary indicator includes multiple secondary indicators. The data sources for calculating the secondary indicators include planning data determined during the distribution network planning stage, borrowed data applying corresponding data from similar distribution networks, and forecast data obtained using conventional forecasting methods. These three types of data allow for the completion of distribution network evaluation during the planning stage. Therefore, this design enables distribution network evaluation during the planning phase, effectively expanding the applicability of the evaluation method.

[0119] 2. In this invention, a comprehensive evaluation method for actively balanced distribution networks applicable to the planning stage includes a self-healing rate index as a primary indicator. The data used to calculate the self-healing rate index consists of planning data and borrowed data. By establishing topology models for different types of feeders and then traversing the topology structure to identify which switches can self-heal, the topology self-healing rate of that type of feeder is statistically obtained. This calculation method employs a different technical approach than existing technologies, thus enabling the acquisition of an accurate self-healing rate index during the planning stage when actual distribution network operation data is unavailable. Therefore, this design can obtain an accurate self-healing rate index during the planning stage when actual distribution network operation data is unavailable, effectively improving the evaluation accuracy. Attached Figure Description

[0120] Figure 1 This is a flowchart of the method described in this invention.

[0121] Figure 2 This is a structural diagram of the system described in this invention.

[0122] Figure 3 This is a structural diagram of the device described in this invention.

[0123] Figure 4 This is a flowchart of the verification process in Embodiment 2 of the present invention.

[0124] Figure 5 This is a topology diagram of each feeder in Embodiment 1 of the present invention. Detailed Implementation

[0125] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0126] Example 1:

[0127] See Figure 1 A comprehensive evaluation method for actively balancing distribution networks applicable to the planning stage, the evaluation method comprising the following steps:

[0128] S1. Obtain distribution network parameters and construct a comprehensive evaluation system for the distribution network in the planning stage. The comprehensive evaluation system includes multiple primary indicators, and each primary indicator includes multiple secondary indicators. Construct a prediction algorithm for each primary indicator and its subordinate secondary indicators. Based on the prediction algorithm, estimate the distribution network according to the comprehensive evaluation system to obtain the evaluation value of each secondary indicator.

[0129] S2. The evaluation values ​​of each secondary indicator are converted into secondary indicator scores through standardization methods;

[0130] S3. Assign values ​​to the weights of each secondary indicator based on the hybrid judgment matrix and the consistency self-correction algorithm, and obtain the primary indicator score and the comprehensive score of the distribution network by combining the secondary indicator weights and secondary indicator scores.

[0131] In S1, the primary indicators include self-healing rate, safety, reliability, economy, flexibility, and carbon contribution.

[0132] The prediction algorithm includes:

[0133] The secondary indicators of the self-healing rate index include the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial overhead feeders, the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial cables, and the self-healing rate of feeder faults considering the restoration of distributed power supply islands.

[0134] In S1, the calculation of the self-healing rate index in the prediction algorithm includes first determining the type of each feeder in the distribution network, and then calculating the self-healing rate of the corresponding feeder using different formulas according to the feeder type; the feeder types in the distribution network include overhead tie feeders, radial overhead feeders, overhead tie cables, and radial cables.

[0135] When a fault occurs in the upstream line of the feeder of the self-contained distributed power source, it can disconnect from the main power grid and be powered by the self-contained distributed power source. This temporarily forms a feeder that considers the islanding and restoration of the distributed power source. At this time, when calculating the self-healing rate of the original feeder of the self-contained distributed power source, the feeder length, number of remote control segments and number of users in the disconnected feeder are removed, and the feeder that is connected to the main power grid is calculated as an independent feeder.

[0136] This design determines the type of each feeder in the distribution network, establishes a topology model for each type of feeder, and enumerates facility outages without considering the influence of the upstream power grid or the impact of faults in the disconnecting switches on both sides adjacent to the circuit breaker (load switch). It analyzes the impact of facility outages on each load point according to three methods: main line facility failure, branch line facility failure, and pre-arranged outage. It traverses which switches in the topology can self-heal and obtains the corresponding self-healing rate by the ratio of self-healing switches to the total number of switches.

[0137] See Figure 5 The self-healing rate of the overhead feeder with connection includes:

[0138] ;

[0139] In the above formula, For overhead feeder lines The topological self-healing rate, For line fault outage rate, For switch failure downtime rate, For distribution transformer outage rate, For feeder Total length, For feeder The number of three remote segments, For feeder The number of users;

[0140] The line fault outage rate Switch failure downtime and transformer outage rate This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0141] The overhead feeder with interconnection is an overhead line that is interconnected with other lines. Users are distributed along the line. When a fault occurs upstream of the overhead feeder, the switching function of the interconnection switch can be used to seamlessly or quickly switch the downstream load to the backup path.

[0142] See Figure 5 The self-healing rate of the radial overhead feeder includes:

[0143] ;

[0144] In the above formula, Radial overhead feeder Topological self-healing rate;

[0145] The radial overhead feeder is an overhead line that is not connected to other lines. Users are distributed along the line, and when a fault occurs, power cannot be transferred.

