CN116683518A - Comprehensive evaluation method for power distribution network considering low-carbon benefits - Google Patents
Comprehensive evaluation method for power distribution network considering low-carbon benefits Download PDFInfo
- Publication number
- CN116683518A CN116683518A CN202310629223.3A CN202310629223A CN116683518A CN 116683518 A CN116683518 A CN 116683518A CN 202310629223 A CN202310629223 A CN 202310629223A CN 116683518 A CN116683518 A CN 116683518A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- power
- index
- power distribution
- carbon
- 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.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 116
- 238000011156 evaluation Methods 0.000 title claims abstract description 107
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 98
- 230000008901 benefit Effects 0.000 title claims abstract description 66
- 238000004146 energy storage Methods 0.000 claims abstract description 63
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 45
- 238000000034 method Methods 0.000 claims abstract description 37
- 239000011159 matrix material Substances 0.000 claims abstract description 35
- 238000004364 calculation method Methods 0.000 claims abstract description 26
- 238000004088 simulation Methods 0.000 claims abstract description 14
- 238000003860 storage Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 14
- 238000010248 power generation Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 8
- 230000010354 integration Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000002347 injection Methods 0.000 claims description 4
- 239000007924 injection Substances 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 238000009413 insulation Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012797 qualification Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 description 10
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000007599 discharging Methods 0.000 description 4
- 238000012937 correction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 230000007423 decrease Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- 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
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
-
- 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
- Y02P90/84—Greenhouse gas [GHG] management systems
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application provides a comprehensive evaluation method of a power distribution network considering low-carbon benefits, which comprises the following steps: establishing a corresponding carbon emission model, analyzing the influence of loads of distributed photovoltaic, energy storage power stations and electric vehicles on the carbon emission of the power distribution network, verifying the rationality and effectiveness of each influence factor through simulation calculation, and establishing a corresponding low-carbon benefit evaluation index according to the influence factors; establishing a comprehensive evaluation index system for the power distribution network, wherein the comprehensive evaluation index system takes low-carbon benefits into account; obtaining subjective weight of an index; obtaining objective weights of indexes; a combination method with stronger interpretation is adopted for subjective and objective weights; and establishing a fuzzy comprehensive judgment matrix by adopting an improved fuzzy comprehensive evaluation method, and multiplying the weight coefficient matrix by the fuzzy comprehensive judgment matrix to obtain the comprehensive score of the power distribution network. According to the application, the low-carbon benefit index is considered, a comprehensive evaluation index system which is comprehensive and suitable for a novel power distribution network is established, and meanwhile, the fuzzy comprehensive evaluation method is improved, so that weak links in the power distribution network can be effectively reflected, and the evaluation result is more accurate.
Description
Technical Field
The application relates to the technical field of power distribution networks, in particular to a comprehensive evaluation method for a power distribution network, which takes low-carbon benefits into account.
Background
Because of the shortage of electric power and the increasing pollution, solar energy becomes a green ecological and clean renewable energy source, wherein the photoelectric technology is mainly used, and the solar energy has extremely high benefit and environmental friendliness. Meanwhile, the photovoltaic output condition is affected by illumination, the electric energy quality of the photovoltaic output condition has certain uncertainty, and particularly in areas with a complex electric power system structure, a certain electric energy quality problem can be caused by large-scale photovoltaic grid connection. Voltage fluctuations, flicker, frequency offset, and frequent generation of harmonics all present new challenges to the safety and reliability of the distribution network.
Large-scale distributed grid-connection makes the form of power supply and grid structure in the distribution system more complicated and decentralized. In the traditional comprehensive evaluation of the power distribution network, firstly, an index system is incomplete, and the evaluation is performed on a certain aspect of the power distribution network or only the advantages or disadvantages of the power distribution network caused by distributed photovoltaic are considered; the method for determining the weight is a single subjective weighting method or an objective weighting method, and although the application of the subjective and objective weight combination model is wider and wider, the rationality and the interpretation of the subjective and objective weight combination model are not strong; and finally, the phenomenon of defect inundation exists in the power distribution network evaluation work, namely, the disadvantages of the low weight index are covered by the advantages of the high weight index. The evaluation value obtained by the linear combination is insensitive to serious defect problems, although it can substantially distinguish the degree of merit between targets.
Disclosure of Invention
In view of the above, the application provides a comprehensive evaluation method of a power distribution network, which aims to solve the problem of how to improve the accuracy of comprehensive evaluation of the power distribution network.
In one aspect, the application provides a comprehensive evaluation method for a power distribution network, which takes low-carbon benefits into account, and comprises the following steps:
step 1: establishing a carbon emission model of the power distribution network;
step 2: analyzing the influence of distributed photovoltaic, energy storage power stations and electric automobile loads on the carbon emission of the distribution network through a typical single-amplitude-emission distribution network model, and determining influence factors;
step 3: verifying the rationality and effectiveness of each influencing factor through simulation calculation, and establishing a corresponding low-carbon benefit evaluation index;
step 4: establishing a comprehensive evaluation index system for the power distribution network, which takes low-carbon benefits into account, and inputting index data;
step 5: obtaining subjective weight of low-carbon benefit evaluation indexes by using an analytic hierarchy process;
step 6: obtaining objective weight of low-carbon benefit evaluation indexes by using an improved CRITIC method;
step 7: calculating subjective and objective combination weights of the low-carbon benefit evaluation indexes;
step 8: and establishing a fuzzy comprehensive judgment matrix by adopting an improved fuzzy comprehensive evaluation method, and multiplying the weight coefficient matrix by the fuzzy comprehensive judgment matrix to obtain the comprehensive score of the power distribution network.
