Power grid asset LCC cost evaluation analysis method based on LCC bidding strategy
Through the LCC cost evaluation and analysis method of power grid assets based on LCC bidding strategy, the problem of low accuracy of cost prediction for the whole life cycle of power transformers is solved, and more accurate cost prediction and more effective bidding plan screening is achieved.
Patent Information
- Application Number
- CN202411451367.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-02-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prediction accuracy of the full life cycle cost of existing power transformers is low, especially the estimation of operation and maintenance costs depends on the bidder's artificial calculations, resulting in inaccurate predictions.
A method for assessing and analysis of grid assets based on LCC bidding strategy is proposed. By obtaining the attribute labels of target equipment, finding the equipment data in the preset database, data mapping and clustering, and calculating the preset cost and impact of bidding equipment Weight, score with grade labels to select the best bidding plan.
It significantly improves the accuracy of the prediction of the full life cycle cost of the power transformer, reduces the error caused by human calculations, can quickly and effectively screen out the best bidding plan, and improves the prediction accuracy of the full life cycle cost of the power transformer.
Smart Images
Figure CN119417501A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of power grid asset evaluation, and in particular relates to a power grid asset LCC cost evaluation and analysis method based on an LCC bidding strategy. Background Art
[0002] With the acceleration of global energy transformation and the rapid innovation of power system infrastructure, the performance excellence and efficient management of power equipment have become key factors supporting the sustainable development of the modern power industry. The traditional "short-sighted" equipment management model, this limitation not only limits the full exploration of equipment potential, but also makes it difficult for assets to achieve optimal economic benefits and operating efficiency during their long life cycle. This management model has lagged far behind the urgent needs of the power industry for efficient, reliable and green development. In this context, life cycle management (LCM), as a forward-looking power equipment management strategy, enables us to clearly understand the equipment from the initial precise planning and scientific procurement, to the mid-term efficient operation and refined maintenance, to the final environmental protection disposal and residual value maximization. Life cycle management is committed to building a closed-loop, collaborative management system to ensure that the equipment can operate in the best state throughout its life cycle, so as to achieve the most reasonable allocation of resources and maximize cost-effectiveness.
[0003] Substation, as the core of the power grid system, its operating status is directly related to the health and vitality of the entire power network. In the complex array of equipment in the substation, the power transformer, with its unique status and role, is like the aorta in the heart. Its stability and efficiency are directly related to the safe, stable operation and economy of the power grid. Therefore, the full life cycle management of power transformers is particularly critical.
[0004] However, the existing life cycle cost prediction of power transformers is often affected by many factors, especially the estimation of operation and maintenance costs, which is highly dependent on the manual calculations of bidders and difficult to achieve accurate predictions, resulting in low and inaccurate predictions of the life cycle costs of power transformers. Summary of the invention
[0005] The purpose of the present invention is to solve the problem that the estimation of operation and maintenance costs is highly dependent on the manual calculations of bidders, which makes it difficult to achieve accurate predictions, resulting in low and inaccurate predictions of the life cycle costs of power transformers, and proposes a power grid asset LCC cost evaluation and analysis method based on the LCC bidding strategy.
[0006] In the implementation of the present invention, a method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy is proposed, and the method comprises:
[0007] Obtaining an attribute tag corresponding to a target device, and searching for device data of a corresponding type of device in a first preset database and a second preset database respectively according to the attribute tag to obtain a first data set and a second data set; the target device is a power grid device to be tendered in a tendering device area; the device data includes a fixed cost, an operation and maintenance cost, and a grade tag of the device;
[0008] According to the fixed costs in the first data set and the second data set, the first data set and the second data set are mapped to the same cost interval map to obtain a target coordinate map;
[0009] Obtaining bidding equipment data given by each bidding equipment, the bidding equipment data including fixed cost, operation and maintenance cost and grade label, mapping the fixed cost of the bidding equipment to the target coordinate map to determine the impact weight of the fixed cost given by the bidding equipment;
[0010] Determine the preset cost of the bidding equipment according to the impact weight, the fixed cost, the operation and maintenance cost of the bidding equipment and the second equipment data set;
[0011] The proportionality coefficient is obtained according to the ratio of fixed cost and operation and maintenance cost in the given bidding assets of each bidding equipment;
[0012] All bidding devices are sorted according to preset costs to obtain a first array, and all bidding devices are sorted according to proportional coefficients to obtain a second array;
[0013] Each bidding device is scored according to the first array, the second array and the grade label corresponding to each bidding device to obtain a final score of each device, and the bidding device with the highest final score is obtained as the bidding device.