[0146] See Figure 5 The self-healing rate of the connecting cable includes:

[0147] ;

[0148] In the above formula, There is a connecting cable. The topological self-healing rate, This refers to the total length of the cable branch lines. This represents the average number of switches within a single ring main unit.

[0149] The aforementioned connecting cable is an overhead line that connects to other lines. Users can only connect it at the ring main unit. In case of a fault, the downstream can transfer the power.

[0150] See Figure 5 The self-healing rate of the radial cable includes:

[0151] ;

[0152] The success rate of the self-healing action of feeder i and the self-healing recovery ratio of the medium-voltage fault section of feeder i This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0153] The radial cable is an overhead line that is not connected to other lines. Users can only connect it to the ring main unit. When a fault occurs, it cannot be transferred to another line.

[0154] The feeder fault self-healing rate considering the islanding restoration of distributed power sources includes:

[0155] ;

[0156] In the above formula, To consider the self-healing rate of feeder faults in distributed generation islanded power restoration, For feeder i, the fault self-healing rate;

[0157] The DG cooperative self-healing gain coefficient include:

[0158] ;

[0159] In the above formula, For feeder The number of users supported by DG in China For feeder The number of users that can be recovered through DG islands in the event of a failure. The correction factor, which takes into account the DG type, DG islanding operation / power transfer, and DG node voltage stability capability in islanding mode within the region, is set to 0-1 and can be obtained through regression of historical data.

[0160] The feeder that considers the islanding restoration of distributed power sources is a type of feeder that is temporarily formed when the upstream line fails, disconnects from the main power grid, and is powered by its own distributed power source.

[0161] The feeder Fault self-healing rate include:

[0162] ;

[0163] In the above formula, The success rate of the self-healing action of feeder i. This represents the self-healing recovery ratio of the medium-voltage fault section of feeder i. , where is the DG collaborative self-healing gain coefficient.

[0164] The secondary indicators of the safety index include the comprehensive voltage qualification rate, line overload rate, distribution transformer overload rate, capacity-to-load ratio, and capacity-to-generation ratio.

[0165] The overall voltage qualification rate includes:

[0166] ;

[0167] The line load rate includes:

[0168] ;

[0169] The transformer overload rate includes:

[0170] ;

[0171] The capacity ratio / generation ratio includes:

[0172] ;

[0173] ;

[0174] The secondary indicators of the reliability index include the line N-1 throughput rate, power supply reliability rate, and absorption reliability rate;

[0175] The throughput of line N-1 includes:

[0176] ;

[0177] The power supply reliability includes:

[0178] ;

[0179] The reliability of the absorption rate includes:

[0180] ;

[0181] The secondary indicators of the economic indicators include the comprehensive line loss rate, the increase in power supply per unit investment, the increase in load per unit investment, the average load rate of the line, the average load rate of the distribution transformer, the new distributed capacity per unit investment, and the increase in distributed power generation per unit investment.

[0182] The overall line loss rate includes:

[0183] ;

[0184] The increased electricity supply per unit investment includes:

[0185] ;

[0186] The increased load per unit investment includes:

[0187] ;

[0188] The average load factor of the line includes:

[0189] ;

[0190] The average load factor of the distribution transformer includes:

[0191] ;

[0192] The unit investment in distributed new capacity includes:

[0193] ;

[0194] The distributed power generation per unit investment includes:

[0195] ;

[0196] The secondary indicators of the flexibility index include the proportion of adjustable and controllable load, the proportion of adjustable and controllable power supply, the proportion of load that can be transferred, the proportion of users that can be disconnected from the grid, the coverage rate of primary and secondary integrated equipment, and the coverage rate of DC transformation of distribution network.

[0197] The adjustable and controllable load percentage includes:

[0198] ;

[0199] The adjustable and controllable power supply ratio includes:

[0200] ;

[0201] The percentage of load that can be supplied includes:

[0202] ;

[0203] The percentage of users who can disconnect from the network includes:

[0204] ;

[0205] The coverage of the primary and secondary fusion equipment includes:

[0206] ;

[0207] The coverage rate of the DC power grid upgrade includes:

[0208] ;

[0209] The secondary indicators of the carbon contribution index include carbon emissions per kilowatt-hour, terminal electrification rate, demand response carbon contribution rate, and distributed power penetration rate.

[0210] The carbon emissions per kilowatt-hour include:

[0211] ;

[0212] The terminal electrification rate includes:

[0213] ;

[0214] The carbon contribution of the demand response includes:

[0215] ;

[0216] The distributed power penetration rate includes:

[0217] ;

[0218] The input to the prediction algorithm is the distribution network parameters. The data sources for these parameters are divided into three categories: planning data, borrowed data, and prediction data. The planning data is the data determined during the distribution network planning stage, i.e., the data known during the planning stage. The borrowed data is the data of the same type of feeder in the distribution network of the same region (such as line fault outage rate, transformer fault outage rate, and switch fault outage rate). The prediction data is the data obtained using conventional prediction methods (such as load and load data).

[0219] In S2, the scoring principles include:

[0220] Based on the type of impact of the magnitude of each secondary indicator on the distribution network, the secondary indicators are divided into extremely large indicators, extremely small indicators, and moderate indicators.

