WO2018176863A1

WO2018176863A1 - Investment efficiency analysis method and device related to power distribution network reliability, and storage medium

Info

Publication number: WO2018176863A1
Application number: PCT/CN2017/112516
Authority: WO
Inventors: 盛万兴; 孟晓丽; 刘科研; 刁赢龙; 胡丽娟; 贾东梨; 何开元; 叶学顺; 董伟杰; 吕琛
Original assignee: 中国电力科学研究院有限公司
Priority date: 2017-04-01
Filing date: 2017-11-23
Publication date: 2018-10-04
Also published as: CN107123982A; CN107123982B

Abstract

An investment efficiency analysis method and device related to power distribution network reliability, and computer readable storage medium. The method comprises: acquiring apparatus change information of a power service area (step 1); calculating, according to a changed apparatus corresponding to the change information, an investment cost (step 2); pairing the investment cost with a pre-arranged power outage incident to obtain an association relationship (step 3); establishing, according to the change information, a prediction model for an reliability increase of a power distribution network for a reliability-related investment (step 4); establishing an investment efficiency calculation model for power distribution network reliability (step 5); and upon an apparatus change in the power service area, employing the investment efficiency calculation model for power distribution network reliability to calculate an investment efficiency for power distribution network reliability (step 6).

Description

Distribution network reliability investment economic benefit analysis method and device, storage medium

Cross-reference to related applications

The present application is filed on the basis of the Chinese Patent Application No. PCT Application No.

Technical field

The invention relates to an economic benefit analysis method, in particular to a distribution network reliability investment economic benefit analysis method and device, and a computer readable storage medium.

Background technique

Studying the calculation and evaluation methods of the economic benefits of distribution network reliability investment is beneficial to the grid company's different reliability investment economic benefits generated by different investment levels, and the positive, negative and comprehensive benefits of different distribution network construction and transformation. Evaluation and assessment.

In order to establish a reliability investment benefit analysis model for distribution network, a reliability and economic data source with high reliability is needed. At present, in the process of reliability management, grid companies will record information such as the time of power outages, power outages, and number of households during power outages. These information are automatically uploaded to the reliability management system through the automatic collection device of the load collection point. Credibility. However, the acquisition of economic data sources is difficult due to the large construction time of the project and the confusion of project subcontracting.

At present, the systems related to the reliability and economy of the distribution network operated by the State Grid Corporation are mainly equipment (asset) operation and maintenance lean management system (PMS2.0) and power quality online monitoring system. In terms of information and data structure, component equipment composition, database input and output variables, the above two systems have unfavorable factors such as information asymmetry, variable relationship disorder, and unsynchronized development platform. Such as According to the above two sets of online database information, it is a challenging task to find and discover relevant mathematical physics statistics and calculation methods and explore the relationship between reliability investment and economic benefits.

Summary of the invention

Embodiments of the present invention provide a method and device for analyzing economic benefits of reliability of distribution network, and a computer readable storage medium.

Embodiments of the present invention provide a method for analyzing economic benefits of reliability of distribution network, including:

Obtaining the change information of the device in the power supply area;

Calculate the investment cost based on the transaction device corresponding to the transaction information;

Match the investment cost with the pre-arranged power outage event to obtain the relationship;

Calculating a calculation model for the reliability investment benefit of the distribution network based on the correlation relationship;

When the equipment in the power supply area changes, the calculation model of the reliability investment benefit of the distribution network is used to calculate the reliability investment benefit of the distribution network.

In the above solution, the relationship is as follows:

In the formula, A ₁ reflects the power shortage caused by unit investment; d is the number of days in the month, m _i is the sum of the power shortages of the day when the power is cut off, C _invest is the reliability investment calculated by the equipment transaction; A ₂ reflects the power outage caused by the unit investment The number of households; z _i is the sum of the number of households in the event of a power outage on the day.

In the above solution, the method further includes: establishing a reliability improvement prediction model of the distribution network before and after the reliability investment according to the transaction information.

In the above solution, the reliability improvement prediction model of the distribution network is obtained by using a Lagrangian multiplier method and a Carlo-Kun-Tucker condition for the least squares support vector machine.

In the above solution, the reliability improvement prediction model of the distribution network is as follows:

Where λ _i : Laerlang multiplier, b: parametric amount, K(x _i , x _j ) represents a kernel function from the sample input space, through nonlinear mapping to the high-dimensional feature space, i=1,. ..,l,j=1,...,l.