Further, in the step 1, a carbon emission model of the power distribution network is built, including:
determining a power distribution network carbon emission model according to the formula (1):
P t calculated according to formula (2):
P t ＝P load,t +P EV,t -P PV,t +P loss,PV,t +P ES,d,t -P ES,c,t (2)
wherein C is op For carbon emission generated in operation phase of distribution network, P t Indicating the injection of the coal-fired unit from the upper level at the moment tPower in, C cef For the carbon emission coefficient of the coal-fired unit, tau is the time interval, T is the statistical time length and P load,t Representing the original load of the power distribution network at the moment t, P EV,t Represents the charging load of the electric automobile at the time t, P PV,t Representing t-moment distributed photovoltaic power generation capacity, P loss,PV,t Represents the line loss at the time t, P ES,c,t Representing the charging power of an energy storage power station at the time t, P ES,d,t And (5) representing the discharge power of the energy storage power station at the time t.
Further, in the step 2, the influence of the loads of the distributed photovoltaic power station, the energy storage power station and the electric vehicle on the carbon emission of the power distribution network is analyzed through a typical single-amplitude power distribution network model, and the influence factors are determined, including:
the general line loss expression of the traditional power distribution network is shown as the formula (3):
wherein P is j For j node load active power, Q j For j node load reactive power, U i For the voltage of i node, r is the resistance value of the unit length of the line, n 1 P is the number of nodes lossi For the i-th line loss, P loss Is the total loss of the circuit;
determining that if the power transmission is reduced and the node voltage is increased according to the formula (1), the line loss can be reduced, and determining the network loss of the power distribution network according to the formula (4) when the distributed photovoltaic and energy storage power station is connected with the m point:
wherein P is PV For distributed photovoltaic power supply power, P Esc Charging power for energy storage power station, P Esd For the discharge power of the energy storage power station, P lossPV The power distribution network loss comprises distributed photovoltaic, energy storage power stations and electric automobile loads.
Further, when the distributed photovoltaic and energy storage power station is connected to p-point, the m-point voltage is determined according to formula (5):
wherein x is l For reactance value of line unit length, U 0 Is the head-end voltage.
Further, in the step 3, during the simulation calculation, the carbon emission simulation calculation is performed on the power distribution network under three different schemes through the IEEE33 node power distribution network model, so as to verify the rationality and effectiveness of each influencing factor;
the first case is that a distributed photovoltaic power station and an energy storage power station are connected, and under three typical photovoltaic output scenes, carbon emission calculation analysis is carried out on four schemes when the energy storage power station is connected and the energy storage power station is connected in a scale of 2MW/4 MW.h, 3MW/6 MW.h and 4MW/8 MW.h respectively;
the second situation is that distributed photovoltaic and electric vehicles are connected, and under three typical photovoltaic output scenes, carbon emission calculation analysis is carried out on four schemes when no electric vehicles are connected and the number of the electric vehicles is 100, 700 and 2000 respectively;
under three typical photovoltaic output scenes, when the energy storage power station is connected with the electric vehicles in a scale of 2MW/4 MW.h and the electric vehicles are connected with 2000 vehicles, simulation calculation analysis is carried out on four schemes of non-scheduled energy storage power stations and unordered electric vehicles, non-scheduled energy storage power stations and ordered electric vehicles and scheduled energy storage power stations and ordered electric vehicles;
and establishing corresponding low-carbon benefit evaluation indexes based on the obtained influence factors according to analysis results of three situations, wherein the low-carbon benefit evaluation indexes comprise distributed photovoltaic power generation rate indexes, distributed photovoltaic rated output duration, light storage configuration indexes, energy storage power station participation rates, charging load indexes, charging efficiency and light storage and charging integration rate indexes.
Further, in the step 4, the established comprehensive evaluation index system considering the low-carbon benefit comprises: grid level, equipment level, operation level, power quality, power supply reliability, low carbon benefit and intelligent level; wherein,,
each secondary index further comprises a subdivided tertiary index, and the tertiary index specifically comprises:
(1) The grid level comprises inter-station connection rate, average line segmentation number, power supply area line rotatable power supply rate, grid structure standardization rate and line 'N-1' passing rate index;
(2) The equipment level comprises high-loss distribution transformation ratio, 10kV line cabling rate, 10kV line insulation rate, power distribution duty ratio of more than 20 years in operation period, line duty ratio of more than 20 years in operation period and switch duty ratio index of more than 20 years in operation period;
(3) The operation level comprises network loss rate, line light load rate, line heavy load rate, distribution transformer light load rate, distribution transformer heavy load rate and capacity ratio index;
(4) The electric energy quality comprises voltage deviation, voltage fluctuation, power grid harmonic waves and three-phase unbalance indexes;
(5) The power supply reliability comprises average power failure time of a user, average power failure frequency of the user, voltage qualification rate, average power consumption and voltage sag rate;
(6) The low-carbon benefit comprises a distributed photovoltaic power generation amount duty ratio, a distributed photovoltaic rated output duration, a photovoltaic configuration ratio, an energy storage power station participation ratio, a charging load duty ratio, a charging efficiency and a photovoltaic storage and charging integration rate index;
(7) The intelligent level comprises intelligent substation duty ratio, distribution automation coverage rate, comprehensive automation rate of the substation, intelligent ammeter coverage rate and three-remote switch controllable rate index.
