CN109492874B - Decision-making method of investment decision-making system of three-layer grading power grid - Google Patents

Decision-making method of investment decision-making system of three-layer grading power grid Download PDF

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CN109492874B
CN109492874B CN201811197486.7A CN201811197486A CN109492874B CN 109492874 B CN109492874 B CN 109492874B CN 201811197486 A CN201811197486 A CN 201811197486A CN 109492874 B CN109492874 B CN 109492874B
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汪颖翔
方仍存
张籍
周玉洁
陈�峰
雷何
杨洁
黄敬择
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

A decision method for a three-layer grading power grid investment decision system is characterized in that an investment scale decision module firstly adopts a Logistic growth model to fit the relation between the current comprehensive grade of the construction situation of a power distribution network and the accumulated investment amount, predicting the target annual power consumption of each area, fitting the relationship between the power consumption and the accumulated investment amount by adopting a polynomial to obtain the corresponding accumulated investment amount, and then, carrying out regional optimization configuration on the investment of the power distribution network according to the accumulated investment amount, then, carrying out project attribute optimization configuration on the important degree of the construction of the power distribution network according to the attributes of each investment project by taking the regional optimization configuration result as the investment constraint of the power distribution network by using an investment direction decision module, determining a project optimization model of the investment of the power distribution network according to the comprehensive evaluation result of each investment project by taking the project attribute optimization configuration result as the attribute investment constraint, and finally, carrying out project optimization. The design not only ensures the integrity, harmony and network performance of the investment of the power distribution network, but also accords with the actual engineering.

Description

Decision-making method of investment decision-making system of three-layer grading power grid
Technical Field
The invention belongs to the field of distribution network automation, and particularly relates to a decision method of a three-level distribution network investment decision system.
Background
In recent years, along with the high-speed increase of national economy of China, the power utilization requirement of the whole society is continuously increased, the investment of power grid enterprises is also continuously increased, the investment scale of a power distribution network is increased more quickly, and the investment benefit of the power distribution network becomes a problem of key attention of power grid companies. However, the problems of unreasonable fund distribution, poor investment balance of the metro-level power grid company and the like generally exist in China provincial power grid companies at present, and meanwhile, clear standards are lacked in the fields of distribution network evaluation and comparison and selection, so that the problems of the aspects of power distribution network investment direction determination, investment project arrangement and the like exist. The investment efficiency of the power distribution network has great promotion space. A complete and reasonable power distribution network investment decision system is of great importance to the intensive and lean management of power grid enterprises.
Currently, research on investment decisions for power distribution networks can be broadly divided into two categories. The first type is comprehensive evaluation research on the current operation situation and planning scheme of the power grid. The comprehensive evaluation theory system is widely researched, namely, the planning and operation scheme of the power grid is comprehensively evaluated by reasonably evaluating the influence of the performance of each power grid on the benefit of the power grid, so that the production, investment and planning of power grid enterprises are guided by the evaluation result. The most widely used comprehensive evaluation theory at home and abroad is Analytic Hierarchy Process (AHP). The idea of AHP is to decompose complex problems by establishing a clear hierarchical structure, introduce a measure theory, standardize human judgment by relative scale through comparison, establish judgment matrices layer by layer, solve the weights of the judgment matrices, and finally calculate the comprehensive weights of the scheme. However, when the AHP method compares two by two, if the information is incomplete, the judgment is uncertain, so that the solution accuracy has a large deviation. The second type is research on distribution network investment distribution under the constraint of a certain investment amount, and relates to the problem of multi-objective optimization.
Disclosure of Invention
In view of the above background, the present invention provides a decision method for a three-tier power distribution network investment decision system, which can ensure the integrity, coordination and network performance of power distribution network investment and meet the actual engineering.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a decision method of a three-layer grading power grid investment decision system comprises an investment scale decision module, an investment direction decision module and an investment project decision module;
the decision method sequentially comprises the following steps:
step A, the investment scale decision module adopts a Logistic growth model to carry out relation S between the comprehensive score of the current situation of power distribution network construction and the accumulated investment amounti＝fi(x) Fitting is carried out, and the planned target annual power consumption L of each area is predicted based on the previous year datai＝hi(t) fitting the relation L of the power consumption and the accumulated investment amount by a polynomiali＝gi(x) Further obtain the corresponding accumulated investment amount
Figure BDA0001829165540000021
Planning the hard investment scale required by a target year, and then performing regional optimization configuration of the power distribution network investment according to the accumulated investment amount;
b, the investment direction decision module takes the area optimization configuration result as the distribution network investment constraint and carries out project attribute optimization configuration according to the importance degree of each investment project attribute in the distribution network construction;
and step C, the investment project decision module firstly takes the project attribute optimization configuration result as the attribute investment constraint, determines a project optimization model of the power distribution network investment according to the comprehensive evaluation result of each investment project, and then performs project optimization, wherein the obtained optimal project combination is the investment decision result.
In the step a, the model for optimizing and configuring the regions takes the minimum sum of the variance of each region in the planning year and the target score margin as a target function, and specifically comprises the following steps:
Figure BDA0001829165540000022
Figure BDA0001829165540000023
in the above formula, diThe difference between the comprehensive score and the target value of the current building situation of the distribution network in the ith area is obtained by weighted summation of the annual investment sum, wherein m is the total number of the areas, C is the total accumulated investment sum, fi(xi) Fitting function, x, of comprehensive score and accumulated investment amount of the ith regional distribution network construction statusiCumulative investment amount for i-th zone, c1、c2、c3The comprehensive scoring target values of the power distribution network construction current situations requiring the investment development area I, the rapid construction investment area I and the stable development investment area I are respectively.