[0014] Optionally, after searching the first preset database and the second preset database for device data of corresponding types of devices according to the attribute tag to obtain the first data set and the second data set, the method further includes:
[0015] Using a K-means algorithm and a fixed cost, the data in the first data set and the second data set are clustered to obtain a plurality of first clustered data sets and a plurality of second clustered data sets; the data clustering is based on a fixed cost interval; and the number of clustered data sets is determined by the fixed cost interval;
[0016] For each cluster data set, it is identified whether there is missing data in the cluster data set. If there is missing data, the missing data is modified according to the average value calculated in the cluster data set.
[0017] Optionally, mapping the first data set and the second data set to the same cost interval map to obtain a target coordinate map according to the fixed costs in the first data set and the second data set includes:
[0018] For all first clustered data sets and all second clustered data sets in the first data set and the second data set;
[0019] Aggregating the first clustering data set and the second clustering data set within each fixed cost interval to obtain an aggregated data set;
[0020] All data in the aggregated data set are standardized by Z-score to obtain a standard data point set; the standard data point set includes a first type of data point and a second type of data point, the first type of data point is a data point generated by the first clustered data set, and the second type of data point is a data point generated by the second clustered data set; each data point corresponds to a full life cycle cost of a device;
[0021] Map all data points in the standard data point set to the cost interval map through a visualization tool to obtain a data point distribution map of all data in the fixed cost interval;
[0022] Obtain the data point distribution diagram corresponding to all fixed cost intervals to obtain the target coordinate diagram.
[0023] Optionally, mapping the fixed cost of the bidding equipment to the target coordinate map to determine the influence weight of the fixed cost given by the bidding equipment includes:
[0024] Determine the corresponding data point distribution area in the target coordinate graph according to the fixed cost of the bidding equipment to obtain the target area;
[0025] Acquire a first target clustering data set and a second target clustering data set corresponding to the target area, and standardize the first target clustering data set, the second target clustering data set, and the fixed cost of the bidding equipment by using the Z-score to obtain a data point corresponding to the fixed cost of the bidding equipment to obtain a target point;
[0026] Mapping the target point to the target area, taking the target point as the center and a preset length as the radius to obtain a target circle, obtaining the number of first-type data points and the number of second-type data points in the target circle to obtain the number of points in the first circle and the number of points in the second circle;
[0027] The first ratio is obtained by calculating the ratio of the number of points in the first circle in the target circle to all the first type of data points in the target area; the second ratio is obtained by calculating the ratio of the number of points in the second circle in the target circle to all the second type of data points in the target area;
[0028] The first ratio and the second ratio are added to obtain a target ratio, and the first ratio is divided by the target ratio to obtain an impact weight of the fixed cost given by the bidding equipment.
[0029] Optionally, determining the preset cost of the bidding equipment according to the impact weight, the fixed cost, the operation and maintenance cost of the bidding equipment, and the second equipment data set includes:
[0030] Obtaining the operation and maintenance costs in the full life cycle costs of the equipment corresponding to all the second type data points in the target circle to obtain an operation and maintenance cost set, and calculating the average value of all the operation and maintenance costs in the operation and maintenance cost set to obtain the historical operation and maintenance cost;
[0031] By formula Get the preset cost of the bidding equipment;
[0032] Among them, Lcc is the preset cost of the bidding equipment, C1 is the fixed cost of the bidding equipment, To influence the weight, C2 is the operation and maintenance cost of the bidding equipment, and C3 is the historical operation and maintenance cost.
[0033] Optionally, scoring each bidding device according to the first array, the second array, and the grade label corresponding to each bidding device to obtain a final score of each device includes:
[0034] Assigning scores to the bidding devices in the first array and the second array respectively according to the ranking to obtain a first score and a second score corresponding to each bidding device;
[0035] For each bidding device, determine a third score corresponding to the bidding device according to the grade label corresponding to the bidding device;
[0036] Corresponding weights are assigned to the first score, second score and third score of each bidding device, and the weighted first score, second score and third score are added together to obtain the final score of the bidding device.
