CN117649118A - Method, system, equipment and medium for evaluating power supply reliability of wind power grid-connected system - Google Patents
Method, system, equipment and medium for evaluating power supply reliability of wind power grid-connected system Download PDFInfo
- Publication number
- CN117649118A CN117649118A CN202311369490.8A CN202311369490A CN117649118A CN 117649118 A CN117649118 A CN 117649118A CN 202311369490 A CN202311369490 A CN 202311369490A CN 117649118 A CN117649118 A CN 117649118A
- Authority
- CN
- China
- Prior art keywords
- power supply
- wind power
- supply reliability
- wind
- connected system
- 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
- 238000000034 method Methods 0.000 title claims abstract description 57
- 230000035699 permeability Effects 0.000 claims abstract description 44
- 238000013210 evaluation model Methods 0.000 claims abstract description 35
- 238000011156 evaluation Methods 0.000 claims description 16
- 230000007704 transition Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 13
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 abstract description 22
- 238000012545 processing Methods 0.000 abstract description 10
- 238000004364 calculation method Methods 0.000 abstract description 7
- 230000006870 function Effects 0.000 description 14
- 230000008859 change Effects 0.000 description 13
- 241001123248 Arma Species 0.000 description 12
- 238000010586 diagram Methods 0.000 description 12
- 238000012360 testing method Methods 0.000 description 12
- 230000008569 process Effects 0.000 description 10
- 238000005070 sampling Methods 0.000 description 8
- 238000004146 energy storage Methods 0.000 description 7
- 230000003287 optical effect Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012983 electrochemical energy storage Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 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 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000001151 other effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012546 transfer Methods 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- 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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/06—Wind turbines or wind farms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- 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
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- 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
- H02J2300/28—The renewable source being wind energy
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Power Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Algebra (AREA)
- Geometry (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
Abstract
The application provides a method, a system, equipment and a medium for evaluating power supply reliability of a wind power grid-connected system, wherein the method comprises the following steps: establishing a power supply reliability evaluation model of the wind power grid-connected system, wherein the power supply reliability evaluation model evaluates the power supply reliability based on the wind power permeability and the operation mode; acquiring power supply reliability indexes of the wind power grid-connected system under different operation modes based on the power supply reliability evaluation model of the wind power grid-connected system; the power supply reliability indexes comprise a power supply shortage probability index and a power supply shortage index. According to the method and the device, the influence of factors such as wind power permeability and operation mode on the power supply reliability index model is comprehensively considered, meanwhile, in the time period selection of simulation data, the time width and the calculation processing strategy of the data selection are optimized, the accuracy and the authenticity of the simulation result are improved, and the power supply reliability is estimated more comprehensively and accurately.
Description
Technical Field
The application belongs to the technical field of wind power generation, and relates to a method, a system, equipment and a medium for evaluating power supply reliability of a wind power grid-connected system.
Background
Wind power generation has obvious randomness, intermittence and fluctuation, and after the output power of a wind power plant is integrated into a power grid system, the power grid is subjected to power impact, voltage disturbance or oscillation, harmonic pollution and other effects. The power supply reliability of the system is also affected by wind power output, particularly when the capacity of a wind power plant connected to a power grid system is large, the power supply reliability of the system is obviously affected, meanwhile, the wind speed and the running state of a fan in the surrounding environment of the wind power plant change and fluctuate in real time, so that the power supply reliability of the system is affected by different degrees, and the power supply capacity of the wind power grid system needs to be assessed again. The existing modeling and evaluation research of the power supply reliability of the wind power grid-connected system mainly adopts probabilistic or deterministic methods such as a Markov chain analysis method, a sequential Monte Carlo simulation method, a recursive simulation method and the like. The considered influencing factors mainly comprise influences of factors such as fluctuation, correlation, wind field wake effect, fan output characteristics, power failure rate or outage rate of wind speed and the like on the power supply reliability index.
However, in the actual operation of the wind power grid-connected system, the wind power permeability will obviously influence the power supply amount of the wind power grid-connected system, and when the wind power is in different operation modes such as off-grid, grid-connected, auxiliary by adopting an energy storage device and the like, the power supply reliability of the system also has certain difference. On the other hand, in the existing simulation calculation process, only running data with a certain duration or a certain year is often selected as a research object, so that a large error exists between a simulation result and an actual situation.
Disclosure of Invention
The application provides an evaluation method, system, equipment and medium for power supply reliability of a wind power grid-connected system, which are used for solving the technical problem of a comprehensive and accurate evaluation method for the power supply reliability of the wind power grid-connected system.
In a first aspect, the present application provides a method for evaluating power supply reliability of a wind power grid-connected system, where the method includes: establishing a power supply reliability evaluation model of the wind power grid-connected system, wherein the power supply reliability evaluation model evaluates the power supply reliability based on the wind power permeability and the operation mode; acquiring power supply reliability indexes of the wind power grid-connected system under different operation modes based on the power supply reliability evaluation model of the wind power grid-connected system; the power supply reliability indexes comprise a power supply shortage probability index and a power supply shortage index.