[0221] The term "extremely large indicator" refers to an indicator whose larger value is better, "extremely small indicator" refers to an indicator whose smaller value is better, and "moderate indicator" refers to an indicator whose value is optimal within the ideal range, and whose value is deducted linearly if it deviates from the range.

[0222] The extremely large indicators include power supply reliability, self-balance coefficient, comprehensive voltage qualification rate, line N-1 pass rate, power supply reliability, absorption reliability, power supply increase per unit investment, load increase per unit investment, distributed new capacity per unit investment, distributed new generation per unit investment, self-healing action success rate, self-healing terminal coverage rate, system fault self-healing rate, network fault self-healing rate, adjustable and controllable load ratio, adjustable and controllable power source ratio, transferable load ratio, off-grid user ratio, primary and secondary integrated equipment coverage rate, distribution network DC transformation coverage rate, terminal electrification rate, demand response carbon contribution, and distributed power source penetration rate.

[0223] The extremely small indicators include line overload rate, comprehensive line loss rate, line overload rate, distribution transformer overload rate, comprehensive line loss rate, and carbon emission value per kilowatt-hour.

[0224] The moderate indicators include, for example, the capacity-to-load ratio / capacity-to-generation ratio, the average line load rate, and the average distribution transformer load rate.

[0225] The S3 includes: The hybrid judgment matrix includes:

[0226] Secondary indicators under the same primary indicator are labeled C11, C12, ..., Cmn, where m is the primary indicator number and n is the secondary indicator number. Each secondary indicator is compared pairwise using the 1-9 scale of the Analytic Hierarchy Process (AHP) to generate a subjective judgment matrix. The subjective judgment matrix It is an n×n positively reciprocal matrix;

[0227] By collecting the distribution network parameters corresponding to each secondary indicator, and after standardization, the entropy weight method is used to transform them into an objective judgment matrix that conforms to the analytic hierarchy process (AHP) scaling. The objective judgment matrix These are positively reciprocal matrices, including:

[0228] ;

[0229] In the above formula, For comparison The second-level indicators and the first The weights obtained from the secondary indicators For the first Each secondary indicator For the first One secondary indicator;

[0230] Subjective judgment matrix and objective judgment matrix The initial mixing judgment matrix is ​​generated by fusion. ,include:

[0231] ;

[0232] In the above formula, This is the fusion coefficient, used to adjust the relative weights of subjective and objective information. ∈[0,1], =1 indicates that the traditional analytic hierarchy process is used completely. =0 indicates that it relies entirely on objective data, and α can be preset according to the application scenario;

[0233] The consistency self-correction algorithm includes:

[0234] Calculate the initial mixed judgment matrix Maximum eigenvalue And calculate the consistency index. :

[0235] ;

[0236] Based on the order of the matrix According to the preset Parameter table lookup for corresponding credential random consistency index ;

[0237] According to the consistency index and the random consistency index of vouchers Calculate the random consistency index :

[0238] ;

[0239] In the above formula, if If so, the judgment matrix is ​​considered to meet the consistency requirement;

[0240] The random consistency index Based on order Refer to the table below to find the relevant information:

[0241] ;

[0242] After the judgment matrix passes the consistency check, the weight of the judgment matrix is ​​calculated, and its weight vector is equal to the eigenvector corresponding to the largest eigenvalue of the judgment matrix.

[0243] If it fails, then the current matrix... corresponding feature vector Perform summation of items in the same row and then normalize the result.

[0244] A contrast matrix is ​​constructed using the normalized eigenvectors. , of which elements ;

[0245] Compare matrices A and A* to find each The largest element in the middle, modify the corresponding ,make Towards Approximation is performed by changing the consistency metric CI each time until the matrix meets the consistency requirements.

[0246] Example 2:

[0247] See Figure 4 This embodiment verifies the effectiveness of the method described in Embodiment 1 based on a typical self-healing planning scheme for a power distribution network in a certain city of a certain province and two typical power supply grids (one urban and one rural scenario):

[0248] First, power balancing was performed on two selected typical power grids, with power supply balancing for urban scenarios and power absorption balancing for rural scenarios. Second, grid element optimization was performed on each grid, taking into account source-load matching. Third, self-healing terminal configuration was performed based on the grid element partitioning results using the primary and secondary fusion self-healing planning method proposed in the study. Then, typical lines were selected to verify the self-healing planning configuration results. Finally, a comprehensive evaluation was conducted on the self-healing capability improvement effect of the two grids after self-healing planning compared with traditional planning.

[0249] The selected urban scenario falls within a Class B power supply area of ​​the urban power grid, covering an area of ​​13.5 km². 2 The main land use is for education, residential, commercial, and administrative offices. The current load of this grid is 99.76MW, with distributed power generation capacity of 21.18MW, representing a distributed power generation penetration rate of 21.22%. The potential for power generation development is lower than the potential for load demand. Therefore, the Chengxi grid was selected as a typical urban grid for ensuring power supply.

[0250] The selected rural scenario falls within a Class C rural power grid area, with a grid area of ​​73.64 km²; the main land use is township. The grid currently has a public line load of 21.91 MW and a distributed power generation capacity of 39.051 MW, indicating potential for rooftop photovoltaic development, with the power generation potential exceeding load demand. Therefore, the central grid was selected as a typical rural grid designed for grid absorption.