[0037] Optionally, the fixed costs include purchasing costs, installation costs, commissioning costs, training costs and disassembly costs.
[0038] Beneficial effects of the present invention:
[0039] The present invention proposes a method for evaluating and analyzing the LCC cost of power grid assets based on an LCC bidding strategy, which includes obtaining an attribute label corresponding to a target device, searching for device data of a corresponding type of device in a first preset database and a second preset database according to the attribute label to obtain a first data set and a second data set; mapping the first data set and the second data set to the same cost interval diagram to obtain a target coordinate diagram according to the fixed costs in the first data set and the second data set; obtaining the bidding device data given by each bidding device, mapping the fixed cost of the bidding device to the target coordinate diagram to determine the influence weight of the fixed cost given by the bidding device; determining the preset cost of the bidding device according to the influence weight, the fixed cost in the bidding device, the operation and maintenance cost, and the second device data set; obtaining a proportional coefficient according to the ratio of the fixed cost to the operation and maintenance cost in the bidding asset given by each bidding device; sorting all bidding devices according to the preset cost to obtain a first array, and sorting all bidding devices according to the proportional coefficient to obtain a second array; scoring each bidding device according to the first array, the second array and the grade label corresponding to each bidding device to obtain a final score of each device, and obtaining the bidding device with the highest final score as the bidding device. By comparing the target equipment with similar equipment in the historical data set and using the historical data of fixed costs and operation and maintenance costs for mapping and analysis, the accuracy of the prediction of the life cycle cost of power transformers can be significantly improved, and the errors caused by human calculations can be reduced. By calculating the proportional coefficient of fixed costs and operation and maintenance costs and sorting them in combination with preset costs, the cost performance of the bidding equipment can be comprehensively evaluated. By scoring the bidding equipment through the LCC bidding strategy, the best bidding plan can be quickly and effectively screened out, thereby improving the prediction accuracy of the life cycle cost of power transformers. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] The present invention will be further described below in conjunction with the accompanying drawings.
[0041] Figure 1 A flowchart of a method for evaluating and analyzing the LCC cost of power grid assets based on an LCC bidding strategy is provided for an embodiment of the present invention. DETAILED DESCRIPTION
[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the present invention, the description of "first", "second", etc. is only used for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.
[0044] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.
[0045] The embodiment of the present invention provides a method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy. Figure 1 , Figure 1 A flowchart of a method for evaluating and analyzing the LCC cost of power grid assets based on an LCC bidding strategy is provided in an embodiment of the present invention. The method comprises the following steps:
[0046] S101, obtaining an attribute tag corresponding to a target device, and searching for device data of a corresponding type of device in a first preset database and a second preset database respectively according to the attribute tag to obtain a first data set and a second data set.
[0047] S102, mapping the first data set and the second data set to the same cost interval map to obtain a target coordinate map according to the fixed costs in the first data set and the second data set.
[0048] S103, obtaining the bidding equipment data given by each bidding equipment, the bidding equipment data including fixed cost, operation and maintenance cost and grade label, mapping the fixed cost of the bidding equipment to the target coordinate map to determine the impact weight of the fixed cost given by the bidding equipment.
[0049] S104: Determine a preset cost of the bidding equipment according to the impact weight, the fixed cost, the operation and maintenance cost of the bidding equipment, and the second equipment data set.
[0050] S105, obtaining a proportionality coefficient according to the ratio of the fixed cost and the operation and maintenance cost in the bid assets given by each bid equipment.
[0051] S106, sorting all bidding devices according to preset costs to obtain a first array, and sorting all bidding devices according to proportionality coefficients to obtain a second array.
[0052] S107, scoring each bidding device according to the first array, the second array and the grade label corresponding to each bidding device to obtain a final score of each device, and obtaining the bidding device with the highest final score as the bidding device.
[0053] The target equipment is the power grid equipment to be tendered in the tender equipment area; the equipment data includes the fixed cost, operation and maintenance cost and grade label of the equipment.