In the step A, the comprehensive scoring of the construction current situation of the power distribution network sequentially comprises the following steps:
a1, establishing a power distribution network investment effect evaluation index system, and dividing each index in the system into a positive index, a negative index and a moderate index, wherein the index system comprises a power supply quality index, a power grid structure index, an equipment level index, a power supply capacity index, an intelligent level index and a power grid efficiency index;
a2, adopting trapezoidal distribution linear membership function fitting to carry out standardization processing on the data of the three indexes so as to determine the membership degree of each index;
and A3, comprehensively evaluating the construction status of the distribution network in each area by adopting a weighted regional hierarchy analysis method according to the membership value of each index.
The step a2 specifically includes:
for the positive indicators, normalization was performed according to the following equation:
Figure BDA0001829165540000031
for negative indicators, normalization is performed according to the following equation:
Figure BDA0001829165540000032
for the fitness index, normalization was performed according to the following formula:
Figure BDA0001829165540000033
in the above formula, rijMembership, x, of the jth index of the ith objectijThe index value of the jth index of the ith object, m the number of the evaluation objects, a1、a2Respectively, the maximum value and the minimum value of the index, [ b, c]Is a moderate range of moderate indexes.
The step A3 sequentially comprises the following steps:
step A31, comparing the importance of each evaluation index of a certain layer in pairs, and adopting an interval number a [ a ]-,a+]Establishing a judgment matrix instead of point values to reflect the uncertainty and ambiguity of evaluation, wherein a-、a+The value of (a) is determined according to the reciprocity scale 1-9;
step A32, assigning a weight lambda to the lower boundary a-of the interval number, counting and taking the expected value to obtain a weighted interval judgment matrix A (a)ij(λ))n×nWherein λ ∈ [0,1 ]]；
Step A33, introducing a weighted geometric mean operator for integration, and recording the integrated result as a matrix A (a)ij)n×n：
Figure BDA0001829165540000041
Step A34, calculating matrix A (a)ij)n×nMaximum eigenvalue λ ofmaxChecking the consistency of the weight vector W 'and solving the weight vector W' (W 'by a feature vector method'1,w'2,…,w'n) Which isIn, AW ═ λmaxW'；
Step A35, normalizing the weight vector W' to obtain the relative weight of each evaluation index of the layer relative to the upper-layer index of the layer;
step A36, calculating layer by layer according to the following formula to obtain the comprehensive score of the construction status of the power distribution network in each area
Figure BDA0001829165540000042
In the above formula, SiIs the percentile rating score, r, of the ith subjectijMembership of the jth index of the ith object, wjIs the weight coefficient of the j index, and n is the order number of the matrix.
In the step C, the project optimization model which takes the project attribute optimization configuration result as the attribute investment constraint and determines the power distribution network investment according to the comprehensive evaluation result of each investment project sequentially comprises the following steps:
step C1, dividing the investment projects into priority guarantee projects and to-be-selected projects according to the importance of the projects, classifying the to-be-selected projects according to the attributes of the projects, establishing an evaluation index system respectively according to the characteristics of the attributes to perform optimal sorting of the same projects, and then completing comprehensive evaluation of each investment project;
step C2, based on the comprehensive evaluation result of each investment project and the project attribute optimization configuration result, establishing an optimal model with the maximum total benefit of the project construction under each attribute as a target:
Figure BDA0001829165540000043
in the above formula, R (p)i) Is an item piIs a total score value of, PkIs a collection of items under the k-th attribute, C (p)i) Is an item piAmount of investment of HkAn investment size constraint for the kth attribute.
The step B comprises the following steps in sequence:
step B1, dividing investment projects into nine types of items according to attributes, namely, meeting the requirement of newly-increased load power supply, sending out the investment projects in a matched manner by a transformer substation, solving a low-voltage transformer area, solving a neck clamp problem, solving heavy load or overload of equipment, eliminating potential safety hazards of the equipment, strengthening a grid structure, transforming high-loss distribution transformers and intelligently constructing, and associating the investment projects with the evaluation indexes of the construction success of the power distribution network;
step B2, constructing a judgment matrix A (a) according to the difference between the current value of each evaluation index and the target value of the planned power gridij)n×nWherein, on a scale of 1-9 reciprocity,
Figure BDA0001829165540000051
aijfor the significance of index i relative to index j, round () is a rounding function, Δ MiIs the current value M of the index iiAnd its target value
Figure BDA0001829165540000052
Percent difference of (c);
step B3, solving the weight w by adopting an analytic hierarchy process, namely the improvement urgency degree of each evaluation index;
step B4, determining an item attribute optimization configuration model:
δ1:δ2:…:δ9＝P1:P2:…:P9
Figure BDA0001829165540000053
Figure BDA0001829165540000054
in the above formula, δkFor the assignment of the k-th attribute, HkInvestment scale for the k-th attribute, CiIs the investment size of the i-th region, PkThe importance of the k-th attribute, nkSet of construction indicators associated with the kth attribute, wiUrgency of improvement for the ith indexAnd (4) degree.
In step B1, the evaluation indexes meeting the attribute association of the newly added load power supply requirement include the medium voltage distribution network N-1 passing rate and the average household distribution capacity, the evaluation indexes sent out by the substation in a matching manner and associated with the attributes include the average medium voltage line power supply radius, the medium voltage distribution network N-1 passing rate and the average medium voltage distribution network load rate, the evaluation indexes for solving the attribute association of the low voltage transformer area are the comprehensive voltage qualification rate, the evaluation indexes for solving the attribute association of the neck comprise the medium voltage distribution network N-1 passing rate and the average medium voltage distribution network load rate, the evaluation indexes for solving the heavy load and overload attribute association comprise the medium voltage distribution network N-1 passing rate, the average household distribution capacity and the average medium voltage distribution network load rate, and the evaluation indexes for eliminating the attribute association of the equipment potential safety hazard comprise the medium voltage line cabling rate, the average medium voltage distribution network load rate, the medium voltage distribution network cable rate and the average distribution network load rate, The system comprises an overhead line insulation rate, evaluation indexes related to attributes of a reinforced grid structure comprise average power supply radius of a medium-voltage line, the N-1 passing rate of a medium-voltage distribution network and the contact rate of a medium-voltage trunk line, the evaluation indexes related to the attributes of the improved high-loss distribution transformer are high-loss distribution transformer ratio, and the evaluation indexes related to intelligent construction attributes comprise distribution automation coverage rate, intelligent electric meter coverage rate and distributed power supply permeability rate.