[0251] Based on the load forecast results of the three methods and the status of charging and swapping facilities in the local power supply company's distribution network planning report, the load in the urban scenario will reach 129.35MW in 2030. Combining the control plan map and the load forecast results, the load distribution in the urban scenario can be obtained.

[0252] Substituting the above data into the method described in Example 1, we obtain the comprehensive evaluation results for urban scenarios and the comprehensive evaluation results for rural scenarios;

[0253] Based on the comprehensive evaluation results of urban and rural scenarios, it can be seen that the method described in Example 1, compared with traditional evaluations that focus on basic indicators such as safety, power supply, and line loss, adds three primary indicators: self-healing capability, flexibility, and carbon contribution. This fully matches the core positioning of the new distribution network as autonomous self-healing, source-grid-load-storage interaction, and low-carbon green development. It focuses on key scenarios of modern active distribution networks, incorporating new indicators such as network fault self-healing rate, adjustable and controllable load / power source ratio, distributed power source penetration rate, terminal electrification, and demand response carbon contribution, accurately reflecting the effectiveness of intelligent, flexible, and low-carbon construction. All secondary indicators in the method described in Example 1 are statistically verifiable, monitorable, and assessable operational and construction data, avoiding the qualitative and quantifiable problems of traditional methods.

[0254] The weight allocation method proposed in Example 1 can design differentiated weights for various scenarios such as urban and rural power grids, which is more in line with the actual characteristics of the power grid and allows for more targeted policy implementation. The evaluation results are more instructive, and the differentiated weights allow the scores to truly reflect the shortcomings, making it easier to formulate targeted urban and rural power grid transformation strategies. In contrast, traditional evaluations use the same weight system for urban and rural power grids, ignoring structural differences. This method sets different weights for security, reliability, economy, self-healing, flexibility, and carbon contribution, adapting to the two types of power grid construction priorities. Among them, urban power grids have dense loads and complex grid structures, requiring higher requirements for security, reliability, and self-healing, so the weight allocation is tilted towards intelligence and flexibility; rural power grids have simple structures and large potential for distributed resources, so the weights emphasize carbon contribution and distributed development, which is more in line with the direction of rural power grid upgrading.

[0255] The scores show that although the evaluation standards for urban and rural power grids are different, the overall score of urban power grids is higher than that of rural power grids, reflecting the problem of unbalanced investment and development in urban and rural power grids. In both examples, the scores of traditional indicators are relatively high, but the scores of new indicators such as flexibility and self-healing are relatively low. This shows that traditional planning methods tend to overlook the construction of flexible interaction and self-healing capabilities of new active distribution networks. Therefore, the evaluation system proposed in this method can effectively guide the planning of new distribution networks, identify gaps based on the scores of each indicator, and determine the focus of the next planning step.

[0256] ;

[0257] ;

[0258] Example 3:

[0259] See Figure 2 A comprehensive evaluation system for actively balanced distribution networks suitable for the planning stage includes: an evaluation system construction module, an indicator scoring module, and a comprehensive scoring module;

[0260] The evaluation system construction module is used to: obtain distribution network parameters, construct a comprehensive evaluation system for the distribution network in the planning stage, the comprehensive evaluation system for the distribution network includes multiple primary indicators, each of the primary indicators includes multiple secondary indicators, construct a prediction algorithm for each primary indicator and its subordinate secondary indicators, and make a prediction of the distribution network based on the prediction algorithm and the comprehensive evaluation system for the distribution network to obtain the evaluation value of each secondary indicator.

[0261] The indicator scoring module is used to: convert the evaluation values ​​of each secondary indicator into secondary indicator scores using a standardized method;

[0262] The comprehensive scoring module is used to: assign weights to each secondary indicator based on the hybrid judgment matrix and the consistency self-correction algorithm, and combine the secondary indicator weights and secondary indicator scores to obtain the primary indicator score and the comprehensive score of the distribution network.

[0263] In the evaluation system construction module, the primary indicators include self-healing rate, safety, reliability, economy, flexibility, and carbon contribution.

[0264] The secondary indicators of the self-healing rate index include the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial overhead feeders, the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial cables, and the self-healing rate of feeder faults considering the restoration of distributed power supply islands.

[0265] The secondary indicators of the safety index include the comprehensive voltage qualification rate, line overload rate, distribution transformer overload rate, capacity-to-load ratio, and capacity-to-generation ratio.

[0266] The secondary indicators of the reliability index include the line N-1 throughput rate, power supply reliability rate, and absorption reliability rate;

[0267] The secondary indicators of the economic indicators include the comprehensive line loss rate, the increase in power supply per unit investment, the increase in load per unit investment, the average load rate of the line, the average load rate of the distribution transformer, the new distributed capacity per unit investment, and the increase in distributed power generation per unit investment.

[0268] The secondary indicators of the flexibility index include the proportion of adjustable and controllable load, the proportion of adjustable and controllable power supply, the proportion of load that can be transferred, the proportion of users that can be disconnected from the grid, the coverage rate of primary and secondary integrated equipment, and the coverage rate of DC transformation of distribution network.