[0054] Based on the LCC cost evaluation and analysis method of power grid assets based on the LCC bidding strategy provided by the embodiment of the present invention, by comparing the target equipment with similar equipment in the historical data set, and using the historical data of fixed costs and operation and maintenance costs for mapping and analysis, the accuracy of the full life cycle cost prediction of the power transformer can be significantly improved, and the error caused by human calculation can be reduced. By calculating the proportional coefficient of fixed costs and operation and maintenance costs and sorting them in combination with preset costs, the cost performance of the bidding equipment can be comprehensively evaluated. Then, by scoring the bidding equipment through the LCC bidding strategy, the best bidding plan can be quickly and effectively screened out, thereby improving the prediction accuracy of the full life cycle cost of the power transformer.
[0055] In one implementation, by systematically comparing and analyzing the attribute labels, fixed costs, and operation and maintenance costs of the target equipment and the bidding equipment, the comprehensive economy and performance of each bidding equipment can be evaluated more accurately. This data-driven decision-making method is more reliable than relying solely on experience or subjective judgment.
[0056] In one implementation, the proportionality coefficient is the ratio between the fixed cost and the operation and maintenance cost. A larger proportionality coefficient indicates that the safety and reliability of the device are higher during the entire operation and maintenance period of the device.
[0057] In one implementation, the bidding devices are sorted in sequence from low to high according to a preset cost to obtain a first array, and the bidding devices are sorted in sequence from high to low according to a proportionality coefficient to obtain a second array.
[0058] In one implementation, the equipment data is mapped to a unified cost range map, and the cost-effectiveness of the bidding equipment is evaluated by calculating the impact weights, which can ensure that all bidding equipment are compared under the same standards, making the evaluation process more fair and transparent.
[0059] In one implementation, by calculating the proportional coefficient of fixed cost and operation and maintenance cost and ranking them according to preset costs, the cost performance of bidding equipment can be comprehensively evaluated, and equipment with the best economic benefits in long-term operation can be identified, thereby optimizing the return on investment.
[0060] In one implementation, by combining fixed costs, operation and maintenance costs, and equipment grade labels, the solution can comprehensively consider the performance and economy of the equipment, avoiding the shortcomings of focusing on only one aspect.
[0061] In one implementation, the first preset database stores historical asset data, attribute tags, and grade tags corresponding to all power grid equipment except the bidding equipment area; the second preset database stores historical asset data, attribute tags, and grade tags corresponding to the bidding equipment area; the attribute tags are the power, operating voltage, temperature resistance level, purpose, weight, and size of the power grid equipment.
[0062] In one embodiment, after searching the first preset database and the second preset database for device data of corresponding types of devices according to the attribute tags to obtain the first data set and the second data set, the method further includes:
[0063] Using the K-means algorithm and the fixed cost, the data in the first data set and the second data set are clustered respectively to obtain a plurality of first cluster data sets and a plurality of second cluster data sets; the data clustering is based on the fixed cost interval; and the number of cluster data sets is determined by the fixed cost interval;
[0064] For each cluster data set, it is identified whether there is missing data in the cluster data set. If there is missing data, the missing data is modified according to the average value calculated in the cluster data set.
[0065] In one implementation, cluster analysis can be used to identify and process missing values in the data, making the data set more complete, which helps improve the accuracy of the analysis and the reliability of subsequent decisions.
[0066] In one implementation, K-means clustering can group devices with similar fixed costs into one category, thereby improving the accuracy of cost analysis, allowing similar devices to be processed together in the analysis, and helping to identify cost patterns and trends.
[0067] In one implementation, the average value within the cluster is used to repair missing data instead of the global average value or other processing methods, which can ensure that the repaired data is more in line with the actual situation and make the analysis results closer to the actual data distribution.
[0068] In one implementation, for example, when the bidding equipment is a 10kV dry-type transformer, the fixed costs in all the first data sets and the second data sets are obtained. For the first data set and the second data set respectively, combined with the fixed costs, the fixed costs of 60,000 yuan and below are clustered into one category, 60,000 yuan to 70,000 yuan (including 70,000 yuan) are clustered into one category, and 10,000 yuan is added successively for clustering until it is added to 150,000 yuan, and 150,000 yuan upwards are clustered into one category. The initial clustering center of K-means is 50,000 yuan, 65,000 yuan, 75,000 yuan, 85,000 yuan, ..., 145,000 yuan, and 160,000 yuan respectively.
