Active distribution network operation reliability assessment method, system, equipment and medium

Abstract

The present invention discloses a method, system, device, and medium for evaluating the operational reliability of an active distribution network, relating to the technical field of distribution network reliability evaluation. In response to a received distribution network reliability evaluation request, the method obtains a variety of historical operating data and historical fault data of the distribution network to be evaluated. Based on the various historical operating data and historical fault data, the method determines the loss degree associated with each historical operating data. The method uses the best-worst method to weight all loss degrees to obtain evaluation weight coefficients corresponding to the loss degrees. The method then performs a weighted summation of all loss degrees and all evaluation weight coefficients to obtain a reliability evaluation value for the distribution network to be evaluated. The method solves the technical problem that existing distribution network reliability evaluation methods fail to consider the impact of indicators such as voltage over-limit, thermal stability over-limit, and short-circuit current over-limit on distribution network operation, resulting in one-sided evaluation results and reduced accuracy of distribution network reliability evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network reliability assessment, and in particular to a method, system, device and medium for assessing the operational reliability of an active distribution network. Background Art

[0002] With the influx of new loads such as distributed renewable energy and electric vehicles, distribution networks have transformed from passive to active. However, distributed renewable energy is susceptible to environmental influences such as temperature and sunlight, resulting in random and fluctuating power output. Furthermore, large-scale charging of new loads such as electric vehicles dramatically increases the power consumption of distribution networks, significantly impacting their safe and stable operation. Therefore, assessing the reliability of distribution network operations is crucial to ensure their stable operation.

[0003] Currently, the existing technology (patent application number: CN201010587104.9) provides a distribution network reliability evaluation method. This patent converts the evaluation of distribution network reliability into an analysis of distribution network outage factors, decouples the distribution network outage factors, and uses the hierarchical analysis method to establish a distribution network reliability evaluation system. At the same time, the existing technology (patent application number: CN202110345829.5) also provides a distribution network reliability comprehensive evaluation method and system. By adopting the main distribution network reliability evaluation indicators and the distribution network reliability reference evaluation indicators, a distribution network reliability comprehensive evaluation index system is established. Among them, the main distribution network reliability evaluation indicators are the main reliability evaluation indicators based on power supply capacity and the main reliability evaluation indicators based on the number of users. The distribution network reliability reference evaluation indicators are the reliability reference evaluation indicators based on the reliability electricity price. However, none of the above methods considers the impact of evaluation dimensions such as voltage over-limit, thermal stability over-limit, and short-circuit current over-limit on distribution network operation, resulting in one-sided evaluation results and reducing the accuracy of distribution network reliability evaluation. Summary of the Invention

[0004] The present invention provides a method, system, device and medium for evaluating the operation reliability of an active distribution network, which solves the technical problem that existing methods for evaluating the reliability of distribution networks fail to take into account the impact of indicators such as voltage over-limit, thermal stability over-limit and short-circuit current over-limit on the operation of distribution networks, resulting in one-sided evaluation results and reduced accuracy of distribution network reliability evaluation.

[0005] A first aspect of the present invention provides a method for evaluating the operational reliability of an active power distribution network, comprising:

[0006] In response to the received distribution network reliability assessment request, obtaining a variety of historical operation data and historical fault data of the distribution network to be assessed;

[0007] determining, based on the plurality of historical operation data and the historical fault data, a loss degree associated with each of the historical operation data;

[0008] Using the best-worst method to weight all the loss degrees, and obtain the evaluation weight coefficient corresponding to the loss degree;

[0009] Performing a weighted summation process on all the loss degrees and all the evaluation weight coefficients to obtain a reliability evaluation value corresponding to the distribution network to be evaluated.

[0010] Optionally, the multiple historical operation data include voltage data, thermal stability assessment data, short-circuit current data, power outage duration data, and power outage number data, and the step of determining the loss degree associated with each of the historical operation data based on the multiple historical operation data and the historical fault data includes:

[0011] Determining voltage over-limit fault data corresponding to the distribution network to be evaluated based on the voltage data and a preset deviation degree function;

[0012] Determining thermal stability over-limit data corresponding to the distribution network to be evaluated based on the thermal stability evaluation data and a preset reverse load rate function;

[0013] Determining node short-circuit current fault data corresponding to the distribution network to be evaluated based on the short-circuit current data and a preset short-circuit current limit table;

[0014] Selecting from the historical fault data respectively the power loss data associated with the voltage over-limit fault data, the thermal stability over-limit data, the node short-circuit current fault data, the power outage duration data, and the power outage number data;

[0015] The power loss data associated with the voltage over-limit fault data, the thermal stability over-limit data, the node short-circuit current fault data, the power outage duration data and the power outage number data are respectively added and processed to obtain the loss degree associated with each of the historical operation data.