In an implementation manner of the first aspect, the building a power supply reliability evaluation model of the wind power grid-connected system includes: a wind speed prediction module is constructed, and the wind speed prediction model is used for acquiring the wind speed prediction sequence of the fan; building a fan output model, wherein the wind power plant output model is used for obtaining fan output based on the wind speed prediction sequence; constructing a fan running state transition model, wherein the fan running state transition model is used for acquiring duration time of three running states of full-running, derating and outage of the fan; building a conventional power supply output model of the wind power grid-connected system, wherein the conventional power supply output model of the wind power grid-connected system is used for obtaining equivalent output of a conventional power supply unit of the wind power grid-connected system; and constructing a power supply reliability evaluation model of the wind power grid system based on the fan output, the duration time of the three running states and the equivalent output of the conventional power supply unit of the wind power grid system.
In an implementation manner of the first aspect, constructing the wind power grid-connected system power supply reliability assessment model based on the fan output, the durations of the three operation states, and the equivalent output of the wind power grid-connected system conventional power supply unit includes: acquiring rated output of the wind power plant based on the number of grid-connected units of the wind power plant and the fan treatment; acquiring wind power permeability based on the rated output of the wind power plant; and constructing a power supply reliability evaluation model of the wind power grid-connected system based on the duration time of the three running states, the equivalent output of the conventional power supply unit of the wind power grid-connected system and the wind power permeability.
In one implementation manner of the first aspect, the constructing a wind speed prediction module includes: determining an optimal order of the autoregressive moving average model based on the red-pool information quantity criterion; and acquiring the wind speed prediction sequence based on an autoregressive moving average model of the optimal order.
In one implementation manner of the first aspect, the building a fan output model includes building the fan output model based on a wind speed-fan output relationship characteristic.
In one implementation manner of the first aspect, constructing a fan operation state transition model includes constructing the fan operation state transition model based on a quasi-sequential monte carlo simulation method.
In one implementation manner of the first aspect, constructing a conventional power supply output model of the wind power grid-connected system includes: and obtaining the equivalent output of the conventional power supply unit based on the number of the conventional power supply unit, the load hours and the forced shutdown rate of the conventional power supply unit.
In a second aspect, the application provides a method and a system for evaluating the power supply reliability of a wind power grid-connected system, wherein the method and the system comprise a construction module, a control module and a control module, wherein the construction module is used for constructing a power supply reliability evaluation model of the wind power grid-connected system, and the power supply reliability evaluation model of the wind power grid-connected system evaluates the power supply reliability based on wind power permeability and an operation mode; the acquisition module is used for acquiring power supply reliability indexes of the wind power grid-connected system under different operation modes based on the power supply reliability evaluation model of the wind power grid-connected system; the power supply reliability indexes comprise a power supply shortage probability index and a power supply shortage index.
In a third aspect, the present application provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the method for evaluating the power supply reliability of a wind power grid-connected system according to the first aspect of the present application.
In a fourth aspect, the present application provides an electronic device, comprising: a memory configured to store a computer program; and a processor communicatively connected to the memory, the processor configured to invoke the computer program to perform the method for evaluating power supply reliability of the wind power grid-connected system according to the first aspect of the present application.
The method, the system, the equipment and the medium for evaluating the power supply reliability of the wind power grid-connected system have the following beneficial effects: the influence of factors such as wind power permeability and operation modes is comprehensively considered, and the power supply reliability index evaluation of the system under different operation modes is considered, wherein the power supply reliability index evaluation comprises two indexes of power supply shortage probability and power supply shortage. Meanwhile, the time width and the calculation processing strategy of data selection are optimized, the system power supply reliability index during the continuous 3-year operation period of the system is simulated, the power supply reliability of the wind power system is comprehensively and accurately estimated, theoretical guidance and planning are provided for the reliable operation of the wind power grid-connected system, and the power supply reliability and income of the wind power grid-connected system are improved.
Drawings
Fig. 1 is a schematic structural diagram of a wind power grid-connected system according to an embodiment of the present application.
Fig. 2 is a flow chart of an evaluation method for power supply reliability of a wind power grid-connected system according to an embodiment of the present application.
Fig. 3 is a schematic flow chart of an evaluation method for power supply reliability of a wind power grid-connected system according to an embodiment of the present application.
Fig. 4 is a flow chart of an evaluation method for power supply reliability of a wind power grid-connected system according to an embodiment of the present application.
FIG. 5 is a schematic diagram of four fan speed sections according to an embodiment of the present application.
FIG. 6 is a schematic diagram illustrating a transition of operation states of a blower according to an embodiment of the present application.
Fig. 7 is a schematic diagram of an evaluation method for power supply reliability of a wind power grid-connected system according to an embodiment of the present application.
FIG. 8 is a schematic diagram showing the relationship between the number of grid-connected fans and the wind power permeability according to an embodiment of the present application.
Fig. 9a to 9f are schematic diagrams showing wind speed prediction distribution and fan operation state change conditions of randomly selecting and simulating 2 fans for 3 years according to an embodiment of the present application.
Fig. 10a to 10f are schematic diagrams showing power supply reliability index simulation situations of a wind turbine under different wind power permeability and different operation modes according to an embodiment of the present application.
FIG. 11a is a schematic diagram showing a change of a system power failure probability index under different wind power permeability conditions according to an embodiment of the present application.
FIG. 11b is a schematic diagram showing the variation of the system power failure probability index under different wind power permeability conditions according to an embodiment of the present application.
Fig. 12 is a schematic architecture diagram of an evaluation system for power supply reliability of a wind power grid system according to an embodiment of the present application.