In the step A1, the power supply quality indexes comprise power supply reliability and comprehensive voltage qualification rate, the power grid structure indexes comprise 110kV line N-1 passing rate, 110kV power transformation N-1 passing rate, 35kV line N-1 passing rate, 35kV power transformation N-1 passing rate, 10kV average power supply radius, 10kV power distribution network N-1 passing rate and 10kV trunk line interconnection rate, the equipment level indexes comprise the insulation rate of an overhead line, the cabling rate of a 10kV line, the per-house distribution transformer capacity and the high-loss distribution transformer ratio, the power supply capacity index comprises a 110kV capacity-load ratio, a35 kV capacity-load ratio and an average load rate of a distribution line, the intelligent level indexes comprise distribution automation coverage rate, intelligent electric meter coverage rate and distributed power supply permeability, and the power grid efficiency indexes comprise input indexes and comprehensive line loss rate.
Compared with the prior art, the invention has the beneficial effects that:
1. the decision making system in the decision making method of the three-layer grading power grid investment decision making system comprises an investment scale decision making module, an investment direction decision making module and an investment project decision making module, the three-layer decision making ring is buckled with each other and pushed in layers, thereby not only meeting the development requirements of the power consumption, the operation index and the economic benefit of each area on a macroscopic level, but also refining to the optimal combination of specific projects, ensuring the integrity, the harmony and the network property of the investment of the power distribution network, being beneficial to realizing accurate investment, meanwhile, the investment scale decision module adopts a Logistic growth model to describe the improvement degree of the index value in the investment construction of the power distribution network, the Logistic growth model is derived from describing the growth rule of the biological population, namely, an upper limit always exists in the population growth in a certain environment, and when the population quantity rises to be close to the upper limit, the actual growth rate is reduced, so that the engineering practice is met. Therefore, the invention not only ensures the integrity, harmony and network property of the investment of the power distribution network, but also conforms to the actual engineering.
2. According to the decision method of the three-layer grading power grid investment decision system, the investment scale decision module establishes a model for area optimization configuration by taking the variance sum of each area in the planning year and the target score margin as the minimum as a target function, so that the balanced development of the power distribution network in each area under limited funds is realized, and the power distribution network is close to the planning power grid as possible. Therefore, the invention realizes the balanced development of the distribution network in each area under limited funds.
3. The comprehensive grading of the construction current situation of the power distribution network in the decision method of the three-layer grading power grid investment decision system comprises the steps of firstly establishing a power distribution network investment performance evaluation index system, then adopting trapezoidal distribution linear membership function fitting to carry out standardized processing on data of the three indexes, then adopting a weighted regional hierarchy analysis method to carry out comprehensive evaluation on the construction current situation of the power distribution network in each area according to the membership value of each index, and effectively solving the problems of subjective fuzziness of an evaluation subject and objective uncertainty of an evaluation object in actual engineering by establishing the regional power distribution network construction performance evaluation index system and providing the weighted regional hierarchy analysis method. Therefore, the method solves the problems of subjective fuzziness of an evaluation subject and objective uncertainty of an evaluation object in actual engineering.
4. According to the decision method of the three-layer grading power grid investment decision system, the investment item decision module classifies investment items according to the importance degree and the attributes of the items in the process of determining the item optimization model of power distribution network investment, index systems are respectively established according to different attributes, comprehensive sequencing optimization is performed under the attributes, and the item decision of the importance degree optimization and the attribute evaluation sequencing has good accuracy and practicability. Therefore, the method has good precision and practicability.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments.
Referring to fig. 1, a decision method of a three-tier power grid investment decision system includes an investment scale decision module, an investment direction decision module and an investment project decision module;
the decision method sequentially comprises the following steps:
step A, the investment scale decision module adopts a Logistic growth model to carry out relation S between the comprehensive score of the current situation of power distribution network construction and the accumulated investment amounti＝fi(x) Fitting is carried out, and the planned target annual power consumption L of each area is predicted based on the previous year datai＝hi(t) fitting the relation L of the power consumption and the accumulated investment amount by a polynomiali＝gi(x) Further obtain the corresponding accumulated investment amount
Figure BDA0001829165540000071
Planning the hard investment scale required by a target year, and then performing regional optimization configuration of the power distribution network investment according to the accumulated investment amount;
b, the investment direction decision module takes the area optimization configuration result as the distribution network investment constraint and carries out project attribute optimization configuration according to the importance degree of each investment project attribute in the distribution network construction;
and step C, the investment project decision module firstly takes the project attribute optimization configuration result as the attribute investment constraint, determines a project optimization model of the power distribution network investment according to the comprehensive evaluation result of each investment project, and then performs project optimization, wherein the obtained optimal project combination is the investment decision result.
In the step a, the model for optimizing and configuring the regions takes the minimum sum of the variance of each region in the planning year and the target score margin as a target function, and specifically comprises the following steps:
Figure BDA0001829165540000081
Figure BDA0001829165540000082
in the above formula, diThe difference between the comprehensive score and the target value of the current building situation of the distribution network in the ith area is obtained by weighted summation of the annual investment sum, wherein m is the total number of the areas, C is the total accumulated investment sum, fi(xi) Fitting function, x, of comprehensive score and accumulated investment amount of the ith regional distribution network construction statusiCumulative investment amount for i-th zone, c1、c2、c3The comprehensive scoring target values of the power distribution network construction current situations requiring the investment development area I, the rapid construction investment area I and the stable development investment area I are respectively.
