CN108921376A - A kind of intelligent distribution network electricity consumption reliability promotes the preferred method and system of object - Google Patents
A kind of intelligent distribution network electricity consumption reliability promotes the preferred method and system of object Download PDFInfo
- Publication number
- CN108921376A CN108921376A CN201810510429.3A CN201810510429A CN108921376A CN 108921376 A CN108921376 A CN 108921376A CN 201810510429 A CN201810510429 A CN 201810510429A CN 108921376 A CN108921376 A CN 108921376A
- Authority
- CN
- China
- Prior art keywords
- index
- distribution network
- reliability
- electricity consumption
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 79
- 230000005611 electricity Effects 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 55
- 239000011159 matrix material Substances 0.000 claims abstract description 31
- 238000011156 evaluation Methods 0.000 claims abstract description 21
- 238000003860 storage Methods 0.000 claims abstract description 8
- 238000011161 development Methods 0.000 claims abstract description 5
- 230000006872 improvement Effects 0.000 claims description 18
- 238000007726 management method Methods 0.000 claims description 12
- 238000004519 manufacturing process Methods 0.000 claims description 10
- 238000013459 approach Methods 0.000 claims description 9
- 238000012546 transfer Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 239000013598 vector Substances 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 230000009466 transformation Effects 0.000 claims description 5
- 230000001737 promoting effect Effects 0.000 claims description 3
- 238000012797 qualification Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 2
- 230000005484 gravity Effects 0.000 claims description 2
- 238000012360 testing method Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 240000002853 Nelumbo nucifera Species 0.000 description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 2
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000406668 Loxodonta cyclotis Species 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000036316 preload Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention discloses the preferred method and system of a kind of intelligent distribution network electricity consumption reliability promotion object.System includes data acquisition module, evaluation module and storage module.Preferred method includes：It establishes intelligent distribution network electricity consumption reliability and promotes demand degree assessment indicator system；Data acquisition module obtains index of correlation data and is stored into storage module；Evaluation module is again normalized each index value of all objects to be selected, nondimensionalization, forms standardization index matrix；Master, the objective weight value of each index are calculated, and seeks the synthetic weights weight values of each index；Weighted normal index matrix is calculated, determines positive and negative absolute ideal solution, calculates the grey relational grade of each object to be selected；Relative similarity degree of each object to be selected relative to positive ideal solution is calculated, and object, output preferred result to storage module are promoted according to the preferred reliability of its size.The present invention can fully assess the true electricity consumption situation of user in intelligent distribution network, and intelligent distribution network electricity consumption reliability is instructed to promote the development of engineering.
Description
Technical field
The present invention relates to Reliability Evaluation field, in particular to a kind of intelligent distribution network electricity consumption reliability promotion pair
The preferred method and system of elephant.
Background technique
As the important channel for improving power supply enterprise's operation and management level and service ability, reliability transformation and promotion work
Make always by the attention of power supply enterprise.With the development of intelligent distribution network, distributed power generation, energy storage etc. obtain the side of electric energy
Formula is enriched constantly, and distribution network electric energy quality problem is outstanding day by day, and the country is for a long time using Middle Voltage as the obtained confession of Statistical Criteria
The limitation of electric reliability assessment system is more obvious：1) power supply of power supply reliability index, especially middle pressure bore is reliable
Property, the problem of powering duration is only considered roughly, can not reflect the true electricity consumption experience of user comprehensively；2) system-oriented, no
Consider that traditional assessment indicator system of electric energy availability has been unable to meet for the fine-grained management of sale of electricity enterprise power distribution network and sale of electricity city
The new demand of depth of field exploitation；3) traditional Reliability Evaluation can not be suitable for intelligent distribution network.It is distributed in intelligent distribution network
The influence that the new factors such as formula power supply and energy storage generate user power utilization process is difficult to embody in Reliability Evaluation.
In addition to this, distribution network reliability promotes engineering only with power supply reliability for primary foundation at this stage, it is difficult to conscientiously
Promote electricity consumption experience.On the other hand, although the research in terms of reliability prediction and method for improving has been achieved for numerous achievements,
But the reliability Promotion Transformation of current intelligent distribution network with scheme it is preferred based on, consider that reliability promotes objective for implementation
It is preferred that.Therefore, in the case where reliability improvement project limited investment, rationally assessing each power distribution network electricity consumption reliability promotion is needed
It asks, is preferably badly in need of promoting the power distribution network of electricity consumption reliability, reliability is promoted into effect and is maximized, for improving user satisfaction
Undoubtedly there is more great meaning with service level.
The present invention constructs intelligence in terms of power grid power supply reliability, user power utilization experience property, improvement project economy three
Power distribution network electricity consumption reliability promotes need assessment index system, proposes a kind of intelligent distribution network electricity consumption reliability promotion object
Preferred method and system.
Summary of the invention
The purpose of the present invention is to overcome the shortcomings of the existing technology and deficiency, proposes that a kind of intelligent distribution network electricity consumption is reliable
Property promoted object preferred method and system.