Fig. 13 is a schematic diagram of an architecture of an electronic device according to an embodiment of the disclosure.
Detailed Description
Other advantages and effects of the present application will become apparent to those skilled in the art from the present disclosure, when the following description of the embodiments is taken in conjunction with the accompanying drawings. The present application may be embodied or carried out in other specific embodiments, and the details of the present application may be modified or changed from various points of view and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that, the illustrations provided in the following embodiments merely illustrate the basic concepts of the application by way of illustration, and only the components related to the application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
The embodiment of the application provides a method, a system, equipment and a medium for evaluating the power supply reliability of a wind power grid-connected system, which can evaluate the power supply reliability of the system under different running modes and wind power permeability conditions and provide theoretical guidance and planning for the reliable running of the wind power grid-connected system.
Referring to fig. 1, a schematic structural diagram of a wind power grid-connected system according to an embodiment of the present application is shown.
Referring to fig. 2, the method for evaluating the power supply reliability of the wind power grid-connected system according to an embodiment of the present application includes the following steps S1 to S2:
s1: and constructing a power supply reliability evaluation model of the wind power grid-connected system, wherein the power supply reliability evaluation model evaluates the power supply reliability based on the wind power permeability and the operation mode.
Specifically, as shown in fig. 3, step S1 includes steps S11 to S15:
s11: and constructing a wind speed prediction module, wherein the wind speed prediction model is used for acquiring the wind speed prediction sequence of the fan.
Specifically, as shown in fig. 4, step S11 includes steps S111 and S112.
S111: the optimal order of the autoregressive moving average model is determined based on the red-cell information volume criterion.
Specifically, an autoregressive moving average (Auto-regressive Moving Average, ARMA) model can better process the correlation of time sequences at different time points, simulate the dynamic change process of various running states of an object at different time periods, and therefore is very suitable for processing wind speed data with obvious randomness and fluctuation, and accurately predict future change sequences of wind speed. The basic expression of ARMA is shown in formula (1).
Wherein ρ is i (i=1, 2, …, k) represents an autoregressive weight coefficient, η j (j=1, 2, …, h) represents a moving average weight coefficient, k, h represent the order of the autoregressive and moving average portions of ARMA, respectively, β r For white noise sequences subject to standard independent distribution (Standard independent distribution, SID), i.e.The order k and h of ARMA dominate the predicted outcome performance of the simulated object.
In order to obtain the optimal ARMA order, the invention adopts a red pool information quantity criterion (Akaike information criterion, AIC) to quasi-measure and optimize the order of the ARMA model. AIC accurate measurement is a standard for measuring the fitting superiority of a statistical model, and the complexity of an estimated model and the superiority of fitted data can be weighed to determine a fitting model with the optimal order. The AIC value may be expressed as shown in formula (2).
AIC＝N A ln(SSR)+2L＝N A ln(SSR)+2(k+h) (2)
Wherein SSR is the sum of squares of residuals of the model, l=k+h is the order term of the model, N A Representing the number of sample observations of the fitted model. The smaller the AIC number, the better the performance of the fitted model.
According to the Pandit-Wu modeling concept, any ARMA (k, h) model can be represented by ARMA (k, k-1), and the SSR values at different orders are compared by successively increasing the ARMA model order k during the calculation until the SSR values are almost smooth and no longer decreasing, at which time AIC is near the minimum value, and the optimal order k of the corresponding model is obtained opt 。
S112: and acquiring the wind speed prediction sequence based on an autoregressive moving average model of the optimal order.
Specifically, ARMA (k) opt ,k opt 1) modeling wind speed distribution of a wind farm, wherein the frequent fluctuation of wind speed data leads to numerical overflow or increase of errors in the data processing process, and in order to avoid the problem, normalization processing is firstly carried out on an original wind speed sequence, as shown in a formula (3).
Wherein { v r0 And { v } is ra Respectively an original wind speed sequence and a normalized wind speed sequence, and tau r And sigma (sigma) r Representing the average and variance values of the original wind speed sequence, respectively. After normalization and fitting treatment are carried out on the original wind speed sequence, the output sequence is corrected to obtain a final wind speed prediction sequence { v } r And as shown in equation (4).
v r ＝σ r v ra +τ r (4)
S12: and constructing a fan output model, wherein the wind power plant output model is used for acquiring the fan output based on the wind speed prediction sequence.
Specifically, the fan output model is established based on the relation characteristic of wind speed and fan output.
Specifically, in order to obtain the fan output based on the predicted wind speed distribution, the relation characteristic of wind speed and fan output needs to be established first. The fan output characteristics may be divided into four sections according to the wind speed range, as shown in FIG. 5. The boundary wind speeds of the sections are respectively cut-in wind speed v in Rated wind speed v n Cut-out wind speed v out Wherein when the wind speed is at 0<v r ≤v in And v r >v out When the two sections are arranged, the fans do not output power; when the wind speed is v in <v r ≤v n When in section, the fan outputs power according to a specific curve; when the wind speed is v n <v r ≤v out When in section, the wind speed maintains rated output; to sum up, the fan output characteristic curve canDescribed by equation (5):
wherein P is wv In order to consider the fan output after wind speed distribution, the output characteristic parameters of the fan output change sections of E, G and J are set as follows.