In the step A, the comprehensive scoring of the construction current situation of the power distribution network sequentially comprises the following steps:
a1, establishing a power distribution network investment effect evaluation index system, and dividing each index in the system into a positive index, a negative index and a moderate index, wherein the index system comprises a power supply quality index, a power grid structure index, an equipment level index, a power supply capacity index, an intelligent level index and a power grid efficiency index;
a2, adopting trapezoidal distribution linear membership function fitting to carry out standardization processing on the data of the three indexes so as to determine the membership degree of each index;
and A3, comprehensively evaluating the construction status of the distribution network in each area by adopting a weighted regional hierarchy analysis method according to the membership value of each index.
The step a2 specifically includes:
for the positive indicators, normalization was performed according to the following equation:
Figure BDA0001829165540000083
for negative indicators, normalization is performed according to the following equation:
Figure BDA0001829165540000091
for the fitness index, normalization was performed according to the following formula:
Figure BDA0001829165540000092
in the above formula, rijMembership, x, of the jth index of the ith objectijThe index value of the jth index of the ith object, m the number of the evaluation objects, a1、a2Respectively, the maximum value and the minimum value of the index, [ b, c]Is a moderate range of moderate indexes.
The step A3 sequentially comprises the following steps:
step A31, comparing the importance of each evaluation index of a certain layer in pairs, and adopting an interval number a [ a ]-,a+]Establishing a judgment matrix instead of point values to reflect the uncertainty and ambiguity of evaluation, wherein a-、a+The value of (a) is determined according to the reciprocity scale 1-9;
step A32 lower bound a of number of intervals-Giving a weight lambda, counting and taking the expected value to obtain a weighted interval judgment matrix A (a)ij(λ))n×nWherein λ ∈ [0,1 ]]；
Step A33, introducing a weighted geometric mean operator for integration, and recording the integrated result as a matrix A (a)ij)n×n：
Figure BDA0001829165540000093
Step A34, calculating matrix A (a)ij)n×nMaximum eigenvalue λ ofmaxChecking the consistency of the weight vector W 'and solving the weight vector W' (W 'by a feature vector method'1,w'2,…,w'n) Where AW' ═ λmaxW'；
Step A35, normalizing the weight vector W' to obtain the relative weight of each evaluation index of the layer relative to the upper-layer index of the layer;
step A36, calculating layer by layer according to the following formula to obtain the comprehensive score of the construction status of the power distribution network in each area
Figure BDA0001829165540000101
In the above formula, SiIs the percentile rating score, r, of the ith subjectijMembership of the jth index of the ith object, wjIs the weight coefficient of the j index, and n is the order number of the matrix.
In the step C, the project optimization model which takes the project attribute optimization configuration result as the attribute investment constraint and determines the power distribution network investment according to the comprehensive evaluation result of each investment project sequentially comprises the following steps:
step C1, dividing the investment projects into priority guarantee projects and to-be-selected projects according to the importance of the projects, classifying the to-be-selected projects according to the attributes of the projects, establishing an evaluation index system respectively according to the characteristics of the attributes to perform optimal sorting of the same projects, and then completing comprehensive evaluation of each investment project;
step C2, based on the comprehensive evaluation result of each investment project and the project attribute optimization configuration result, establishing an optimal model with the maximum total benefit of the project construction under each attribute as a target:
Figure BDA0001829165540000102
in the above formula, R (p)i) Is an item piIs a total score value of, PkIs a collection of items under the k-th attribute, C (p)i) Is an item piAmount of investment of HkAn investment size constraint for the kth attribute.
The step B comprises the following steps in sequence:
step B1, dividing investment projects into nine types of items according to attributes, namely, meeting the requirement of newly-increased load power supply, sending out the investment projects in a matched manner by a transformer substation, solving a low-voltage transformer area, solving a neck clamp problem, solving heavy load or overload of equipment, eliminating potential safety hazards of the equipment, strengthening a grid structure, transforming high-loss distribution transformers and intelligently constructing, and associating the investment projects with the evaluation indexes of the construction success of the power distribution network;
step B2, constructing a judgment matrix A (a) according to the difference between the current value of each evaluation index and the target value of the planned power gridij)n×nWherein, on a scale of 1-9 reciprocity,
Figure BDA0001829165540000103
aijfor the significance of index i relative to index j, round () is a rounding function, Δ MiIs the current value M of the index iiAnd its target value
Figure BDA0001829165540000104
Percent difference of (c);
step B3, solving the weight w by adopting an analytic hierarchy process, namely the improvement urgency degree of each evaluation index;
step B4, determining an item attribute optimization configuration model:
δ1:δ2:…:δ9＝P1:P2:…:P9
Figure BDA0001829165540000111
Figure BDA0001829165540000112
in the above formula, δkFor the assignment of the k-th attribute, HkInvestment scale for the k-th attribute, CiIs the investment size of the i-th region, PkThe importance of the k-th attribute, nkSet of construction indicators associated with the kth attribute, wiThe degree of improvement urgency of the i-th index.
In step B1, the evaluation indexes meeting the attribute association of the newly added load power supply requirement include the medium voltage distribution network N-1 passing rate and the average household distribution capacity, the evaluation indexes sent out by the substation in a matching manner and associated with the attributes include the average medium voltage line power supply radius, the medium voltage distribution network N-1 passing rate and the average medium voltage distribution network load rate, the evaluation indexes for solving the attribute association of the low voltage transformer area are the comprehensive voltage qualification rate, the evaluation indexes for solving the attribute association of the neck comprise the medium voltage distribution network N-1 passing rate and the average medium voltage distribution network load rate, the evaluation indexes for solving the heavy load and overload attribute association comprise the medium voltage distribution network N-1 passing rate, the average household distribution capacity and the average medium voltage distribution network load rate, and the evaluation indexes for eliminating the attribute association of the equipment potential safety hazard comprise the medium voltage line cabling rate, the average medium voltage distribution network load rate, the medium voltage distribution network cable rate and the average distribution network load rate, The system comprises an overhead line insulation rate, evaluation indexes related to attributes of a reinforced grid structure comprise average power supply radius of a medium-voltage line, the N-1 passing rate of a medium-voltage distribution network and the contact rate of a medium-voltage trunk line, the evaluation indexes related to the attributes of the improved high-loss distribution transformer are high-loss distribution transformer ratio, and the evaluation indexes related to intelligent construction attributes comprise distribution automation coverage rate, intelligent electric meter coverage rate and distributed power supply permeability rate.
