CN112907154A - Power grid physical asset input-output evaluation method - Google Patents
Power grid physical asset input-output evaluation method Download PDFInfo
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- CN112907154A CN112907154A CN202110392876.5A CN202110392876A CN112907154A CN 112907154 A CN112907154 A CN 112907154A CN 202110392876 A CN202110392876 A CN 202110392876A CN 112907154 A CN112907154 A CN 112907154A
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Abstract
The invention relates to a power grid physical asset input-output evaluation method, which comprises the following steps: determining the data source of each physical asset according to the evaluation range of the physical asset; constructing a physical asset analysis and evaluation index system, defining physical asset analysis dimensions, and defining and calculating methods of each index; and unifying the dimension values of the multi-source data, and calculating each relevant index to obtain an evaluation result. Compared with the prior art, the method can efficiently and accurately analyze the input and output benefits of the physical assets of the power grid so as to improve the quality of physical asset management.
Description
Technical Field
The invention relates to the technical field of power grid asset management, in particular to a power grid physical asset input-output evaluation method.
Background
The power grid enterprise is a natural enterprise with dense assets and technology, the number of physical devices is large, the variety is large, and the complexity of construction, operation and maintenance management is high. The power grid physical assets mainly comprise overhead power transmission lines, cable power transmission lines, transformers, current conversion equipment, distribution lines, power distribution equipment, automatic control equipment, instruments and meters, production management tools, transportation equipment, auxiliary production equipment and instruments, houses, buildings and other related assets. In the power grid enterprise asset structure, the proportion of the real asset is up to more than 80%.
With the increasing investment of power grids, the scale of the physical assets of power grid enterprises is continuously increased, and the enhancement and the improvement of the management level of the physical assets of the power grids become important means for realizing scientific and orderly planning and development of the power grids, guaranteeing the safe operation of the power grids and improving the utilization efficiency of the assets. Under the background that the management informatization level is rapidly improved and the economy is more and more emphasized, the conversion from the traditional equipment management which only emphasizes the technical attributes to the physical asset management which comprehensively considers the value and the technical benefits is urgently needed to be realized.
Traditional power grid physical asset management mostly adopts the mode of manual account and card establishment, and fixed asset checking can only adopt to derive the checking list from financial department fixed asset card system, carries out checking to the physical object according to the checking list again, causes that work efficiency is low, intensity of labour is big, the data accuracy is lower, along with the very fast development of power grid technology, the electric power enterprise also can receive the multiple factor restriction, leads to the asset input and output level lower, is unfavorable for promoting asset efficiency quality.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a power grid physical asset input-output evaluation method to efficiently and accurately analyze the power grid physical asset input-output benefits so as to improve the physical asset management quality.
The purpose of the invention can be realized by the following technical scheme: a power grid physical asset input-output evaluation method comprises the following steps:
s1, determining the data source of each physical asset according to the evaluation range of the physical asset;
s2, constructing a physical asset analysis and evaluation index system, defining physical asset analysis dimensions, and defining and calculating methods of each index;
and S3, unifying the dimension values of the multi-source data, and calculating each relevant index to obtain an evaluation result.
Further, the evaluation range of the physical assets includes ten types of physical assets, specifically: the system comprises a power transmission line, power transformation equipment, distribution lines and equipment, communication lines and equipment, automatic control equipment and instruments, production management tools, transportation equipment, auxiliary production equipment and tools, houses and buildings.
Further, the data source of the physical asset includes a PMS (power production management system), an ERP (enterprise resource planning), a TMS (communication management system), a financial statement, a regulation and control system, a marketing system, and an electric energy quality on-line monitoring system.
Further, the physical asset analysis and evaluation index system comprises an evaluation dimension, an evaluation index and an index calculation formula.
Further, the evaluation dimension comprises power grid development investment, asset cost, asset safety, asset quality, asset efficiency and asset benefit, wherein the power grid development investment is the sum of all expenses invested to meet the power transmission and distribution capacity in a certain period and is used for evaluating the fund utilization efficiency and the asset management level;
the asset cost comprises a power transmission and distribution cost index for evaluating the asset management level, a unit asset power transmission and distribution cost index, a unit electricity sales power transmission and distribution cost index and a unit newly increased capacity cost index; and depreciation cost indicators for evaluating the aging degree of the assets;
the asset safety comprises an equipment failure rate index and a forced outage rate index for evaluating the asset safety level;
the asset quality comprises a power supply reliability index, an electric energy quality index and a facility reliability index;
the asset efficiency comprises an electricity selling quantity index, a load rate index, an asset use state index and a spare part index;
the asset benefits include a net asset profitability index and an economic incremental value index.