Further, in the step 5, obtaining subjective weights of the low-carbon benefit evaluation indexes by using an analytic hierarchy process includes:
and comparing the indexes of each layer in pairs by using a analytic hierarchy process, constructing an importance judgment matrix, and carrying out hierarchical single sequencing and consistency check and hierarchical total sequencing and consistency check to obtain the subjective weight of the low-carbon benefit evaluation index.
Further, in the step 6, obtaining objective weights of the low carbon benefit evaluation index by using an improved CRITIC method includes:
(1) Carrying out Min-Max normalization processing on the data;
(2) The method is expressed in a standard deviation form, and index variability coefficients are obtained;
(3) Expressed in the form of pearson correlation coefficients, the index conflict coefficient is calculated, specifically, the formula (6):
wherein R is ij The pearson correlation coefficient of index i and index j, A j To improve the collision coefficient of the jth index of the CRITIC method;
(4) Obtaining index information entropy
(5) Weight calculation, calculation according to formula (7):
wherein S is j ' is the standard deviation of the sample normalized by the jth index, e j Entropy of j index information, beta j To improve the objective weight of the jth index of the CRITIC method.
Further, in the step 7, the subjective and objective combination weight of the low-carbon benefit evaluation index is calculated, including:
the subjective and objective combination weights are obtained according to the formulas (8) and (9):
wherein alpha is zj Is j thSubjective weight of individual index, beta kj Objective weight of jth index, w j Is a combined weight value.
Further, in the step 8, a fuzzy comprehensive judgment matrix is established by adopting an improved fuzzy comprehensive evaluation method, and the weight coefficient matrix is multiplied with the fuzzy comprehensive judgment matrix to obtain a comprehensive score of the power distribution network, including:
(1) Determining an evaluation index and an evaluation grade;
(2) Determining a weight set;
(3) Determining a membership function and calculating membership degree, constructing a fuzzy comprehensive evaluation matrix, and improving the fuzzy comprehensive evaluation matrix, wherein the fuzzy comprehensive evaluation matrix is shown as a formula (10) -a formula (12):
wherein r is j ' i In order to adopt the membership degree of the jth index under the ith scheme after the improved fuzzy comprehensive evaluation method,as the average membership of index j, BIAS ji And (3) the deviation value of the membership degree of the ith scheme which is lower than the average value in the jth index. S is S i Is the comprehensive evaluation value of the i-th region.
Compared with the prior art, the application has the remarkable advantages that:
(1) The rationality and the effectiveness of the low-carbon benefit evaluation index constructed by the carbon emission influence factors of the power distribution network are verified through simulation calculation, a comprehensive index system is established, and the method is different from the traditional evaluation model which only evaluates a certain aspect of the power distribution network or only considers the advantages or disadvantages brought by distributed photovoltaic to the power distribution network so as to adapt to the characteristics of a modern novel power distribution network;
(2) Compared with the traditional weighting methods such as linear combination, a combination weighting method with stronger interpretation and rationality is established;
(3) Weak links in the power distribution network can be found more easily by improving the fuzzy comprehensive evaluation method, and differentiation of evaluation results is increased, so that the accuracy and reliability of evaluation are improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a first flowchart of a comprehensive evaluation method for a power distribution network, which is provided by an embodiment of the application and takes low-carbon benefits into account;
FIG. 2 is a comprehensive evaluation index system of a power distribution network provided by an embodiment of the application;
fig. 3 is a second flowchart of a comprehensive evaluation method for a power distribution network, which is provided by an embodiment of the present application and takes into account low carbon benefits.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Referring to fig. 1, the application relates to a comprehensive evaluation method for a power distribution network, which takes low-carbon benefits into account, and comprises the following steps:
step 1: establishing a carbon emission model of the power distribution network;
step 2: analyzing the influence of distributed photovoltaic, energy storage power stations and electric automobile loads on the carbon emission of the distribution network through a typical single-amplitude-emission distribution network model, and determining influence factors;
step 3, verifying the rationality and effectiveness of each influencing factor through simulation calculation, and establishing a corresponding low-carbon benefit evaluation index;
step 4, a comprehensive evaluation index system for taking low-carbon benefits into account is established for the novel power distribution network, and index data are input;
step 5, obtaining subjective weight of the low-carbon benefit evaluation index by using an analytic hierarchy process;
step 6, obtaining objective weight of the low-carbon benefit evaluation index by using an improved CRITIC method;
step 7, calculating subjective and objective combination weights of the low-carbon benefit evaluation indexes;
and 8, establishing a fuzzy comprehensive judgment matrix by adopting an improved fuzzy comprehensive evaluation method, and multiplying the weight coefficient matrix by the fuzzy comprehensive judgment matrix to obtain the comprehensive score of the power distribution network.