[0016] Optionally, the step of determining voltage over-limit fault data corresponding to the distribution network to be evaluated based on the voltage data and a preset deviation degree function includes:

[0017] Inputting the actual voltage value of the voltage data and the nominal voltage associated with the actual voltage value into a preset deviation function to generate a deviation value;

[0018] Inputting the voltage level of the voltage data into a preset voltage deviation limit table to match the corresponding voltage deviation range;

[0019] Determining whether the deviation value is within the voltage deviation range;

[0020] If the deviation value is not within the voltage deviation range, the deviation value is used as voltage over-limit fault data.

[0021] Optionally, the step of determining thermal stability over-limit data corresponding to the distribution network to be evaluated based on the thermal stability evaluation data and a preset reverse load rate function includes:

[0022] Inputting the new energy output value of the thermal stability assessment data, the load value associated with the new energy output value, and the transformer capacity value into a preset reverse load rate function to generate a reverse load rate;

[0023] Determining whether the reverse load rate is greater than a preset load allowable threshold;

[0024] If the reverse load rate is greater than the preset load allowable threshold, the reverse load rate is used as thermal stability limit-crossing data.

[0025] Optionally, the step of weighting all the loss degrees using the best-worst value assignment method to obtain evaluation weight coefficients corresponding to the loss degrees includes:

[0026] Selecting the loss degree associated with the power outage duration data from all the loss degrees as the optimal indicator;

[0027] Screening out the loss degree associated with the short-circuit current fault data from all the loss degrees as the worst indicator, eliminating the best indicator and the worst indicator, and using the remaining loss degrees as decision indicators;

[0028] Entering the optimal index and the worst index into a preset survey score library respectively, and matching the corresponding optimal score table and worst score table;

[0029] Inputting the decision indicator into the optimal score table to match the corresponding first preference value;

[0030] Inputting the decision indicator into the worst score table to match the corresponding second preference value;

[0031] using all of the first preference values ​​to construct a first comparison vector, and using all of the second preference values ​​to construct a second comparison vector;

[0032] The first comparison vector and the second comparison vector are input into a preset optimal weight function to generate an evaluation weight coefficient corresponding to the loss degree.

[0033] Optionally, the step of determining the node short-circuit current fault data corresponding to the distribution network to be evaluated based on the short-circuit current data and a preset short-circuit current limit table includes:

[0034] Inputting the category information of the short-circuit current data into a preset short-circuit current limit table to match the corresponding short-circuit current limit;

[0035] Determining whether the node short-circuit current in the short-circuit current data is greater than or equal to the short-circuit current limit;

[0036] When the node short-circuit current is greater than or equal to the current threshold, the node short-circuit current is used as node short-circuit current fault data.

[0037] Optionally, the power loss data includes distributed new energy power abandonment loss data, new load power abandonment loss data, residential and industrial and commercial power abandonment loss data, and distribution network equipment damage loss data.

[0038] A second aspect of the present invention provides an active distribution network operation reliability assessment system, comprising:

[0039] A response module, configured to obtain, in response to a received distribution network reliability assessment request, a variety of historical operation data and historical fault data of the distribution network to be assessed;

[0040] A first analysis module is configured to determine a loss degree associated with each of the historical operation data based on the plurality of historical operation data and the historical fault data;

[0041] The second analysis module is used to perform weighting processing on all the loss degrees using the best-worst method to obtain the evaluation weight coefficients corresponding to the loss degrees;

[0042] The evaluation module is used to perform weighted sum processing on all the loss degrees and all the evaluation weight coefficients to obtain a reliability evaluation value corresponding to the distribution network to be evaluated.

[0043] A third aspect of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the active distribution network operation reliability assessment method as described in any one of the above items.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the active power distribution network operation reliability assessment method as described in any one of the above items.

[0045] It can be seen from the above technical solutions that the present invention has the following advantages:

[0046] 1) This invention assesses the operational reliability of active distribution networks. It not only considers the impact of household power outage duration and frequency on distribution network operation, but also comprehensively considers factors such as voltage over-limit, thermal stability over-limit, and short-circuit current over-limit. This allows for a more accurate assessment of the operational reliability of active distribution networks with widespread access to distributed power sources, such as distributed photovoltaics. Compared to existing distribution network reliability assessment methods, this invention comprehensively analyzes distribution network operation based on factors such as voltage over-limit, thermal stability over-limit, short-circuit current over-limit, household power outage duration, and frequency, improving the accuracy of distribution network reliability assessment.