Further, the power transmission and distribution cost index and the depreciation cost index both directly acquire data from the ERP;
the unit asset power transmission and distribution cost indexes are specifically as follows:
unit asset transmission and distribution cost is equal to current year transmission and distribution cost/current year average asset original value
The average asset original value of the current year is (original asset value at the end of the previous year + original asset value at the end of the current year)/2
The unit electricity selling quantity power transmission and distribution cost indexes are specifically as follows:
the power transmission and distribution cost per unit electricity sale is equal to the current year power transmission and distribution cost/current year electricity sale cost
The unit newly increased capacity cost index is specifically as follows:
and the unit new capacity cost is the original value of the new assets/the new capacity in the current year.
Further, the equipment failure rate index is specifically:
the failure rate of the equipment is equal to the failure times of the equipment in the current year/total number of the equipment multiplied by 100 percent
The forced outage rate index specifically comprises:
the forced equipment outage rate is the forced equipment outage times in the same year/the total equipment in the same year/100.
Further, the power supply reliability index comprises power supply reliability, power failure frequency and number of the users in power failure, the power supply reliability is directly obtained from the power quality on-line monitoring system, and the power failure frequency specifically comprises:
power failure number of users ═ sigma number of power failure users at each time
The number of the households in the power failure is as follows:
power failure time house number ═ sigma (duration of each power failure x number of users per power failure)
The electric energy quality index is specifically a voltage qualified rate, and the voltage qualified rate is directly obtained from an electric energy quality online monitoring system;
the facility reliability index is specifically a power transmission and transformation availability coefficient which is directly obtained from an electric energy quality online monitoring system.
Further, the electricity sales volume index includes a total annual electricity sales volume, a unit asset electricity sales volume, a unit newly added asset electricity sales volume, a unit capacity electricity sales volume and a unit newly added capacity electricity sales volume, the total annual electricity sales volume is directly obtained from the marketing system, and the unit asset electricity sales volume specifically includes:
unit asset selling electricity quantity is selling electricity quantity/average asset original value
Average asset origin value (initial asset origin value + end asset origin value)/2
The unit newly-increased asset electricity selling amount specifically comprises the following steps:
new electricity selling quantity (electricity selling quantity in the same year-electricity selling quantity in last year)/new original value of the new assets in the same year
The unit capacity electricity selling amount is specifically as follows:
unit capacity selling electricity quantity is selling electricity quantity/current year average main transformer capacity
Average main transformer capacity (initial main transformer capacity + end main transformer capacity)/2
The unit newly increased capacity electricity selling amount specifically comprises the following steps:
the load rate index is specifically the annual average load rate of the main transformer, and the annual average load rate of the main transformer is obtained from a regulation and control system;
the asset use state index is specifically an asset transport rate, and the asset transport rate is specifically:
the total original value of the assets at the end of the transport period/the total original value of the assets at the end of the transport period is multiplied by 100 percent
The spare part index is specifically spare part turnover rate, and spare part turnover rate specifically is:
the spare part turnover rate is 3 years and less, and the number of spare parts/the total number of spare parts is multiplied by 100%.
Further, the net asset profitability index and the economic added value index are both directly obtained from the financial statement.
Compared with the prior art, the invention has the following advantages:
according to the invention, the evaluation range of the physical assets of the power grid is set to ten types of physical assets, the current PMS, ERP, TMS, financial statements, a regulation and control system, a marketing system and an electric energy quality online monitoring system are combined, and a physical asset analysis and evaluation index system is constructed, so that the acquisition efficiency and accuracy of data sources can be ensured, the accuracy of the statistical results of important physical asset index information in the physical asset evaluation statements and reports of the power grid enterprise is improved, the uncertainty of manual operation is avoided, and the purpose of efficiently and accurately analyzing the input-output benefits of the physical assets can be realized.