The purpose of the present invention is realized by the following technical solution.
A kind of intelligent distribution network electricity consumption reliability promotes the optimum decision system of object, includes：
Data acquisition module is communicated with the backstage production management system of power grid enterprises and marketing message management system,
And the data of the corresponding index of each intelligent distribution network to be selected are acquired, also part index number is manually entered for network operation personnel
Data.
Evaluation module approaches ideal method (technique for order preference by based on improvement using one kind
Similarity to ideal solution, TOPSIS) and grey correlation analysis comprehensive estimation method to each intelligence to be selected
The electricity consumption reliability of energy power distribution network promotes demand and is assessed, and preferably goes out to need most the intelligence for carrying out the transformation of electricity consumption reliability and matches
Power grid.
Storage module stores data and assessment result.
It include following step using a kind of above-mentioned method of the optimum decision system of intelligent distribution network electricity consumption reliability promotion object
Suddenly：
S1, intelligent match is established in terms of power grid power supply reliability, user power utilization experience property, improvement project economy three
Power grid electricity consumption reliability promotes Needs index system.
S2, data acquisition module pass through power grid backstage production management system, marketing message management system and manually
The mode of input obtains the data of index of correlation, and is stored among memory module
S3, evaluation module call related data, and each index value of all objects to be selected are normalized, dimensionless
Change, forms standardization index matrix.
S4, evaluation module obtain each index using analytic hierarchy process (AHP) (analytic hierarchy process, AHP)
Subjective weighted value, obtain the objective weight value of each index using entropy assessment is improved, and seek the synthetic weights weight values of each index.
S5, evaluation module calculate weighted normal index matrix, determine positive and negative absolute ideal solution, calculate each object to be selected
Grey relational grade.
S6, evaluation module calculate relative similarity degree R (i) of each object to be selected relative to positive ideal solution, and big according to R (i)
Small preferred reliability promotes object, and preferred result is output among memory module.
The intelligent distribution network electricity consumption reliability that the step S1 is referred to promotes Needs index system, specific comprising referring to
It marks as follows：
1) isolated island abundant intensity probability
Different moments during isolated operation, the output power of distributed generation resource might not be able to satisfy negative in isolated island
The demand of lotus, load, distributed generation resource watt level can be its respectively one of which in probabilistic model.Considering two
In the case where all possible combination of person, the probability that distributed generation resource meets load point power demand in isolated island i can use orphan
Island abundant intensity probability ρiIt indicates, calculation formula is as follows：
In formula,WithRespectively represent the payload and distributed electrical under the jth kind operating status of isolated island i
The size of source gross capability；NdiFor all possible operating status quantity (isolated island internal loading and the distributed electrical source power of isolated island i
All possible combinations mode)；ρi,jIndicate that isolated island i is in the probability under jth kind operating status.
To reflect this entire effect, system isolated operation abundant intensity probability is defined, calculation formula is as follows：
In formula,For the probability for forming isolated island i operation after system jam, specific value can pass through Monte Carlo method
Emulation obtains；NdFor the scene quantity of be likely to form isolated island.
2) load transfer efficiency
N-1 failure is the most commonly seen failure of power distribution network, after failure occurs, if power distribution network is capable of providing more loads
Turn then to mean that power distribution network network structure is stronger for line channel.In fact, power distribution network is provided after N-1 failure occurs
The safe transfer of sub-load can only be met, for the capacity of trunk of load transfer in order to assess intelligent distribution network load transfer
Efficiency, while considering the case where failure leads to load loss, load transfer efficiency index is defined, calculation formula is as follows：
In formula,It indicates under i-th of N-1 fault condition, the limit transmitted power of route j；Respectively indicate system
The power for being removed for the no load of guarantee and route j being needed to transmit；PLIt is that preload total amount occurs for failure；For N-1 event
Hinder quantity.Work as ηiWhen for negative value, indicate that distribution has load loss, and η under the failureiIt is smaller, it indicates that load loss amount is bigger, uses
It is bigger that electric reliability promotes demand.
To reflect this entire effect, defines system loading and turn for efficiency, calculation formula is as follows：
In formula,The probability of i-th of N-1 failure occurs for system；NN-1For the quantity of all possible N-1 failure.
3) power supply reliability index completion rate
According to south electric network《110kV and following distribution network planning guideline》, power distribution network must be according to negative in planning
To division of the power supply area grade, formulating different power supplies to different grades of power supply area can for lotus density and planning and development positioning
By rate objectives of examination, can specifically see the table below：
The power supply reliability objectives of examination (%) of all kinds of service areas of table 1
The present invention is according to the power supply reliability objectives of examination of service area and the power supply reliability of actual count, definition power supply
Reliability index completion rate, calculation formula are as follows：
In formula, RSaIndicate power supply reliability assessment target value, RS-1Indicate the power supply reliability of actual count.