[0047] 2) This invention introduces a best-worst approach to assess the operational reliability of active distribution networks, determining the weights for each evaluation metric, such as the duration and number of outages caused by voltage over-limit, thermal stability over-limit, and short-circuit current over-limit. This results in more reasonable assessments of the operational reliability of active distribution networks and better reflects the interplay between these metrics. Compared to other weighting methods, the best-worst approach is more efficient, eliminating the need for complex iterative calculations and rapidly obtaining optimal or near-optimal weighting values. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flowchart of a method for evaluating the reliability of an active power distribution network provided in the first embodiment of the present invention;

[0050] Figure 2 A flowchart of a method for evaluating the reliability of an active distribution network operation provided in the second embodiment of the present invention;

[0051] Figure 3 This is a structural block diagram of an active distribution network operation reliability assessment system provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0052] Embodiments of the present invention provide a method, system, device, and medium for evaluating the operational reliability of an active distribution network, which are used to address the technical problem that existing methods for evaluating the reliability of distribution networks fail to consider the impact of indicators such as voltage over-limit, thermal stability over-limit, and short-circuit current over-limit on the operation of distribution networks, resulting in one-sided evaluation results and reduced accuracy of distribution network reliability evaluation.

[0053] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 , Figure 1 This is a flowchart of the steps of a method for evaluating the operation reliability of an active distribution network provided in Example 1 of the present invention.

[0055] The present invention provides a method for evaluating the operational reliability of an active distribution network, comprising:

[0056] Step 101: In response to a received distribution network reliability assessment request, obtain various historical operation data and historical fault data of the distribution network to be assessed;

[0057] It should be noted that the distribution network reliability assessment request refers to a request for reliability assessment of the distribution network to be assessed, so as to facilitate adjustments within the distribution network to be assessed.

[0058] Historical fault data refers to the distributed renewable energy power loss data, new load power loss data, residential and industrial and commercial load power loss data, and distribution network equipment damage loss data recorded by the distribution network monitoring system each time a distribution network fault occurs.

[0059] In an embodiment of the present invention, in response to a received distribution network reliability assessment request, a variety of historical operation data and historical fault data of the distribution network to be assessed are obtained, wherein the various historical operation data include voltage data, thermal stability assessment data, short-circuit current data, power outage duration data, power outage number data, etc.

[0060] Step 102: Determine the loss degree associated with each historical operation data based on the plurality of historical operation data and historical fault data;

[0061] In an embodiment of the present invention, voltage data, thermal stability assessment data and short-circuit current data are input into a preset discrimination model to obtain voltage over-limit fault data, thermal stability over-limit data and node short-circuit current fault data corresponding to the distribution network to be assessed, and electric power loss data associated with power outage duration data, power outage number data, voltage over-limit fault data, thermal stability over-limit data and node short-circuit current fault data are selected from historical fault data. The electric power loss data associated with the power outage duration data, power outage number data, voltage over-limit fault data, thermal stability over-limit data and node short-circuit current fault data are respectively added and processed to obtain the loss degree associated with each historical operation data.

[0062] Step 103: Use the best-worst method to weight all loss degrees and obtain the evaluation weight coefficient corresponding to the loss degree;

[0063] In one embodiment, the Best-Worst Method (BWM method) is used to weight all loss degrees to obtain evaluation weight coefficients corresponding to the loss degrees.

[0064] In an embodiment of the present invention, the loss degree associated with the power outage duration data is screened out from all the loss degrees as the optimal indicator, and then the loss degree associated with the node short-circuit current data is screened out as the worst indicator, and the remaining loss degrees are used as decision indicators. A preset questionnaire survey score table is obtained, and all decision indicators are input into the preset questionnaire survey score table. The corresponding first comparison vector and second comparison vector are matched, and the best and worst method is used to compare and analyze the first comparison vector and the second comparison vector to obtain the evaluation weight coefficient corresponding to each loss degree.

[0065] Step 104: Perform weighted sum processing on all loss degrees and all evaluation weight coefficients to obtain a reliability evaluation value of the distribution network to be evaluated.

[0066] In this embodiment of the present invention, a weighted sum of all loss degrees and all assessment weight coefficients is performed to obtain a reliability assessment value of the distribution network to be assessed. Here, the reliability assessment value = power outage duration weight coefficient * power outage duration loss degree + power outage number weight coefficient * power outage number loss degree + voltage over-limit weight coefficient * voltage over-limit loss degree + thermal stability over-limit weight coefficient * thermal stability over-limit loss degree + node current weight coefficient * node current loss degree.