The invention is based on big data storage and analysis technology to realize the acquisition and analysis of mass data, realizes the automation of the analysis and evaluation process of the physical assets by an informatization means, reduces the working difficulty, improves the working efficiency and the analysis accuracy, improves the working efficiency of the physical asset management information statistics and analysis, saves a large amount of manpower, realizes the monitoring and management of the main physical assets of the power grid in a normalized, real-time, normalized and informatization manner, and provides powerful quantitative data support for the formulation of the working decision of the physical asset management.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic illustration of fixed asset investment data in an embodiment;
FIG. 3 is a schematic diagram of power transmission and distribution cost data in an embodiment;
FIG. 4 is a schematic diagram of unit asset power transmission and distribution cost data in an embodiment;
FIG. 5 is a schematic diagram illustrating the power transmission and distribution cost data of a unit electricity sales amount in the embodiment;
FIG. 6 is a diagram illustrating depreciation cost data in an embodiment;
FIG. 7 is a schematic diagram of equipment failure rate data in an embodiment;
FIG. 8 is a graph of forced outage rate data for an embodiment;
FIG. 9 is a schematic diagram of power supply reliability data in an embodiment;
FIG. 10 is a graph illustrating voltage yield data for an example embodiment;
FIG. 11 is a schematic diagram showing the unit property electricity sales data in the embodiment;
FIG. 12 is a graph illustrating transformer load rate data for an exemplary embodiment;
FIG. 13 is a schematic illustration of asset rate data in an embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, a power grid physical asset input-output evaluation method includes the following steps:
s1, determining the data source of each physical asset according to the evaluation range of the physical asset, wherein the evaluation range of the physical asset comprises ten types of physical assets, and specifically comprises the following steps: the system comprises a power transmission line, power transformation equipment, power distribution lines and equipment, communication lines and equipment, automatic control equipment and instruments, production management tools, transportation equipment, auxiliary production equipment and tools, houses and buildings;
the data source of the physical assets comprises a PMS, an ERP, a TMS, a financial statement, a regulation and control system, a marketing system and an electric energy quality on-line monitoring system;
s2, constructing a physical asset analysis and evaluation index system, defining physical asset analysis dimensions, and defining and calculating methods of each index;
and S3, unifying the dimension values of the multi-source data, and calculating each relevant index to obtain an evaluation result.
According to the invention, analysis and evaluation are carried out according to six dimensions of power grid development investment, asset cost, safety, quality, efficiency and benefit. Based on basic index data such as power transmission and distribution cost, unit capacity increasing cost, equipment failure rate, forced outage rate, power supply reliability, voltage qualification rate, power transmission and transformation availability factor, electricity sales quantity, main transformer average load rate, asset use state, spare part turnover rate, net asset profitability, economic added value and the like, the input-output benefit change trend and reasons of different regions and different years are analyzed through index parameters such as proportion, growth rate and the like.
And (3) by combining environmental factors, based on enterprise related requirements and rationality principles, judging and evaluating the degree of realizing the asset management target and the capability of guaranteeing the degree of realizing the asset management target, predicting risks in aspects of asset input-output efficiency, demand-capability matching, capability guarantee, rationality and effectiveness of related strategies and the like, and proposing suggestions.
The physical asset analysis and evaluation index system comprises an evaluation dimension, an evaluation index and an index calculation formula. The power grid development investment is the sum of all expenses invested for meeting the power transmission and distribution capacity in a certain period, including basic construction, technical transformation, sporadic purchase, major repair projects, others, parts, research and development expenses, management and consultation expenses, education and training, equity investment and the like, and can be obtained from an annual comprehensive plan. FIG. 2 shows the data of the fixed asset investment of each province company from 2015 to 2018, wherein the total fixed asset investment of each province company from 2015 to 2018 is 20134 billion yuan, wherein the investment of each province company is 18301.5 billion yuan, which accounts for 90.9% of the total investment. In provincial companies, the highest cumulative investment in four years is Shandong (1879.5 Yi Yuan), Jiangsu (1525 Yi Yuan), Zhejiang (1306.6 Yi Yuan) and Henan (1149.7 Yi Yuan), and the lowest is Jilin (257.7 Yi Yuan), Heilongjiang (265.8 Yi Yuan), Qinghai (281.2 Yi Yuan) and Ningxia (303 Yi Yuan). It can be seen from the figure that the investment of fixed assets of most provincial companies is mainly influenced by the quantity of electricity sold, and other factors are also influenced by national policies, extra-high voltage required by company strategy, rural power grid transformation, power distribution network construction and the like.
From 2015 to 2018, the cumulative investment of fixed assets of provincial companies is 52.8% of the original value of assets in 2015, wherein the highest proportion is Xinjiang (112.4%), Tibet (109.3%), Mongolian (83.7%) and Jibei (81.3%), and the lowest proportion is Shanghai (34.5%), Jiangsu (34.1%) and Liaoning (31.9%). The fixed asset investment of provincial companies is basically consistent with the new asset, wherein the new asset scale of the North Ji company in four years is only 25.6% of the original asset value in 2015, and the difference between the new asset scale and the fixed asset investment (81.3%) in four years is 55.7%.