4) electricity consumption reliability：In statistical time, all users obtain the hourage and statistical time of available powers supplies
Ratio is denoted as RRSL。
In formula：tmRepresent total power off time of m-th of user in statistical time in the power distribution network；M represents the power distribution network
Middle user's fee sum；T represents statistics duration.
5) user power utilization satisfaction：Total user off the net is matched with this by the number of customer complaint in power distribution network to be assessed 1 year
The ratio between number, is denoted as
In formula：NucRepresent the customer complaint total degree received in the power distribution network 1 year；M represents the power distribution network and falls into a trap expense
Family sum.
6) customer charge grade：According to China《Code for design of electric power supply systems GB 50052-2009》, customer charge presses
Its load character and significance level are divided into superfine load, first order load, two stage loads and three stage loads.When one it is to be assessed right
When as middle load different there are multiple grades, using wherein highest load level as the load level of the object.
Since customer charge grade is non-quantized index, this example adopts each stage load from inferior grade to high-grade respectively
Quantified with 1,0.75,0.5,0.25, i.e., the superfine load index value is assigned a value of 1, and three-level load amplitude is 0.25.
7) rate of qualified voltage：In statistical time, the voltage qualification duration of subscriber's drop unit and the ratio of statistical time,
It is denoted as VER (%).
In formula：tvmRepresent voltage qualification hourage of m-th of user's fee in statistical time in power distribution network.
8) O&M cost is invested
Wherein
In formula, CiFor the equal years value of initial outlay expense；CeFor the equal years value of remanent value of equipment；CmFor operation expense,
And the expenses such as via net loss；PLIt is the predicted load of distribution planning horizon；CoIt is initial outlay expense；K is discount rate；n
It is the equipment operation time limit；CrIt is the residual value in end of term equipment life.
9) electricity production ratio：During statistics, in evaluation object the gross national product Yu total electricity consumption of all users it
Than being denoted as：VOC (member/kWh)
In formula：GDP represents the country (area) total output value of a certain power distribution network or area during statistics, unit：Hundred million
Member；Q represents the total electricity consumption of a certain power distribution network or area during statistics, unit：Hundred million kWh.(times/year).
The step S3 is comprised the steps of：
There is j-th of evaluation index values of m objects to be selected, n evaluation index, i-th of assessment object to be denoted as xij。
The initial data of all objects to be selected is pre-processed, processing formula is as follows：
It is bigger for index value, the bigger index of electricity consumption reliability requirement is promoted, is had：
rij=xij/miax xij (12)
It is smaller for index value, the bigger index of electricity consumption reliability requirement is promoted, is had：
After turning to nondimensional achievement data, the standardization index matrix for obtaining each object to be selected is as follows：
Wherein, rijIt is nondimensionalization treated the numerical value of j-th of index of i-th of object to be selected, closer to 1, explanation
This index performance of the object is poorer, more there is the demand for promoting reliability.
Step S4 is comprised the steps of：
S401：The subjective weighted value of each index is obtained using AHP method,
Each index importance is compared two-by-two using three scales, establishes comparator matrix A：
Wherein,
With range method development of judgment matrix C=(cij)n×n：
In formula, riIt is the sum of every row element of matrix A；cbIt is according to element pair very poor under certain standard for a constant
Relative importance and determination, c is taken under normal circumstancesb=9；R=rmax-rmin, rmax=max (r1, r2..., rn), rmin=
min(r1, r2..., rn)。
The maximum eigenvalue of judgment matrix C is sought, and carries out consistency check, introduces compatibility index CI test and judge
The consistency of matrix, wherein：
CI=(λmax-n)/(n-1) (18)
Generally work as CI<When 0.1, it is believed that the consistency of judgment matrix can receive；Work as CI>It, should be again to judgement when 0.1
Matrix make it is suitably modified, then to the matrix after correction recalculate weight and carry out consistency desired result.
After the maximum eigenvalue of judgment matrix passes through consistency desired result, the corresponding feature vector of maximum eigenvalue is calculated
ω’A=(ω 'A1, ω 'A2... ω 'An), to being normalized, certain grade of each index is obtained relative to its upper level index
Relative weighting ωA=(ωA1, ωA2... ωAn)。
S402：The objective weight value of each index is obtained using entropy assessment is improved,
For index j, the feature specific gravity of object i to be selected is：
The entropy of index j is：
Then the objective weight of index j is：
In formula,It is the average value of all entropy for not being 1；
S403：The synthetic weights weight values of each index are as follows：
ωj=φ ωAj+(1-φ)ωEj (23)
The present invention comprehensively considers the characteristics of two kinds of tax power methods, takes Φ=0.5.
Weight vectors ω is normalized, wherein
Step S5 is comprised the steps of：
S501：By Standard Process and standard summary weight vectors, can acquire weighted normal matrix B=
[bij]m×n, wherein：
S502：The positive and negative ideal solution data sequence of ideal object is respectively defined as：
S503：Grey relational grade is calculated, the calculation method of grey relational grade is as follows：
In formula,ρ is resolution ratio, generally takes 0.5.