[0067] In an embodiment of the present invention, in response to a received distribution network reliability assessment request, a variety of historical operating data and historical fault data of the distribution network to be assessed are obtained, and based on the various historical operating data and historical fault data, the loss degree associated with each historical operating data is determined, and the best-worst method is used to weight all the loss degrees to obtain the assessment weight coefficients corresponding to the loss degrees, and all the loss degrees and all the assessment weight coefficients are weighted and summed to obtain the reliability assessment value of the distribution network to be assessed. This solves the technical problem that the existing distribution network reliability assessment method does not take into account the impact of indicators such as voltage over-limit, thermal stability over-limit, and short-circuit current over-limit on the operation of the distribution network, resulting in one-sidedness of the assessment results and reducing the accuracy of the distribution network reliability assessment. The present invention comprehensively considers the impact of indicators such as user power outage duration, power outage number, voltage over-limit, thermal stability over-limit, and short-circuit current over-limit on the operation of the distribution network, thereby more accurately assessing the operational reliability of the active distribution network.

[0068] See also Figure 2 , Figure 2This is a flowchart of the steps of a method for evaluating the operation reliability of an active distribution network provided in the second embodiment of the present invention.

[0069] The present invention provides a method for evaluating the operational reliability of an active power distribution network, comprising:

[0070] Step 201: In response to a received distribution network reliability assessment request, obtain various historical operation data and historical fault data of the distribution network to be assessed;

[0071] In an embodiment of the present invention, in response to a received distribution network reliability evaluation request, a variety of historical operation data and historical fault data of the distribution network to be evaluated are acquired from a distribution network monitoring system.

[0072] It should be noted that the various historical operating data include voltage data, thermal stability assessment data, short-circuit current data, power outage duration data, and power outage number data.

[0073] Step 202: Determine voltage over-limit fault data corresponding to the distribution network to be evaluated based on the voltage data and a preset deviation function;

[0074] Furthermore, step 202 includes the following sub-steps:

[0075] S11. Inputting the actual voltage value of the voltage data and the nominal voltage associated with the actual voltage value into a preset deviation function to generate a deviation value;

[0076] In the embodiment of the present invention, the actual voltage value of the voltage data and the nominal voltage associated with the actual voltage value are input into a preset deviation function for solution to obtain a deviation value.

[0077] It should be noted that the deviation degree function is specifically:

[0078]

[0079] in, is the actual value of voltage, is the nominal voltage, is the deviation value.

[0080] S12. Input the voltage level of the voltage data into a preset voltage deviation limit table to match the corresponding voltage deviation range.

[0081] In an embodiment of the present invention, as shown in Table 1, the voltage level of the voltage data is input into a preset voltage deviation limit table to match the corresponding voltage deviation interval.

[0082] Table 1

[0083]

[0084] S13: Determine whether the deviation value is within the voltage deviation range.

[0085] In the embodiment of the present invention, it is determined whether the deviation value deviates from the voltage deviation interval.

[0086] S14. If the deviation value is not within the voltage deviation range, the deviation value is used as voltage over-limit fault data.

[0087] In the embodiment of the present invention, when the deviation value deviates from the voltage deviation interval, it is determined that a voltage over-limit fault occurs in the distribution network and the deviation value is used as voltage over-limit fault data.

[0088] Step 203: Determine thermal stability over-limit data corresponding to the distribution network to be evaluated based on the thermal stability evaluation data and the preset reverse load rate function;

[0089] Furthermore, step 203 includes the following sub-steps:

[0090] S21, inputting the new energy output value, the load value associated with the new energy output value, and the transformer capacity value of the thermal stability assessment data into a preset reverse load rate function to generate a reverse load rate;

[0091] In an embodiment of the present invention, a preset reverse load rate function is used to analyze the new energy output value of the thermal stability assessment data, the load value associated with the new energy output value, and the transformer capacity value to obtain the reverse load rate.

[0092] It should be noted that the reverse load rate function is specifically:

[0093]

[0094] in, For the new energy output value, is the load value, is the transformer capacity value, is the reverse load rate.

[0095] S22, determining whether the reverse load rate is greater than a preset load allowable threshold;

[0096] In the embodiment of the present invention, it is determined whether the reverse load rate exceeds a preset load allowable threshold.

[0097] S23: If the reverse load rate is greater than the preset load allowable threshold, the reverse load rate is used as thermal stability over-limit data.

[0098] In an embodiment of the present invention, when the reverse load rate exceeds a preset load allowable threshold, the reverse load rate is used as thermal stability over-limit data.

[0099] Step 204: Determine node short-circuit current fault data corresponding to the distribution network to be evaluated based on the short-circuit current data and the short-circuit current limit table;

[0100] Furthermore, step 204 includes the following sub-steps:

[0101] S31, inputting the category information of the short-circuit current data into a preset short-circuit current limit table to match the corresponding short-circuit current limit;

[0102] In an embodiment of the present invention, as shown in Table 2, the category information of the short-circuit current data is input into a preset short-circuit current limit table to match the corresponding short-circuit current limit, wherein the category information includes the voltage level and the power supply area type.