Aspects of asset cost include:
analysis of power transmission and distribution costs — index data includes power transmission and distribution costs (including major repair operation and maintenance costs, depreciation costs, material costs, and repair costs), unit asset power transmission and distribution costs, unit electricity sales power transmission and distribution costs, and the like. The factors influencing the power transmission and distribution price are analyzed from the dimensions of different years, different regions and different asset types through index data such as proportion, year-round trend change and the like, the differences of the power transmission and distribution cost level and different dimensions are analyzed and evaluated in combination with the supervision requirement of the power transmission and distribution cost, the key field and link are determined, and optimization suggestions are provided. The power transmission and distribution cost is characterized in that the total power transmission and distribution cost, the cost composition proportion and the variation trend of each region are analyzed through statistics, the reason of variation is analyzed, the variation trend of the power grid assets is combined for comparative analysis, and both the power transmission and distribution cost index and the depreciation cost index directly acquire data from the ERP. And analyzing the change of cost management after the verification of the power transmission and distribution price by utilizing the power transmission and distribution cost difference and combining the national power transmission and distribution price cost supervision requirement. In the power transmission and distribution cost, the material cost and the repair cost are analyzed through statistics, the repair cost and the component proportion of the material cost, the proportion of the material cost to the repair cost relative to the assets, the proportion of the material cost to the repair cost in the power transmission and distribution cost, the statistics of the repair cost proportions of different types of assets and the variation trend, the reasons generated by variation are analyzed, and the decision support is provided for the technical improvement and major repair of a company; the major repair operation and maintenance cost is obtained by analyzing the major repair operation and maintenance cost (including daily overhaul and production major repair), the major repair operation and maintenance cost of unit assets and the change trend through statistics, analyzing the reasons of major repair operation and maintenance cost change (such as policy change of external factors, local supervision and the like, equipment state in enterprises, power consumption change and the like), and knowing the resource investment condition required by the assets in the operation and maintenance stage.
Fig. 3 is a schematic diagram of annual change data of transmission and distribution costs in an embodiment, wherein annual transmission and distribution costs of provincial companies are increased from 6070.38 billion to 8342.12 billion in 2014 to 2018, the annual compound growth rate is 8.27%, the growth rate is lower than 10.21% of grid assets, and the annual transmission and distribution costs and the grid assets keep a synchronous ascending trend.
In 2014 to 2018, the transmission and transformation cost of the unit assets of the provincial companies is reduced from 0.194 (yuan/yuan) to 0.180 (yuan/yuan), and the annual compound growth rate is-1.89%. After depreciation cost is eliminated, the power transmission and transformation cost of the unit asset is reduced from 0.134 (yuan/yuan) to 0.121 (yuan/yuan), the annual composite growth rate is-2.53 percent, and the power transmission and transformation cost control level is improved year by year.
In 2014 to 2018, the power transmission and transformation cost of unit electricity sales of provincial companies is increased from 0.178 (yuan/kWh) to 0.20 (yuan/kWh), and the annual compound growth rate is 2.90%.
In 2018, in the power transmission and distribution cost of provincial companies, the depreciation cost accounts for 32.8%, the overhaul cost accounts for 15.2% (wherein the material cost is 5.7%, and the overhaul cost is 9.5%) and the other cost accounts for 52.0%.
The unit asset power transmission and distribution cost is obtained by analyzing the unit asset power transmission and distribution cost through statistics and analysis, and analyzing the variation trend, the regional difference and the reasons thereof, wherein the unit asset power transmission and distribution cost indexes are as follows:
unit asset transmission and distribution cost is equal to current year transmission and distribution cost/current year average asset original value
The average asset original value of the current year is (original asset value at the end of the previous year + original asset value at the end of the current year)/2
Fig. 4 is a schematic diagram of the transmission and distribution cost data of the unit assets in the embodiment, in 2018, the transmission and distribution cost of the unit assets of the company is 0.18 yuan/yuan, wherein the highest provinces are shanxi (0.42 yuan/yuan) and Sichuan (0.33 yuan/yuan), and the lowest provinces are Tibet (0.06 yuan/yuan), Shanghai (0.10 yuan/yuan), Mongolian (0.12 yuan/yuan), Beijing (0.14 yuan/yuan) and Tianjin (0.14 yuan/yuan).
The annual compound growth rate of the power transmission and distribution cost of companies is 8.27% from 2014 to 2018, wherein the provinces of the companies are Shandong (26.2%), Tibet (23.1%) and Beijing (14.7%) at the highest, and Liaoning (1.2%) and Jibei (2.6%) at the lowest.
