CN107506863A - One kind is based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology - Google Patents

Abstract

The invention discloses a method for predicting operation and maintenance costs of physical assets of a power grid based on big data. The method includes a multi-source data collection platform, a data warehouse, a MapReduce model, and a data visualization display platform. The power grid physical asset operation and maintenance cost prediction system provided by the present invention applies big data analysis technology, ETL technology, data warehouse technology and data visualization technology, takes the asset life cycle theory as the guiding ideology, and uses the asset list in the PMS system and ERP system Based on big data analysis methods such as cluster analysis, classification analysis, and correlation analysis, statistical relationships can be established for massive unstructured data, providing information on the scale and development trend of power grid physical asset operation, maintenance, and maintenance costs. Medium and long-term analysis and forecast. The invention realizes the high-efficiency integration of multiple business system data in the operation and maintenance management of the physical assets of the power grid, and improves the accuracy of the maintenance operation and maintenance cost prediction.

Description

A method for predicting operation and maintenance costs of physical assets in power grids based on big data

technical field

The present invention relates to a forecasting method, and more specifically relates to the forecasting of operation and maintenance costs of physical assets of a power grid based on big data analysis technology.

technical background

Physical assets refer to fixed assets, low-value durable goods, material consumables and other assets that have physical form and have clear and quantifiable value items. Power grid enterprises are asset-intensive enterprises, and the proportion of physical assets in their asset structure is as high as 80%. Primary equipment, secondary equipment, communication networks, instruments, buildings, vehicles, consumables and other tangible assets are all physical assets. The scale and utilization efficiency of physical assets directly determine the production capacity of an enterprise. With the continuous increase of investment in power grids, the scale of physical assets and operation and maintenance costs of power grid companies will continue to grow.

In order to predict in advance the financial pressure and various risks that the company will face in the future, and to put forward scientific suggestions for investment, operation and maintenance, and disposal opinions, it is urgent to establish a set of scientific and effective methods and tools for predicting the operation and maintenance costs of physical assets. The existing operation and maintenance cost prediction method is based on accounting principles, mainly using the linear regression method to find the mutual influence relationship between the physical asset scale of the power grid and the operation and maintenance cost, determine the target variable and related variables, and then use regression The method calculates the regression equation between variables, and predicts the future operation and maintenance cost based on historical data. The main disadvantage of this method is that the model does not consider the exact status of the physical assets of the power grid, such as the utilization of various physical assets, the incidence of defects, the actual service life and other information, so it can only conduct short-term and rough analysis and prediction, and different types of There is a large deviation in the forecast of physical asset operation, maintenance and repair costs. Due to the huge amount of data on the exact status of physical assets, traditional statistical forecasting methods cannot use these data for correlation analysis.

In the design process of the present invention, the latest research results at home and abroad are referred to, among which there are 2 related patents at home and abroad, and 3 documents:

The patent "an information processing system and method supporting seven-dimensional ledger equipment management", inventor Li Youcheng, etc., application number CN201510494092.8 discloses an information processing system and method supporting seven-dimensional ledger equipment management, using In order to solve the technical problem that the current asset management cannot meet the requirements of equipment asset management and the efficiency of asset use is not high, the system includes: an equipment management plan determination unit, which is used to analyze the feasibility of the equipment asset plan to determine the equipment management plan; The relationship establishment unit is used to generate the unique ID code of the equipment based on the equipment management plan, and establish the corresponding relationship between the equipment in the asset management business system and the financial management business system according to the equipment management scheme and the equipment ID card code; the equipment ledger management unit , which is used to link the equipment account, asset card and physical objects based on the corresponding relationship in the entire life cycle of the equipment asset, so as to realize the orderly management of the equipment account in seven dimensions. The consistency and accuracy of information between equipment ledgers, asset cards and physical objects has been realized.

This patent proposes the management and summary of the ledger data of physical assets, the relevant technical guarantee of the consistency of accounts, cards and objects, and also mentions the theoretical method of the whole life cycle, but does not analyze and utilize the ledger data of real assets.