[0103] Table 2

[0104]

[0105] S32, determining whether the node short-circuit current in the short-circuit current data is greater than or equal to the short-circuit current limit;

[0106] In the embodiment of the present invention, it is determined whether the node short-circuit current in the short-circuit current data is greater than or equal to the short-circuit current limit, that is, I f <I fmax Among them, I f is the node short-circuit current, I fmax is the short-circuit current limit.

[0107] S33. When the node short-circuit current is greater than or equal to the current threshold, the node short-circuit current is used as node short-circuit current fault data.

[0108] In an embodiment of the present invention, when the node short-circuit current is greater than or equal to the current threshold, it indicates that a short-circuit current over-limit fault has occurred in the distribution network, and the node short-circuit current is used as node short-circuit current fault data.

[0109] Step 205: Selecting power loss data associated with voltage over-limit fault data, thermal stability over-limit data, node short-circuit current fault data, power outage duration data, and power outage number data from the historical fault data;

[0110] In an embodiment of the present invention, power loss data associated with voltage over-limit fault data, thermal stability over-limit data, node short-circuit current fault data, power outage duration data and power outage number data are selected from historical fault data respectively. For example, according to the timing information of voltage over-limit fault data, thermal stability over-limit data, node short-circuit current fault data, power outage duration data and power outage number data, matching is performed from historical fault data to obtain associated power loss data.

[0111] Step 206 : summing up the power loss data associated with the voltage over-limit fault data, thermal stability over-limit data, node short-circuit current fault data, power outage duration data, and power outage number data to obtain the loss degree associated with each historical operation data.

[0112] It should be noted that the electricity loss data includes the loss data of distributed new energy power abandonment, the loss data of new load power abandonment, the loss data of residential and industrial and commercial power abandonment, and the loss data of distribution network equipment damage.

[0113] In this embodiment of the present invention, the corresponding power loss data for voltage over-limit fault data, thermal stability over-limit data, node short-circuit current fault data, power outage duration data, and power outage count data are summed to obtain the loss degree associated with each historical operating data (i.e., the loss degree caused by voltage over-limit fault data, the loss degree caused by thermal stability over-limit data, the loss degree caused by node short-circuit current fault data, the loss degree caused by power outage duration data, and the loss degree caused by power outage count data). For example, the loss degree = distributed renewable energy power curtailment loss data + new load power curtailment loss data + residential and industrial and commercial power curtailment loss data + distribution network equipment damage loss data.

[0114] Step 207: Use the best-worst method to weight all loss degrees and obtain the evaluation weight coefficient corresponding to the loss degree;

[0115] Furthermore, step 207 includes the following sub-steps:

[0116] S41. Filtering out the loss degree associated with the power outage duration data from all loss degrees as the optimal indicator;

[0117] In the embodiment of the present invention, the loss degree associated with the power outage duration data is selected from all the loss degrees and set as the optimal index c B .

[0118] S42, selecting the loss degree associated with the node short-circuit current fault data from all loss degrees as the worst indicator, eliminating the best indicator and the worst indicator, and using the remaining loss degrees as the decision indicator;

[0119] In the embodiment of the present invention, the loss degree associated with the node short-circuit current fault data is selected from all the loss degrees and set as the worst indicator c W , and eliminate the best and worst indicators, and use the remaining loss degree as the decision indicator.

[0120] In another embodiment, the total loss degree is used as the decision indicator, wherein all decision indicators include voltage over-limit reliability index, thermal stability over-limit reliability index, short-circuit current reliability index, power outage duration reliability index, power outage number reliability index, etc. Assuming that there are n decision indicators in total, it is expressed as {c1, c2, ..., c n}, and set the outage duration reliability index as the optimal index c B , set the short-circuit current reliability index as the worst index c W .

[0121] S43. Input the optimal index and the worst index into the preset survey score library respectively, and match the corresponding optimal score table and the worst score table;

[0122] S44. Input the decision indicator into the optimal score table to match the corresponding first preference value;

[0123] S45. Input the decision indicator into the worst score table to match the corresponding second preference value;

[0124] S46. The first preference value refers to the preference value of the optimal indicator to the decision indicator.

[0125] S47. The second preference value refers to the preference value of the worst indicator to the decision indicator.

[0126] In an embodiment of the present invention, the optimal indicator and the worst indicator are respectively input into a preset survey score library, matched with the corresponding optimal score table and the worst score table, the decision indicator is input into the optimal score table, matched with the corresponding first preference value, and the decision indicator is input into the worst score table, matched with the corresponding second preference value.

[0127] It should be noted that the optimal score table and the worst score table are obtained by power industry practitioners using numbers between 1 and 9 to score.