[0069] In one embodiment, according to the fixed costs in the first data set and the second data set, mapping the first data set and the second data set to the same cost interval map to obtain a target coordinate map includes:
[0070] For all first clustered data sets and all second clustered data sets in the first data set and the second data set;
[0071] Aggregating the first clustering data set and the second clustering data set within each fixed cost interval to obtain an aggregated data set;
[0072] All data in the aggregated data set are standardized by Z-score to obtain a standard data point set; the standard data point set includes a first type of data point and a second type of data point, the first type of data point is a data point generated by the first clustered data set, and the second type of data point is a data point generated by the second clustered data set; each data point corresponds to a full life cycle cost of a device;
[0073] Map all data points in the standard data point set to the cost interval map through a visualization tool to obtain a data point distribution map of all data in the fixed cost interval;
[0074] Obtain the data point distribution diagram corresponding to all fixed cost intervals to obtain the target coordinate diagram.
[0075] In one implementation, through Z-score standardization, data in different fixed cost ranges can be unified to the same scale, eliminating dimensional differences between different data sets, making comparison and analysis of data more intuitive and reliable.
[0076] In one implementation, by standardizing and visualizing the data, analysis errors caused by differences in data scales can be reduced. After standardization, the position of the data points in the graph can truly reflect the actual cost of the equipment throughout its life cycle, thereby improving the accuracy of the analysis.
[0077] In one implementation, the mean and standard deviation of the fixed cost of all data points in the aggregated data set are calculated. For each data point, its difference from the mean is calculated and divided by the standard deviation to obtain the standardized value of the data point. The visualization tool can be Matplotlib, Seaborn, Plotly, Bokeh, etc.
[0078] In one embodiment, mapping the fixed cost of the bidding device to the target coordinate map to determine the impact weight of the fixed cost given by the bidding device includes:
[0079] Determine the corresponding data point distribution area in the target coordinate graph according to the fixed cost of the bidding equipment to obtain the target area;
[0080] Obtaining a first target clustering data set and a second target clustering data set corresponding to the target area, and normalizing the first target clustering data set, the second target clustering data set, and the fixed cost of the bidding equipment by using a Z-score to obtain a data point corresponding to the fixed cost of the bidding equipment to obtain a target point;
[0081] Map the target point to the target area, take the target point as the center and the preset length as the radius to obtain a target circle, obtain the number of first-type data points and the number of second-type data points in the target circle to obtain the number of points in the first circle and the number of points in the second circle;
[0082] The first ratio is obtained by calculating the ratio of the number of points in the first circle in the target circle to all the first type of data points in the target area; the second ratio is obtained by calculating the ratio of the number of points in the second circle in the target circle to all the second type of data points in the target area;
[0083] The first ratio and the second ratio are added to obtain the target ratio, and the first ratio is divided by the target ratio to obtain the impact weight of the fixed cost given by the bidding equipment.
[0084] In one implementation, by mapping the fixed cost of the bidding equipment to the target area and calculating the corresponding data point distribution, the relative position and impact of the equipment within a specific cost range can be accurately evaluated, which can more accurately reflect the impact of the equipment's position in the cost space on the overall cost analysis.
[0085] In one implementation, the proportion of data points in the target circle is calculated and the impact weight is determined accordingly, which can ensure that the evaluation process is based on actual data rather than subjective estimates, improve the objectivity and accuracy of the evaluation, and help make more informed decisions. The preset length is determined by the technician.
[0086] In one implementation, by comparing the proportions of different types of data points within the target circle, one can gain in-depth understanding of the cost structure of the equipment and the market competition, and identify the equipment option with the best cost-effectiveness.
[0087] In one embodiment, determining the preset cost of the bidding device according to the impact weight, the fixed cost of the bidding device, the operation and maintenance cost, and the second device data set includes:
[0088] Obtain the operation and maintenance costs in the full life cycle costs of the equipment corresponding to all the second type data points in the target circle to obtain an operation and maintenance cost set, and calculate the average value of all operation and maintenance costs in the operation and maintenance cost set to obtain the historical operation and maintenance cost;
[0089] By formula Get the preset cost of the bidding equipment;
[0090] Among them, Lcc is the preset cost of the bidding equipment, C1 is the fixed cost of the bidding equipment, To influence the weight, C2 is the operation and maintenance cost of the bidding equipment, and C3 is the historical operation and maintenance cost.