Step S6 is comprised the steps of：
S601：Relative similarity degree is calculated, grey correlation analysis is based on, each object to be selected can be calculated relative to positive ideal solution
Relative similarity degree R (i)：
Relative similarity degree reflect object to be selected and positive ideal object or negative ideal object in situation variation close to journey
Degree.According to the size of R (i), each object to be selected is ranked up, preferably object, and by preferred result be output to memory module it
In.
Compared with prior art, the present invention having the following advantages that and technical effect：
1, a kind of intelligent distribution network electricity consumption reliability designed by the present invention promotes the preferred method and system of object, first
It is secondary in terms of power grid power supply reliability, user power utilization experience property, improvement project economy three, establish it is a set of can either be complete
The true electricity consumption situation of user is reflected in face, and the electricity consumption reliability that intelligent distribution network can be instructed to be transformed promotes the assessment of demand degree and refers to
Mark system.
2, the comprehensive estimation method based on improvement approximatioss and grey correlation analysis designed by the present invention, utilizes improvement
Entropy assessment calculates the objective weight of each index, the subjective weight of each index is calculated using AHP method, and then seek each index
Comprehensive weight, the characteristics of both having reflected initial data, it is also considered that the practical experience of expert.
3, designed by the present invention based on the comprehensive estimation method for improving approximatioss and grey correlation analysis, using absolute
Ideal solution preferably solves the problems, such as number of objects to be selected variation bring " inverse sequence ", accurately can preferably go out be badly in need of into
The intelligent distribution network that row electricity consumption reliability is promoted helps power grid enterprises to realize maximum benefit using limited resource.
Detailed description of the invention
Fig. 1 is a kind of preferred flow charts of the preferred method of intelligent distribution network electricity consumption reliability promotion object in example；
Fig. 2 is that the intelligent distribution network electricity consumption reliability that step S1 is referred in example promotes Needs index system figure.
Fig. 3 is a kind of optimum decision system structure chart of intelligent distribution network electricity consumption reliability promotion object in example.
Fig. 4 is that different ideal solutions utilize the resulting approach degree comparison diagram of Euclidean distance method calculating in embodiment.
Fig. 5 is the approach degree comparison diagram for calculating gray relative analysis method and euclidean distance method, sciagraphy in example.
Specific embodiment
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are not
It is limited to this, is that those skilled in the art can refer to existing skill if place is not described in detail especially it is noted that having below
Art understand or realize.
Attached drawing 1 is a kind of preferred method flow chart of intelligent distribution network electricity consumption reliability promotion object, and basic step is：
Firstly, establishing intelligent distribution network from three power grid power supply reliability, user power utilization experience property and improvement project economy levels
Electricity consumption reliability promotes demand degree assessment indicator system；Data acquisition module is by system automatic collection or is manually entered
Mode obtains index of correlation data, and is stored into storage module；Evaluation module again carries out each index value of all objects to be selected
Normalization, nondimensionalization form standardization index matrix；Then the subjective weighted value of each index is obtained using AHP method, used
It improves entropy assessment and obtains the objective weight value of each index, and seek the synthetic weights weight values of each index；Secondly, calculating weighted normal
Change index matrix, determine positive and negative absolute ideal solution, calculates the grey relational grade of each object to be selected；Again, it is each to be selected right to calculate
Object is promoted as the relative similarity degree R (i) relative to positive ideal solution, and according to the preferred reliability of R (i) size, and is exported excellent
Select result to storage module.
Attached drawing 2 is that the intelligent distribution network electricity consumption reliability that step S1 is referred to promotes Needs index system figure.In specific
Rong Wei：
The first class index that intelligent distribution network electricity consumption reliability promotes Needs index system includes power grid power supply reliability
Index, user power utilization experience property index and improvement project economic index.Power grid power supply reliability index includes that 3 second levels refer to
Mark：Isolated island abundant intensity probability, load transfer efficiency, power supply reliability index completion rate；User power utilization experience property index includes 4
A two-level index：Electricity consumption reliability, electricity consumption satisfaction, customer charge grade, rate of qualified voltage；Improvement project economic index
Including 2 two-level index：Invest O&M cost and electricity production ratio.
Attached drawing 3 is a kind of system construction drawing for the optimum decision system that intelligent distribution network electricity consumption reliability promotes object, specific to wrap
It includes：Data acquisition module is communicated with the backstage production management system of power grid enterprises and marketing message management system, and is adopted
The data for collecting the corresponding index of each intelligent distribution network to be selected, are also manually entered part index number data for network operation personnel；
Evaluation module approaches the comprehensive estimation method of ideal method and grey correlation analysis to each intelligence to be selected based on improvement using a kind of
The electricity consumption reliability of energy power distribution network promotes demand and is assessed, and preferably goes out to need most the intelligence for carrying out the transformation of electricity consumption reliability and matches
Power grid；Storage module stores data and assessment result.
Embodiment
It is further described below with reference to example, 5 novel intelligent power distribution networks for choosing somewhere herein are research pair
As carrying out implementation analysis of the invention.