[0128] S48. Use all first preference values ​​to construct a first comparison vector, and all second preference values ​​to construct a second comparison vector;

[0129] In the embodiment of the present invention, all first preference values ​​are used to construct the first comparison vector A B =(a B1 ,a B2 ,……,a Bn ) T , where a Bj is the optimal index c B For decision indicator c j The second comparison vector A is constructed using all second preference values. W =(a 1W ,a2W ,……,a nW ) T , where a iW is the worst indicator c W For decision indicator c i degree of preference.

[0130] S47 . Input the first comparison vector and the second comparison vector into a preset optimal weight function to generate an evaluation weight coefficient corresponding to the loss degree.

[0131] In an embodiment of the present invention, the first comparison vector and the second comparison vector are input into a preset optimal weight function, and the optimal weight function is solved to obtain an evaluation weight coefficient corresponding to the loss degree.

[0132] It should be noted that the optimal weight function is specifically:

[0133]

[0134] in, To evaluate the weight coefficient, is the evaluation weight coefficient number, is the consistency indicator, is the optimal indicator weight, is the worst indicator weight, For constraints.

[0135] It should be noted that the optimal weight function is a mathematical model constructed based on the constraints that the indicator weights are non-negative and their sum is 1.

[0136] Step 208: Perform weighted sum processing on all loss degrees and all evaluation weight coefficients to obtain a reliability evaluation value corresponding to the distribution network to be evaluated.

[0137] In an embodiment of the present invention, the loss degree and the loss degree associated assessment weight system are multiplied to obtain a first multiplication value, and all the first multiplication values ​​are added together to obtain a reliability assessment value corresponding to the distribution network to be assessed, wherein the reliability assessment value = power outage duration weight coefficient * power outage duration loss degree + power outage number weight coefficient * power outage number loss degree + voltage over-limit weight coefficient * voltage over-limit loss degree + thermal stability over-limit weight coefficient * thermal stability over-limit loss degree + node current weight coefficient * node current loss degree.

[0138] In an embodiment of the present invention, in response to a received distribution network reliability assessment request, a variety of historical operating data and historical fault data of the distribution network to be assessed are obtained, and based on the various historical operating data and historical fault data, the loss degree associated with each historical operating data is determined, and the best-worst method is used to weight all the loss degrees to obtain the assessment weight coefficients corresponding to the loss degrees, and all the loss degrees and all the assessment weight coefficients are weighted and summed to obtain the reliability assessment value of the distribution network to be assessed. This solves the technical problem that the existing distribution network reliability assessment method does not take into account the impact of indicators such as voltage over-limit, thermal stability over-limit, and short-circuit current over-limit on the operation of the distribution network, resulting in one-sidedness of the assessment results and reducing the accuracy of the distribution network reliability assessment. The present invention comprehensively considers the impact of indicators such as user power outage duration, power outage number, voltage over-limit, thermal stability over-limit, and short-circuit current over-limit on the operation of the distribution network, thereby more accurately assessing the operational reliability of the active distribution network.

[0139] See also Figure 3 , Figure 3 This is a structural block diagram of a method for evaluating the operation reliability of an active distribution network provided in Example 3 of the present invention.

[0140] The response module 301 is configured to obtain, in response to a received distribution network reliability assessment request, a plurality of historical operation data and historical fault data corresponding to the distribution network to be assessed;

[0141] A first analysis module 302 is configured to determine a loss degree associated with each historical operation data based on a plurality of historical operation data and historical fault data;

[0142] The second analysis module 303 is used to weight all loss degrees using the best-worst method to obtain an assessment weight coefficient corresponding to the loss degree;

[0143] The evaluation module 304 is configured to perform weighted sum processing on all loss degrees and all evaluation weight coefficients to obtain a reliability evaluation value corresponding to the distribution network to be evaluated.

[0144] Furthermore, the various historical operating data include voltage data, thermal stability assessment data, short-circuit current data, power outage duration data, and power outage number data. The first analysis module 302 includes:

[0145] The first analysis submodule is used to determine voltage over-limit fault data corresponding to the distribution network to be evaluated based on the voltage data and a preset deviation degree function;

[0146] The second analysis submodule is used to determine the thermal stability over-limit data corresponding to the distribution network to be evaluated based on the thermal stability evaluation data and the preset reverse load rate function;

[0147] The third analysis submodule is used to determine the node short-circuit current fault data corresponding to the distribution network to be evaluated based on the short-circuit current data and the short-circuit current limit table;

[0148] The first screening submodule is used to select the power loss data associated with the voltage over-limit fault data, the thermal stability over-limit data, the node short-circuit current fault data, the power outage duration data and the power outage number data from the historical fault data;

[0149] The fourth analysis submodule is used to sum up the power loss data associated with voltage over-limit fault data, thermal stability over-limit data, node short-circuit current fault data, power outage duration data and power outage number data to obtain the loss degree associated with each historical operation data.