The unit electricity selling and distributing cost is obtained by analyzing the electricity selling and distributing cost of the unit by statistics, analyzing the variation trend, the regional difference and the reason thereof, and analyzing the cost space under the power transmission and distribution price policy by combining the electricity purchasing income and the electricity selling income. The unit electricity selling quantity power transmission and distribution cost index specifically comprises:
the power transmission and distribution cost per unit electricity sale is equal to the current year power transmission and distribution cost/current year electricity sale cost
Fig. 5 is a schematic diagram of unit electricity sales data in the embodiment, in 2018, the unit electricity sales power transmission and distribution cost of the company is 0.197 yuan/kWh, wherein the highest is tibet (0.581 yuan/kWh) and sichuan (0.402 yuan/kWh), and the lowest is Qinghai (0.126 yuan/kWh) and Shanxi (0.133 yuan/kWh). The company unit sales electricity transmission and distribution cost (without depreciation) is 0.132 yuan/kWh, the highest of which is Sichuan (0.334 yuan/kWh) and Shanxi (0.299 yuan/kWh), and the lowest of which is Qinghai (0.075 yuan/kWh), Shanxi (0.084 yuan/kWh) and Tianjin (0.095 yuan/kWh).
The annual compound increasing rate of unit electricity sales of provincial companies from 2014 to 2018 is 2.95%, wherein the fastest is Shandong (33.07%) and Beijing (13.41%), and the lowest is Chongqing (-4.12%) and Fujian (-4.82%). The annual compound increase rate of the unit power selling and power transmission and distribution cost (without depreciation) of a company is 2.27%, wherein the fastest are Shandong (19.55%), Henan (10.80%) and Beijing (8.90%), and the lowest are Chongqing (-3.16%) and Xinjiang (-2.79%).
The unit newly increased capacity cost is the condition of statistically analyzing the unit newly increased capacity cost, analyzing the change trend, the regional difference and the reason thereof and evaluating the resource utilization efficiency during asset allocation. The cost index of unit newly increased capacity is specifically as follows:
and the unit new capacity cost is the original value of the new assets/the new capacity in the current year.
The depreciation cost analysis is a method and a strategy for analyzing depreciation cost and change trend of each type of asset in the current year in service life, analyzing factors influencing the depreciation cost, considering reasonable purchase of fixed assets and reducing the depreciation cost. Fig. 6 is a schematic diagram of depreciation cost data in the embodiment, wherein the depreciation cost scale of provincial companies is increased from 1882 million yuan to 2735 million yuan in 2014 to 2018, and the annual average composite growth rate is 9.80%. For 5 years, the proportion of relative assets is reduced from 6.01% to 5.89%, and the proportion of the relative assets in the power transmission and distribution cost is increased from 27.17% to 32.78%.
The asset safety comprises an equipment failure rate index and a forced outage rate index which are used for evaluating the asset safety level, wherein the equipment failure rate index specifically comprises the following steps:
the failure rate of the equipment is equal to the failure times of the equipment in the current year/total number of the equipment multiplied by 100 percent
The forced outage rate index is specifically as follows:
the forced equipment outage rate is the forced equipment outage times in the same year/the total equipment in the same year/100.
Fig. 7 and 8 are data schematic diagrams of equipment failure rate and forced outage rate in the embodiment, and the failure rate of the company transmission and distribution assets is reduced from 0.17% to 0.09% from 2014 to 2018. The failure frequency is up to 2014 years, and the frequency is 32261. The minimum number is 2017, and the number of faults is 23250. The frequency of equipment faults in the last five years can be found, the frequency of the power distribution equipment faults is prominent, and the overall fault rate of transmission and distribution is reduced year by year.
In 2014 to 2018, the forced outage rate of the power transmission equipment is between 0.0473 times/hundred kilometers/year and 0.033 times/hundred kilometers/year. The maximum forced outage number is 2018 and 635. The lowest is 2014, 428 times.
In 2014 to 2018, the forced outage rate of the power transformation equipment is between 0.0064 times/hundred machines/year and 0.0036 times/hundred machines/year. The forced outage times are 2016 and 403, respectively. The lowest is 2018, 275 times.
The asset quality comprises a power supply reliability index, an electric energy quality index and a facility reliability index, the power supply reliability index comprises a power supply reliability rate, a power failure household number and a power failure household number, the power supply reliability rate is directly obtained from an electric energy quality on-line monitoring system, and the power failure household number specifically comprises:
power failure number of users ═ sigma number of power failure users at each time
The number of households in power failure is as follows:
power failure time house number ═ sigma (duration of each power failure x number of users per power failure)
The electric energy quality index is specifically a voltage qualification rate, and the voltage qualification rate is directly obtained from an electric energy quality on-line monitoring system;
the facility reliability index is specifically a power transmission and transformation availability coefficient which is directly obtained from an electric energy quality on-line monitoring system.