Preferably, in step 1, a carbon emission model of the power distribution network is built, which is specifically as follows:
P t ＝P load,t +P EV,t -P PV,t +P loss,PV,t +P ES,d,t -P ES,c,t (2)
wherein C is op Carbon emission generated in operation stage of novel power distribution network, P t The injection power from the upper coal-fired unit at the moment t is represented as C cef And (3) the carbon emission coefficient of the coal-fired unit, wherein tau is a time interval, and T is a statistical duration. Wherein P is t And can be divided into an original load P at the moment t of the power distribution network load,t Electric automobile charging load P at time t EV,t T-moment distributed photovoltaic power generation amount P PV,t Line loss P at time t loss,PV,t Energy storage electricity at time tStation charging power P ES,c,t Discharge power P of energy storage power station at t moment ES,d,t 。
Preferably, in step 2, the influence of distributed photovoltaic, energy storage power stations and electric automobile loads on the carbon emission of the distribution network is analyzed through a typical single-amplitude-emission distribution network model, and influence factors are determined. The injection power of the superior power supply depends on the load size of the distribution network, the size of the distributed photovoltaic power generation capacity, the charging and discharging size of the energy storage power station and the line loss. The circuit loss part is influenced by the load, the charging and discharging size of the energy storage power station and the size of the distributed photovoltaic power generation, and the specific analysis is as follows:
the general line loss expression of the traditional power distribution network is as follows:
wherein P is j For j node load active power, Q j For j node load reactive power, U i For the voltage of i node, r is the resistance value of the unit length of the line, n 1 P is the number of nodes lossi For the i-th line loss, P loss Is the total loss of the line.
The power transmission can be reduced by the formula (1), the line loss can be effectively reduced by increasing the node voltage, when the distributed photovoltaic and energy storage power station is connected with the point m,
wherein P is PV Is distributed photovoltaic power. P (P) Esc Charging power for energy storage power station, P Esd For the discharge power of the energy storage power station, P lossPV The energy storage power station is a power distribution network loss comprising distributed photovoltaic, energy storage power stations and electric automobile loads.
The distributed photovoltaic power supply can effectively reduce power transmission when being connected with the power distribution network, and reduces as the access capacity of the distributed photovoltaic power supply increases. And the energy storage power station increases the network loss in the charging stage and reduces the network loss in the discharging stage.
When the distributed photovoltaic and energy storage power station is connected with the p point, the voltage of the m node is,
wherein x is l For reactance value of line unit length, U 0 Is the head-end voltage.
When the m node precedes the p node, the voltage drop between every two nodes decreases with the increase in the distributed photovoltaic power access capacity. When the m node is behind the p node, the voltage reduction amplitude between every two nodes is not influenced by the distributed photovoltaic power supply, but the voltage of the front-stage line is improved due to the distributed photovoltaic power supply, so that compared with the voltage of the m node which is not connected with the distributed photovoltaic power supply, the voltage of the m node is also improved. Similarly, when the energy storage power station is connected, the charging stage will reduce the voltage of the m node, and the discharging stage will raise the voltage of the m node.
Preferably, in the step 3, through an IEEE33 node power distribution network model, carbon emission simulation calculation is performed on the power distribution network under three different schemes, and the rationality and the effectiveness of each influencing factor are verified;
the first situation is that a distributed photovoltaic power station and an energy storage power station are connected, and under three typical photovoltaic output scenes, carbon emission calculation analysis is carried out on the non-energy storage power station and four schemes of 2MW/4 MW.h (the charging power of an energy storage system is 2MW, the capacity is 4 MW.h), 3MW/6 MW.h and 4MW/8 MW.h respectively.
And in the second case, the distributed photovoltaic and electric automobiles are connected, and under three typical photovoltaic output scenes, carbon emission calculation analysis is carried out on the non-electric automobiles and four schemes of 100, 700 and 2000 respectively.
And under the scene of three typical photovoltaic output forces, when the scale of the energy storage power station is 2MW/4 MW.h, the number of the electric vehicles is 2000, and four schemes of non-dispatching energy storage power stations and non-disordered charging of the electric vehicles, non-dispatching energy storage power stations and ordered charging of the electric vehicles and dispatching of the energy storage power stations and ordered charging of the electric vehicles are carried out simulation calculation analysis.
Corresponding low-carbon benefit evaluation indexes are established according to influence factors, wherein the low-carbon benefit evaluation indexes comprise distributed photovoltaic power generation capacity ratio, distributed photovoltaic rated output duration, light storage configuration ratio, energy storage power station participation rate, charging load ratio, charging efficiency and light storage and charging integration rate indexes.