Firstly, establishing intelligence from three power grid power supply reliability, user power utilization experience property and improvement project economy levels
It can power distribution network electricity consumption reliability promotion demand degree assessment indicator system.Wherein, power grid power supply reliability index includes 3 second levels
Index：Isolated island abundant intensity probability, load transfer efficiency, power supply reliability index completion rate；User power utilization experience property index includes
4 two-level index：Electricity consumption reliability, electricity consumption satisfaction, customer charge grade, rate of qualified voltage；Improvement project economic index
Including 2 two-level index：Invest O&M cost and electricity production ratio.
The indices initial data statistical result of 5 novel intelligent power distribution networks is as shown in table 2：
Each power distribution network original index data of table 2
Each index value of all objects to be selected is normalized, nondimensionalization, it is as follows to form standardization index matrix：
Then the subjective weighted value of each index is obtained using AHP method, obtains the objective power of each index using entropy assessment is improved
Weight values, and the synthetic weights weight values of each index are sought, and compare with traditional entropy assessment, the results are shown in Table 3：
Comparison between the different tax power methods of table 3
Secondly, it is as follows that weighted normal index matrix is calculated：
It determines positive and negative absolute ideal solution, calculates the grey relational grade of each object to be selected, then calculate each object phase to be selected
For the relative similarity degree R (i) of positive ideal solution.Meanwhile in order to verify the effect for introducing absolute ideal solution, by intelligent distribution network 5
Leave out, remaining 4 intelligent distribution networks is reappraised, as a result as shown in Figure 3.
It on the other hand, is the accuracy of grey relational grade used in the verifying present invention, the present embodiment is by gray relative analysis method
The approach degree result calculated with euclidean distance method, sciagraphy compares, and concrete condition is as shown in Figure 4.
As seen from Table 3, traditional entropy assessment determines the weight of each index according to initial data distribution situation completely, right
Gap is lesser between 4 data such as power supply reliability index completion rate, electricity consumption reliability, electricity consumption satisfaction, rate of qualified voltage
The weight of index, imparting is obviously less than normal, and weight shared by two indexs of isolated island abundant intensity and customer charge grade is excessive, deviates
Practical experience.This defect can preferably be made up by improving entropy assessment.Combination weights method proposed by the present invention combines AHP
The advantages of method and improvement entropy assessment, to the key factor in Practical Project --- investment O&M cost and customer charge etc.
Grade, power supply reliability index completion rate, isolated island abundant intensity probability etc. are able to reflect the finger of user power utilization experience and electricity consumption reliability
Mark assigns higher weight, has both considered the distribution situation of initial data, it is also considered that the practical operating experiences of expert.
It is promoted in engineering with the distribution network reliability that power supply reliability is main foundation, each power distribution network carries out reliability
5. the preferred sequence of promotion is>④>③>②>①.But similar power distribution network 4., customer charge grade is not high, and electricity production is compared
Low, investment O&M cost is but very high, and from the reality of power grid, it is clearly not conform to that its priority level, which is mentioned higher position,
Reason.This example has paid the utmost attention to investment O&M cost, customer charge grade, isolated island abundant intensity probability, power supply reliability index
The indexs such as completion rate, to reliability promoted engineering object to be selected carried out preferably, by this kind of investment O&M cost of power distribution network 2 with
Isolated island abundant intensity probability is lower, and load level is higher and rate of qualified voltage is relatively low, is easy to cause user power utilization experience and electricity consumption
The poor power distribution network of reliability is put into the position being preferentially transformed, and can preferably meet the power requirement of user, guarantees Service Quality
Amount, Social benefit and economic benefit is maximized.
Available from figure 4, before not leaving out power distribution network 5, traditional TOPSIS method based on relative ideal solution obtains each
2. the preferred sequence that power distribution network carries out the promotion of electricity consumption reliability is>⑤>①>④>3. preferential suitable after leaving out power distribution network 5
2. sequence then becomes>①>③>④.The preferred sequence of both adjustment front and back, power distribution network 3 and power distribution network 4 is reversed, and is produced
The phenomenon that inverse sequence, the accuracy of assessment result is directly influenced.And improved TOPSIS method used in the present invention introduces absolutely
Ideal solution can effectively avoid inverse sequencing problem caused by number of objects variation to be selected, before and after adjusting number of objects to be selected,
There is no variations for the preferred sequence of remaining object to be selected, illustrate the accuracy and reasonability of improved TOPSIS method used.
As shown in figure 5, it is big to calculate resulting approach degree using Euclidean distance method in the case where introducing absolute ideal solution
It is small to be ordered as 5.>②>①>③>4. and to calculate resulting approach degree size sequence consistent for gray relative analysis method and sciagraphy,
It is 2.>⑤>①>③>4. illustrating that gray relative analysis method is more accurate than Euclidean distance method in terms of calculating approach degree.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, it is other it is any without departing from the spirit and principles of the present invention made by change, modification, substitution, combination, letter
Change, should be equivalent substitute mode, be included within the scope of the present invention.