[0269] The secondary indicators of the carbon contribution index include carbon emissions per kilowatt-hour, terminal electrification rate, demand response carbon contribution rate, and distributed power penetration rate.

[0270] The distribution network parameters include planning data, borrowed data, and forecast data. The planning data is the data determined during the distribution network planning stage. The borrowed data is the data of the same type of feeder in the distribution network of the same region. The forecast data is the data obtained by using conventional forecasting methods.

[0271] In the evaluation system construction module, the calculation of the self-healing rate index in the prediction algorithm includes first determining the type of each feeder in the distribution network, and then calculating the self-healing rate of the corresponding feeder using different formulas according to the feeder type.

[0272] The self-healing rate of the overhead feeder with connection includes:

[0273] ;

[0274] In the above formula, For overhead feeder lines The topological self-healing rate, For line fault outage rate, For switch failure downtime rate, For distribution transformer outage rate, For feeder Total length, For feeder The number of three remote segments, For feeder The number of users;

[0275] The line fault outage rate Switch failure downtime and transformer outage rate This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0276] The self-healing rate of the radial overhead feeder includes:

[0277] ;

[0278] In the above formula, Radial overhead feeder Topological self-healing rate;

[0279] The self-healing rate of the connecting cable includes:

[0280] ;

[0281] In the above formula, There is a connecting cable. The topological self-healing rate, This refers to the total length of the cable branch lines. This represents the average number of switches within a single ring main unit.

[0282] The self-healing rate of the radial cable includes:

[0283] ;

[0284] The feeder fault self-healing rate considering the islanding restoration of distributed power sources includes:

[0285] ;

[0286] In the above formula, To consider the self-healing rate of feeder faults in distributed generation islanded power restoration, For feeder i, the fault self-healing rate;

[0287] The fault self-healing rate of feeder i include:

[0288] ;

[0289] In the above formula, The success rate of the self-healing action of feeder i. This represents the self-healing recovery ratio of the medium-voltage fault section of feeder i. The gain coefficient for DG synergistic self-healing;

[0290] The success rate of the self-healing action of feeder i and the self-healing recovery ratio of the medium-voltage fault section of feeder i This was obtained by applying data from the same feeder type within the same regional power distribution network.

[0291] The DG cooperative self-healing gain coefficient include:

[0292] ;

[0293] In the above formula, For feeder The number of users supported by DG in China For feeder The number of users that can be recovered through DG islands in the event of a failure. To comprehensively consider the DG type, DG islanding operation / power transfer, and the voltage stability capability of DG nodes in islanding mode, the correction coefficient is set to 0-1 and can be obtained through regression analysis of historical data.

[0294] In the comprehensive scoring module, the hybrid judgment matrix includes:

[0295] Secondary indicators under the same primary indicator are labeled C11, C12, ..., Cmn, where m is the primary indicator number and n is the secondary indicator number. Each secondary indicator is compared pairwise using the 1-9 scale of the Analytic Hierarchy Process (AHP) to generate a subjective judgment matrix. The subjective judgment matrix It is an n×n positively reciprocal matrix;

[0296] By collecting the distribution network parameters corresponding to each secondary indicator, and after standardization, the entropy weight method is used to transform them into an objective judgment matrix that conforms to the analytic hierarchy process (AHP) scaling. The objective judgment matrix These are positively reciprocal matrices, including:

[0297] ;

[0298] In the above formula, For comparison The second-level indicators and the first The weights obtained from the secondary indicators For the first Each secondary indicator For the first One secondary indicator;

[0299] Subjective judgment matrix and objective judgment matrix The initial mixing judgment matrix is ​​generated by fusion. ,include:

[0300] ;

[0301] In the above formula, This is the fusion coefficient, used to adjust the relative weights of subjective and objective information. ∈[0,1], =1 indicates that the traditional analytic hierarchy process is used completely. =0 indicates that it relies entirely on objective data, and α can be preset according to the application scenario;

[0302] The consistency self-correction algorithm includes:

[0303] Calculate the initial mixed judgment matrix Maximum eigenvalue And calculate the consistency index. :

[0304] ;

[0305] Based on the order of the matrix According to the preset Parameter table lookup for corresponding credential random consistency index ;

[0306] According to the consistency index and the random consistency index of vouchers Calculate the random consistency index :

[0307] ;

[0308] In the above formula, if If so, the judgment matrix is ​​considered to meet the consistency requirement;

[0309] After the judgment matrix passes the consistency check, the weight of the judgment matrix is ​​calculated, and its weight vector is equal to the eigenvector corresponding to the largest eigenvalue of the judgment matrix.

[0310] If it fails, then the current matrix... corresponding feature vector Perform summation of items in the same row and then normalize the result.

[0311] A contrast matrix is ​​constructed using the normalized eigenvectors. , of which elements ;

[0312] Compare matrices A and A* to find each The largest element in the middle, modify the corresponding ,make Towards Approximation is performed by changing the consistency metric CI each time until the matrix meets the consistency requirements.

[0313] Example 4:

[0314] See Figure 3 An active balancing distribution network comprehensive evaluation device suitable for the planning stage includes a memory and a processor, wherein the memory is used to store computer program code and transmit the computer program code to the processor;

[0315] The processor is configured to execute, according to instructions in the computer program code, the active balancing distribution network comprehensive evaluation method applicable to the planning stage as described in Embodiment 1.