With reference to fig. 2, the established comprehensive evaluation index system includes 7 secondary indexes of grid level, equipment level, operation level, electric energy quality, power supply reliability, low carbon benefit and intelligent level, and the subdivision tertiary index under each secondary index is specifically as follows:
(1) The grid level comprises inter-station connection rate, average line segmentation number, power supply area line rotatable power supply rate, grid structure standardization rate and line 'N-1' passing rate index.
(2) The equipment level comprises high loss distribution ratio, 10kV line cabling rate, 10kV line insulation rate, power distribution ratio of over 20 years in operation period, line ratio of over 20 years in operation period and switch ratio index of over 20 years in operation period.
(3) The operation level comprises network loss rate, line light load rate, line heavy load rate, distribution transformer light load rate, distribution transformer heavy load rate and capacity ratio index.
(4) The power quality includes voltage deviation, voltage fluctuation, grid harmonics and three-phase imbalance index.
(5) The power supply reliability comprises average power failure time of users, average power failure frequency of users, voltage qualification rate, average power consumption and voltage sag rate.
(6) The low-carbon benefit comprises indexes of distributed photovoltaic power generation rate duty ratio, distributed photovoltaic rated output time length, photo-storage configuration ratio, energy storage power station participation rate, charging load duty ratio, charging efficiency and photo-storage and charging integration rate.
(7) Intelligent level intelligent substation duty ratio, distribution automation coverage rate, comprehensive automation rate of the substation, intelligent ammeter coverage rate and three-remote switch controllable rate index.
Preferably, in step 5, the indexes of each layer are compared in pairs by using a hierarchical analysis method, an importance judgment matrix is constructed, hierarchical single sorting and consistency check thereof, and hierarchical total sorting and consistency check thereof are performed, and subjective weights of the indexes are obtained.
Preferably, in step 6, objective weights of the indexes are obtained by using an improved CRITIC method according to index properties, specifically as follows:
(1) Data is subjected to Min-Max normalization processing
(2) Expressed in the form of standard deviation, and the index variability coefficient is obtained
(3) Expressed in the form of pearson correlation coefficient, and the index conflict coefficient is obtained
Wherein R is ij The pearson correlation coefficient of index i and index j, A j To improve the collision coefficient of the jth index of CRITIC method.
(4) Obtaining index information entropy
(5) Weight calculation
Wherein S is j ' is the standard deviation of the sample normalized by the jth index, e j Entropy of j index information, beta j To improve the objective weight of the jth index of the CRITIC method.
Preferably, in step 7, subjective weights are more focused on expert experiences, objective weights are more focused on information of the data, and subjective and objective combination weights of indexes are calculated, specifically as follows:
wherein alpha is zj Subjective weight, beta, of the j-th index kj Objective weight of jth index, w j Is a combined weight value.
Advantages of this mathematical model: the objective function gives consideration to objective weight, so that the deviation degree between the objective function and the combined weight is minimum, and the deviation to which direction is determined by the following constraint function, so that the objective function is ensured to fall in the intersection of subjective and objective weight, and the subjective weight is more focused on the expert experience.
Preferably, in step 8, an improved fuzzy comprehensive evaluation method is adopted to establish a fuzzy comprehensive judgment matrix, and the weight coefficient matrix is multiplied by the fuzzy comprehensive judgment matrix to obtain a comprehensive score of the power distribution network, which is specifically as follows:
(1) Determining an evaluation index and an evaluation grade
(2) Determining a set of weights
(3) Determination of membership function and membership degree calculation to construct fuzzy comprehensive evaluation matrix
The fuzzy comprehensive evaluation matrix is improved as shown in the following formula.
Wherein r is j ' i In order to adopt the membership degree of the jth index under the ith scheme after the improved fuzzy comprehensive evaluation method,as the average membership of index j, BIAS ji The ith index being lower than the average valueDeviation value of the degree of membership of the scheme. S is S i Is the comprehensive evaluation value of the i-th region.
Specifically, referring to fig. 3, to verify the effectiveness of the scheme of the present application, comprehensive evaluation is performed on the distribution network of 5 regions, including the following steps:
step 1, establishing a carbon emission model of a power distribution network;
step 2, analyzing the influence of loads of distributed photovoltaics, energy storage power stations and electric vehicles on the carbon emission of the distribution network through a typical single-amplitude-emission distribution network model, and determining influence factors;
step 3, carrying out carbon emission simulation calculation under three different schemes through an IEEE33 node power distribution network, verifying rationality and effectiveness of each influencing factor, wherein tables 1-3 show daily carbon emission of the power distribution network under three conditions, the unit is T, and establishing corresponding low-carbon benefit evaluation indexes;
TABLE 1 daily carbon emissions from energy storage power stations of different scales to distribution network
TABLE 2 daily carbon emission impact of electric vehicle charging load at different scales
TABLE 3 daily carbon emissions for simultaneous access to distribution networks
The low-carbon benefit evaluation index comprising the distributed photovoltaic power generation rate duty ratio, the distributed photovoltaic rated output time length, the photo-storage configuration ratio, the participation rate of the energy storage power station, the charging load duty ratio, the charging efficiency and the photo-storage and charging integration rate is established.