According to the wind speed-fan output model, fan output based on predicted wind speed distribution can be obtained through simulation by bringing a wind speed prediction sequence.
The power supply reliability of the system is mainly evaluated by indexes such as power supply quantity, power loss probability, missing electric quantity and the like. For a wind power grid-connected system, the power supply reliability of the system is determined by a plurality of factors such as the grid-connected number of fans, the wind power permeability, the system load capacity, the conventional power supply output of the system, the system operation mode and the like. The wind speed distribution rule and the wind power running state have direct influence on the real-time output power of wind power, the wind power permeability determines the wind power grid-connected capacity, and the change of the system running mode also affects the power supply reliability. Therefore, the influence of the wind power permeability and the running mode is comprehensively considered, and a reasonable wind speed prediction model and a wind power output model which are close to the real situation are established, so that the method is a premise for accurately evaluating the power supply reliability of the system.
S13: and constructing a fan running state transition model, wherein the fan running state transition model is used for acquiring duration time of three running states of full running, derating and shutdown of the fan.
Specifically, the state of the fan in the running process is fluctuated and changed in real time, and the output and the reliability of the fan are affected by the running state in real time, so that a Quasi-sequential Monte Carlo simulation (Quasi-Sequential Monte Carlo, QMC) method is adopted to build a running state transition model of the fan, and the duration time of the fan in different running states is determined to correct the power supply reliability model of the system.
The sequential Monte Carlo simulation method adopts random sampling to simulate the statistical distribution of the actual target problem, so that the optimization result of the problem is obtained by fitting approximation. The simulation method is used for efficiently processing complex practical problems including multiple uncertain factors, high nonlinearity and multiple dimensions through the strategies of continuous iterative sampling and piecewise processing, and is very suitable for simulating the problems with multiple running states and continuous change characteristics with time sequence and indexes thereof.
Because the output and load data of the wind power grid-connected system are all in the unit of hours, the method is improved and adjusted on the basis of the traditional sequential Monte Carlo method, simulation calculation is carried out in the unit of hours in each year of calculation of the fitted model, and an average value is taken as a simulation result, so that the simulation result has higher precision and is closer to the real situation, and the improved simulation method is called as a Quasi-sequential Monte Carlo simulation method (Quasi-Sequential Monte Carlo, QMC). The invention establishes a running state transition model of the wind power plant based on a QMC method and calculates duration time of different running states.
As shown in fig. 6, the running state transition of the fan is considered to have three running states of Full Supply (FS), derated Output (DO) and shutdown (shutdown, SD), and the probabilities of the fan in the three running states are λ respectively FS ，λ DO ，λ SD . Thus, the fan may have 6 operational state transition situations, each state transition and its probability being full-derated (λ) FS-DO ) Full-stop (lambda) FS-SD ) Derating-full hair (lambda) DO-FS ) Stop-full hair (lambda) SD-FS ) Derating-off-line (lambda) DO-SD ) Shutdown-derate (lambda) SD-DO )。
The probability of setting the fan in three running states of full sending, derating and stopping is shown in a formula (7).
Setting the current running state of the fanState u ai The total running state is N s (according to the above arrangement, the blower has three operating states N s =3), the characteristics of the blower are uniform for each duration of the operating state, and the operating state u of the blower at the next moment ai+1 By random sampling, i.e. u ai+1 State probability lambda of (2) ui+1 N (0, 1). N (0, 1) represents a normal distribution with a mean of 0 and a variance of 1. u (u) ai+1 May be converted into N s The duration of each operating condition of the fan is represented by equation (8).
Let u be ab Representing that the system is in the b-th running state in the a-th year, and the fan is in the u-th running state ab The duration of the state is T (u ab ). According to equation (8), the duration of three operating states of the wind farm, full, derated, and shutdown, may be specifically expressed as shown in equation (9).
S14: and constructing a conventional power supply output model of the wind power grid-connected system, wherein the conventional power supply output model of the wind power grid-connected system is used for obtaining the equivalent output of a conventional power supply unit of the wind power grid-connected system.
Specifically, the wind power grid-connected system further comprises a conventional power supply unit, and the output of the conventional power supply unit also influences the power supply reliability of the system. Therefore, the influence of the output of the conventional power supply unit needs to be considered when the power supply reliability evaluation model of the wind power grid-connected system is constructed.
Specifically, the capacity of a conventional power unit of the wind power grid-connected system is set to be P c Average fault time of conventional power supply unit is T cf The average fault time sequence of the conventional power supply unit is T cr The forced shutdown rate beta of the conventional power supply unit can be expressed as shown in a formula (10),
β＝T cr /(T cf +T cr )×100％ (10)
in order to integrate and match the fan prediction model, the probability sampling mode is still adopted to simulate the value condition of the output of the conventional power supply unit, and the probability functions epsilon-N are distributed normally according to the number of the conventional power supply unit and the load hours c (8736,11) randomly comparing the forced shutdown rate beta to be used as the output value condition of the conventional power supply unit, as shown in a formula (11).
Wherein P is ce Representing the equivalent output of a conventional power supply unit.
S15: and constructing a power supply reliability evaluation model of the wind power grid system based on the fan output, the duration time of the three running states and the equivalent output of the conventional power supply unit of the wind power grid system.
Specifically, as shown in fig. 7, step S15 includes steps S151 to S153.