In the step A1, the power supply quality indexes comprise power supply reliability and comprehensive voltage qualification rate, the power grid structure indexes comprise 110kV line N-1 passing rate, 110kV power transformation N-1 passing rate, 35kV line N-1 passing rate, 35kV power transformation N-1 passing rate, 10kV average power supply radius, 10kV power distribution network N-1 passing rate and 10kV trunk line interconnection rate, the equipment level indexes comprise the insulation rate of an overhead line, the cabling rate of a 10kV line, the per-house distribution transformer capacity and the high-loss distribution transformer ratio, the power supply capacity index comprises a 110kV capacity-load ratio, a35 kV capacity-load ratio and an average load rate of a distribution line, the intelligent level indexes comprise distribution automation coverage rate, intelligent electric meter coverage rate and distributed power supply permeability, and the power grid efficiency indexes comprise input indexes and comprehensive line loss rate.
The modules of the invention are described as follows:
an investment scale decision module: and aiming at the determined total investment amount, the provincial network company needs to distribute the investment amount to each local city. On the basis of meeting the power consumption requirements of various regions and cities, the module comprehensively considers factors such as grid structures and operational benefits, establishes a set of scientific and simplified index system for comprehensively evaluating and analyzing the construction conditions of the power distribution network, lays a foundation for subsequent investment decision optimization work, simultaneously realizes the balanced development of the grid structures of the power distribution network of each region, and stimulates the increase of the operational benefits.
An investment direction decision module: for the allocated investment amount of a certain region, how to select the most urgent investment direction with the maximum benefit is important. The module analyzes the current situation of the regional power grid and a planning target, and determines the importance of each project attribute to the construction of the regional power distribution network, so that the investment amount of each attribute is determined to ensure the maximization of the investment efficiency. The importance of each attribute to the construction of the power distribution network is determined according to the difference between the current status of each attribute and the planning target, and the resource investment can be matched to ensure higher investment efficiency.
An investment item decision module: the module classifies investment projects reported in various cities according to project attributes, comprehensively evaluates and sorts the similar projects, and obtains an investment project optimal combination under the constraint of the attribute investment amount so as to ensure higher investment accuracy.
Weighted geometric mean operator: in step a33, the calculation can be simplified by introducing a weighted geometric mean operator for integration.
Attribute importance degree: for a certain attribute, the more indexes associated with the attribute, the greater the urgency for improving the indexes, and the higher the importance degree of the attribute to the construction of the power distribution network.
Example 1:
referring to fig. 1, a decision method of a three-tier power grid investment decision system includes an investment scale decision module, an investment direction decision module and an investment project decision module;
the decision method is based on data obtained by researching operation data of 11 city companies in a certain province by a power distribution network of a national power grid company from 2014 to 2017 and annual end summary statistical data serving as basic data, the total investment of the power distribution network in the province in 2018 is set to be 180 hundred million yuan, statistical and calculation software is MATLAB and SPSS, and the decision method is sequentially carried out according to the following steps:
step 1, establishing an index system for evaluating the investment success of the power distribution network, and dividing each index in the system into three types of positive indexes, negative indexes and moderate indexes, wherein the index system comprises a power supply quality index, a power grid structure index, an equipment level index, a power supply capacity index, an intelligent level index and a power grid efficiency index, the power supply quality index comprises a power supply reliability and a comprehensive voltage qualification rate, the power grid structure index comprises a 110kV line N-1 passing rate, a 110kV power transformation N-1 passing rate, a35 kV line N-1 passing rate, a35 kV power transformation N-1 passing rate, a 10kV average power supply radius, a 10kV power distribution network N-1 passing rate and a 10kV main line interconnection rate, the equipment level index comprises an overhead line insulation rate, a 10kV line cabling rate, a user average distribution capacity and a high loss distribution ratio, the power supply capacity indexes comprise a 110kV capacity-load ratio, a35 kV capacity-load ratio and an average load rate of a distribution line, the intelligent level indexes comprise a distribution automation coverage rate, an intelligent electric meter coverage rate and a distributed power supply permeability, and the power grid efficiency indexes comprise an input index and a comprehensive line loss rate;
step 2, adopting trapezoidal distribution linear membership function fitting to carry out standardization processing on the data of the three indexes so as to determine the membership degree of each index,
for the positive indicators, normalization was performed according to the following equation:
Figure BDA0001829165540000131
for negative indicators, normalization is performed according to the following equation:
Figure BDA0001829165540000132
for the fitness index, normalization was performed according to the following formula:
Figure BDA0001829165540000133
in the above formula, rijMembership, x, of the jth index of the ith objectijThe index value of the jth index of the ith object, m the number of the evaluation objects, a1、a2Respectively, the maximum value and the minimum value of the index, [ b, c]A moderate interval which is a moderate index;
step 3, according to the membership value of each index, a weighting inter-level analytic method is adopted to comprehensively evaluate the construction current situation of each regional power distribution network, and the method specifically comprises the following steps:
step 3-1, comparing the importance of each evaluation index of a certain layer pairwise, and adopting an interval number a [ a ]-,a+]Establishing a judgment matrix instead of point values to reflect the uncertainty and ambiguity of evaluation, wherein a-、a+The value of (a) is determined according to the reciprocity scale 1-9;
step 3-2, assigning a weight lambda to the lower boundary a-of the interval number, counting and taking the expected value to obtain a weighted interval judgment matrix A (a)ij(λ))n×nWherein λ ∈ [0,1 ]]；
Step 3-3, introducing a weighted geometric mean operator for integration, and recording the integrated operator as a matrix A (a)ij)n×n：
Figure BDA0001829165540000141