[0150] Furthermore, the first analysis submodule includes:

[0151] a first analyzing unit, configured to input an actual voltage value of the voltage data and a nominal voltage associated with the actual voltage value into a preset deviation function to generate a deviation value;

[0152] A first matching unit is used to input the voltage level of the voltage data into a preset voltage deviation limit table to match the corresponding voltage deviation interval;

[0153] The second analysis unit is used to determine whether the deviation value is within the voltage deviation range;

[0154] If the deviation value is not within the voltage deviation range, the deviation value is used as voltage over-limit fault data.

[0155] Furthermore, the second analysis submodule includes:

[0156] The third analysis unit is configured to input the new energy output value, the load value associated with the new energy output value, and the transformer capacity value of the thermal stability assessment data into a preset reverse load rate function to generate a reverse load rate;

[0157] a fourth analyzing unit, configured to determine whether the reverse load rate is greater than a preset load allowable threshold;

[0158] If the reverse load rate is greater than the preset load allowable threshold, the reverse load rate is used as thermal stability limit-crossing data.

[0159] Furthermore, the second analysis module includes:

[0160] The second screening submodule is used to screen out the loss degree associated with the power outage duration data from all the loss degrees as the optimal indicator;

[0161] The third screening submodule is used to screen out the loss degree associated with the node short-circuit current fault data from all the loss degrees as the worst indicator, and eliminate the best indicator and the worst indicator, and use the remaining loss degrees as the decision indicator;

[0162] The matching submodule is used to input the optimal index and the worst index into the preset survey score library respectively, and match the corresponding optimal score table and the worst score table;

[0163] Input the decision indicator into the optimal score table to match the corresponding first preference value;

[0164] Enter the decision indicator into the worst score table and match the corresponding second preference value;

[0165] a construction submodule, configured to respectively use all first preference values ​​to construct a first comparison vector and all second preference values ​​to construct a second comparison vector;

[0166] The fifth analysis submodule is used to input the first comparison vector and the second comparison vector into a preset optimal weight function to generate an evaluation weight coefficient corresponding to the loss degree.

[0167] Furthermore, the third analysis submodule includes:

[0168] a matching unit, configured to input the category information of the short-circuit current data into a preset short-circuit current limit table and match the corresponding short-circuit current limit;

[0169] a fifth analyzing unit, configured to determine whether the node short-circuit current in the short-circuit current data is greater than or equal to the short-circuit current limit;

[0170] When the node short-circuit current is greater than or equal to the current threshold, the node short-circuit current is used as node short-circuit current fault data.

[0171] Furthermore, the power loss data includes distributed new energy power abandonment loss data, new load power abandonment loss data, residential and industrial and commercial power abandonment loss data, and distribution network equipment damage loss data.

[0172] Embodiment 4 of the present invention further provides an electronic device, comprising: a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the active distribution network operation reliability assessment method as described in any of the above embodiments.

[0173] The fifth embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the method for evaluating the operational reliability of an active power distribution network according to any of the above embodiments is implemented.

[0174] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0176] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0177] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0178] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0179] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the operational reliability of an active distribution network, characterized in that: include: In response to the received distribution network reliability assessment request, obtaining a variety of historical operation data and historical fault data of the distribution network to be assessed; determining, based on the plurality of historical operation data and the historical fault data, a loss degree associated with each of the historical operation data; Using the best-worst method to weight all the loss degrees, and obtain the evaluation weight coefficient corresponding to the loss degree; Performing a weighted summation process on all the loss degrees and all the evaluation weight coefficients to obtain a reliability evaluation value corresponding to the distribution network to be evaluated; The multiple historical operating data include voltage data, thermal stability assessment data, short-circuit current data, power outage duration data, and power outage number data. The step of determining the loss degree associated with each of the historical operating data based on the multiple historical operating data and the historical fault data includes: Determining voltage over-limit fault data corresponding to the distribution network to be evaluated based on the voltage data and a preset deviation degree function; Determining thermal stability over-limit data corresponding to the distribution network to be evaluated based on the thermal stability evaluation data and a preset reverse load rate function; Determining node short-circuit current fault data corresponding to the distribution network to be evaluated based on the short-circuit current data and a preset short-circuit current limit table; Selecting from the historical fault data respectively the power loss data associated with the voltage over-limit fault data, the thermal stability over-limit data, the node short-circuit current fault data, the power outage duration data, and the power outage number data; The power loss data associated with the voltage over-limit fault data, the thermal stability over-limit data, the node short-circuit current fault data, the power outage duration data and the power outage number data are respectively added and processed to obtain the loss degree associated with each of the historical operation data.