Claims (6)
1. the optimum decision system that a kind of intelligent distribution network electricity consumption reliability promotes object, it is characterised in that include：
Data acquisition module, for being communicated with the backstage production management system of power grid enterprises and marketing message management system,
And acquire the data of the corresponding index of each intelligent distribution network to be selected or power supply network staff is manually entered part index number number
According to；
Evaluation module approaches ideal method (technique for order preference by based on improvement using one kind
Similarity to ideal solution, TOPSIS) and grey correlation analysis comprehensive estimation method to each intelligence to be selected
The electricity consumption reliability of energy power distribution network promotes demand and is assessed, and preferably goes out the intelligent power distribution for needing most and carrying out the transformation of electricity consumption reliability
Net；
Storage module stores data and assessment result.
2. the preferred method of the optimum decision system of object is promoted using a kind of intelligent distribution network electricity consumption reliability described in claim 1,
It is characterized in that comprising the steps of：
S1, intelligent distribution network use is established in terms of power grid power supply reliability, user power utilization experience property, improvement project economy three
Electric reliability promotes Needs index system；
S2, data acquisition module are inputted by power grid backstage production management system, marketing message management system and manually
Mode obtain the data of corresponding index, and be stored among memory module；
S3, evaluation module call the data of the corresponding index, and each index value of all objects to be selected are normalized, nothing
Dimension forms standardization index matrix；
S4, evaluation module obtain the subjectivity of each index using analytic hierarchy process (AHP) (analytic hierarchy process, AHP)
Weighted value obtains the objective weight value of each index using entropy assessment is improved, and seeks the synthetic weights weight values of each index；
S5, evaluation module calculate weighted normal index matrix, determine positive and negative absolute ideal solution, calculate the ash of each object to be selected
The color degree of association；
S6, evaluation module calculate relative similarity degree R (i) of each object to be selected relative to positive ideal solution, and are selected according to R (i) size
It selects reliability and promotes object, and preferred result is output among memory module.
3. preferred method according to claim 2, it is characterised in that：The intelligent distribution network electricity consumption that the step S1 is referred to can
Promoting Needs index system by property includes following index：
First class index includes power grid power supply reliability index, user power utilization experience property index and improvement project economic index；Electricity
Net power supply reliability index includes 3 two-level index：Isolated island abundant intensity probability, load transfer efficiency, power supply reliability index are complete
At rate；User power utilization experience property index includes 4 two-level index：Electricity consumption reliability, electricity consumption satisfaction, customer charge grade, electricity
Press qualification rate；Improvement project economic index includes 2 two-level index：Invest O&M cost and electricity production ratio.
4. preferred method according to claim 2, it is characterised in that：The step S4 is specifically included：
S401：The subjective weighted value of each index is obtained using AHP method,
Each index importance is compared two-by-two using three scales, establishes comparator matrix A：
Wherein,
With range method development of judgment matrix C=(cij)n×n：
In formula, riIt is the sum of every row element of matrix A；cbIt is according to the relatively heavy of element pair very poor under certain standard for a constant
Want degree and determination, R=rmax-rmin, rmax=max (r1, r2..., rn), rmin=min (r1, r2..., rn)；
The maximum eigenvalue of judgment matrix C is sought, and carries out consistency check, introduces compatibility index CI test and judge matrix
Consistency, wherein：
CI=(λmax-n)/(n-1)
Work as CI<When 0.1, it is believed that the consistent performance of judgment matrix receives；Work as CI>When 0.1, judgment matrix work suitably should be repaired again
Change, then weight is recalculated to the matrix after correction and carries out consistency desired result；
After the maximum eigenvalue of judgment matrix passes through consistency desired result, the corresponding feature vector ω ' of maximum eigenvalue is calculatedA=
(ω’A1, ω 'A2... ω 'An), to being normalized, obtain opposite power of certain grade of each index relative to its upper level index
Weight ωA=(ωA1, ωA2... ωAn)；
S402：The objective weight value of each index is obtained using entropy assessment is improved,
There is m objects to be selected, and n evaluation index, for index j, the feature specific gravity of object i to be selected is：
The entropy of index j is：
Then the objective weight of index j is：
In formula,It is the average value of all entropy for not being 1；
S403：The synthetic weights weight values of each index are as follows：
ωj=φ ωAj+(1-φ)ωEj,
The characteristics of comprehensively considering two kinds of tax power methods, takes Φ=0.5；
Weight vectors ω is normalized, wherein
5. preferred method according to claim 2, it is characterised in that：Step S5 is comprised the steps of：
S501：By Standard Process and standard summary weight vectors, weighted normal matrix B=[b is acquiredij]m×n, wherein：
S502：The positive and negative ideal solution data sequence of ideal object is respectively defined as：
S503：Grey relational grade is calculated, the calculation method of grey relational grade is as follows：
In formula,ρ is resolution ratio.