The patent "asset management information processing method and device based on big data analysis", inventor Xu Wanrong, etc., application number: CN201510066350.2 relates to a method and device for asset management information processing based on big data analysis, including client, application server, The database server, the application server is connected with the client and the database server respectively; the application server includes a data integration module, an asset status analysis module, a target strategy formulation module, a planning module, a management system research and development module, and a support element research and development module module, implementation process monitoring module and performance evaluation module. Compared with the existing technology, the invention uses system engineering methods, various evaluation models and logical calculation methods in different analysis modules to coordinate and control the testing assets in planning, design, procurement, construction, operation and maintenance, transformation, scrap disposal, etc. The performance information of the asset's life cycle has the functions of analyzing, monitoring and predicting the entire process of asset management.

This method proposes the integration and utilization of physical asset data and monitors its key indicators, but does not consider the prediction of operation and maintenance costs by establishing a data model.

Literature "Evaluation Index System of Power Grid Physical Assets", Li Peidong et al., Electric Power Construction, 2014. Based on the characteristics of power grid physical asset management, combined with the asset life cycle management theory and the asset wall model, aiming at the core asset evaluation problem in the asset management strategy, from the four aspects of scale structure analysis, health level analysis, utilization efficiency analysis, and retirement analysis Build a power grid physical asset evaluation index system in two dimensions. The research results can provide a reference for continuously deepening asset life-cycle management, predicting the risk of technical transformation in operation and maintenance, and realizing economical and reliable operation of power grids.

This document proposes the related methods of establishing the physical asset wall of the power grid, as well as the application of the asset wall in the analysis and evaluation of physical assets, but the asset wall and operation and maintenance adopt a simple positive correlation, and do not consider other influences on operation and maintenance costs the elements of.

Literature "Research on Analysis Method of 'Asset Wall' of Power Grid Physical Assets", Liu Yihe et al., East China Electric Power, 2014. Taking the asset operation and maintenance of Shanghai Electric Power Company as an example, this document expounds the establishment of asset walls, the analysis of expected service life, the prediction of future technological transformation, the prediction of replacement scale, the analysis of the relationship between equipment defects and commissioning time, and the prediction process of operation and maintenance work.

This paper proposes a method of predicting the scale of future operation and maintenance by using asset wall translation, but it does not consider that the current power grid construction is still in the acceleration period, and the impact of new and scrapped assets on a certain waveform, so the overall estimated waveform will have some Variety.

Literature "An Index Evaluation System for the Life Cycle of Assets Management under the New Environment of Power Grid", Jian Deng, Applied Mechanics and Materials, 2013. This document discusses that the life cycle asset management evaluation index system is the basis for scientifically evaluating the asset management level of the power grid and the carrier of the quality operation of the power grid. Aiming at the new requirements for the whole life cycle of asset management in the new environment, the paper constructs a four-layer tree evaluation index system to comprehensively evaluate the asset management level to maximize the benefits.

This literature uses a tree structure to explain the direct relationship between physical asset indicators, and thus analyzes the direct relationship between indicators, which has certain significance for determining the impact coefficient of indicators on the scale of operation and maintenance, but the relevant research methods have not been applied to the cost of operation and maintenance on the forecast.

Contents of the invention

The purpose of the present invention is to provide a power grid physical assets operation and maintenance cost prediction system based on big data analysis technology in order to overcome the above-mentioned defects in the prior art. The system can collect equipment ledger information data and The cost of operation and maintenance is guided by the theory of the whole life cycle of assets. Through big data analysis methods, a statistical relationship is established for massive unstructured data, and the scale and development trend of the cost of operation and maintenance of physical assets of the power grid are provided for medium and long-term analysis and prediction.

In order to achieve the above object, the present invention adopts following technical scheme:

A method for predicting the cost of operation and maintenance of physical assets of a power grid based on big data, the method comprising the following steps:

a. The power grid includes an enterprise resource management system and an engineering production management system;

b. Obtain the physical asset information ledger and operation and maintenance expense ledger from the enterprise resource management system, and obtain the physical asset operation status ledger and equipment defect records from the engineering production management system;

c. The physical asset information ledger has uniformly coded all equipment, and each equipment corresponds to a specific code. There are N equipment in the power grid, and the code corresponding to the i-th equipment is B _i , i=1, 2, ... , N;

d. Obtain the original value, net value and operation and maintenance fee information of physical assets coded as B _i from the physical asset information ledger and the operation and maintenance maintenance cost ledger, and obtain the coded as B from the physical asset operation status ledger and equipment defect records The utilization rate and defect rate of _i , i=1, 2, ..., N;

e. Process the data obtained in step d

e1. Calculate the total original value of physical assets Y

Among them, Y _i is the original value of the physical asset coded as B _i ;

e2. Calculate the total net value of physical assets J

Where J _i is the net asset value of physical assets coded as B _i ;