[0091] In one implementation, the introduction of impact weights quantifies the impact of fixed costs on preset costs, making the final cost estimate more realistic. This method can take into account the actual impact of fixed costs in the overall cost structure and improve the scientific nature of cost forecasting.
[0092] In one embodiment, scoring each bidding device according to the first array, the second array, and the grade label corresponding to each bidding device to obtain a final score of each device includes:
[0093] Assigning scores to the bidding devices in the first array and the second array respectively according to the ranking to obtain a first score and a second score corresponding to each bidding device;
[0094] For each bidding device, determine a third score corresponding to the bidding device according to the grade label corresponding to the bidding device;
[0095] Corresponding weights are assigned to the first score, second score and third score of each bidding device, and the weighted first score, second score and third score are added together to obtain the final score of the bidding device.
[0096] In one implementation, a first score and a second score are assigned to each bidding device according to the ranking, so that the scoring process is based on objective ranking results, avoiding the bias caused by subjective evaluation, and helping to improve the fairness and transparency of the scoring.
[0097] In one implementation, various aspects of the score are integrated into the final score through weighted synthesis, which helps to make more scientific and rational decisions and ensures that all important factors are fully considered, thereby improving the accuracy of the decision.
[0098] In one implementation, the first scores of all equipment are given the same weight, the second scores of all equipment are given the same weight, and the third scores of all equipment are given the same weight. The grade label corresponding to the bidding equipment is determined by the bidder's supply performance, bidding response, key technical parameters, component materials, resource strength, and quality control, as follows: All the supply performance of the bidder is sorted from large to small and divided into 3 grades, with scores of 15, 13, 11, and 10 points respectively. The top 10% gets 15 points, 10%-30% (including 30%) gets 13 points, 30%-60% (including 60%) gets 11 points, and the rest get 10 points. The overall response of the bid is good, the technical parameters are complete and complete in the bidding documents, the technical deviation table is carefully filled in or clearly states that there is no technical deviation, there is a quality commitment letter, and the test report is complete, 5 points; if the technical deviation is not clearly stated, other materials have 3 points, and the test report is incomplete, 2 points. If the load loss of the distribution transformer is better than the purchase standard value by more than 10%, 5 points will be awarded, if it is better than the purchase standard value by 5%-10% (inclusive), 3 points will be awarded, and if it is better than the purchase standard value by less than 5% (inclusive), 2 points will be awarded. If the no-load loss is better than the purchase standard value by more than 20%, 10 points will be awarded, if it is better than the purchase standard value by 10%-20% (inclusive), 8 points will be awarded, and if it is better than the purchase standard value by less than 10% (inclusive), 5 points will be awarded. If the noise level is better than the purchase standard value by more than 12dB (inclusive), 5 points will be awarded, if it is better than the purchase standard value by 8 (inclusive)-12dB, 3 points will be awarded, and if it is better than the purchase standard value by less than 8dB, 2 points will be awarded. Materials of key components: core materials, copper conductors, insulating oil, etc. are selected from well-known brands (international and domestic top 500 companies, listed companies, and companies with good quality recognized by the majority of the society), and the overall product is verified to be of excellent and reliable quality through operation, 15 points will be awarded; if the above-mentioned component materials are mainly selected from well-known brands, and some are selected from general brand professional manufacturers, 10 points will be awarded; if the above-mentioned component materials are selected from well-known brands in small quantities, and mainly from general brand professional manufacturers, 5 points will be awarded. Other component materials: tap changers, pressure relief valves, high and low insulators, oil tanks, etc., use well-known brands (international and domestic top 500 companies, listed companies, and companies with good quality recognized by the majority of the society) with advanced technology and mature operation, and get 5 points; the above component materials use general brand professional manufacturers' products, and are reliable and available products, get 3 points. If the variety, quantity and precision of the installed equipment are all better than the manufacturing capacity requirements, 15 points will be awarded; if it meets all manufacturing capacity requirements and the main tooling is better than the manufacturing capacity requirements, 10 points will be awarded; if the variety, quantity and precision basically meet the product manufacturing needs, 5 points will be awarded. Advanced equipment, scientific and standardized methods, and the ability to test all type test items will be awarded 10 points; excellent equipment, rigorous and standardized methods, meeting the needs of factory testing, and the ability to test some type