Fig. 9 is a schematic diagram of power supply reliability data in the embodiment, in 2018, the company city power supply reliability is 99.955%, and the rural power supply reliability is 99.795%.
In 2014 to 2018, the reliability of power supply in cities and rural areas of companies is reduced and increased firstly, the highest reliability is 99.967% and 99.878% in 2014, and the lowest reliability is 99.946% and 99.782% in 2016. The overall power supply reliability is at a higher level.
Fig. 10 is a schematic diagram of voltage qualification rate data in the embodiment, wherein the comprehensive voltage qualification rates in cities and rural areas of a company are increased year by year from 2014 to 2018, the highest voltage qualification rates in 2018 reach 99.995% and 99.752%, and the overall voltage qualification rate is at a higher level.
The asset efficiency comprises an electricity sales quantity index, a load rate index, an asset use state index and a spare part index, the electricity sales quantity index comprises annual total electricity sales quantity, unit asset electricity sales quantity, unit newly increased asset electricity sales quantity, unit capacity electricity sales quantity and unit newly increased capacity electricity sales quantity, the annual total electricity sales quantity is directly obtained from a marketing system, and the unit asset electricity sales quantity specifically comprises the following components:
unit asset selling electricity quantity is selling electricity quantity/average asset original value
Average asset origin value (initial asset origin value + end asset origin value)/2
The unit newly-increased asset electricity selling quantity specifically comprises the following steps:
new electricity selling quantity (electricity selling quantity in the same year-electricity selling quantity in last year)/new original value of the new assets in the same year
The specific electricity selling amount per unit capacity is as follows:
unit capacity selling electricity quantity is selling electricity quantity/current year average main transformer capacity
Average main transformer capacity (initial main transformer capacity + end main transformer capacity)/2
The unit newly increased capacity electricity selling amount is specifically as follows:
new electricity selling quantity per new unit capacity (electricity selling quantity in the same year-electricity selling quantity in last year)/new capacity in the same year
The load rate index is specifically the annual average load rate of the main transformer, and the annual average load rate of the main transformer is obtained from a regulation and control system;
the asset use state index is specifically an asset transport rate, and the asset transport rate is specifically:
the total original value of the assets at the end of the transport period/the total original value of the assets at the end of the transport period is multiplied by 100 percent
The spare part index is the spare part turnover rate, and the spare part turnover rate is as follows:
the spare part turnover rate is 3 years and less, and the number of spare parts/the total number of spare parts is multiplied by 100%.
Fig. 11 is a data diagram of unit asset electricity sales amount in the embodiment, the unit asset electricity sales amount of the company is in a decreasing trend year by year from 2014 to 2018, the unit asset electricity sales amount is decreased from 1.03 kWh/yuan to 0.85 kWh/yuan, and the unit asset electricity sales amount of the company is decreased by 17.05%, namely 0.18 kWh/yuan in 2018 compared with 2014, and the annual compound growth rate is-4.92%.
In 2018, the electricity sold by a company is 42366.33 hundred million kWh, compared with 34624.78 hundred million kWh in 2014, the electricity sold by the company is increased by 7741.55 hundred million kWh, the electricity is increased by 22.36%, the annual compound growth rate of 5 years is 5.17%, the electricity is far lower than the annual compound growth rate of assets (10.21%), and the asset utilization efficiency and the benefit are reduced year by year.
In 2018, the unit asset electricity sales amount (0.85 kWh/yuan) is basically equal to the newly increased unit asset electricity sales amount (0.87 kWh/yuan), and the investment accuracy of newly increased unit asset power grid assets is gradually improved in nearly 4 years.
Fig. 12 is a graph of transformer load rate data for an example embodiment, and in 2018, most of the transformer asset utilization efficiency was at a lower level. According to value statistics, the load rate of the transformer with the voltage of 220kV or more is 69.90% of the proportion below 40%, and 49.41% of the proportion below 30%; 110kV or less transformer with load rate of 40% or less 65.72%, 30% or less 47.05%.
Fig. 13 is a schematic diagram of the asset in-transit rate data in the embodiment, between 2013 and 2018, the in-transit rate of the company asset is 99.44% -99.09%, wherein the in-transit rate in 2018 is 99.18%, which is slightly reduced compared with 2013. In the three types of assets of the transmission, transformation and distribution, the on-line rate of the transformation assets is low, the on-line rate in 2018 is 98.40%, and the on-line rate is 0.24% lower than 98.64% in 2013.