In the above solution, the least squares support vector is calculated as follows:

y=f(x)=ω·φ(x)+b (4)

The Lagrangian multiplier method is used for the least squares support vector machine as follows:

The Carol-Kun-Tuck condition is as follows:

Where, the training sample set {(x _i , y _i )}, i=1,...,n,n is the capacity of the training sample, x _i ∈R ⁿ is the sample input, and y _i ∈R is the sample output value ; ω is the weight vector, b is the paranoid amount; φ(x) is the mapping from the low-dimensional space to the high-dimensional space; e _i is the error, e∈R ^l×1 is the error vector; λ: Lagrangian multiplier , λ ∈ R ^{l × 1} ; λ = [λ ₁ , λ ₂ , ... λ _l ] ^T , E = [1, 1, ... 1] ^T is a l × 1 dimensional column vector, Y = [y ₁ , y ₂ ,..., y _l ] ^T , I is a unit matrix, K is a suitable kernel function, and

In the above solution, the reliability economic benefit analysis and evaluation model of the distribution network is as follows:

Where, A ₃ is the comprehensive reliability index of the distribution network reliability, (RS-2) _i+1 is the power supply reliability index predicted by the distribution network reliability improvement prediction model, (RS-2) _i is the investment The reliability index of the previous cycle, h is the total number of regional power supply households, C _invest is the investment amount of the forecast period, and A ₂ is the number of households in the pre-arranged power outage caused by unit investment.

In the above solution, the investment cost of the device transaction is calculated as follows:

Total investment in distribution network construction = base price + labor fee × 153.1% + equipment fee × 107.51% (8)

In the formula, the base price, labor cost and equipment fee of different equipment are determined according to actual conditions.

The embodiment of the invention further provides a distribution network reliability investment benefit analysis device, the device comprising: a database module, a data processing module, an input module, an analysis module and an output module;

The database module is configured to collect and store the change information of the power supply area device;

The data processing module is configured to calculate an investment cost of the transaction device corresponding to the transaction information, and match the investment cost with the pre-arranged power outage event to obtain an association relationship;

The input module is configured to transmit data processed by the data processing module to the analysis module;

The analysis module is configured to establish a calculation model of the reliability investment benefit of the distribution network according to the association relationship; and obtain a distribution network through the calculation model of the reliability investment benefit of the distribution network according to the data transmitted by the input module Reliability investment benefit;

The output module is configured to output a distribution network reliability investment benefit value.

A computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of any of the above methods.

Embodiments of the present invention have the following beneficial effects:

(1) The embodiment of the present invention is based on the problem that the investment data is not credible in the reliability economic benefit analysis of the traditional distribution network, and the calculation model of the reliability investment of the distribution network is established by the PMS2.0 system equipment transaction information, thereby solving the problem. The source of investment data in the analysis of grid reliability economic benefits.

(2) The embodiment of the invention provides a method for predicting the reliability of the distribution network in a certain period of time through the historical data when the topology of the distribution network is unknown, thereby solving the historical power outage and reliability at the macro level. The investment data can be used to obtain the problem of reliability improvement of the distribution network.

(3) In the embodiment of the present invention, a pre-arranged power outage is considered, and an integrated reliability economic benefit analysis model of the distribution network is established, so that the model can more accurately reflect the influence of the distribution network investment on the reliability of the distribution network.

DRAWINGS

1 is a flow chart of a method for analyzing economic benefits of reliability investment of a distribution network according to an embodiment of the present invention;

2 is a schematic diagram of matching a PMS2.0 system with a power quality online monitoring system according to an embodiment of the present invention;

FIG. 3 is a flowchart of reliability prediction of a power distribution network to a distribution network according to an embodiment of the present invention.

detailed description

In order to better understand the present invention, the contents of the present invention will be further described below in conjunction with the drawings and examples.

At present, there is an urgent need for a method for analyzing the economic benefits of distribution network reliability investment, linking the reliability investment with economic benefits, and establishing a reliability investment economic benefit calculation model.

Based on this, an embodiment of the present invention provides a method for analyzing the economic benefit of reliability of a distribution network, which is applied to a device (asset) operation and maintenance lean management system (PMS2.0). As shown in FIG. 1 , the method includes the following steps. :

Step 1: Obtain the change information of the device in the power supply area;

Here, the equipment (asset) operation and maintenance lean management system (PMS2.0) is a unified equipment (asset) operation and maintenance lean management system for the operation and maintenance department, covering the operation and maintenance maintenance business and production management process. Realize the life-cycle management of equipment (assets) from planning, installation, operation, decommissioning, reuse, and retirement.

The device (asset) change information is recorded in the PMS2.0 system, including device physical parameter information (device category, device type, voltage level), device change information (device addition method, commissioning time), and the like. According to the classification of the station equipment, the interval unit, the overhead line, the cable line, and the primary equipment in the substation, the data in the PMS2.0 system is exported in the form of excel.

The grid reliability data is stored in the power quality online monitoring system, in which “power outage end time”, “power outage nature”, and “number of households in power outage” are structured data. The reliability data of the distribution network of a power supply area is exported in the form of excel, and the data is preprocessed. The power outage data is divided into pre-arranged power outage data and fault power outage data according to the nature of power outage.

Step 2: Calculate the investment cost based on the transaction device corresponding to the transaction information;

Here, in actual application, according to the physical parameter information of the transaction equipment and the relevant provisions of the "20kV and below distribution network construction budget preparation and calculation standards", "State Grid power material budget price" two documents, calculate equipment changes cost of investment.

According to the relevant provisions of the "20kV and below distribution network project construction budget and calculation standards" document, the construction network construction cost is composed of installation engineering fees, equipment purchase costs, other expenses and dynamic costs. The contents and calculation formulas of each fee are shown in Table 1.

Table 1

Based on the calculation formula in Table 1, the total investment reliability of the distribution network caused by equipment changes can be calculated by the following formula. The formula is as follows:

Total investment in distribution network construction = base price + labor fee × 153.1% + equipment fee × 107.51% (1)

In formula (1), the base price, labor cost and equipment fee of different equipment are determined according to the actual actual value.

Step 3: Match the investment cost with the pre-arranged power outage event to obtain the relationship;

Here, the time parameter information of the device change is matched with the construction power failure time in the power quality online monitoring system, and the power outage data information of the distribution network caused by the device change construction is obtained, and the data fusion method of the two information systems is as shown in FIG. 2 Show.

That is to say, the association relationship may be referred to as an association relationship between the construction and distribution network power outage data information.

By matching the PMS2.0 system equipment transaction information with the power failure online power monitoring online pre-arranged power outage termination date, the equipment transaction information is linked with the distribution network reliability data to obtain the monthly variable operation of each voltage level. The total amount of power shortage caused and the total number of households during power outages. Furthermore, by matching the date field, the distribution network construction investment can be correlated with the construction power outage, so as to realize the analysis and research on the economic benefit of the distribution network reliability investment.

The formula for calculating the power shortage caused by unit investment is as follows:

In the formula, A ₁ reflects the lack of power supply caused by unit investment. The larger the value of A ₁ , the more power shortage is caused by unit investment. d is the number of days in the month, m _i is the sum of the power shortages of the day's power outage, and C _invest is the reliability investment calculated by the device.

The formula for calculating the number of households during power outage caused by unit investment is as follows:

In the formula, A ₂ reflects the number of households in the event of power outage caused by unit investment. The larger the value of A ₂ is, the more households are in the event of power outage caused by unit investment. d is the number of days in the month, and z _i is the sum of the number of households in the event of a power outage on the day. C _invest is a reliability investment through device computing calculations.

By linking the investment in construction network construction with the construction power outage, you can get the pre-arranged power consumption caused by the unit investment in the investment month.

Step 4: Based on the transaction information, establish a reliability improvement prediction model for the distribution network before and after the reliability investment;

Here, in actual application, step 4 and steps 2, 3, and 5 have no order of execution.

Under the condition that the topology of the distribution network is unknown, the reliability improvement prediction model of the distribution network before and after the reliability investment is established, and the reliability of the power supply before and after the investment of the reliability investment data can be obtained. As a result, the pre-arranged power failure is calculated. The impact of network reliability, so the reliability prediction of this part only predicts the impact of fault power outage on the reliability of the distribution network.

Wherein, using the distribution network reliability improvement prediction model, the reliability of the fault power failure can be predicted.

The reliability of the distribution network for the month and the forecast monthly month, the forecast of the number of severe weather days, the total monthly investment amount of the distribution network in the previous year, and the current reliability level of the predicted site are all four factors. Related. To this end, a reliability estimation based on the least squares support vector machine is proposed.

As shown in Fig. 3, the sample matrix is formed by preprocessing the historical data, normalizing the data, determining the parameters, solving the objective function to obtain the regression equation, and using the regression equation to predict the reliability.