S151: and obtaining rated output of the wind power plant based on the number of grid-connected units of the wind power plant and the output of the fan.
Specifically, the number of grid-connected units of the simulated wind power plant is N w Rated capacity of a single wind turbine generator in year a is P wo (u an ) The rated output of the wind farm is shown in formula (12).
P WS (u an )＝N w ·P wo (u an ) (12)
Wherein P is ws (u an ) And the output of the wind farm in the rated running state in the a-th year is shown.
S152: and obtaining the wind power permeability based on the rated output of the wind power plant.
Specifically, on the basis of the formula (12), the wind power permeability of the wind power grid-connected system can be derived from the formula (13).
η＝P ws (μ am )/L p ×100％ (13)
Wherein eta is wind power permeability, L p Is the peak load of the test system. It can be seen that the number of the grid-connected fans determines the wind power permeability. And the power supply reliability spectrum evaluation under different wind power permeability can be simulated by selecting different fan grid connection numbers.
It should be noted that, in operation, the wind power grid-connected system needs to satisfy the real-time power balance, as shown in formula (14). Namely, the power supply reliability value condition under the wind power permeability correction system needs to meet the formula (14).
P ws (μ ab )+P ce ＝L p (14)
S153: and constructing a power supply reliability evaluation model of the wind power grid-connected system based on the duration time of the three running states, the equivalent output of the conventional power supply unit of the wind power grid-connected system and the wind power permeability.
Specifically, the rated output capacity and the wind power permeability of the wind power grid connection are determined by considering the number of different wind turbines, so that the power supply reliability of a wind power grid connection system can be greatly influenced.
Therefore, based on the operation state duration time acquired by the fan operation state transfer module, the equivalent output of a conventional power unit of the wind power grid-connected system acquired by combining the conventional power output model and the power supply reliability value condition of the wind power permeability correction system are adopted, and a system power supply reliability index function based on a QMC method is established, as shown in a formula (15).
Wherein T is s For the simulated run period, PF (u a ) And t is the running time, and is the power supply reliability index in the running state of the a year.
Simulated transportation is setThe total number of the line years is Y years, and the number of hours per year is N H The power supply reliability indexes of all small time periods in different years are respectively simulated and calculated, and then the power supply reliability index function of the system can be discretized according to the years; on the other hand, the operating states of the fans are transitionally changed in real time, and the characteristics of the fans remain constant for the duration of each operating state, so that the system can also be discretized according to the operating state.
And accumulating the power supply reliability indexes of the wind power grid-connected system in different years and under different running states, and then taking an average value to obtain an equivalent system power supply reliability index, namely a power supply reliability evaluation model of the wind power grid-connected system. As shown in equation (16).
Wherein u is ab Represents that the wind turbine generator system is in the b-th running state in the a-th year, T (u) ab ) Indicating that the wind turbine generator is in u ab Runtime of state. N (N) s Is the total number of running states of the system (according to the number of running states of the fans, N is s ＝3)
S2: acquiring power supply reliability indexes of the wind power grid-connected system under different operation modes based on the power supply reliability evaluation model of the wind power grid-connected system; the power supply reliability indexes comprise a power supply shortage probability index and a power supply shortage index.
Specifically, in an embodiment, a wind power grid-connected system power supply reliability evaluation model is utilized, under three operation modes of off-grid of a fan, grid connection of the fan and auxiliary assistance of an electrochemical energy storage device, two power supply reliability indexes of power supply shortage probability and power supply shortage in the continuous 3-year operation period of the simulation system are taken as final simulation results, and the average value of the power supply capacity indexes of 3 years is taken as a final simulation result, so that the power supply reliability of the wind power grid-connected system is evaluated.
Specifically, the hour-level wind speed, the fan state, the conventional power output and the load in 3 years are selected as data simulation units, and the corresponding rated output and wind power permeability of the wind power plant are obtained.
Specifically, in the wind farm grid-connected operation mode, the power failure probability (power supply insufficient Probability, PSIP) of the wind farm grid-connected system in a certain sampling simulation period can be calculated by using the formula (17).
Wherein PF is PSIP (u ab ) Indicating that the wind turbine generator is in state u ab And (3) a value function of whether the power supply state is sufficient or not, wherein the value condition of the function is modified by adopting wind power permeability, as shown in a formula (18).
The power shortage (power supply quantity shortage, PSQS) of the wind power grid-connected system in a certain sampling simulation time period can be calculated by a formula (19).
Wherein PF is PSQS (u ab ) Indicating that the wind turbine generator is in state u ab And a value function of the lost electric quantity due to the load reduction is shown in a formula (20).
Specifically, when the wind farm is in an off-grid operation mode, the system only depends on a conventional power supply unit to meet load power supply, and at the moment, the power supply shortage probability index function of the system is as shown in formula (21).
The index function of the shortage of power supply is shown as a formula (22)
Specifically, as the uncertainty of the wind power running state and different wind power permeability have different influences on the power supply reliability of the grid-connected system, the electrochemical energy storage device (Battery Energy Storage Device, BESD) is considered to assist in improving the power supply reliability of the wind power grid-connected system. On the one hand, in the period that the output of the wind power plant exceeds the load demand of the system, the energy storage is utilized to absorb and store redundant electric energy under the BESD-based auxiliary operation mode; on the other hand, when the output of the wind farm cannot meet the load demand of the system, the energy storage actively releases a proper amount of electric quantity to the system so as to supplement the insufficient load demand.