The asset benefits comprise a net asset profitability index and an economic added value index, and the net asset profitability index and the economic added value index are directly obtained from the financial statement.
As can be seen from the data analysis in the embodiment, in the aspect of cost control, the purchase and sale price difference is not enough to support the power transmission and distribution cost, the existing cost management level is far less than the requirement specified by the state (the operation and maintenance cost is less than or equal to 4.5%), and the new added assets increase the asset operation cost of the company. The cost management level must be improved, the related management cost is reduced, the cost configuration is optimized, the material cost and the overhaul cost of each province have larger proportion difference in the power transmission and distribution cost, the power transmission and distribution cost management must be enhanced, and the cost precision is improved;
in the aspect of asset efficiency, under the condition of considering the safety of a power grid, the existing power grid has the capacity of accommodating part of the electricity quantity increase demand, and the operation cost pressure of an enterprise is increased by adding new assets. Therefore, an asset allocation strategy is proposed and optimized, the investment of newly added power grid assets should be slowed down in the future by provincial companies with low asset utilization efficiency, and otherwise, the investment of a certain scale can be maintained, so that the asset utilization efficiency is improved.
Claims (10)
1. A power grid physical asset input-output evaluation method is characterized by comprising the following steps:
s1, determining the data source of each physical asset according to the evaluation range of the physical asset;
s2, constructing a physical asset analysis and evaluation index system, defining physical asset analysis dimensions, and defining and calculating methods of each index;
and S3, unifying the dimension values of the multi-source data, and calculating each relevant index to obtain an evaluation result.
2. The method for evaluating input and output of a physical asset of a power grid according to claim 1, wherein the evaluation range of the physical asset comprises ten types of physical assets, specifically: the system comprises a power transmission line, power transformation equipment, distribution lines and equipment, communication lines and equipment, automatic control equipment and instruments, production management tools, transportation equipment, auxiliary production equipment and tools, houses and buildings.
3. The method for evaluating the input and output of the physical asset of the power grid as claimed in claim 1, wherein the data source of the physical asset comprises a PMS, an ERP, a TMS, a financial statement, a regulation and control system, a marketing system and an on-line monitoring system of power quality.
4. The method for evaluating input and output of physical assets of the power grid according to claim 3, wherein the physical asset analysis and evaluation index system comprises an evaluation dimension, an evaluation index and an index calculation formula.
5. The method for evaluating the real asset input and output of the power grid according to claim 4, wherein the evaluation dimensions comprise power grid development input, asset cost, asset safety, asset quality, asset efficiency and asset benefit, and the power grid development input is the sum of all expenses input for meeting the power transmission and distribution capacity in a certain period and is used for evaluating the fund utilization efficiency and the asset management level;
the asset cost comprises a power transmission and distribution cost index for evaluating the asset management level, a unit asset power transmission and distribution cost index, a unit electricity sales power transmission and distribution cost index and a unit newly increased capacity cost index; and depreciation cost indicators for evaluating the aging degree of the assets;
the asset safety comprises an equipment failure rate index and a forced outage rate index for evaluating the asset safety level;
the asset quality comprises a power supply reliability index, an electric energy quality index and a facility reliability index;
the asset efficiency comprises an electricity selling quantity index, a load rate index, an asset use state index and a spare part index;
the asset benefits include a net asset profitability index and an economic incremental value index.
6. The method for evaluating the input and output of the physical assets of the power grid according to claim 5, wherein the power transmission and distribution cost index and the depreciation cost index both directly acquire data from an ERP;
the unit asset power transmission and distribution cost indexes are specifically as follows:
unit asset transmission and distribution cost is equal to current year transmission and distribution cost/current year average asset original value
The average asset original value of the current year is (original asset value at the end of the previous year + original asset value at the end of the current year)/2
The unit electricity selling quantity power transmission and distribution cost indexes are specifically as follows:
the power transmission and distribution cost per unit electricity sale is equal to the current year power transmission and distribution cost/current year electricity sale cost
The unit newly increased capacity cost index is specifically as follows:
and the unit new capacity cost is the original value of the new assets/the new capacity in the current year.
7. The method for evaluating input and output of physical assets of a power grid according to claim 5, wherein the equipment failure rate index specifically comprises:
the failure rate of the equipment is equal to the failure times of the equipment in the current year/total number of the equipment multiplied by 100 percent
The forced outage rate index specifically comprises:
the forced equipment outage rate is the forced equipment outage times in the same year/the total equipment in the same year/100.