Calculate the newness rate E _j of the physical asset in the jth year

e3. Calculate the total operation and maintenance fee W

Where W _i is the operation and maintenance cost of assets coded as B _i ;

e4. Calculate the average defect rate

Where F _i is the defect rate of assets coded as B _i ;

e5. Calculate the average utilization rate

Where U _i is the asset utilization efficiency of this type of asset;

e6. Use linear fitting and "least square method" to calculate the influence curve p(E _j ) of the newness rate on the defect rate

Process and collect the newness rate and defect rate data pair set {(E _j ,F _j )}(j=1,2,...,M) for M years, and find the function Where e is a natural base number, which minimizes the square sum of errors E ² , where E ² =∑[p(E _j )-F _j ];

Find the fitting function p(x) of order n, where 1≤n≤5, and get the influence coefficient vector α;

e7. Using multiple linear regression analysis and "ordinary least square method" to calculate the influence coefficient of average utilization efficiency and average defect rate on operation and maintenance costs

Process and collect three sets of corresponding data sets {(U _j , F _j , _{W j )} } ( _{j =} 1,2,..., M), use the Matlab mathematical tool polyfit program module to solve the function q(β, U _j , F _j ), where β is the undetermined influence coefficient vector, so that the square sum E ² of the error is the smallest, where E ² =∑[q( U _j , F _j )-W _j ];

The n-order fitting function q(β, U _j , F _j ) can be obtained, where 1≤n≤5, and the selected influence coefficient vector β can be obtained;

e8. Calculate the actual average retirement age of physical assets

Where S _i is the actual retirement age of the corresponding asset coded as B _i ;

f. Build a predictive model

f1. Predict the scale of decommissioned and scrapped assets S in the jth year

C is the total scale of invested assets, P={P _j },j=1,...,M, where P _j is the scale of assets scrapped in the jth year of the equipment obtained from statistics; find the decommissioning and scrapping of assets related to the number of years in service Curve function f(s,x), s is the scale of invested assets, x is the retirement age, the method adopted is the fitting method, and the function f(s,x) is solved by using the Matlab mathematical tool polyfit program module;

Based on the obtained function f(s,x), the scale of assets decommissioned and scrapped in the jth year is:

Where s _i is the scale of assets invested in the i-th year;

f2. Predict the asset size R _j in the jth year

f3. Predict the asset renewal rate E _j in the jth year

Among them, R _nj is the scale of the net asset value in the jth year, and R _oj is the scale of the original asset value in the jth year;

g. Predict the operation and maintenance cost W _j in the jth year

According to the binary function q(β,U _j ,F _j ) obtained in step e8, where β has been obtained in step e7, and the data obtained in step e1--step e8, calculate the operation and maintenance cost of the j-th year:

where U _j is the utilization rate in the jth year, G _j is the electricity sales in the jth year, based on the electricity sales in that year, and the annual growth rate of electricity sales is set at 5%.

Compared with the prior art, the present invention has the following advantages:

A power grid physical asset operation and maintenance cost prediction system based on big data analysis technology. By integrating massive data such as physical asset value scale, quantity scale, new asset scale, asset decommissioning and scrapping records, defects, and historical maintenance operation and maintenance costs, Realize medium and long-term analysis and prediction of operation and maintenance costs, with an intuitive display mode. Compared with the method of using linear regression to predict future operation and maintenance costs, this method has a clearer prediction process and more accurate prediction results.