Step 4, a comprehensive evaluation index system for calculating low-carbon benefits is established for the novel power distribution network, wherein the comprehensive evaluation index system comprises 7 secondary indexes including grid level, equipment level, running level, electric energy quality, power supply reliability, low-carbon benefits and intelligent level, and 38 tertiary indexes, and the three secondary indexes are specifically shown in fig. 2, and index data are imported;
step 5, obtaining subjective weight of the index by using an analytic hierarchy process;
step 6, obtaining objective weights of indexes by using an improved CRITIC method;
step 7, based on subjective weight more focused on expert experience, objective weight more focused on data information, and subjective and objective combination weight of indexes is calculated;
from the flowchart shown in fig. 1 and the above steps 5 to 7, the comprehensive weights of the respective indexes can be obtained as shown in table 4.
Table 4 subjective and objective weights and combined weights for the indexes
/>
And 8, establishing a fuzzy comprehensive judgment matrix by adopting an improved fuzzy comprehensive evaluation method, and multiplying the weight coefficient matrix by the fuzzy comprehensive judgment matrix to obtain the comprehensive score of the power distribution network. The comprehensive scores are shown in Table 5, compared with the evaluation values before correction without the fuzzy comprehensive evaluation method.
Table 5 comparison of evaluation values before and after correction
The weak links in the power distribution network can be effectively identified by improving the fuzzy evaluation method, and as part of indexes in the A, C area are far lower than the average level, the membership degree of the corresponding indexes becomes lower, the differentiation of the evaluation result is increased, and the weak links in the power distribution network are more easily found.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (10)
1. The comprehensive evaluation method of the power distribution network considering the low-carbon benefits is characterized by comprising the following steps of:
step 1: establishing a carbon emission model of the power distribution network;
step 2: analyzing the influence of distributed photovoltaic, energy storage power stations and electric automobile loads on the carbon emission of the distribution network through a typical single-amplitude-emission distribution network model, and determining influence factors;
step 3: verifying the rationality and effectiveness of each influencing factor through simulation calculation, and establishing a corresponding low-carbon benefit evaluation index;
step 4: establishing a comprehensive evaluation index system for the power distribution network, which takes low-carbon benefits into account, and inputting index data;
step 5: obtaining subjective weight of low-carbon benefit evaluation indexes by using an analytic hierarchy process;
step 6: obtaining objective weight of low-carbon benefit evaluation indexes by using an improved CRITIC method;
step 7: calculating subjective and objective combination weights of the low-carbon benefit evaluation indexes;
step 8: and establishing a fuzzy comprehensive judgment matrix by adopting an improved fuzzy comprehensive evaluation method, and multiplying the weight coefficient matrix by the fuzzy comprehensive judgment matrix to obtain the comprehensive score of the power distribution network.
2. The comprehensive evaluation method for the power distribution network, which takes into account low carbon benefits, according to claim 1, wherein in the step 1, the establishment of a power distribution network carbon emission model comprises:
determining a power distribution network carbon emission model according to the formula (1):
P t calculated according to formula (2):
P t ＝P load,t +P EV,t -P PV,t +P loss,PV,t +P ES,d,t -P ES,c,t (2)
wherein C is op For carbon emission generated in operation phase of distribution network, P t The injection power from the upper coal-fired unit at the moment t is represented as C cef For the carbon emission coefficient of the coal-fired unit, tau is the time interval, T is the statistical time length and P load,t Representing the original load of the power distribution network at the moment t, P EV,t Represents the charging load of the electric automobile at the time t, P PV,t Representing t-moment distributed photovoltaic power generation capacity, P loss,PV,t Represents the line loss at the time t, P ES,c,t Representing the charging power of an energy storage power station at the time t, P ES,d,t And (5) representing the discharge power of the energy storage power station at the time t.
3. The comprehensive evaluation method for the power distribution network, which takes into account the low-carbon benefits, according to claim 2, wherein in the step 2, the influence factors are determined by analyzing the influence of the loads of the distributed photovoltaic power station, the energy storage power station and the electric automobile on the carbon emission of the power distribution network through a typical single-amplitude power distribution network model, and the method comprises the following steps:
the general line loss expression of the traditional power distribution network is shown as the formula (3):
wherein P is j For j node load active power, Q j For j node load reactive power, U i For the voltage of i node, r is the resistance value of the unit length of the line, n 1 Is a section ofPoint number, P lossi For the i-th line loss, P loss Is the total loss of the circuit;
determining that if the power transmission is reduced and the node voltage is increased according to the formula (1), the line loss can be reduced, and determining the network loss of the power distribution network according to the formula (4) when the distributed photovoltaic and energy storage power station is connected with the m point:
wherein P is PV For distributed photovoltaic power supply power, P Esc Charging power for energy storage power station, P Esd For the discharge power of the energy storage power station, P lossPV The power distribution network loss comprises distributed photovoltaic, energy storage power stations and electric automobile loads.