When the wind farm is in BESD auxiliary operation mode, a sequence { P } of energy storage charging and discharging power is set bs(i) Sequence { P at the previous point in time by energy storage, wind farm and conventional power supply bs(i-1) }、{P ws(i) { P } ce(i-1) The power variation strategy is designed as shown in formula (23).
The power balance condition of the wind power grid-connected system based on the BESD auxiliary operation mode is shown in a formula (24).
P ws (μ ab )+P ce +P bs ＝L p (24)
And under the BESD auxiliary operation mode, the power shortage probability of the wind power grid-connected system in a certain sampling simulation time period can be calculated by using a formula (17). Wherein PF is PSIP (u ab ) Indicating that the wind turbine generator is in state u ab And (3) a value function of whether the power supply state is sufficient or not, wherein the value condition of the function is modified by adopting the wind power permeability, as shown in a formula (25).
The probability of the power shortage of the wind power grid-connected system in a certain sampling simulation time period can be calculated by a formula (19. Wherein, PF PSQS (u ab ) Indicating that the wind turbine generator is in state u ab And a value function of the lost electric quantity due to the load reduction is shown in a formula (26).
In an embodiment, the method and the device are used for testing the effectiveness and the accuracy of the wind power grid-connected system power supply reliability evaluation model, and comparing and analyzing the change rule of the system power supply reliability under different wind power permeability and running modes. And selecting an RBTS system to test the evaluation model of the application.
It should be noted that, the RBTS test system mainly includes 5 load buses, 11 power units with capacity ranging from 5MW to 40MW, the capacity of the total assembly machine is 240MW, the annual peak load is 185MW, the load type mainly includes hour load, day load, week load and season load within one year, and the RBTS test system has comprehensive basic characteristics of the power system and is very suitable for reliability test.
The embodiment selects the load data type of the hour level, and comprises 8736 hour level load data in one year so as to simulate the load change condition of the system most thoroughly and accurately. The capacity of the conventional power supply unit is recorded as a sequence { P } c }＝{P c1 ,,P c2 ,,…,P c11 The average fault time sequence of the conventional power supply unit is recorded as { T } cf }＝{T cf1 ,T cf2 ,…,T cf11 The average fault time sequence of the conventional power supply unit is recorded as { T } cr }＝{T cr1 ,T cr2 ,…,T cr11 }。
Performing calculation test based on RBTS test system, and setting capacity P of single wind turbine generator set wo Is of the order of 3MW,grid-connected number N of wind turbine generators w The energy storage rated capacity is set to be 30MW and the simulated year number is set to be Y=3 by comparison with 15, 20, 25, 30, 35 and 40 respectively. And respectively carrying out power supply reliability simulation under three operation modes of wind power plant grid connection, wind power plant off-grid and BESD assistance.
Testing the obtained ARMA (k, k-1) model with the optimal order of k opt Order 4, so the test uses the ARMA (4, 3) model for wind speed prediction.
The relationship between the number of fan-connected units under test and the wind power permeability is shown in fig. 8, wherein the wind power permeability rises with the increase of the number of fan-connected units.
2 fans were randomly selected during the test, and the simulated wind speed forecast profile and fan operating state change for each year are shown in fig. 9a to 9 f. It can be seen that the wind speed average value of the wind farm is about 12m/s, and a normal distribution rule is shown. The operation state change process of the selected 2 fans in the same year and different years is random and irregular.
The simulation of the power supply reliability index of the fan under the conditions of different wind power permeabilities and different running modes is shown in fig. 10a to 10 f. The change conditions of the system power shortage probability index and the power shortage index under different wind power permeability conditions are shown in fig. 11a and 11b respectively.
It can be seen that the power supply shortage probability and the power supply shortage amount of the fan in all operation modes are reduced along with the increase of the wind-electricity permeability, namely the power supply reliability index performance of the fan is improved along with the increase of the wind-electricity permeability. The power supply shortage probability and the power supply shortage quantity of the fan in the off-grid operation mode are far greater than those of the other two operation modes, and meanwhile, the power supply reliability index of the fan in the BESD auxiliary operation mode is slightly better than that of the fan in the grid-connected operation mode. When the grid-connected quantity of the fans reaches 35 and the wind power permeability reaches 56.7568%, the power supply shortage probability of the fans in two operation modes such as grid-connected operation and BESD assistance is less than 1%, and the power supply shortage is only about 0.1MW and about 0.065MW respectively, which indicates that the system power supply reliability at the moment is superior enough, and the investment and operation and maintenance cost of the system are increased by considering the higher wind power permeability, so that the 35 fan grid-connected operation modes under BESD assistance are recommended as the preferred operation modes of the wind power grid-connected system, and the power supply reliability and income of the wind power grid-connected system are both improved. The power supply reliability evaluation model provided by the application can provide theoretical guidance and planning for the reliable operation of the wind power grid-connected system, and improves the power supply reliability and income of the wind power grid-connected system.
The protection scope of the method for evaluating the power supply reliability of the wind power grid-connected system according to the embodiment of the application is not limited to the step execution sequence listed in the embodiment, and all the schemes realized by increasing or decreasing steps and replacing steps according to the prior art made according to the principles of the application are included in the protection scope of the application.