The system can not only predict the overall operation and maintenance costs of power grid companies at all levels, but also realize independent prediction and analysis of local power grids, single-type assets, and single-voltage levels, enhancing the accuracy and reliability of data.

detailed description

In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

A method for predicting the cost of operation and maintenance of physical assets of a power grid based on big data, the method comprising the following steps:

a. The power grid includes an enterprise resource management system and an engineering production management system. These systems are universal and universal in the information construction of modern power grids, and record the value and use of physical assets in detail;

b. Obtain the physical asset information ledger and the operation and maintenance expense ledger from the enterprise resource management system, and obtain the physical asset operation status ledger and equipment defect records from the engineering production management system. The present invention uses ETL technology to capture and process these data Transform and store in the data warehouse;

c. The physical asset information ledger has uniformly coded all equipment, and each equipment corresponds to a specific code. There are N equipment in the power grid, and the code corresponding to the i-th equipment is B _i , i=1, 2, ... , N, the data from multiple systems in the data warehouse can be associated through the unified coding of equipment;

d. Obtain the original value, net value and operation and maintenance fee information of physical assets coded as B _i from the physical asset information ledger and the operation and maintenance maintenance cost ledger, and obtain the coded as B from the physical asset operation status ledger and equipment defect records The utilization rate and defect rate of _i , i=1, 2, ..., N, after the data is processed by the asset code and classification comparison table, it is stored in the intermediate data table of the data warehouse. At this time, the data has been sorted into half structured data;

The intermediate data table is a data table format used to store the smallest data granularity in the data warehouse, retaining all the fields of the original data, and merging data from multiple systems in the intermediate data table;

e. Process the data obtained in step d

e1. Calculate the total original value of physical assets Y

Among them, Y _i is the original value of the physical asset coded as B _i ;

The original value of the asset is the total cost incurred when the object is capitalized, mainly including equipment fees and installation fees;

e2. Calculate the total net value of physical assets J

Where J _i is the net asset value of physical assets coded as B _i ;

The net asset value is the usable value calculated according to a certain depreciation rate according to the type and age of the asset;

Calculate the newness rate E _j of the physical asset in the jth year

This newness rate calculation method takes into account all samples in the power grid, which is more accurate than the newness rate obtained by traditional valuation methods, especially when analyzing partial assets, the real composition of assets can be fully considered;

e3. Calculate the total operation and maintenance fee W

Where W _i is the operation and maintenance cost of assets coded as B _i ;

e4. Calculate the average defect rate

Where F _i is the defect rate of assets coded as B _i ;

There is a direct positive correlation between the defect rate and the operation and maintenance cost, the higher the defect rate, the higher the operation and maintenance cost;

e5. Calculate the average utilization rate

Where U _i is the asset utilization efficiency of this type of asset;

There is a positive correlation between the average utilization rate and the operation and maintenance cost, the higher the average utilization rate, the higher the operation and maintenance cost;

e6. Use linear fitting and "least square method" to calculate the influence curve p(E _j ) of the newness rate on the defect rate

Process and collect the newness rate and defect rate data pair set {(E _j ,F _j )}(j=1,2,...,M) for M years, and find the function Where e is a natural base number, which minimizes the square sum of errors E ² , where E ² =∑[p(E _j )-F _j ];

Find the fitting function p(x) of order n, where 1≤n≤5, and get the influence coefficient vector α;

The newness rate of assets is an important indicator affecting the defect rate of assets. The impact on the defect rate is shown as a bathtub curve. The defect rate is high in the early stage of asset operation and decommissioning, and the defect rate is low in the operation process;

Since the age components of the overall physical assets are relatively rich, and the age of assets is between 1 and 30, the influence coefficient vector α appears as an irregular curve;

e7. Using multiple linear regression analysis and "ordinary least square method" to calculate the influence coefficient of average utilization efficiency and average defect rate on operation and maintenance costs

Process and collect three sets of corresponding data sets {(U _j , F _j , _{W j )} } ( _{j =} 1,2,..., M), use the Matlab mathematical tool polyfit program module to solve the function q(β, U _j , F _j ), where β is the undetermined influence coefficient vector, so that the square sum E ² of the error is the smallest, where E ² =∑[q( U _j , F _j )-W _j ];

The n-order fitting function q(β, U _j , F _j ) can be obtained, where 1≤n≤5, and the selected influence coefficient vector β can be obtained;