2. The method for evaluating the operational reliability of an active power distribution network according to claim 1, wherein: The step of determining voltage over-limit fault data corresponding to the distribution network to be evaluated based on the voltage data and a preset deviation degree function includes: Inputting the actual voltage value of the voltage data and the nominal voltage associated with the actual voltage value into a preset deviation function to generate a deviation value; Inputting the voltage level of the voltage data into a preset voltage deviation limit table to match the corresponding voltage deviation range; Determining whether the deviation value is within the voltage deviation range; If the deviation value is not within the voltage deviation range, the deviation value is used as voltage over-limit fault data.

3. The method for evaluating the operational reliability of an active power distribution network according to claim 1, wherein: The step of determining thermal stability over-limit data corresponding to the distribution network to be evaluated based on the thermal stability evaluation data and a preset reverse load rate function includes: Inputting the new energy output value of the thermal stability assessment data, the load value associated with the new energy output value, and the transformer capacity value into a preset reverse load rate function to generate a reverse load rate; Determining whether the reverse load rate is greater than a preset load allowable threshold; If the reverse load rate is greater than the preset load allowable threshold, the reverse load rate is used as thermal stability limit-crossing data.

4. The method for evaluating the operational reliability of an active power distribution network according to claim 1, wherein: The step of using the best-worst method to weight all the loss degrees to obtain the evaluation weight coefficients corresponding to the loss degrees includes: Selecting the loss degree associated with the power outage duration data from all the loss degrees as the optimal indicator; Screening out the loss degree associated with the node short-circuit current fault data from all the loss degrees as the worst indicator, eliminating the best indicator and the worst indicator, and using the remaining loss degrees as decision indicators; Entering the optimal index and the worst index into a preset survey score library respectively, and matching the corresponding optimal score table and worst score table; Inputting the decision indicator into the optimal score table to match the corresponding first preference value; Inputting the decision indicator into the worst score table to match the corresponding second preference value; using all of the first preference values ​​to construct a first comparison vector, and using all of the second preference values ​​to construct a second comparison vector; The first comparison vector and the second comparison vector are input into a preset optimal weight function to generate an evaluation weight coefficient corresponding to the loss degree.

5. The method for evaluating the operational reliability of an active power distribution network according to claim 1, wherein: The step of determining the node short-circuit current fault data corresponding to the distribution network to be evaluated based on the short-circuit current data and a preset short-circuit current limit table includes: Inputting the category information of the short-circuit current data into a preset short-circuit current limit table to match the corresponding short-circuit current limit; Determining whether the node short-circuit current in the short-circuit current data is greater than or equal to the short-circuit current limit; When the node short-circuit current is greater than or equal to the short-circuit current limit, the node short-circuit current is used as node short-circuit current fault data.

6. The method for evaluating the operational reliability of an active power distribution network according to claim 1, wherein: The power loss data includes distributed new energy power abandonment loss data, new load power abandonment loss data, residential and industrial and commercial power abandonment loss data, and distribution network equipment damage loss data.

7. An active distribution network operation reliability assessment system, characterized in that: include: A response module, configured to obtain, in response to a received distribution network reliability assessment request, a variety of historical operation data and historical fault data of the distribution network to be assessed; A first analysis module is configured to determine a loss degree associated with each of the historical operation data based on the plurality of historical operation data and the historical fault data; The second analysis module is used to perform weighting processing on all the loss degrees using the best-worst method to obtain the evaluation weight coefficients corresponding to the loss degrees; An evaluation module, configured to perform a weighted summation process on all the loss degrees and all the evaluation weight coefficients to obtain a reliability evaluation value corresponding to the distribution network to be evaluated; The various historical operating data include voltage data, thermal stability assessment data, short-circuit current data, power outage duration data, and power outage number data. The first analysis module includes: A first analysis submodule is configured to determine voltage over-limit fault data corresponding to the distribution network to be evaluated based on the voltage data and a preset deviation degree function; A second analysis submodule is configured to determine thermal stability over-limit data corresponding to the distribution network to be evaluated based on the thermal stability evaluation data and a preset reverse load rate function; A third analysis submodule is configured to determine node short-circuit current fault data corresponding to the distribution network to be evaluated based on the short-circuit current data and a preset short-circuit current limit table; a first screening submodule, configured to select, from the historical fault data, power loss data associated with the voltage over-limit fault data, the thermal stability over-limit data, the node short-circuit current fault data, the power outage duration data, and the power outage number data; The fourth analysis submodule is used to sum up the power loss data associated with the voltage over-limit fault data, the thermal stability over-limit data, the node short-circuit current fault data, the power outage duration data and the power outage number data to obtain the loss degree associated with each historical operation data.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the active distribution network operation reliability assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the active power distribution network operation reliability assessment method according to any one of claims 1 to 6 is implemented.

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