Wherein, the matrix of the criterion layer index is:
Figure BDA0001829165540000142
step 3-4, calculating matrix A (a)ij)n×nMaximum eigenvalue λ ofmaxChecking the consistency of the weight vector W 'and solving the weight vector W' (W 'by a feature vector method'1,w'2,…,w'n) Where AW' ═ λmaxW', the weights of the criteria level indicators are shown in table 1:
TABLE 1 criterion layer index weights
Figure BDA0001829165540000143
3-5, normalizing the weight vector W' to obtain the relative weight of each evaluation index of the layer relative to the upper-layer index of the layer;
3-6, calculating layer by layer upwards according to the following formula to obtain the comprehensive score of the construction current situation of the power distribution network in each area, wherein the result is shown in a table 2:
Figure BDA0001829165540000144
in the above formula, SiIs the percentile rating score, r, of the ith subjectijMembership of the jth index of the ith object, wjThe weight coefficient of the j index is, and n is the order number of the matrix;
TABLE 2 comprehensive evaluation results of the construction effect of each city over the years
Figure BDA0001829165540000145
Figure BDA0001829165540000151
Step 4, the investment scale decision module adoptsRelation S between comprehensive score and accumulated investment amount of distribution network construction current situation by using Logistic growth modeli＝fi(x) Fitting is carried out, and the electricity consumption L of each area in 2018 years is predicted based on the data of the previous yearsi＝hi(t) fitting the relation L of the power consumption and the accumulated investment amount by a polynomiali＝gi(x) Further obtain the corresponding accumulated investment amount
Figure BDA0001829165540000154
Namely, the hard investment scale required in 2018 (see table 3), then based on the economic acceleration of cities and cities in the province, the comprehensive scores of the investment effect in the past years and the development trend thereof, JZ, JM, HG and ES are areas I, YC, XY, XG, XN and ES to be urgently developed, are areas II for rapid construction, WH and EZ are areas III for smooth development, the comprehensive score target values in the planning years are 70, 80 and 90 respectively, a model for area optimization configuration is established by taking the variance and minimum of the areas in the planning years and the target score margin as a target function, and area optimization configuration of the investment of the power distribution network is performed according to the accumulated investment to obtain the preliminary investment allocation scale of the areas, wherein the model for area optimization configuration is as follows:
Figure BDA0001829165540000152
Figure BDA0001829165540000153
in the above formula, diThe difference between the comprehensive score and the target value of the current building situation of the distribution network in the ith area is obtained by weighted summation of the annual investment sum, wherein m is the total number of the areas, C is the total accumulated investment sum, fi(xi) Fitting function, x, of comprehensive score and accumulated investment amount of the ith regional distribution network construction statusiCumulative investment amount for i-th zone, c1、c2、c3Respectively obtaining a comprehensive scoring target value of the power distribution network construction current situation requiring the investment development area I, the rapid investment construction area I and the stable investment development area I;
TABLE 3 regional distribution of investment scales for distribution networks (Yi Yuan)
Figure BDA0001829165540000161
Step 5, dividing investment projects into nine types of items which meet newly-increased load power supply requirements, are matched and sent out by a transformer substation, solve a low-voltage transformer area, solve a 'neck', solve heavy load or overload of equipment, eliminate potential safety hazards of the equipment, strengthen a grid structure, reform a high-loss distribution transformer and intelligently construct, and associating the items with evaluation indexes of power distribution network construction success, wherein the evaluation indexes which meet newly-increased load power supply requirements and are associated comprise medium-voltage distribution network N-1 passing rate and average distribution transformer capacity, the evaluation indexes which are matched and sent out by the transformer substation comprise medium-voltage line average power supply radius, medium-voltage distribution network N-1 passing rate and medium-voltage distribution network average load rate, the evaluation indexes which solve low-voltage transformer area attribute association are comprehensive voltage qualification rate, and the evaluation indexes which solve 'neck' attribute association comprise medium-voltage distribution network N-1 passing rate, average medium-voltage distribution network passing rate, The evaluation indexes related to solving equipment heavy load and overload property comprise a medium-voltage distribution network N-1 passing rate, a household average distribution capacity and a medium-voltage distribution network average load rate, the evaluation indexes related to eliminating equipment safety hidden trouble property comprise a medium-voltage line cabling rate and an overhead line insulation rate, the evaluation indexes related to strengthening grid structure property comprise a medium-voltage line average power supply radius, a medium-voltage distribution network N-1 passing rate and a medium-voltage trunk line contact rate, the evaluation indexes related to transforming high-loss distribution and change property are high-loss distribution and change ratio, and the evaluation indexes related to intelligent construction property comprise a distribution automation coverage rate, an intelligent ammeter coverage rate and a distributed power supply permeability rate;
step 6, constructing a judgment matrix A (a) by taking the difference between the current value of each evaluation index and the target value of the planned power grid as the basisij)n×nWherein, on a scale of 1-9 reciprocity,
Figure BDA0001829165540000171
aijfor the significance of index i relative to index j, round () is a rounding function, Δ MiIs the current value M of the index iiAnd its target value
Figure BDA0001829165540000172
Percent difference of (c);
and 7, solving the weight w by adopting an analytic hierarchy process, namely the improvement urgency degree of each evaluation index, wherein the associated index values and the index weights of the EZ city distribution network are shown in a table 4:
TABLE 4 EZ City distribution network associated index values and index weights
Figure BDA0001829165540000173
Step 8, determining an item attribute optimization configuration model:
δ1:δ2:…:δ9＝P1:P2:…:P9
Figure BDA0001829165540000174
Figure BDA0001829165540000175
in the above formula, δkFor the assignment of the k-th attribute, HkInvestment scale for the k-th attribute, CiIs the investment size of the i-th region, PkThe importance of the k-th attribute, nkSet of construction indicators associated with the kth attribute, wiThe degree of improvement urgency of the i-th index;
as can be seen from Table 4, when the investment scale of the distribution network in EZ city is 3.54 billion yuan, the importance of each project attribute and the result of investment distribution are shown in Table 5.