The influence coefficient vector β takes into account both the average defect rate and the average utilization rate, so it appears as a curved surface;

e8. Calculate the actual average retirement age of physical assets

Where S _i is the actual retirement age of the corresponding asset coded as B _i ;

Since the physical asset data stored in the intermediate table of the data warehouse is semi-structured data, the traditional SQL query tool cannot perform statistical analysis on the data. Therefore, the present invention introduces a MapReduce big data analysis tool, which can analyze massive unstructured data. Statistical analysis of the data;

After using MapReduce to model and calculate the data, the calculation results are stored in the result data table of the data warehouse;

The result data table is a data table after dimensionality reduction in the data warehouse, which is used to store summary data, which can be directly read and displayed;

f. Build a predictive model

f1. Predict the scale of decommissioned and scrapped assets S in the jth year

C is the total scale of invested assets, P={P _j },j=1,...,M, where P _j is the scale of assets scrapped in the jth year of the equipment obtained from statistics; find the decommissioning and scrapping of assets related to the number of years in service Curve function f(s,x), s is the scale of invested assets, x is the retirement age, the method adopted is the fitting method, and the function f(s,x) is solved by using the Matlab mathematical tool polyfit program module;

Based on the obtained function f(s,x), the scale of assets decommissioned and scrapped in the jth year is:

Where s _i is the scale of assets invested in the i-th year;

f2. Predict the asset size R _j in the jth year

f3. Predict the asset renewal rate E _j in the jth year

Among them, R _nj is the scale of the net asset value in the jth year, and R _oj is the scale of the original asset value in the jth year;

Use the MapReduce big data analysis tool to model and calculate the data, and store the calculation results in the result data table of the data warehouse;

g. Predict the operation and maintenance cost W _j in the jth year

According to the binary function q(β,U _j ,F _j ) obtained in step e8, where β has been obtained in step e7, and the data obtained in step e1--step e8, calculate the operation and maintenance cost of the j-th year:

where U _j is the utilization rate in the jth year, G _j is the electricity sales in the jth year, based on the electricity sales in that year, and the annual growth rate of electricity sales is set at 5%;

Use the MapReduce big data analysis tool to model and calculate the data, store the calculation results in the result data table of the data warehouse, and visualize the final prediction results through the data display platform.

Claims (1)

1. one kind is based on big data power network physical assets O＆M cost of overhaul Forecasting Methodology, it is characterised in that this method includes down Row step:

A, the power network includes ERP System, engineering production management system；

B, physical assets information account and O＆M cost of overhaul account are obtained from ERP System, from engineering production management System obtains physical assets running status account, equipment deficiency record；

C, physical assets information account is by all devices Unified coding, corresponding one specific coding of each equipment, the electricity N platform equipment is shared in net, B is encoded to corresponding to i-th equipment_i, i=1,2 ..., N；

D, obtained from physical assets information account and O＆M cost of overhaul account and be encoded to B_iPhysical assets initial value, net value with And O＆M maintenance charge information, it is encoded to B from physical assets running status account, equipment deficiency record acquisition_iUtilization rate and Ratio of defects, i=1,2 ..., N；

E, the data acquired in step d are handled

E1, calculate the total initial value Y of physical assets

Wherein Y_iIt is that physical assets is encoded to B_iInitial asset value；

E2, calculate the total net value J of physical assets

Wherein J_iIt is that physical assets is encoded to B_iNet asset value；

Calculate the newness rate E in physical assets jth year_j

<mrow> <msub> <mi>E</mi> <mi>j</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>J</mi> <mi>j</mi> </msub> <msub> <mi>Y</mi> <mi>j</mi> </msub> </mfrac> </mrow>

E3, calculate total O＆M cost of overhaul W

Wherein W_iIt is that assets are encoded to B_iThe O＆M cost of overhaul use；

E4, calculate average defect rate

Wherein F_iIt is that assets are encoded to B_iAssets ratio of defects；

E5, calculate average utilization

Wherein U_iFor the assets utilization efficiency of such assets；

E6, influence curve p (E of the newness rate to ratio of defects is calculated using linear fit and " least square method "_j)