[0316] Example 5:

[0317] A computer program product includes a computer program that is executed by a processor as described in Example 1, which is a comprehensive evaluation method for an active balancing distribution network suitable for the planning phase.

[0318] The above description is only a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or changes made by those skilled in the art based on the content disclosed in the present invention should be included within the scope of protection set forth in the claims.

Claims

1. A comprehensive evaluation method for actively balanced distribution networks applicable to the planning stage, characterized in that, The evaluation method includes the following steps: S1. Obtain distribution network parameters and construct a comprehensive evaluation system for the distribution network in the planning stage. The comprehensive evaluation system includes multiple primary indicators, and each primary indicator includes multiple secondary indicators. Construct a prediction algorithm for each primary indicator and its subordinate secondary indicators. Based on the prediction algorithm, estimate the distribution network according to the comprehensive evaluation system to obtain the evaluation value of each secondary indicator. S2. The evaluation values ​​of each secondary indicator are converted into secondary indicator scores through standardization methods; S3. Assign values ​​to the weights of each secondary indicator based on the hybrid judgment matrix and the consistency self-correction algorithm, and obtain the primary indicator score and the comprehensive score of the distribution network by combining the secondary indicator weights and secondary indicator scores.

2. The comprehensive evaluation method for actively balancing distribution networks applicable to the planning stage according to claim 1, characterized in that, In S1, the primary indicators include self-healing rate, safety, reliability, economy, flexibility, and carbon contribution. The secondary indicators of the self-healing rate index include the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial overhead feeders, the self-healing rate of overhead feeders with tie lines, the self-healing rate of radial cables, and the self-healing rate of feeder faults considering the restoration of distributed power supply islands. The secondary indicators of the safety index include the comprehensive voltage qualification rate, line overload rate, distribution transformer overload rate, capacity-to-load ratio, and capacity-to-generation ratio. The secondary indicators of the reliability index include the line N-1 throughput rate, power supply reliability rate, and absorption reliability rate; The secondary indicators of the economic indicators include the comprehensive line loss rate, the increase in power supply per unit investment, the increase in load per unit investment, the average load rate of the line, the average load rate of the distribution transformer, the new distributed capacity per unit investment, and the increase in distributed power generation per unit investment. The secondary indicators of the flexibility index include the proportion of adjustable and controllable load, the proportion of adjustable and controllable power supply, the proportion of load that can be transferred, the proportion of users that can be disconnected from the grid, the coverage rate of primary and secondary integrated equipment, and the coverage rate of DC transformation of distribution network. The secondary indicators of the carbon contribution index include carbon emissions per kilowatt-hour, terminal electrification rate, demand response carbon contribution rate, and distributed power penetration rate. The distribution network parameters include planning data, borrowed data, and forecast data. The planning data is the data determined during the distribution network planning stage. The borrowed data is the data of the same type of feeder in the distribution network of the same region. The forecast data is the data obtained by using conventional forecasting methods.

3. The comprehensive evaluation method for actively balancing distribution networks applicable to the planning stage according to claim 2, characterized in that, In S1, the calculation of the self-healing rate index in the prediction algorithm includes first determining the type of each feeder in the distribution network, and then calculating the self-healing rate of the corresponding feeder using different formulas according to the feeder type. The self-healing rate of the overhead feeder with connection includes: ； In the above formula, For overhead feeder lines The topological self-healing rate, For line fault outage rate, For switch failure downtime rate, For distribution transformer outage rate, For feeder Total length, For feeder The number of three remote segments, For feeder The number of users; The line fault outage rate Switch failure downtime and transformer outage rate This was obtained by applying data from the same feeder type within the same regional power distribution network. The self-healing rate of the radial overhead feeder includes: ； In the above formula, Radial overhead feeder Topological self-healing rate; The self-healing rate of the connecting cable includes: ； In the above formula, There is a connecting cable. The topological self-healing rate, This refers to the total length of the cable branch lines. This represents the average number of switches within a single ring main unit. The self-healing rate of the radial cable includes: ； The feeder fault self-healing rate considering the islanding restoration of distributed power sources includes: ； In the above formula, To consider the self-healing rate of feeder faults in distributed generation islanded power restoration, For feeder i, the fault self-healing rate; The feeder Fault self-healing rate include: ； In the above formula, The success rate of the self-healing action of feeder i. This represents the self-healing recovery ratio of the medium-voltage fault section of feeder i. The gain coefficient for DG synergistic self-healing; The success rate of the self-healing action of feeder i and the self-healing recovery ratio of the medium-voltage fault section of feeder i This was obtained by applying data from the same feeder type within the same regional power distribution network. The DG cooperative self-healing gain coefficient include: ； In the above formula, For feeder The number of users supported by DG in China For feeder The number of users who can be recovered through DG islands in the event of a failure. To comprehensively consider the DG type, DG islanding operation / power transfer, and the voltage stability capability of DG nodes in islanding mode, the correction coefficient is set to a value of 0-1, which can be obtained through regression analysis of historical data.