6. preferred method according to claim 2, it is characterised in that：The step S6 is specifically included：
S601：Relative similarity degree is calculated, grey correlation analysis is based on, calculates opposite patch of each object to be selected relative to positive ideal solution
Recency R (i)：
Relative similarity degree reflects the degree of closeness of object to be selected and positive ideal object or negative ideal object in situation variation；Root
According to the size of R (i), each object to be selected is ranked up, preferably object, and preferred result is output among memory module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810510429.3A CN108921376B (en) | 2018-05-24 | 2018-05-24 | Optimal selection method and system for electricity reliability improvement object of intelligent power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810510429.3A CN108921376B (en) | 2018-05-24 | 2018-05-24 | Optimal selection method and system for electricity reliability improvement object of intelligent power distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108921376A true CN108921376A (en) | 2018-11-30 |
| CN108921376B CN108921376B (en) | 2022-02-15 |
Family
ID=64403275
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810510429.3A Active CN108921376B (en) | 2018-05-24 | 2018-05-24 | Optimal selection method and system for electricity reliability improvement object of intelligent power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108921376B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110363443A (en) * | 2019-07-19 | 2019-10-22 | 广东电网有限责任公司 | The operation situation evaluation method and device of power spot market |
| CN110490471A (en) * | 2019-08-23 | 2019-11-22 | 广西电网有限责任公司电力科学研究院 | A kind of remodeling method of distribution network reliability differentiation |
| CN110782171A (en) * | 2019-10-29 | 2020-02-11 | 青海格尔木鲁能新能源有限公司 | Method and device for determining demand side resource demand response benefit value and computing equipment |
| CN112101813A (en) * | 2020-09-24 | 2020-12-18 | 贵州电网有限责任公司 | Comprehensive evaluation and sequencing method for testing of distribution automation equipment |
| CN112381377A (en) * | 2020-11-10 | 2021-02-19 | 国网上海市电力公司 | Power grid connection mode evaluation method, device, equipment and storage medium |
| CN112990634A (en) * | 2019-12-16 | 2021-06-18 | 中车唐山机车车辆有限公司 | Power quality evaluation method and device and terminal equipment |
| CN113191634A (en) * | 2021-04-29 | 2021-07-30 | 中国电力科学研究院有限公司 | Method and system for evaluating health state of electricity consumption behavior of industrial user |
| CN115481906A (en) * | 2022-09-22 | 2022-12-16 | 中航机载***共性技术有限公司 | Production line evaluation method, device, equipment and medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105023065A (en) * | 2015-07-24 | 2015-11-04 | 国网山东省电力公司经济技术研究院 | Power grid planning risk evaluation system and method based on grey correlation degree TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) |
| CN105427053A (en) * | 2015-12-07 | 2016-03-23 | 广东电网有限责任公司江门供电局 | Relative influence analysis model applied to evaluation of distribution network construction and renovation schemes and power supply quality indexes |
| CN105787817A (en) * | 2016-04-11 | 2016-07-20 | 深圳供电局有限公司 | Circuit breaker standby method and system considering aging failure model |
| CN106203854A (en) * | 2016-07-13 | 2016-12-07 | 广东工业大学 | A kind of DC distribution net Economic Analysis Method and system |
| CN106384302A (en) * | 2016-09-30 | 2017-02-08 | 南方电网科学研究院有限责任公司 | Power distribution network reliability evaluation method and system |
| CN106875035A (en) * | 2016-12-30 | 2017-06-20 | 国网四川省电力公司经济技术研究院 | A kind of active distribution network reliability assessment optimization method |
| CN107506843A (en) * | 2017-07-03 | 2017-12-22 | 国网上海市电力公司 | A kind of short-term load forecasting method and device |
-
2018
- 2018-05-24 CN CN201810510429.3A patent/CN108921376B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105023065A (en) * | 2015-07-24 | 2015-11-04 | 国网山东省电力公司经济技术研究院 | Power grid planning risk evaluation system and method based on grey correlation degree TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) |
| CN105427053A (en) * | 2015-12-07 | 2016-03-23 | 广东电网有限责任公司江门供电局 | Relative influence analysis model applied to evaluation of distribution network construction and renovation schemes and power supply quality indexes |
| CN105787817A (en) * | 2016-04-11 | 2016-07-20 | 深圳供电局有限公司 | Circuit breaker standby method and system considering aging failure model |
| CN106203854A (en) * | 2016-07-13 | 2016-12-07 | 广东工业大学 | A kind of DC distribution net Economic Analysis Method and system |
| CN106384302A (en) * | 2016-09-30 | 2017-02-08 | 南方电网科学研究院有限责任公司 | Power distribution network reliability evaluation method and system |
| CN106875035A (en) * | 2016-12-30 | 2017-06-20 | 国网四川省电力公司经济技术研究院 | A kind of active distribution network reliability assessment optimization method |
| CN107506843A (en) * | 2017-07-03 | 2017-12-22 | 国网上海市电力公司 | A kind of short-term load forecasting method and device |
Non-Patent Citations (4)
| Title |
|---|
| IN-SU BAE ET AL.: ""Reliability evaluation of customers in a microgrid"", 《IEEE》 * |