M newness rate is collected in processing, and ratio of defects data are to gathering { (E_j,F_j) (j=1,2 ..., M), find a functionWherein e is the nature truth of a matter, makes the quadratic sum E of error²Minimum, wherein E²=∑ [p (E_j)-F_j]；

The fitting function p (x) of n ranks is obtained, wherein 1≤n≤5, obtain influenceing coefficient vector α；

E7, average utilization efficiency calculated with average defect rate to transporting using multiple linear regression analysis and " common least square method " Tie up the influence coefficient of the cost of overhaul

M utilization rate U is collected in processing_j, average defect rate F_jWith O＆M cost of overhaul W_jThree groups of corresponding data set { (U_j,F_j, W_j) (j=1,2 ..., M), with Matlab mathematical tool polyfit program modules to function q (β, U_j,F_j) solved, its Middle β is influence coefficient vector undetermined, makes the quadratic sum E of error²Minimum, wherein E²=∑ [q (U_j, F_j)-W_j]；

Fitting function q (β, the U of n ranks can be obtained_j,F_j), wherein 1≤n≤5, obtain selected influence coefficient vector β；

E8, calculating physical assets actual average scrap the age

Wherein S_iIt is that assets are encoded to B_iCorresponding assets actually scrap the age；

F, forecast model is established

F1, the retired asset size S scrapped for predicting jth year

C is invested assets total scale, P={ P_j, j=1 ..., M, wherein P_jTo count the obtained assets scrapped in equipment jth year Scale；The retired retirement curve function f (s, x) that assets are related to entering to use as a servant the time limit is asked, s is invested assets scale, and x is to scrap year Limit, the method taken is fitting process, function f (s, x) solve using Matlab mathematical tool polyfit program modules into Row solves；

Based on the function f (s, x) tried to achieve, the asset size that jth year is retired to scrap is：

<mrow> <msub> <mi>S</mi> <mi>j</mi> </msub> <mo>=</mo> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>j</mi> </munderover> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow>

Wherein s_iFor the asset size put into 1 year；

F2, the asset size R for predicting jth year_j

<mrow> <msub> <mi>R</mi> <mi>j</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>j</mi> </munderover> <msub> <mi>s</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>S</mi> <mi>j</mi> </msub> <mo>+</mo> <mi>J</mi> </mrow>

F3, the assets newness rate E for predicting jth year_j

<mrow> <msub> <mi>E</mi> <mi>j</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>R</mi> <mrow> <mi>n</mi> <mi>j</mi> </mrow> </msub> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>j</mi> </mrow> </msub> </mfrac> </mrow>

Wherein, R_njFor the Net asset value scale in jth year, R_ojFor the initial asset value scale in jth year；

G, the O＆M cost of overhaul W in jth year is predicted_j

Binary function q (β, the U obtained according to step e8_j,F_j), wherein β obtains in step e7, and step e1-- steps e8 The acquired data of step, the O＆M cost of overhaul for calculating jth year are used：

<mrow> <msub> <mi>W</mi> <mi>j</mi> </msub> <mo>=</mo> <mi>q</mi> <mrow> <mo>(</mo> <mi>&amp;beta;</mi> <mo>,</mo> <msub> <mi>U</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>F</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>q</mi> <mrow> <mo>(</mo> <mi>&amp;beta;</mi> <mo>,</mo> <msub> <mi>U</mi> <mi>j</mi> </msub> <mo>,</mo> <mi>p</mi> <mo>(</mo> <msub> <mi>E</mi> <mi>j</mi> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mo>=</mo> <mi>q</mi> <mrow> <mo>(</mo> <mi>&amp;beta;</mi> <mo>,</mo> <msub> <mi>U</mi> <mi>j</mi> </msub> <mo>,</mo> <mi>p</mi> <mo>(</mo> <mfrac> <msub> <mi>R</mi> <mrow> <mi>n</mi> <mi>j</mi> </mrow> </msub> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>j</mi> </mrow> </msub> </mfrac> <mo>)</mo> <mo>)</mo> </mrow> </mrow>

Wherein U_jFor the utilization rate in jth year,G_jFor the electricity sales amount in jth year, using electricity sales amount then as radix, sale of electricity year Growth rate is set as 5%.