TABLE 5 importance degree of each attribute and its investment distribution result
Figure BDA0001829165540000181
And 9, dividing the investment projects into priority guarantee projects and to-be-selected projects according to the importance of the projects, classifying the to-be-selected projects according to the attributes of the projects, establishing an evaluation index system for optimizing and sequencing the same projects according to the characteristics of the attributes, and then completing comprehensive evaluation of the investment projects, wherein 8 items of 'reinforced grid structure type' are reported in 2018 in EZ city, and the total investment is calculated to be 9200 ten thousand yuan. The project evaluation indexes of the attributes are shown in table 6, the investment scale of the attributes is 6627 ten thousand yuan, and the evaluation data of each project is shown in table 7:
TABLE 6 comprehensive evaluation index system for 'reinforced grid structure class' project
Figure BDA0001829165540000182
TABLE 7 evaluation of "reinforced grid structure class" project in EZ City
Figure BDA0001829165540000183
Step 10, based on the comprehensive evaluation result of each investment project and the project attribute optimization configuration result, establishing an optimal model with the maximum total benefit of the project construction under each attribute as a target, and obtaining the optimal investment combination of construction projects in 2018 of the EZ city:
Figure BDA0001829165540000191
in the above formula, R (p)i) Is an item piIs a total score value of, PkIs a collection of items under the k-th attribute, C (p)i) Is an item piAmount of investment of HkAn investment size constraint for the kth attribute.

Claims (6)

1. A decision method of a three-layer grading power grid investment decision system is characterized by comprising the following steps:
the decision system comprises an investment scale decision module, an investment direction decision module and an investment project decision module;
the decision method sequentially comprises the following steps:
step A, the investment scale decision module adopts a Logistic growth model to carry out relation S between the comprehensive score of the current situation of power distribution network construction and the accumulated investment amounti＝fi(x) Fitting is carried out, and the planned target annual power consumption L of each area is predicted based on the previous year datai＝hi(t) fitting the relation L of the power consumption and the accumulated investment amount by a polynomiali＝gi(x) Further obtain the corresponding accumulated investment amount
Figure FDA0003331671720000013
The method comprises the following steps of planning the hard investment scale required by a target year, and then performing regional optimization configuration of power distribution network investment according to the accumulated investment amount, wherein a model of the regional optimization configuration takes the variance and minimum of each region in the planned year and a target score margin as a target function, and specifically comprises the following steps:
Figure FDA0003331671720000011
Figure FDA0003331671720000012
in the above formula, diThe difference between the comprehensive score and the target value of the current building situation of the distribution network in the ith area is obtained by weighted summation of the annual investment sum, wherein m is the total number of the areas, C is the total accumulated investment sum, fi(xi) Fitting function, x, of comprehensive score and accumulated investment amount of the ith regional distribution network construction statusiCumulative investment amount for i-th zone, c1、c2、c3Respectively as the urgent waiting investment area IThe comprehensive scoring target value of the power distribution network construction current situation of the rapid construction investment area I and the stable development investment area I;
the comprehensive scoring of the power distribution network construction current situation sequentially comprises the following steps:
a1, establishing a power distribution network investment effect evaluation index system, and dividing each index in the system into a positive index, a negative index and a moderate index, wherein the index system comprises a power supply quality index, a power grid structure index, an equipment level index, a power supply capacity index, an intelligent level index and a power grid efficiency index;
a2, adopting trapezoidal distribution linear membership function fitting to carry out standardization processing on the data of the three indexes so as to determine the membership degree of each index;
step A3, according to the membership value of each index, a weighting zone hierarchy analysis method is adopted to comprehensively evaluate the construction status of each regional power distribution network, and the method sequentially comprises the following steps:
step A31, comparing the importance of each evaluation index of a certain layer in pairs, and adopting an interval number a [ a ]-,a+]Establishing a judgment matrix instead of point values to reflect the uncertainty and ambiguity of evaluation, wherein a-、a+The value of (a) is determined according to the reciprocity scale 1-9;
step A32 lower bound a of number of intervals-Giving a weight lambda, counting and taking the expected value to obtain a weighted interval judgment matrix A (a)ij(λ))n×nWherein λ ∈ [0,1 ]]；
Step A33, introducing a weighted geometric mean operator for integration, and recording the integrated result as a matrix A (a)ij)n×n：
Figure FDA0003331671720000021
Step A34, calculating matrix A (a)ij)n×nMaximum eigenvalue λ ofmaxChecking the consistency of the weight vector W 'and solving the weight vector W' (W 'by a feature vector method'1,w'2,…,w'n) Where AW' ═ λmaxW'；
Step A35, normalizing the weight vector W' to obtain the relative weight of each evaluation index of the layer relative to the upper-layer index of the layer;
step A36, calculating layer by layer according to the following formula to obtain the comprehensive score of the construction status of the power distribution network in each area
Figure FDA0003331671720000022
In the above formula, SiIs the percentile rating score, r, of the ith subjectijMembership of the jth index of the ith object, wjThe weight coefficient of the j index is, and n is the order number of the matrix;
b, the investment direction decision module takes the area optimization configuration result as the distribution network investment constraint and carries out project attribute optimization configuration according to the importance degree of each investment project attribute in the distribution network construction;
and step C, the investment project decision module firstly takes the project attribute optimization configuration result as the attribute investment constraint, determines a project optimization model of the power distribution network investment according to the comprehensive evaluation result of each investment project, and then performs project optimization, wherein the obtained optimal project combination is the investment decision result.