The embodiment of the application also provides an evaluation system for the power supply reliability of the wind power grid-connected system, which can realize the evaluation method for the power supply reliability of the wind power grid-connected system, but the implementation device for the evaluation method for the power supply reliability of the wind power grid-connected system comprises but is not limited to the structure of the evaluation system for the power supply reliability of the wind power grid-connected system, and all the structural deformation and replacement of the prior art according to the principles of the application are included in the protection scope of the application.
As shown in fig. 12, the system for evaluating power supply reliability of a wind power grid-connected system according to this embodiment includes a construction module 10 and an acquisition module 20.
The construction module 10 is used for constructing a power supply reliability evaluation model of the wind power grid-connected system, and the power supply reliability evaluation model evaluates the power supply reliability based on the wind power permeability and the running mode;
the acquiring module 20 is configured to acquire power supply reliability indexes of the wind power grid-connected system under different operation modes based on the power supply reliability evaluation model of the wind power grid-connected system; the power supply reliability indexes comprise a power supply shortage probability index and a power supply shortage index.
The application also provides electronic equipment. As shown in fig. 13, the present embodiment provides an electronic apparatus 90, the electronic apparatus 90 including: a memory 901 configured to store a computer program; and a processor 902 communicatively connected to the memory 901 and configured to invoke the computer program to perform the method of evaluating the power supply reliability of the wind power grid system.
The memory 901 includes: ROM (Read Only Memory image), RAM (Random Access Memory), magnetic disk, USB flash disk, memory card or optical disk, etc.
The processor 902 is connected to the memory 901, and is configured to execute a computer program stored in the memory 901, so that the electronic device executes the method for evaluating the power supply reliability of the wind power grid-connected system.
Preferably, the processor 902 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; but also digital signal processors (Digital Signal Processor, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field programmable gate arrays (Field Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules/units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or units may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules or units, which may be in electrical, mechanical or other forms.
The modules/units illustrated as separate components may or may not be physically separate, and components shown as modules/units may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules/units may be selected according to actual needs to achieve the purposes of the embodiments of the present application. For example, functional modules/units in various embodiments of the present application may be integrated into one processing module, or each module/unit may exist alone physically, or two or more modules/units may be integrated into one module/unit.
Those of ordinary skill would further appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Embodiments of the present application also provide a computer-readable storage medium. Those of ordinary skill in the art will appreciate that all or part of the steps in a method implementing the above embodiments may be implemented by a program to instruct a processor, where the program may be stored in a computer readable storage medium, where the storage medium is a non-transitory (non-transitory) medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state disk, a magnetic tape (magnetic tape), a floppy disk (floppy disk), an optical disk (optical disk), and any combination thereof. The storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Embodiments of the present application may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the processes or functions described in accordance with the embodiments of the present application are produced in whole or in part. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.).
The computer program product is executed by a computer, which performs the method according to the preceding method embodiment. The computer program product may be a software installation package, which may be downloaded and executed on a computer in case the aforementioned method is required.
The descriptions of the processes or structures corresponding to the drawings have emphasis, and the descriptions of other processes or structures may be referred to for the parts of a certain process or structure that are not described in detail.
The foregoing embodiments are merely illustrative of the principles of the present application and their effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those of ordinary skill in the art without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications and variations which may be accomplished by persons skilled in the art without departing from the spirit and technical spirit of the disclosure be covered by the claims of this application.
Claims (10)
1. The method for evaluating the power supply reliability of the wind power grid-connected system is characterized by comprising the following steps of:
establishing a power supply reliability evaluation model of the wind power grid-connected system, wherein the power supply reliability evaluation model evaluates the power supply reliability based on the wind power permeability and the operation mode;
acquiring power supply reliability indexes of the wind power grid-connected system under different operation modes based on the power supply reliability evaluation model of the wind power grid-connected system; the power supply reliability indexes comprise a power supply shortage probability index and a power supply shortage index.
2. The method for evaluating the power supply reliability of a wind power grid-connected system according to claim 1, comprising: the construction of the wind power grid-connected system power supply reliability evaluation model comprises the following steps:
a wind speed prediction module is constructed, and the wind speed prediction model is used for acquiring the wind speed prediction sequence of the fan;
building a fan output model, wherein the wind power plant output model is used for obtaining fan output based on the wind speed prediction sequence;
constructing a fan running state transition model, wherein the fan running state transition model is used for acquiring duration time of three running states of full-running, derating and outage of the fan;
building a conventional power supply output model of the wind power grid-connected system, wherein the conventional power supply output model of the wind power grid-connected system is used for obtaining equivalent output of a conventional power supply unit of the wind power grid-connected system;
and constructing a power supply reliability evaluation model of the wind power grid system based on the fan output, the duration time of the three running states and the equivalent output of the conventional power supply unit of the wind power grid system.
3. The method for evaluating the power supply reliability of a wind power grid-connected system according to claim 2, comprising: the wind power grid-connected system power supply reliability assessment model is constructed based on the fan output, the duration time of the three running states and the equivalent output of the conventional power supply unit of the wind power grid-connected system, and comprises the following steps:
acquiring rated output of the wind power plant based on the number of grid-connected units of the wind power plant and the fan treatment;
acquiring wind power permeability based on the rated output of the wind power plant;
and constructing a power supply reliability evaluation model of the wind power grid-connected system based on the duration time of the three running states, the equivalent output of the conventional power supply unit of the wind power grid-connected system and the wind power permeability.