test items will be awarded 8 points.If the enterprise has closed workshops and cleanrooms, and the temperature, humidity, lighting and dust reduction of the production environment are all better than those specified in national or industry regulations, and the production plant area is more than 60,000 square meters, 10 points will be awarded; if the enterprise has closed workshops and cleanrooms, and the temperature, humidity, lighting and dust reduction of the production environment are all in line with national or industry regulations, and the production plant area is between 12,000 square meters and 60,000 square meters, 8 points will be awarded; if the enterprise can only meet the general manufacturing capacity needs and the production plant area is less than 12,000 square meters, 5 points will be awarded. If the design software and design drawings are all independently developed, and the enterprise has achieved several important application results for improving the quality of the bidding products through the combination of industry, academia and research, and is a high-efficiency energy-saving transformer promotion enterprise of the national energy-saving product benefit project (or the product has the China energy-saving product certification), 10 points will be awarded; if the design drawings and part of the design software are independently developed, but the main design software is introduced, 8 points will be awarded; if the design drawings or design software are all purchased, 5 points will be awarded. The corresponding grade labels of the bidding equipment corresponding to the bidding enterprises whose total scores are in the top 10% (inclusive) are recorded as first grade labels, the grade labels corresponding to the bidding enterprises whose total scores are in the top 10%-30% (inclusive) are recorded as second grade labels, the grade labels corresponding to the bidding equipment corresponding to the bidding enterprises whose total scores are in the top 30%-60% (inclusive) are recorded as third grade labels, and the remaining grade labels are recorded as fourth grade labels.
[0099] In one implementation, for the first data group and the second data group, the device corresponding to the first data is recorded as 100 points, and the device corresponding to the last data is recorded as 0, and the points are assigned in sequence according to the linear relationship. For example, when there are 6 devices, the device ranked first in the data group is 100 points, followed by 80 points, 60 points, 40 points, 20 points and 0 points respectively; if there are devices with the same ranking, such as the second device and the third device have the same ranking, the points assigned are 100 points for the first device, followed by 80 points, 80 points, 40 points, 20 points and 0 points respectively; the points assigned for the grade labels are 100 points for the first grade label, 75 points for the second grade label, 50 points for the third grade label, and 30 points for the fourth grade label; the scores of all bidding equipment in the first data group and the second data group are obtained, the score of the first data group accounts for 50%, the score of the second data group accounts for 30%, and the score of the grade label accounts for 20%, and the final score of the bidding equipment is obtained, and the highest score is used as the bid. If the scores are the same, the highest score in the first data group is obtained.
[0100] In one embodiment, the fixed costs include purchase cost, installation cost, commissioning cost, training cost, and removal cost.
[0101] In one implementation, the operation and maintenance costs include equipment operation energy costs, equipment maintenance labor costs, and equipment failure repair costs.
[0102] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.
Claims
1. A method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy, characterized in that: The method comprises: Obtaining an attribute tag corresponding to a target device, and searching for device data of a corresponding type of device in a first preset database and a second preset database respectively according to the attribute tag to obtain a first data set and a second data set; the target device is a power grid device to be tendered in a tendering device area; the device data includes a fixed cost, an operation and maintenance cost, and a grade tag of the device; According to the fixed costs in the first data set and the second data set, the first data set and the second data set are mapped to the same cost interval map to obtain a target coordinate map; Obtaining bidding equipment data given by each bidding equipment, the bidding equipment data including fixed cost, operation and maintenance cost and grade label, mapping the fixed cost of the bidding equipment to the target coordinate map to determine the impact weight of the fixed cost given by the bidding equipment; Determine the preset cost of the bidding equipment according to the impact weight, the fixed cost, the operation and maintenance cost of the bidding equipment and the second equipment data set; The proportionality coefficient is obtained according to the ratio of fixed cost and operation and maintenance cost in the given bidding assets of each bidding equipment; All bidding devices are sorted according to preset costs to obtain a first array, and all bidding devices are sorted according to proportional coefficients to obtain a second array; Each bidding device is scored according to the first array, the second array and the grade label corresponding to each bidding device to obtain a final score of each device, and the bidding device with the highest final score is obtained as the bidding device.