2. The decision method of the investment decision system of the three-tier power distribution network according to claim 1, characterized in that:
the step a2 specifically includes:
for the positive indicators, normalization was performed according to the following equation:
Figure FDA0003331671720000031
for negative indicators, normalization is performed according to the following equation:
Figure FDA0003331671720000032
for the fitness index, normalization was performed according to the following formula:
Figure FDA0003331671720000033
in the above formula, rijMembership, x, of the jth index of the ith objectijThe index value of the jth index of the ith object, m the number of the evaluation objects, a1、a2Respectively, the maximum value and the minimum value of the index, [ b, c]Is a moderate range of moderate indexes.
3. The decision method of the investment decision system of the three-tier power distribution network according to claim 1, characterized in that:
in the step C, the project optimization model which takes the project attribute optimization configuration result as the attribute investment constraint and determines the power distribution network investment according to the comprehensive evaluation result of each investment project sequentially comprises the following steps:
step C1, dividing the investment projects into priority guarantee projects and to-be-selected projects according to the importance of the projects, classifying the to-be-selected projects according to the attributes of the projects, establishing an evaluation index system respectively according to the characteristics of the attributes to perform optimal sorting of the same projects, and then completing comprehensive evaluation of each investment project;
step C2, based on the comprehensive evaluation result of each investment project and the project attribute optimization configuration result, establishing an optimal model with the maximum total benefit of the project construction under each attribute as a target:
Figure FDA0003331671720000041
in the above formula, R (p)i) Is an item piIs a total score value of, PkIs a collection of items under the k-th attribute, C (p)i) Is an item piAmount of investment of HkInvestment size constraint for kth attribute。
4. The decision method of the investment decision system of the three-tier power distribution network according to claim 1, characterized in that:
the step B comprises the following steps in sequence:
step B1, dividing investment projects into nine types of items according to attributes, namely, meeting the requirement of newly-increased load power supply, sending out the investment projects in a matched manner by a transformer substation, solving a low-voltage transformer area, solving a neck clamp problem, solving heavy load or overload of equipment, eliminating potential safety hazards of the equipment, strengthening a grid structure, transforming high-loss distribution transformers and intelligently constructing, and associating the investment projects with the evaluation indexes of the construction success of the power distribution network;
step B2, constructing a judgment matrix A (a) according to the difference between the current value of each evaluation index and the target value of the planned power gridij)n×nWherein, on a scale of 1-9 reciprocity,
Figure FDA0003331671720000042
aijfor the significance of index i relative to index j, round () is a rounding function, Δ MiIs the current value M of the index iiAnd its target value
Figure FDA0003331671720000043
Percent difference of (c);
step B3, solving the weight w by adopting an analytic hierarchy process, namely the improvement urgency degree of each evaluation index;
step B4, determining an item attribute optimization configuration model:
δ1:δ2:…:δ9＝P1:P2:…:P9
Figure FDA0003331671720000044
Figure FDA0003331671720000045
in the above formula, δkFor the assignment of the k-th attribute, HkInvestment scale for the k-th attribute, CiIs the investment size of the i-th region, PkThe importance of the k-th attribute, nkSet of construction indicators associated with the kth attribute, wiThe degree of improvement urgency of the i-th index.
5. The decision method of the investment decision system of the three-tier power distribution network according to claim 4, characterized in that:
in step B1, the evaluation indexes meeting the attribute association of the newly added load power supply requirement include the medium voltage distribution network N-1 passing rate and the average household distribution capacity, the evaluation indexes sent out by the substation in a matching manner and associated with the attributes include the average medium voltage line power supply radius, the medium voltage distribution network N-1 passing rate and the average medium voltage distribution network load rate, the evaluation indexes for solving the attribute association of the low voltage transformer area are the comprehensive voltage qualification rate, the evaluation indexes for solving the attribute association of the neck comprise the medium voltage distribution network N-1 passing rate and the average medium voltage distribution network load rate, the evaluation indexes for solving the heavy load and overload attribute association comprise the medium voltage distribution network N-1 passing rate, the average household distribution capacity and the average medium voltage distribution network load rate, and the evaluation indexes for eliminating the attribute association of the equipment potential safety hazard comprise the medium voltage line cabling rate, the average medium voltage distribution network load rate, the medium voltage distribution network cable rate and the average distribution network load rate, The system comprises an overhead line insulation rate, evaluation indexes related to attributes of a reinforced grid structure comprise average power supply radius of a medium-voltage line, the N-1 passing rate of a medium-voltage distribution network and the contact rate of a medium-voltage trunk line, the evaluation indexes related to the attributes of the improved high-loss distribution transformer are high-loss distribution transformer ratio, and the evaluation indexes related to intelligent construction attributes comprise distribution automation coverage rate, intelligent electric meter coverage rate and distributed power supply permeability rate.
6. The decision method of the investment decision system of the three-tier power distribution network according to claim 1, characterized in that:
in the step A1, the power supply quality indexes comprise power supply reliability and comprehensive voltage qualification rate, the power grid structure indexes comprise 110kV line N-1 passing rate, 110kV power transformation N-1 passing rate, 35kV line N-1 passing rate, 35kV power transformation N-1 passing rate, 10kV average power supply radius, 10kV power distribution network N-1 passing rate and 10kV trunk line interconnection rate, the equipment level indexes comprise the insulation rate of an overhead line, the cabling rate of a 10kV line, the per-house distribution transformer capacity and the high-loss distribution transformer ratio, the power supply capacity index comprises a 110kV capacity-load ratio, a35 kV capacity-load ratio and an average load rate of a distribution line, the intelligent level indexes comprise distribution automation coverage rate, intelligent electric meter coverage rate and distributed power supply permeability, and the power grid efficiency indexes comprise input indexes and comprehensive line loss rate.
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