4. The comprehensive evaluation method for the power distribution network, which is characterized by taking low-carbon benefits into account according to claim 3,
when the distributed photovoltaic and energy storage power station is connected to the p point, the voltage of the m point is determined according to the formula (5):
wherein x is l For reactance value of line unit length, U 0 Is the head-end voltage.
5. The comprehensive evaluation method for the power distribution network, which takes into account the low-carbon benefits, according to claim 1, wherein in the step 3, when the simulation calculation is performed, the carbon emission simulation calculation is performed on the power distribution network under three different schemes through an IEEE33 node power distribution network model, and the rationality and the effectiveness of each influencing factor are verified;
the first case is that a distributed photovoltaic power station and an energy storage power station are connected, and under three typical photovoltaic output scenes, carbon emission calculation analysis is carried out on four schemes when the energy storage power station is connected and the energy storage power station is connected in a scale of 2MW/4 MW.h, 3MW/6 MW.h and 4MW/8 MW.h respectively;
the second situation is that distributed photovoltaic and electric vehicles are connected, and under three typical photovoltaic output scenes, carbon emission calculation analysis is carried out on four schemes when no electric vehicles are connected and the number of the electric vehicles is 100, 700 and 2000 respectively;
under three typical photovoltaic output scenes, when the energy storage power station is connected with the electric vehicles in a scale of 2MW/4 MW.h and the electric vehicles are connected with 2000 vehicles, simulation calculation analysis is carried out on four schemes of non-scheduled energy storage power stations and unordered electric vehicles, non-scheduled energy storage power stations and ordered electric vehicles and scheduled energy storage power stations and ordered electric vehicles;
and establishing corresponding low-carbon benefit evaluation indexes based on the obtained influence factors according to analysis results of three situations, wherein the low-carbon benefit evaluation indexes comprise distributed photovoltaic power generation rate indexes, distributed photovoltaic rated output duration, light storage configuration indexes, energy storage power station participation rates, charging load indexes, charging efficiency and light storage and charging integration rate indexes.
6. The comprehensive evaluation method for the power distribution network according to claim 1, wherein in the step 4, the established comprehensive evaluation index system for the low-carbon benefit comprises: grid level, equipment level, operation level, power quality, power supply reliability, low carbon benefit and intelligent level; wherein,,
each secondary index further comprises a subdivided tertiary index, and the tertiary index specifically comprises:
(1) The grid level comprises inter-station connection rate, average line segmentation number, power supply area line rotatable power supply rate, grid structure standardization rate and line 'N-1' passing rate index;
(2) The equipment level comprises high-loss distribution transformation ratio, 10kV line cabling rate, 10kV line insulation rate, power distribution duty ratio of more than 20 years in operation period, line duty ratio of more than 20 years in operation period and switch duty ratio index of more than 20 years in operation period;
(3) The operation level comprises network loss rate, line light load rate, line heavy load rate, distribution transformer light load rate, distribution transformer heavy load rate and capacity ratio index;
(4) The electric energy quality comprises voltage deviation, voltage fluctuation, power grid harmonic waves and three-phase unbalance indexes;
(5) The power supply reliability comprises average power failure time of a user, average power failure frequency of the user, voltage qualification rate, average power consumption and voltage sag rate;
(6) The low-carbon benefit comprises a distributed photovoltaic power generation amount duty ratio, a distributed photovoltaic rated output duration, a photovoltaic configuration ratio, an energy storage power station participation ratio, a charging load duty ratio, a charging efficiency and a photovoltaic storage and charging integration rate index;
(7) The intelligent level comprises intelligent substation duty ratio, distribution automation coverage rate, comprehensive automation rate of the substation, intelligent ammeter coverage rate and three-remote switch controllable rate index.
7. The comprehensive evaluation method for the power distribution network according to claim 1, wherein in the step 5, subjective weights of the low-carbon benefit evaluation indexes are obtained by using a hierarchical analysis method, and the method comprises the following steps:
and comparing the indexes of each layer in pairs by using a analytic hierarchy process, constructing an importance judgment matrix, and carrying out hierarchical single sequencing and consistency check and hierarchical total sequencing and consistency check to obtain the subjective weight of the low-carbon benefit evaluation index.
8. The comprehensive evaluation method for power distribution network according to claim 1, wherein in the step 6, objective weights of the low-carbon benefit evaluation indexes are obtained by using an improved CRITIC method, and the method comprises the following steps:
(1) Carrying out Min-Max normalization processing on the data;
(2) The method is expressed in a standard deviation form, and index variability coefficients are obtained;
(3) Expressed in the form of pearson correlation coefficients, the index conflict coefficient is calculated, specifically, the formula (6):
wherein R is ij The pearson correlation coefficient of index i and index j, A j To improve the collision coefficient of the jth index of the CRITIC method;
(4) Obtaining index information entropy
(5) Weight calculation, calculation according to formula (7):
wherein S is j ' is the standard deviation of the sample normalized by the jth index, e j Entropy of j index information, beta j To improve the objective weight of the jth index of the CRITIC method.