8. The method for evaluating input and output of physical assets of a power grid according to claim 5, wherein the power supply reliability indexes comprise a power supply reliability, a power failure household number and a power failure household number, the power supply reliability is directly obtained from an electric energy quality online monitoring system, and the power failure household number specifically comprises:
power failure number of users ═ sigma number of power failure users at each time
The number of the households in the power failure is as follows:
power failure time house number ═ sigma (duration of each power failure x number of users per power failure)
The electric energy quality index is specifically a voltage qualified rate, and the voltage qualified rate is directly obtained from an electric energy quality online monitoring system;
the facility reliability index is specifically a power transmission and transformation availability coefficient which is directly obtained from an electric energy quality online monitoring system.
9. The method for evaluating the input and output of physical assets of a power grid according to claim 5, wherein the electricity sales volume indexes comprise annual total electricity sales volume, unit asset electricity sales volume, unit newly added asset electricity sales volume, unit capacity electricity sales volume and unit newly added capacity electricity sales volume, the annual total electricity sales volume is directly obtained from a marketing system, and the unit asset electricity sales volume specifically comprises:
unit asset selling electricity quantity is selling electricity quantity/average asset original value
Average asset origin value (initial asset origin value + end asset origin value)/2
The unit newly-increased asset electricity selling amount specifically comprises the following steps:
new electricity selling quantity (electricity selling quantity in the same year-electricity selling quantity in last year)/new original value of the new assets in the same year
The unit capacity electricity selling amount is specifically as follows:
unit capacity selling electricity quantity is selling electricity quantity/current year average main transformer capacity
Average main transformer capacity (initial main transformer capacity + end main transformer capacity)/2
The unit newly increased capacity electricity selling amount specifically comprises the following steps:
new electricity selling quantity per new unit capacity (electricity selling quantity in the same year-electricity selling quantity in last year)/new capacity in the same year
The load rate index is specifically the annual average load rate of a main transformer, and the annual average load rate of the main transformer is obtained from a regulation and control system;
the asset use state index is specifically an asset transport rate, and the asset transport rate is specifically:
the total original value of the assets at the end of the transport period/the total original value of the assets at the end of the transport period is multiplied by 100 percent
The spare part index is specifically spare part turnover rate, and spare part turnover rate specifically is:
the spare part turnover rate is 3 years and less, and the number of spare parts/the total number of spare parts is multiplied by 100%.
10. The method for evaluating the input and output of the physical asset of the power grid according to claim 5, wherein the net asset profitability index and the economic added value index are both directly obtained from financial statements.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080071588A1 (en) * | 1997-12-10 | 2008-03-20 | Eder Jeff S | Method of and system for analyzing, modeling and valuing elements of a business enterprise |
| US20170124464A1 (en) * | 2015-10-28 | 2017-05-04 | Fractal Industries, Inc. | Rapid predictive analysis of very large data sets using the distributed computational graph |
| CN110428166A (en) * | 2019-07-31 | 2019-11-08 | 国网安徽省电力有限公司 | Power grid physical assets evaluation method based on regressing fitting model |
| CN112132440A (en) * | 2020-09-18 | 2020-12-25 | 国网四川省电力公司经济技术研究院 | Multi-dimensional benefit index analysis method based on DEA theory |
| CN112529418A (en) * | 2020-12-14 | 2021-03-19 | 国网四川省电力公司经济技术研究院 | Power distribution network investment benefit evaluation method based on matter element extension evaluation model |
-
2021
- 2021-04-13 CN CN202110392876.5A patent/CN112907154A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080071588A1 (en) * | 1997-12-10 | 2008-03-20 | Eder Jeff S | Method of and system for analyzing, modeling and valuing elements of a business enterprise |
| US20170124464A1 (en) * | 2015-10-28 | 2017-05-04 | Fractal Industries, Inc. | Rapid predictive analysis of very large data sets using the distributed computational graph |
| CN110428166A (en) * | 2019-07-31 | 2019-11-08 | 国网安徽省电力有限公司 | Power grid physical assets evaluation method based on regressing fitting model |
| CN112132440A (en) * | 2020-09-18 | 2020-12-25 | 国网四川省电力公司经济技术研究院 | Multi-dimensional benefit index analysis method based on DEA theory |
| CN112529418A (en) * | 2020-12-14 | 2021-03-19 | 国网四川省电力公司经济技术研究院 | Power distribution network investment benefit evaluation method based on matter element extension evaluation model |
Non-Patent Citations (1)
| Title |
|---|
| 刘璐等: ""基于灰色关联和TOPSIS 模型的电网实物资产综合价值评价"", 《山东电力技术》, vol. 45, no. 242, pages 8 - 13 * |
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