2. A method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy according to claim 1, characterized in that: After searching the first preset database and the second preset database for device data of corresponding types of devices according to the attribute tag to obtain the first data set and the second data set, the following further comprises: Using a K-means algorithm and a fixed cost, the data in the first data set and the second data set are clustered to obtain a plurality of first clustered data sets and a plurality of second clustered data sets; the data clustering is based on a fixed cost interval; and the number of clustered data sets is determined by the fixed cost interval; For each cluster data set, it is identified whether there is missing data in the cluster data set. If there is missing data, the missing data is modified according to the average value calculated in the cluster data set.
3. A method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy according to claim 2, characterized in that: According to the fixed costs in the first data set and the second data set, mapping the first data set and the second data set to the same cost interval map to obtain a target coordinate map includes: For all first clustered data sets and all second clustered data sets in the first data set and the second data set; Aggregating the first clustering data set and the second clustering data set within each fixed cost interval to obtain an aggregated data set; All data in the aggregated data set are standardized by Z-score to obtain a standard data point set; the standard data point set includes a first type of data point and a second type of data point, the first type of data point is a data point generated by the first clustered data set, and the second type of data point is a data point generated by the second clustered data set; each data point corresponds to a full life cycle cost of a device; Map all data points in the standard data point set to the cost interval map through a visualization tool to obtain a data point distribution map of all data in the fixed cost interval; Obtain the data point distribution diagram corresponding to all fixed cost intervals to obtain the target coordinate diagram.
4. A method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy according to claim 3, characterized in that: Mapping the fixed cost of the bidding equipment to the target coordinate map to determine the influence weight of the fixed cost given by the bidding equipment includes: Determine the corresponding data point distribution area in the target coordinate graph according to the fixed cost of the bidding equipment to obtain the target area; Acquire a first target clustering data set and a second target clustering data set corresponding to the target area, and standardize the first target clustering data set, the second target clustering data set, and the fixed cost of the bidding equipment by using the Z-score to obtain a data point corresponding to the fixed cost of the bidding equipment to obtain a target point; Mapping the target point to the target area, taking the target point as the center and a preset length as the radius to obtain a target circle, obtaining the number of first-type data points and the number of second-type data points in the target circle to obtain the number of points in the first circle and the number of points in the second circle; The first ratio is obtained by calculating the ratio of the number of points in the first circle in the target circle to all the first type of data points in the target area; the second ratio is obtained by calculating the ratio of the number of points in the second circle in the target circle to all the second type of data points in the target area; The first ratio and the second ratio are added to obtain a target ratio, and the first ratio is divided by the target ratio to obtain an impact weight of the fixed cost given by the bidding equipment.
5. A method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy according to claim 4, characterized in that: Determining the preset cost of the bidding equipment according to the impact weight, the fixed cost, the operation and maintenance cost of the bidding equipment, and the second equipment data set includes: Obtaining the operation and maintenance costs in the full life cycle costs of the equipment corresponding to all the second type data points in the target circle to obtain an operation and maintenance cost set, and calculating the average value of all the operation and maintenance costs in the operation and maintenance cost set to obtain the historical operation and maintenance cost; By formula Get the preset cost of the bidding equipment; Among them, Lcc is the preset cost of the bidding equipment, C1 is the fixed cost of the bidding equipment, To influence the weight, C2 is the operation and maintenance cost of the bidding equipment, and C3 is the historical operation and maintenance cost.
6. The method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy according to claim 1 is characterized in that: Scoring each bidding device according to the first array, the second array, and the grade label corresponding to each bidding device to obtain a final score of each device includes: Assigning scores to the bidding devices in the first array and the second array respectively according to the ranking to obtain a first score and a second score corresponding to each bidding device; For each bidding device, determine a third score corresponding to the bidding device according to the grade label corresponding to the bidding device; Corresponding weights are assigned to the first score, second score and third score of each bidding device, and the weighted first score, second score and third score are added together to obtain the final score of the bidding device.
7. The method for evaluating and analyzing the LCC cost of power grid assets based on the LCC bidding strategy according to claim 1 is characterized in that: The fixed costs include purchase cost, installation cost, commissioning cost, training cost and disassembly cost.