4. The comprehensive evaluation method for actively balancing distribution networks applicable to the planning stage according to claim 2, characterized in that, The S3 includes: The hybrid judgment matrix includes: Each secondary indicator is numbered, and pairwise comparisons are performed on each secondary indicator using the 1–9 scale of the analytic hierarchy process to generate a subjective judgment matrix. Simultaneously, by collecting the distribution network parameters corresponding to each secondary indicator, and after standardization, the entropy weight method is used to transform them into an objective judgment matrix conforming to the analytic hierarchy process (AHP) scaling. ; Subjective judgment matrix and objective judgment matrix The initial mixing judgment matrix is ​​generated by fusion. Consistency is verified by a consistency self-correction algorithm. When the consistency verification is successful, the weight of the judgment matrix is ​​calculated, and its weight vector is equal to the eigenvector corresponding to the largest eigenvalue of the judgment matrix. When the consistency check fails, the secondary indicators under the same primary indicator are labeled C11, C12, ..., Cmn, where m is the primary indicator number and n is the secondary indicator number. The secondary indicators are then compared pairwise using the 1-9 scale of the Analytic Hierarchy Process (AHP) to generate a subjective judgment matrix. The subjective judgment matrix It is an n×n positively reciprocal matrix; By collecting the distribution network parameters corresponding to each secondary indicator, and after standardization, the entropy weight method is used to transform them into an objective judgment matrix that conforms to the analytic hierarchy process (AHP) scaling. The objective judgment matrix These are positively reciprocal matrices, including: ； In the above formula, For comparison The second-level indicators and the first The weights obtained from the secondary indicators For the first Each secondary indicator For the first One secondary indicator; Subjective judgment matrix and objective judgment matrix The initial mixing judgment matrix is ​​generated by fusion. ,include: ； In the above formula, This is the fusion coefficient, used to adjust the relative weights of subjective and objective information. ∈[0,1], =1 indicates that the traditional analytic hierarchy process is used completely. =0 indicates that it relies entirely on objective data, and α can be preset according to the application scenario; The consistency self-correction algorithm includes: Calculate the initial mixed judgment matrix Maximum eigenvalue And calculate the consistency index. : ； Based on the order of the matrix According to the preset Parameter table lookup for corresponding credential random consistency index ; According to the consistency index and the random consistency index of vouchers Calculate the random consistency index : ； In the above formula, if If so, the judgment matrix is ​​considered to meet the consistency requirement; After the judgment matrix passes the consistency check, the weight of the judgment matrix is ​​calculated, and its weight vector is equal to the eigenvector corresponding to the largest eigenvalue of the judgment matrix. If it fails, then the current matrix... Corresponding feature vector Add the features in the same row and normalize them to obtain the normalized feature vectors, and then construct the comparison matrix. and make According to fixed step size direction The corresponding Gradually get closer, Each step closer to verifying consistency metrics This continues until the matrix meets the consistency requirements.

5. A comprehensive evaluation system for actively balancing distribution networks suitable for the planning stage, characterized in that, include: The evaluation system includes a construction module, an indicator scoring module, and a comprehensive scoring module. The evaluation system construction module is used to: obtain distribution network parameters, construct a comprehensive evaluation system for the distribution network in the planning stage, the comprehensive evaluation system for the distribution network includes multiple primary indicators, the primary indicators include self-healing rate indicators, safety indicators, reliability indicators, economic indicators, flexibility indicators and carbon contribution indicators, each of the primary indicators includes multiple secondary indicators, construct a prediction algorithm for the self-healing rate indicator, predict the distribution network according to the comprehensive evaluation system for the distribution network, and obtain the evaluation values ​​of each secondary indicator; The indicator scoring module is used to: convert the evaluation values ​​of each secondary indicator into secondary indicator scores using a standardized method; The comprehensive scoring module is used to: assign weights to each secondary indicator based on the hybrid judgment matrix and the consistency self-correction algorithm, and combine the secondary indicator weights and secondary indicator scores to obtain the primary indicator score and the comprehensive score of the distribution network.

6. A comprehensive evaluation system for actively balancing distribution networks suitable for the planning stage, as described in claim 5, is characterized in that... In the evaluation system construction module, the secondary indicators of the self-healing rate index include self-healing rate of overhead feeder with tie, self-healing rate of radial overhead feeder, self-healing rate of overhead feeder with tie cable, self-healing rate of radial cable, and feeder fault self-healing rate considering the restoration of distributed power supply islands. The secondary indicators of the safety index include the comprehensive voltage qualification rate, line overload rate, distribution transformer overload rate, capacity-to-load ratio, and capacity-to-generation ratio. The secondary indicators of the reliability index include the line N-1 throughput rate, power supply reliability rate, and absorption reliability rate; The secondary indicators of the economic indicators include the comprehensive line loss rate, the increase in power supply per unit investment, the increase in load per unit investment, the average load rate of the line, the average load rate of the distribution transformer, the new distributed capacity per unit investment, and the increase in distributed power generation per unit investment. The secondary indicators of the flexibility index include the proportion of adjustable and controllable load, the proportion of adjustable and controllable power supply, the proportion of load that can be transferred, the proportion of users that can be disconnected from the grid, the coverage rate of primary and secondary integrated equipment, and the coverage rate of DC transformation of distribution network. The secondary indicators of the carbon contribution index include carbon emissions per kilowatt-hour, terminal electrification rate, demand response carbon contribution rate, and distributed power penetration rate. The distribution network parameters include planning data, borrowed data, and forecast data. The planning data is the data determined during the distribution network planning stage. The borrowed data is the data of the same type of feeder in the distribution network of the same region. The forecast data is the data obtained by using conventional forecasting methods.