4. The method for evaluating power supply reliability of a wind power grid-connected system according to claim 2, wherein the constructing a wind speed prediction module comprises:
determining an optimal order of the autoregressive moving average model based on the red-pool information quantity criterion;
and acquiring the wind speed prediction sequence based on an autoregressive moving average model of the optimal order.
5. The method for evaluating power supply reliability of a wind power grid-connected system according to claim 2, wherein the constructing a fan output model comprises constructing the fan output model based on a relational characteristic of wind speed and fan output.
6. The method for evaluating power supply reliability of a wind power grid-connected system according to claim 2, wherein constructing a fan operation state transition model comprises constructing the fan operation state transition model based on a quasi-sequential monte carlo simulation method.
7. The method for evaluating the power supply reliability of a wind power grid-connected system according to claim 2, wherein constructing a conventional power supply output model of the wind power grid-connected system comprises:
and obtaining the equivalent output of the conventional power supply unit based on the number of the conventional power supply unit, the load hours and the forced shutdown rate of the conventional power supply unit.
8. An evaluation system for power supply reliability of a wind power grid-connected system is characterized by comprising:
the building module is used for building a power supply reliability evaluation model of the wind power grid-connected system, and the power supply reliability evaluation model is used for evaluating the power supply reliability based on the wind power permeability and the running mode;
the acquisition module is used for acquiring power supply reliability indexes of the wind power grid-connected system under different operation modes based on the power supply reliability evaluation model of the wind power grid-connected system; the power supply reliability indexes comprise a power supply shortage probability index and a power supply shortage index.
9. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the method for evaluating the power supply reliability of a wind power grid-connected system according to any one of claims 1 to 7.
10. An electronic device, the device comprising:
a memory storing a computer program;
the processor is in communication connection with the memory, and executes the method for evaluating the power supply reliability of the wind power grid-connected system according to any one of claims 1 to 7 when the computer program is called.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311369490.8A CN117649118A (en) | 2023-10-20 | 2023-10-20 | Method, system, equipment and medium for evaluating power supply reliability of wind power grid-connected system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311369490.8A CN117649118A (en) | 2023-10-20 | 2023-10-20 | Method, system, equipment and medium for evaluating power supply reliability of wind power grid-connected system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117649118A true CN117649118A (en) | 2024-03-05 |
Family
ID=90042283
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311369490.8A Pending CN117649118A (en) | 2023-10-20 | 2023-10-20 | Method, system, equipment and medium for evaluating power supply reliability of wind power grid-connected system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117649118A (en) |
-
2023
- 2023-10-20 CN CN202311369490.8A patent/CN117649118A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113937763B (en) | Wind power prediction method, device, equipment and storage medium | |
| CN103117546B (en) | A kind of Ultrashort-term slide prediction method for wind power | |
| CN110457821B (en) | Wind power curve multi-target comprehensive evaluation method and device and server | |
| CN115456304A (en) | Offshore wind farm reliability index calculation method and device considering typhoon influence | |
| CN112632773B (en) | Reliability prediction method for wind turbine generator | |
| CN112906757A (en) | Model training method, optical power prediction method, device, equipment and storage medium | |
| Dumas et al. | Deep learning-based multi-output quantile forecasting of PV generation | |
| CN117744916A (en) | Method and device for predicting energy storage capacity, computer equipment and readable storage medium | |
| CN115526429A (en) | Decoupling analysis method for wind power prediction error, processor and storage medium | |
| CN115496603A (en) | Artificial intelligence technology-based new energy day-ahead transaction decision method for power market | |
| CN115713320A (en) | Operation and maintenance strategy optimization method and device for equipment, equipment and storage medium | |
| Zhou et al. | A Hybrid Method for Ultrashort‐Term Wind Power Prediction considering Meteorological Features and Seasonal Information | |
| CN117691583B (en) | Power dispatching system and method for virtual power plant | |
| CN104156885A (en) | Fast wind power capacity reliability calculating method based on reliability function | |
| Tena-García et al. | Implementing data reduction strategies for the optimal design of renewable energy systems | |
| CN117557125A (en) | Method and system for evaluating flexible resource adjustment capability of power system | |
| CN113205259A (en) | Power grid scheduling decision evaluation method and device and terminal equipment | |
| CN116415722A (en) | Photovoltaic power generation amount prediction method | |
| CN117649118A (en) | Method, system, equipment and medium for evaluating power supply reliability of wind power grid-connected system | |
| CN115964951A (en) | Power system operation reliability assessment method and device and electronic equipment | |
| CN115577854A (en) | Quantile regression wind speed interval prediction method based on EEMD-RBF combination | |
| CN115688994A (en) | Comprehensive evaluation method and device for power prediction deviation of photovoltaic power station | |
| Ghaffari et al. | Optimal economic load dispatch based on wind energy and risk constrains through an intelligent algorithm | |
| Shu et al. | Non-sequential simulation methods for reliability analysis of power systems with photovoltaic generation | |
| Liu et al. | A Self-Data-Driven Method for Lifetime Prediction of PV Arrays Considering the Uncertainty and Volatility |
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 |