Regression principle of least squares support vector machine: Given a set of training samples {(x _i , y _i )}, i=1,...,n, where n is the capacity of the training sample, x _i ∈R ⁿ For sample input, y _i ∈R is the sample output value. For the nonlinear regression of the training samples, the core idea of the least squares support vector machine is to map the training samples to the high-dimensional feature space through a nonlinear mapping φ(x), and then perform linear regression in the high-dimensional feature space. The time regression function is:

y=f(x)=ω·φ(x)+b (4)

Where ω is the weight vector, b is the paranoid quantity, and φ(x) is the mapping from the low dimensional space to the high dimensional space. The optimization problem of the least squares support vector machine at this time is:

Where e _i is the error, e _∈ R ^{l × 1} is the error vector, C (C > 0) is the regularization parameter, and the degree of punishment of the control error. Introducing the Lagrange multiplier λ, λ∈R ^l×1 , and transforming the equation (5) into

Conditional by Carol-Kun-Tuck

To eliminate ω and e, the solution of equation (7) is:

Where λ=[λ ₁ ,λ ₂ ,...λ _l ] ^T , E=[1,1,...1] ^T is a l×1 dimensional column vector, Y=[y ₁ , y ₂ ,. .., y _l ] ^T , I is the identity matrix, K is a suitable kernel function, and

In order to simplify the calculation process, the kernel function in the original space is used to replace the dot product operation in the high dimensional feature space.

The prediction model of the least squares support vector machine is:

Where λ _i , b can be obtained from the linear equation of the above formula, and K(x _i , x _j ) represents the kernel function from the sample input space through the nonlinear mapping to the high-dimensional feature space. The radial basis function (RBF) function is used as the kernel function in the least squares support vector machine model.

Each input sample selected by the predictive model input sample contains four characteristic indicators: the reliability and forecast monthly month of the distribution network, the forecast of severe weather days in the month, the total investment amount of the distribution network in the previous year before the forecast, and the current forecast position. Reliability level.

According to the historical data sample, the prediction model of the least squares support vector machine can predict the reliability index of the fault blackout only in a certain month.

Step 5: According to the association relationship, establish a calculation model for the economic benefit of the reliability investment of the distribution network;

Comprehensively consider the negative benefits and positive benefits of distribution network reliability investment, and establish a calculation model for the economic benefits of distribution network reliability investment.

The calculation model for the economic benefits of reliability investment is as follows:

Step 6: When the equipment in the power supply area changes, the calculation model of the reliability investment benefit of the distribution network is used to calculate the reliability investment benefit of the distribution network.

It is easy to see that the value of A ₃ is greater than 0, indicating that the implementation of reliability investment can bring economic benefits, and the larger the value, the greater the reliability and economic benefit.

In order to implement the above method, the embodiment of the present invention further provides a distribution network reliability investment benefit analysis device, the device comprising: a database module, a data processing module, an input module, an analysis module and an output module connected in sequence;

The data processing module is configured to calculate an investment cost of the transaction device, and match the investment cost with a pre-arranged power outage event to obtain an association relationship;

The analysis module is further configured to establish a reliability investment according to the fault power failure information. The post-distribution power grid reliability improvement prediction model predicts the reliability of the fault power outage.

In practical applications, each of the above modules may be implemented by a processor in a distribution network reliability investment benefit analysis device; specifically, the processor is configured to execute the steps of any of the above methods when the computer program is run; the computer program stores On the memory of the distribution network reliability investment benefit analysis device.

It should be noted that, in the distribution network reliability investment benefit analysis device provided by the above embodiments, only the division of each of the above program modules is illustrated in the analysis of the distribution network reliability investment benefit, and in actual application, according to the needs The above processing is performed by different program modules, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the embodiment of the distribution network reliability investment benefit analysis device and the distribution network reliability investment benefit analysis method provided by the above embodiments are the same concept, and the specific implementation process is detailed in the method embodiment, and details are not described herein again.

Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Thus, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware. Moreover, the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (system), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. The work specified in one or more blocks of a flow or a flow and/or a block diagram of a flowchart Able device.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

Based on this, an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the foregoing methods are implemented.

The above are only the embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalents, improvements, etc., which are made within the spirit and principles of the present invention, are included in the present invention to be approved. Within the scope of the claims.

Industrial applicability

The solution provided by the embodiment of the present invention acquires the transaction information of the equipment in the power supply area; calculates the investment cost according to the changed device; matches the investment cost with the pre-arranged power outage event, acquires the association relationship; and establishes the reliability according to the transaction information Pre-investment distribution network reliability improvement prediction model; establish a calculation model of distribution network reliability investment benefit; when the power supply area equipment changes, use the distribution network reliability investment benefit calculation model to calculate distribution network reliability investment Benefits enable the model to more accurately reflect the impact of distribution grid investments on distribution grid reliability.