9. The comprehensive evaluation method for a power distribution network according to claim 1, wherein in the step 7, the subjective and objective combination weights of the low-carbon benefit evaluation index are calculated, and the method comprises the steps of:
the subjective and objective combination weights are obtained according to the formulas (8) and (9):
wherein alpha is zj Subjective weight, beta, of the j-th index kj Objective weight of jth index, w j Is a combined weight value.
10. The comprehensive evaluation method for the power distribution network according to claim 1, wherein in the step 8, a fuzzy comprehensive evaluation matrix is established by adopting an improved fuzzy comprehensive evaluation method, and the comprehensive score of the power distribution network is obtained by multiplying a weight coefficient matrix by the fuzzy comprehensive evaluation matrix, and the method comprises the following steps:
(1) Determining an evaluation index and an evaluation grade;
(2) Determining a weight set;
(3) Determining a membership function and calculating membership degree, constructing a fuzzy comprehensive evaluation matrix, and improving the fuzzy comprehensive evaluation matrix, wherein the fuzzy comprehensive evaluation matrix is shown as a formula (10) -a formula (12):
wherein r 'is' ji In order to adopt the membership degree of the jth index under the ith scheme after the improved fuzzy comprehensive evaluation method,as the average membership of index j, BIAS ji And (3) the deviation value of the membership degree of the ith scheme which is lower than the average value in the jth index. S is S i Is the comprehensive evaluation value of the i-th region.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310629223.3A CN116683518A (en) | 2023-05-31 | 2023-05-31 | Comprehensive evaluation method for power distribution network considering low-carbon benefits |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310629223.3A CN116683518A (en) | 2023-05-31 | 2023-05-31 | Comprehensive evaluation method for power distribution network considering low-carbon benefits |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116683518A true CN116683518A (en) | 2023-09-01 |
Family
ID=87784786
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310629223.3A Pending CN116683518A (en) | 2023-05-31 | 2023-05-31 | Comprehensive evaluation method for power distribution network considering low-carbon benefits |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116683518A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117913827A (en) * | 2024-03-18 | 2024-04-19 | 广东电网有限责任公司广州供电局 | Optimization method of complex power distribution network considering trigger function |
-
2023
- 2023-05-31 CN CN202310629223.3A patent/CN116683518A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117913827A (en) * | 2024-03-18 | 2024-04-19 | 广东电网有限责任公司广州供电局 | Optimization method of complex power distribution network considering trigger function |
| CN117913827B (en) * | 2024-03-18 | 2024-05-28 | 广东电网有限责任公司广州供电局 | Optimization method of complex power distribution network considering trigger function |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112308427B (en) | New energy consumption constraint factor evaluation method and system based on combined weighting-ash correlation | |
| CN109886472B (en) | Power distribution area capacity method with uncertain distributed photovoltaic and electric automobile access | |
| CN108717608A (en) | Million kilowatt beach photovoltaic plant accesses electric network synthetic decision-making technique and system | |
| CN104318317A (en) | Black-start scheme optimization method based on distributive integrated energy supply system | |
| Ramalakshmi | Optimal siting and sizing of distributed generation using fuzzy-EP | |
| CN116683518A (en) | Comprehensive evaluation method for power distribution network considering low-carbon benefits | |
| CN112380694A (en) | Power distribution network optimization planning method based on differential reliability requirements | |
| CN113610359A (en) | Photovoltaic access power distribution network adaptability evaluation method based on quantitative hierarchical index system | |
| CN113536694A (en) | Robust optimization operation method, system and device of comprehensive energy system and storage medium | |
| CN107221936A (en) | A kind of optimal load flow computational methods and device containing wind power plant | |
| Gangwar et al. | Management of energy storage dispatch in unbalanced distribution networks using opendss | |
| CN109102196B (en) | Power distribution network diversified load digestion capacity assessment method and system | |
| CN115719967A (en) | Active power distribution network energy storage device optimal configuration method for improving power supply reliability | |
| CN112736913B (en) | Method for analyzing influence factors of power optimization mode of power distribution network containing distributed power supply | |
| CN113595069A (en) | Method for improving photovoltaic absorption capacity of power distribution network | |
| CN115051392A (en) | Reactive power coordination optimization control method containing new energy, stored energy and flexible load | |
| Permana et al. | Impact of Photovoltaic Interconnection on Power Flow of Selayar Island Power System South Sulawesi | |
| CN111092453A (en) | Power grid key line identification method for multipoint access photovoltaic power supply | |
| CN108899917A (en) | A kind of capacitor group and Static Var Compensator（SVC）The distribution network var compensation device of cooperated integration | |
| CN212966176U (en) | Experimental microgrid construction for verifying various microgrid structures | |
| CN115907981B (en) | Low-carbon joint transaction optimization method for virtual power plant | |
| Chege et al. | Optimal Placement of Distributed Generation and Capacitors in Radial Distribution Networks Using Hybrid Evolution Programming Algorithm | |
| Wu et al. | A Distribution Network Flexible Resource Capacity Configuration Method with Large Renewable Energy Sources Access | |
| CN108334981A (en) | A kind of layering Multipurpose Optimal Method towards micro-capacitance sensor complex energy management | |
| Mingming et al. | Scheme recommendation of regional wind power access system based on analytic hierarchy process |
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 |