7. A comprehensive evaluation system for actively balancing distribution networks suitable for the planning stage, as described in claim 5, is characterized in that... In the evaluation system construction module, the prediction algorithm for the self-healing rate index includes determining the type of each feeder in the distribution network and calculating the self-healing rate of the corresponding feeder using different formulas according to the feeder type. The self-healing rate of the overhead feeder with connection includes: ； In the above formula, For overhead feeder lines The topological self-healing rate, For line fault outage rate, For switch failure downtime rate, For distribution transformer outage rate, For feeder Total length, For feeder The number of three remote segments, For feeder The number of users; The line fault outage rate Switch failure downtime and transformer outage rate This was obtained by applying data from the same feeder type within the same regional power distribution network. The self-healing rate of the radial overhead feeder includes: ； In the above formula, Radial overhead feeder Topological self-healing rate; The self-healing rate of the connecting cable includes: ； In the above formula, There is a connecting cable. The topological self-healing rate, This refers to the total length of the cable branch lines. This represents the average number of switches within a single ring main unit. The self-healing rate of the radial cable includes: ； The feeder fault self-healing rate considering the islanding restoration of distributed power sources includes: ； In the above formula, To consider the self-healing rate of feeder faults in distributed generation islanded power restoration, For feeder i, the fault self-healing rate; The fault self-healing rate of feeder i include: ； In the above formula, The success rate of the self-healing action of feeder i. This represents the self-healing recovery ratio of the medium-voltage fault section of feeder i. The gain coefficient for DG synergistic self-healing; The success rate of the self-healing action of feeder i and the self-healing recovery ratio of the medium-voltage fault section of feeder i This was obtained by applying data from the same feeder type within the same regional power distribution network. The DG cooperative self-healing gain coefficient include: ； In the above formula, For feeder The number of users supported by DG in China For feeder The number of users who can be recovered through DG islands in the event of a failure. To comprehensively consider the DG type, DG islanding operation / power transfer, and the voltage stability capability of DG nodes in islanding mode, the correction coefficient is set to a value of 0-1, which can be obtained through regression analysis of historical data.

8. A comprehensive evaluation system for actively balancing distribution networks suitable for the planning stage, as described in claim 6, is characterized in that... In the comprehensive scoring module, the hybrid judgment matrix includes: Secondary indicators under the same primary indicator are labeled C11, C12, ..., Cmn, where m is the primary indicator number and n is the secondary indicator number. Each secondary indicator is compared pairwise using the 1-9 scale of the analytic hierarchy process (AHP) to generate a subjective judgment matrix. The subjective judgment matrix It is an n×n positively reciprocal matrix; By collecting the distribution network parameters corresponding to each secondary indicator, and after standardization, the entropy weight method is used to transform them into an objective judgment matrix that conforms to the analytic hierarchy process (AHP) scaling. The objective judgment matrix These are positively reciprocal matrices, including: ； In the above formula, For comparison The second-level indicators and the first The weights obtained from the secondary indicators For the first Each secondary indicator For the first One secondary indicator; Subjective judgment matrix and objective judgment matrix The initial mixing judgment matrix is ​​generated by fusion. ,include: ； In the above formula, This is the fusion coefficient, used to adjust the relative weights of subjective and objective information. ∈[0,1], =1 indicates that the traditional analytic hierarchy process is used completely. =0 indicates that it relies entirely on objective data, and α can be preset according to the application scenario; The consistency self-correction algorithm includes: Calculate the initial mixed judgment matrix Maximum eigenvalue And calculate the consistency index. : ； Based on the order of the matrix According to the preset Parameter table lookup for corresponding credential random consistency index ; According to the consistency index and the random consistency index of vouchers Calculate the random consistency index : ； In the above formula, if If so, the judgment matrix is ​​considered to meet the consistency requirement; After the judgment matrix passes the consistency check, the weight of the judgment matrix is ​​calculated, and its weight vector is equal to the eigenvector corresponding to the largest eigenvalue of the judgment matrix. If it fails, then the current matrix... Corresponding feature vector Add the features in the same row and normalize them to obtain the normalized feature vectors, and then construct the comparison matrix. and make According to fixed step size direction The corresponding Gradually get closer, Each step closer to verifying consistency metrics This continues until the matrix meets the consistency requirements.

9. A comprehensive evaluation device for actively balancing distribution networks suitable for the planning stage, characterized in that, It includes a memory and a processor, wherein the memory is used to store computer program code and transfer the computer program code to the processor; The processor is configured to execute, according to instructions in the computer program code, the active balancing distribution network comprehensive evaluation method applicable to the planning stage as described in any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed by a processor as described in any one of claims 1 to 4, which is a comprehensive evaluation method for active balancing distribution networks applicable to the planning phase.

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