Claims

A distribution network reliability investment benefit analysis method, the method comprising:

Obtaining the change information of the device in the power supply area;

Calculate the investment cost based on the transaction device corresponding to the transaction information;

Match the investment cost with the pre-arranged power outage event to obtain the relationship;

Calculating a calculation model for the reliability investment benefit of the distribution network based on the correlation relationship;

When the equipment in the power supply area changes, the calculation model of the reliability investment benefit of the distribution network is used to calculate the reliability investment benefit of the distribution network.
The reliability investment economic benefit analysis method of the distribution network according to claim 1, wherein the relationship is as follows:

In the formula, A 1 reflects the power shortage caused by unit investment; d is the number of days in the month, m i is the sum of the power shortages of the day when the power is cut off, C invest is the reliability investment calculated by the equipment transaction; A 2 reflects the power outage caused by the unit investment The number of households; z i is the sum of the number of households in the event of a power outage on the day.
The method for analyzing a reliability investment economic benefit of a distribution network according to claim 1, wherein the method further comprises: establishing a reliability improvement prediction model of the distribution network before and after the reliability investment according to the transaction information.
The method according to claim 3, wherein said distribution network reliability improvement prediction model adopts a Lagrangian multiplier method and a Carol-Kun-Tuck condition for a least squares support vector machine Get it together.
The reliability investment economic benefit analysis method for a distribution network according to claim 4, wherein the distribution network reliability improvement prediction model is as follows:

Where λ i : Laerlang multiplier, b: parametric amount, K(x i , x j ) represents a kernel function from the sample input space, through nonlinear mapping to the high-dimensional feature space, i=1,. ..,l,j=1,...,l.
The distribution system reliability investment economic benefit analysis method according to claim 4, wherein the least squares support vector is calculated as follows:

y=f(x)=ω·φ(x)+b (4)

The Lagrangian multiplier method is used for the least squares support vector machine as follows:

The Carol-Kun-Tuck condition is as follows:

Where, the training sample set {(x i , y i )}, i=1,...,n,n is the capacity of the training sample, x i ∈R n is the sample input, and y i ∈R is the sample output value ; ω is the weight vector, b is the paranoid amount; φ(x) is the mapping from the low-dimensional space to the high-dimensional space; e i is the error, e∈R l×1 is the error vector; λ: Lagrangian multiplier , λ ∈ R l × 1 ; λ = [λ 1 , λ 2 , ... λ l ] T , E = [1, 1, ... 1] T is a l × 1 dimensional column vector, Y = [y 1 , y 2 ,..., y l ] T , I is a unit matrix, K is a suitable kernel function, and
The economic benefit analysis method for distribution network reliability investment according to claim 1, wherein the reliability economic benefit analysis and evaluation model of the distribution network is as follows:

Where, A 3 is the comprehensive reliability index of the distribution network reliability, (RS-2) i+1 is the power supply reliability index predicted by the distribution network reliability improvement prediction model, (RS-2) i is the investment The reliability index of the previous cycle, h is the total number of regional power supply households, C invest is the investment amount of the forecast period, and A 2 is the number of households in the pre-arranged power outage caused by unit investment.
The economic benefit analysis method for reliability investment of a distribution network according to claim 1, wherein the investment cost of the equipment change is calculated by the following formula:

Total investment in distribution network construction = base price + labor fee × 153.1% + equipment fee × 107.51% (8)

In the formula, the base price, labor cost and equipment fee of different equipment are determined according to actual conditions.
A distribution network reliability investment benefit analysis device, the device comprises: a database module, a data processing module, an input module, an analysis module and an output module;

The database module is configured to collect and store the change information of the power supply area device;

The data processing module is configured to calculate an investment cost of the transaction device corresponding to the transaction information, and match the investment cost with the pre-arranged power outage event to obtain an association relationship;

The input module is configured to transmit data processed by the data processing module to the analysis module;

The analysis module is configured to establish a calculation model of the reliability investment benefit of the distribution network according to the association relationship; and obtain a distribution model according to the data transmitted by the input module through a calculation model of the reliability investment benefit of the distribution network Network reliability investment benefit;

The output module is configured to output a distribution network reliability investment benefit value.
A computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to perform the steps of the method of any one of claims 1 to 8.