| STEFANIA CONTI ET AL.: "Probability of adequacy evaluation considering power output correlation of renewable generators in Smart Grids", 《ELECTRICAL POWER AND ENERGY SYSTEMS》 * |
| 袁广修 等: ""计及微网孤岛运行方式的配电网可靠性评估"", 《电网技术》 * |
| 麻兴斌 等: "《电网运行的可靠性、适应性和经济型性研究》", 30 August 2014, 山东大学出版社 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110363443A (en) * | 2019-07-19 | 2019-10-22 | 广东电网有限责任公司 | The operation situation evaluation method and device of power spot market |
| CN110490471A (en) * | 2019-08-23 | 2019-11-22 | 广西电网有限责任公司电力科学研究院 | A kind of remodeling method of distribution network reliability differentiation |
| CN110490471B (en) * | 2019-08-23 | 2023-07-11 | 广西电网有限责任公司电力科学研究院 | Transformation method for power distribution network power supply reliability differentiation |
| CN110782171A (en) * | 2019-10-29 | 2020-02-11 | 青海格尔木鲁能新能源有限公司 | Method and device for determining demand side resource demand response benefit value and computing equipment |
| CN112990634A (en) * | 2019-12-16 | 2021-06-18 | 中车唐山机车车辆有限公司 | Power quality evaluation method and device and terminal equipment |
| CN112101813A (en) * | 2020-09-24 | 2020-12-18 | 贵州电网有限责任公司 | Comprehensive evaluation and sequencing method for testing of distribution automation equipment |
| CN112101813B (en) * | 2020-09-24 | 2023-05-02 | 贵州电网有限责任公司 | Comprehensive evaluation sequencing method for testing of power distribution automation equipment |
| CN112381377A (en) * | 2020-11-10 | 2021-02-19 | 国网上海市电力公司 | Power grid connection mode evaluation method, device, equipment and storage medium |
| CN113191634A (en) * | 2021-04-29 | 2021-07-30 | 中国电力科学研究院有限公司 | Method and system for evaluating health state of electricity consumption behavior of industrial user |
| CN115481906A (en) * | 2022-09-22 | 2022-12-16 | 中航机载***共性技术有限公司 | Production line evaluation method, device, equipment and medium |
| CN115481906B (en) * | 2022-09-22 | 2024-04-16 | 中航机载***共性技术有限公司 | Production line evaluation method, device, equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108921376B (en) | 2022-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108921376A (en) | A kind of intelligent distribution network electricity consumption reliability promotes the preferred method and system of object | |
| CN107563680B (en) | Power distribution network reliability assessment method based on AHP and entropy weight method | |
| CN108876131B (en) | Reserve project optimization method based on power grid effect contribution degree | |
| CN109829604A (en) | A kind of grid side energy-accumulating power station operational effect comprehensive estimation method | |
| CN111754090B (en) | Big data-based power user demand side regulation and control method | |
| CN110689240A (en) | Fuzzy comprehensive evaluation method for economic operation of power distribution network | |
| CN108428045A (en) | A kind of distribution network operation health state evaluation method | |
| CN107871214A (en) | Method for establishing comprehensive evaluation index system of multi-energy complementary energy supply system | |
| CN111695718A (en) | Power grid development aid decision-making method considering investment demand and planning target | |
| CN106980910A (en) | Long Electric Power Load calculating system and method | |
| CN112766730A (en) | Distribution network planning scheme adaptability evaluation method for distributed resource access | |
| CN112465286A (en) | Method for evaluating service capability of electric vehicle charging network | |
| CN113240330A (en) | Multi-dimensional value evaluation method and scheduling strategy for demand side virtual power plant | |
| CN112734274A (en) | Mining and comprehensive evaluation method for low-carbon power grid operation leading influence factors | |
| CN112561319A (en) | Comprehensive evaluation method for energy system of data center | |
| Domnikov et al. | Methodological approach to the research of energy cogeneration systems operational reliability indicators | |
| CN115983664A (en) | Comprehensive evaluation method for trading effect of energy storage participation in electric power market based on fuzzy analysis | |
| CN115689620A (en) | Physical model and data-driven coal price and electricity price conduction model construction method | |
| CN115907539A (en) | Method, system and device for constructing new energy utilization evaluation index system in power market | |
| CN114819292A (en) | Park microgrid intelligent scheduling method and system based on comprehensive safety evaluation | |
| Dong et al. | Study on operation evaluation of charging and discharging network based on improved analytic hierarchy process and fuzzy synthetic evaluation | |
| CN112365169A (en) | Power grid operation evaluation analysis method under future low-carbon energy framework | |
| Ye et al. | Short term output prediction method of runoff type medium and small hydropower stations | |
| Ren et al. | Decision-making approach in charging mode for electric vehicle based on cumulative prospect theory | |
| Tao et al. | Distributed Energy Storage Cluster Control Method for DC Microgrid Considering Flexibility |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |