CN113496316A - Capacity configuration method, device, system and medium based on source-load time sequence coupling - Google Patents
Capacity configuration method, device, system and medium based on source-load time sequence coupling Download PDFInfo
- Publication number
- CN113496316A CN113496316A CN202111052829.2A CN202111052829A CN113496316A CN 113496316 A CN113496316 A CN 113496316A CN 202111052829 A CN202111052829 A CN 202111052829A CN 113496316 A CN113496316 A CN 113496316A
- Authority
- CN
- China
- Prior art keywords
- wind
- load
- light
- source
- capacity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000008878 coupling Effects 0.000 title claims abstract description 96
- 238000010168 coupling process Methods 0.000 title claims abstract description 96
- 238000005859 coupling reaction Methods 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 title claims abstract description 55
- 230000008859 change Effects 0.000 claims description 31
- 238000012216 screening Methods 0.000 claims description 18
- 230000009467 reduction Effects 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 12
- 238000010977 unit operation Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000000630 rising effect Effects 0.000 claims description 9
- 230000009194 climbing Effects 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 6
- -1 period Chemical class 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 230000035699 permeability Effects 0.000 abstract description 9
- 238000010248 power generation Methods 0.000 description 13
- 230000008569 process Effects 0.000 description 8
- 238000012545 processing Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000005457 optimization Methods 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 238000004590 computer program Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- 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
-
- 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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load 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
- 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
-
- 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
- H02J2300/20—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
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- 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)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Artificial Intelligence (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Power Engineering (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Control Of Eletrric Generators (AREA)
Abstract
The invention discloses a capacity configuration method, a device, a system and a medium based on source-load time sequence coupling, wherein a plurality of load time periods are obtained by dividing the acquired daily load curve; calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples; carrying out scene classification on the wind and light joint output sample based on the source load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set; and establishing a capacity configuration model under the extreme scene set and the clustering scene set, and solving a global optimal solution of the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station. Scene classification is carried out on the wind and light combined output sample by considering source-load time sequence coupling characteristics under different load periods, a multi-scene capacity configuration model is established under different scene sets to describe wind and light uncertainty, and the overall optimal solution of the model is used as the optimal configuration capacity, so that high permeability of wind and light energy can be still realized under the influence of different scenes.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a capacity configuration method, device, system and medium based on source-load time sequence coupling.
Background
Wind power and solar power generation can effectively relieve the current fossil fuel crisis and solve the problem of environmental pollution, but wind power and solar power generation are influenced by natural meteorological conditions, have the characteristics of intermittence and volatility, and lead wind and light to be difficult to be connected to the grid.
At present, fluctuation of wind and light output can be effectively stabilized by establishing a water, wind and light combined power generation system, capacity configuration among different energy sources needs to be optimized in the water, wind and light combined power generation system so as to achieve higher energy utilization rate, however, wind and light uncertainty under different scenes is not considered for matching among different energy sources, loads and power station operation cost in the existing capacity configuration, so that high permeability of the wind and light energy sources under the influence of different scenes is difficult to achieve, and therefore the energy utilization rate is influenced.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to provide a capacity allocation method, device, system and medium based on source-to-charge time-series coupling, which aims to improve the permeability of wind-solar power generation in multiple scenes.
The technical scheme of the invention is as follows:
a capacity configuration method based on source-load time sequence coupling comprises the following steps:
the acquired daily load curve is divided into a plurality of load time periods;
calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples;
carrying out scene classification on the wind and light joint output sample based on the source-load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set;
and establishing a capacity configuration model under the extreme scene set and the clustering scene set, and solving a global optimal solution of the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station.
In one embodiment, the time-division of the acquired daily load curve to obtain a plurality of load time periods includes:
acquiring a daily load curve and a preset defining coefficient for time interval division;
according to the defined coefficients and formula
、
、
And
dividing the daily load curve into time intervals, and respectively calculating to obtain load peak time intervals
Load trough time
Load rise period
And load down period
Wherein
、
、
And
for the purpose of the definition of the coefficients,
is the load value at the moment t of the daily load curve,
the load value at the moment of the daily load curve T + delta T,
is the maximum load value of the daily load curve,
is the minimum load value of the daily load curve, t is the time,
are time intervals.
In one embodiment, the calculating the source-to-charge time-series coupling characteristics of each load period according to the collected wind-solar combined contribution samples includes:
respectively obtaining wind and light output forces of a plurality of wind power stations and photovoltaic output forces of a plurality of photovoltaic power stations, and obtaining a plurality of wind and light combined output samples through an adder;
according to the time interval division results of each wind-solar combined output sample and daily load curve, respectively according to formulas
wherein the content of the first and second substances,
for the combined wind and light output at the time t,
the wind-light combined output at the time of t + delta t,
the wind and light combined output median at the peak load time,
the wind-light combined output median of the load valley period,
the number of periods of the load rise period,
for the number of periods of the load reduction period,
is a unit time interval.
In one embodiment, the performing scene classification on the wind-solar combined output sample based on the source-load time-sequence coupling characteristics of each load period to obtain an extreme scene set and a clustering scene set includes:
screening the wind and light combined output sample according to a preset extreme condition, and forming a wind and light combined output curve with the source-charge time sequence coupling characteristics meeting the preset extreme condition in the wind and light combined output sample into an extreme scene set;
and clustering the residual samples except the extreme scene set in the wind-light joint output samples to obtain the clustered scene set.
In one embodiment, the screening the wind and light joint output sample according to a preset extreme condition, and forming the extreme scene set by using the wind and light joint output curve, in which the source-to-charge time sequence coupling characteristics of the wind and light joint output sample meet the preset extreme condition, includes:
obtaining a screening parameter, wherein the screening parameter comprises a first confidence
The second confidence level
The third confidence
The fourth confidence level
Upper limit of output
Lower limit of output
Upper limit value of output change rate
And lower limit of output rate of change
；
Push buttonFormula (II)
Screening the wind-light combined output sample to obtain an extreme peak regulation scene set meeting a first preset extreme condition, wherein,
for a first wind-solar combined output curve meeting the first preset extreme condition,
is a set of first wind-solar combined output curves,
is the probability of scene occurrence;
according to the formula
Screening the wind-light combined output sample to obtain an extreme climbing scene set meeting a second preset extreme condition, wherein,
for a second wind-solar combined contribution curve meeting said second preset extreme condition,
is a set of second wind-solar combined output curves,
is the probability of the scene occurring.
In one embodiment, the clustering the remaining samples excluding the extreme scene set from the wind-solar combined contribution samples to obtain the clustered scene set includes:
and taking the source-load time sequence coupling characteristic of each sample curve in the residual samples as a clustering characteristic, and clustering the residual samples by a density peak value clustering algorithm to obtain the clustering scene set.
In an embodiment, the establishing a capacity configuration model under the extreme scene set and the clustering scene set and solving a global optimal solution of the capacity configuration model to obtain an optimal configuration capacity of the wind and photovoltaic power station includes:
establishing a capacity configuration model under the extreme scene set and the clustering scene set, wherein the capacity configuration model comprises an upper layer model and a lower layer model;
calculating an upper layer objective function of the upper layer model under the constraint of an upper layer constraint set to obtain the configuration capacity, the unit operation power and the controllable load regulation power of the wind-solar power station, and transmitting the unit operation power and the controllable load regulation power to the lower layer model;
optimizing the unit operating power and the controllable load adjusting power of the lower layer objective function of the lower layer model under the constraint of the lower layer constraint set to obtain new unit operating power and controllable load adjusting power, and feeding the new unit operating power and controllable load adjusting power back to the upper layer model for iterative computation;
and the upper layer model and the lower layer model are iterated and circulated for a plurality of times, and the iteration is stopped and the optimal configuration capacity of the wind and light power station is output until a global optimal solution is found.
A capacity configuration apparatus based on source-to-charge timing coupling, comprising:
the time interval division module is used for carrying out time interval division on the acquired daily load curve to obtain a plurality of load time intervals;
the characteristic calculation module is used for calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples;
the scene classification module is used for carrying out scene classification on the wind-light joint output sample based on the source load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set;
and the capacity configuration module is used for establishing a capacity configuration model under the extreme scene set and the clustering scene set and solving a global optimal solution of the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station.
Yet another embodiment of the present invention further provides a capacity configuration system based on source-to-load sequential coupling, the system comprising at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for capacity configuration based on source-to-charge sequential coupling described above.
Yet another embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the above-described method for capacity configuration based on source-to-charge sequential coupling.
Has the advantages that: compared with the prior art, the method, the device, the system and the medium for capacity configuration based on source-load time sequence coupling carry out scene classification on the wind-light joint output sample by considering the source-load time sequence coupling characteristics under different load periods, a multi-scene capacity configuration model is established under different scene sets to describe wind-light uncertainty, the overall optimal solution of the model is used as the optimal configuration capacity, and high permeability of wind-light energy can still be realized under the influence of different scenes.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flowchart of a capacity allocation method based on source-to-charge time sequence coupling according to an embodiment of the present invention;
fig. 2 is a sectional schematic view of a daily load curve in the capacity configuration method based on source-load time-series coupling according to the embodiment of the present invention;
fig. 3 is a schematic diagram of an extreme scene set of a capacity configuration method based on source-to-charge time sequence coupling according to an embodiment of the present invention;
fig. 4 is a schematic clustering diagram in the capacity allocation method based on source-to-load sequential coupling according to the embodiment of the present invention;
fig. 5 is a schematic diagram of a clustering scenario set in the capacity allocation method based on source-to-load sequential coupling according to the embodiment of the present invention;
fig. 6 is a functional block diagram of a capacity allocation apparatus based on source-to-charge sequential coupling according to an embodiment of the present invention;
fig. 7 is a schematic hardware structure diagram of a capacity configuration system based on source-to-load timing coupling according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is described in further detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating a capacity allocation method based on source-to-charge timing coupling according to an embodiment of the present invention. The capacity configuration method based on source-load time sequence coupling provided by the embodiment is suitable for the case of performing wind-light capacity configuration on a water-wind-light combined power station, and is applied to a system comprising terminal equipment, a network and a server, wherein the network is a medium for directly providing a communication link between the terminal equipment and the server, and can include various connection types, such as a wired connection type, a wireless communication link or an optical fiber cable; the operating system on the terminal device may include an iPhone operating system (iOS system), an android system, or another operating system, the terminal device is connected to the server through a network to implement interaction, so as to perform operations such as receiving or sending data, and may specifically be various electronic devices that have a display screen and support web browsing, including but not limited to a smart phone, a tablet computer, a portable computer, a desktop server, and the like. As shown in fig. 1, the method specifically includes the following steps:
and S100, carrying out time interval division on the acquired daily load curve to obtain a plurality of load time intervals.
In the embodiment, a daily load curve is generated by collecting historical load data in advance, the generated daily load curve is obtained in advance when capacity configuration is carried out, time interval division is carried out on the daily load curve according to the change characteristics of the load along with time (namely the time sequence change characteristics of the load), a plurality of load time intervals are obtained, and corresponding source load time sequence coupling characteristic calculation can be carried out on different load time intervals by carrying out time interval division on the daily load curve, so that the rationality of the capacity configuration is further improved.
In one embodiment, the time-division of the acquired daily load curve to obtain a plurality of load time periods includes:
acquiring a daily load curve and a preset defining coefficient for time interval division;
according to the defined coefficients and formula
、
、
And
dividing the daily load curve into time intervals, and respectively calculating to obtain load peak time intervals
Load trough time
Load rise period
And load down period
Wherein
、
、
And
for the purpose of the definition of the coefficients,
is the load value at the moment t of the daily load curve,
the load value at the moment of the daily load curve T + delta T,
is the maximum load value of the daily load curve,
is the minimum load value of the daily load curve, t is the time,
are time intervals.
In this embodiment, the daily load curve is generated in advance according to the collected historical load data, the specific historical load data can collect the average historical load in the past period, the corresponding daily load curve is obtained by the historical load data fitting and is stored in the database for calling, and the preset defining coefficient
、
、
And
the defining coefficient can adopt a system default value, can be adjusted according to actual requirements in the actual capacity configuration process, divides the daily load curve according to corresponding formulas through the defining coefficient, and respectively calculates to obtain the load peak time period
Load trough time
Load rise period
And load down period
In the peak load period
The maximum load value and the load valley period of the daily load curve are included
The minimum load value and the load rising period of the daily load curve are included
The load increase rate of the load curve is greater than the limit coefficient
Time period of load drop
Load reduction rate of the load curve of the middle dayLess than a defined coefficient
According to the method, corresponding load time intervals are obtained by dividing based on the change characteristic of the load value in the daily load curve along with time, so that the load characteristics in different time intervals can be considered subsequently to calculate the capacity configuration, and the utilization rate of energy is improved.
And S200, calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples.
In the embodiment, a wind-solar power station group is arranged for the combined water-wind-solar power generation system, the wind-solar power station group specifically comprises a plurality of wind power stations and a plurality of photovoltaic power stations, electric energy output by each wind power station and each photovoltaic power station is connected to a large power grid through a grid-connected inverter and a distribution transformer, and then is output by the large power grid through the distribution transformer to supply power to users, in addition, the combined water-wind-solar power generation system also comprises a plurality of water power stations and controllable loads to adjust uncertainty and fluctuation of system power, capacity of each wind power station is configured during capacity configuration, wind power output samples and photovoltaic output samples corresponding to each wind power station and each photovoltaic power station are collected firstly, the wind power output samples and the photovoltaic processing samples pass through an adder to obtain combined wind-solar power output samples, and then according to the collected wind-solar power combined wind power output samples and the divided load time intervals, and extracting source charge time sequence coupling characteristics under each load time period, wherein the source charge time sequence coupling characteristics represent wind-light joint output characteristics under different load time periods, and an accurate data basis is provided for capacity configuration by combining the characteristics between energy and loads.
In one embodiment, the method for calculating the source-load time sequence coupling characteristics of each load period according to the collected wind-solar combined contribution samples comprises the following steps:
respectively obtaining wind and light output forces of a plurality of wind power stations and photovoltaic output forces of a plurality of photovoltaic power stations, and obtaining a plurality of wind and light combined output samples through an adder;
according to the time interval division results of each wind-solar combined output sample and daily load curve, respectively according to formulas
wherein the content of the first and second substances,
for the combined wind and light output at the time t,
the wind-light combined output at the time of t + delta t,
the wind and light combined output median at the peak load time,
the wind-light combined output median of the load valley period,
the number of periods of the load rise period,
for the number of periods of the load reduction period,
is a unit time interval.
In this embodiment, the peak load period is obtained according to the division
Load trough time
Load rise period
And load down period
Obtaining peak load periods by means of different period extractors
Wind and light combined output and load valley time period
Wind-solar combined output and load rise time period
Combined wind and light output and load reduction period
The maximum wind-light combined output at the load peak time is further obtained by respectively solving the maximum value, the median, the minimum value and the average value of the change rate
Wind-solar combined minimum output in load valley period
Wind-solar combined output change rate average value in load rising period
Wind and light combined output change rate average value in load reduction period
Subtracting the wind-light combined output median at the load valley period from the wind-light combined output median at the load peak period to obtain the peak regulation capacity demand index caused by wind and light
The five characteristics are used as source-charge time sequence coupling characteristics for subsequent processes such as scene classification and capacity configuration, and wind-light capacity configuration is carried out by combining the source-charge time sequence characteristics, so that the influence of the source-charge time sequence characteristics on the capacity configuration is optimized as much as possible, and the utilization rate of wind-light resources is improved.
S300, carrying out scene classification on the wind and light combined output sample based on the source load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set.
In the embodiment, after the source-charge time sequence coupling characteristics of each load period are obtained, scene classification is performed on the collected wind and light combined output samples, the collected wind and light combined output samples are specifically classified into an extreme scene set and a clustering scene set, and wind and light combined output curves with different source-charge time sequence coupling characteristics are divided into corresponding scenes, so that wind and light uncertainties under different scenes can be considered based on the extreme scene set and the clustering scene set during wind and light capacity configuration, and therefore the permeability of wind and light power generation under the influence of different scenes is improved.
In one embodiment, the scene classification of the wind-solar combined output sample is performed based on the source-load time sequence coupling characteristics of each load period to obtain an extreme scene set and a clustering scene set, and the method includes:
screening the wind and light combined output sample according to a preset extreme condition, and forming a wind and light combined output curve with the source-charge time sequence coupling characteristics meeting the preset extreme condition in the wind and light combined output sample into an extreme scene set;
and clustering the residual samples except the extreme scene set in the wind-light joint output samples to obtain the clustered scene set.
In the embodiment, when scene classification is performed, logical operation is performed on the source charge time sequence coupling characteristics, the wind and light combined output sample is screened according to the preset extreme knowledge, that is, a wind and light combined output curve under the preset extreme condition of the source charge time sequence coupling characteristic load in the wind and light combined output sample is screened, the corresponding scene set forms an extreme scene set, after the extreme scene set is screened, the residual samples except the extreme scene set in the wind and light combined output sample are further clustered, curves with the source charge time sequence coupling characteristics in the residual samples having commonalities are clustered together, so that a plurality of clustering scenes are obtained to form a clustering scene set, that is, in the embodiment, the scenes under the extreme condition are screened, the scenes under the common condition are clustered, and the configuration of multi-scene capacity is realized while the data processing amount is saved as much as possible, the processing efficiency is improved.
In one embodiment, screening the wind and light joint output sample according to a preset extreme condition, and forming the extreme scene set by using the wind and light joint output curve, in which the source-to-charge time sequence coupling characteristics of the wind and light joint output sample meet the preset extreme condition, includes:
obtaining a screening parameter, wherein the screening parameter comprises a first confidence
The second confidence level
The third confidence
The fourth confidence level
Upper limit of output
Lower limit of output
Upper limit value of output change rate
And lower limit of output rate of change
；
According to the formula
Screening the wind-light combined output sample to obtain an extreme peak regulation scene set meeting a first preset extreme condition, wherein,
for a first wind-solar combined output curve meeting the first preset extreme condition,
is a set of first wind-solar combined output curves,
is the probability of scene occurrence;
according to the formula
Screening the wind-light combined output sample to obtain an extreme climbing scene set meeting a second preset extreme condition, wherein,
for a second wind-solar combined contribution curve meeting said second preset extreme condition,
is a set of second wind-solar combined output curves,
is the probability of the scene occurring.
In this embodiment, when an extreme scene set in the wind and light combined output sample is screened, the wind and light combined maximum output at the load peak time period in the source-load time sequence coupling feature is subjected to wind and light combined maximum output
Wind-solar combined minimum output in load valley period
Wind-solar combined output change rate average value in load rising period
Wind and light combined output change rate average value in load reduction period
Performing logic operation to respectively screen an extreme peak regulation scene set meeting a first preset extreme condition and an extreme climbing scene set meeting a second preset extreme condition, wherein the extreme peak regulation scene set and the extreme climbing scene set form an extreme scene set, and a first wind-light joint output curve in the extreme peak regulation scene set
The first preset extreme condition to be met is the wind-solar combined maximum output during the peak load period
Not more than
Probability (i.e. of
) Not exceeding
And wind-light minimum output force in load valley period
Not less than
Probability (i.e. of
) Not exceeding
(ii) a Concentrated second wind-solar combined output curve in extreme climbing scene
The second preset extreme condition to be met is the average value of the wind-light joint output change rate in the load rising period
Not more than
Probability (i.e. of
) Not exceeding
And the wind-solar combined output change rate average value in the load reduction period
Not less than
Probability (i.e. of
) Not exceeding
。
In one embodiment, the clustering the remaining samples excluding the extreme scene set from the wind-solar combined contribution samples to obtain the clustered scene set includes:
and taking the source-load time sequence coupling characteristic of each sample curve in the residual samples as a clustering characteristic, and clustering the residual samples by a density peak value clustering algorithm to obtain the clustering scene set.
In this embodiment, when performing clustering processing on the remaining samples, for each sample curve in the remaining samples, the source-to-charge time sequence coupling characteristic corresponding to each sample curve is obtained, that is, the source-to-charge time sequence coupling characteristic includes
Wherein
Is the ith sampleThe wind-solar combined maximum output in the peak load period of the curve,
the wind-solar combined minimum output for the load trough period of the ith sample curve,
is the peak shaving capacity requirement index of the ith sample curve,
is the average value of the wind-solar combined output change rate of the load rising period of the ith sample curve,
is the average value of the wind-light combined output change rate of the load reduction period of the ith sample curve, and is
=（
) As a cluster feature, the element of (c),
as a new sample set, wherein
，
The total sample curve number of the wind-light joint output sample,
is the number of sample curves for the remaining samples,
is the number of sample curves for the extreme peak shaver scene set,
the number of sample curves of the extreme climbing scene set.
Then obtaining the preset stage distance
Calculating the local density values of all sample points in the sample set
And relative distance to high local density points
And in local density
As abscissa, in relative distance
And obtaining a clustering decision chart of the sample as an ordinate. Specifically, the following formula can be used:
then obtaining a preset clustering center threshold value
By the formula
Calculating the cluster center value of each sample point
Will be
Is identified as a cluster center, thereby obtaining a sample set
The cluster center sample point of (2) is recorded as
And k is the number of scenes, each clustering center corresponds to one scene, after the clustering centers are determined, the sample points of other non-clustering centers determine the scene categories corresponding to which clustering centers belong respectively according to the sequence of the local density from high to low, namely, each non-clustering center sample point is classified into the category to which the clustering center with the local density larger than that of the sample point and closest distance belongs, and finally, a wind-light output classification graph is obtained, so that the clustering processing of common scenes is realized, the clustering center samples with the common characteristics are extracted and obtained, and the data processing efficiency is improved.
S400, establishing a capacity configuration model under the extreme scene set and the clustering scene set, and solving a global optimal solution of the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station.
In the embodiment, after the multi-scene set is obtained, the wind and light uncertainty is described through the extreme scene set and the clustering scene set, the multi-scene wind and light capacity configuration model considering the source load time sequence coupling characteristics is established under the extreme scene set and the clustering scene set, the global optimal solution is obtained for the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station, and the capacity configuration is carried out through the multi-scene wind and light capacity configuration model considering the source load time sequence coupling characteristics, so that the wind and light energy can still combine the source load time sequence characteristics to realize the penetration rate under the influence of different scenes, and the energy utilization rate is improved.
In one embodiment, establishing a capacity configuration model under the extreme scene set and the clustering scene set and solving a global optimal solution of the capacity configuration model to obtain an optimal configuration capacity of the wind and photovoltaic power station includes:
establishing a capacity configuration model under the extreme scene set and the clustering scene set, wherein the capacity configuration model comprises an upper layer model and a lower layer model;
calculating an upper layer objective function of the upper layer model under the constraint of an upper layer constraint set to obtain the configuration capacity, the unit operation power and the controllable load regulation power of the wind-solar power station, and transmitting the unit operation power and the controllable load regulation power to the lower layer model;
optimizing the unit operating power and the controllable load adjusting power of the lower layer objective function of the lower layer model under the constraint of the lower layer constraint set to obtain new unit operating power and controllable load adjusting power, and feeding the new unit operating power and controllable load adjusting power back to the upper layer model for iterative computation;
and the upper layer model and the lower layer model are iterated and circulated for a plurality of times, and the iteration is stopped and the optimal configuration capacity of the wind and light power station is output until a global optimal solution is found.
In this embodiment, a capacity configuration model of a double-layer structure is constructed under an extreme scene set and a clustering scene set, that is, the capacity configuration model includes an upper layer model and a lower layer model which are connected with each other, wherein the upper layer model and the lower layer model respectively have corresponding objective functions and constraint sets to perform global optimal solution calculation, specifically, the upper layer model has an upper layer objective function with a minimum planned annual economic cost and a maximum wind-light absorption power, so as to obtain a wind-light capacity configuration scheme within a system acceptance capacity range, the upper layer objective function includes a wind-light power station investment cost, a wind-light power station operation cost, a hydropower station operation cost, a controllable load adjustment cost and an economic cost for converting wind-light power generation power, and the specific model is a wind-light power station investment cost, a wind-light power station operation cost, a controllable load adjustment cost and an economic cost for converting wind-light power generation power into
Wherein d is the mark rate,
for the study period;
、
investment costs of unit capacity of the wind power station n and the photovoltaic power station m are respectively;
、
、
the unit operation costs of the wind power station n, the photovoltaic power station m and the hydropower station i are respectively;
adjusting the cost for the unit power of the controllable load j;
、
respectively the operating power of the wind power station n and the photovoltaic power station m at the moment t under the scene s;
、
、
respectively determining a comprehensive output coefficient of the hydropower station i, a generating flow of the hydropower station i at the moment t under the scene s, and a generating head of the hydropower station i at the moment t under the scene s;
adjusting power of a controllable load j at the moment t under a scene s;
、
respectively configuring the capacities of a wind power station n and a photovoltaic power station m;
、
、
、
the number of wind power stations, photovoltaic power stations, hydropower stations and controllable loads respectively;
is an economic conversion coefficient;
is the probability of scene s occurring. Specifically, under the constraint of the installed capacity of the wind and light power station, the upper layer model calculates the configuration capacity of the wind and light power station, the unit operation power and the controllable load regulation power, and transmits the unit operation power and the controllable load regulation power to the lower layer model.
The lower layer model is optimized according to the configuration scheme of the upper layer model by taking the minimum load tracking degree as an objective function, the load tracking degree is defined by the sum of the normalized absolute difference f1 between the combined output change rate of water, wind and light and the change rate of the load power and the absolute difference f2 between the wind output and the load power, and the specific model is shown as the following formula:
wherein the content of the first and second substances,
water-wind-light under s scene
A rate of change of output over a period of time;
for high energy-carrying load to participate in regulationThe system load after the section is
A rate of change of output over a period of time;
the water-wind-light combined output at the time t;
the load power of the system at the time t. The lower layer model calculates to obtain new unit operation power and controllable load regulation power under the constraint of a lower layer constraint set comprising power balance constraint, line transmission capacity constraint, wind-light power station output constraint, cascade hydropower station generating capacity, generating head, reservoir capacity and water quantity balance constraint and controllable negative power regulation constraint, feeds the calculation result as an operation strategy back to the upper layer model, continuously calculates and optimizes configuration capacity, unit operation power and controllable load regulation power of the wind-light power station by the upper layer model, and transmits the configuration capacity, the unit operation power and the controllable load regulation power to the lower layer model for cycle iteration, and performing optimization calculation through mutual iteration of an upper layer and a lower layer until a global optimal solution is found, stopping iteration and outputting the optimal configuration capacity of the wind-light power station, and realizing multi-scene wind-light capacity optimization considering source-load time sequence coupling characteristics so as to improve the wind-light power generation permeability under the influence of different scenes as much as possible.
To better understand the implementation process of the capacity configuration method based on source-to-load sequential coupling provided by the present invention, the following describes the capacity configuration process based on source-to-load sequential coupling in detail by referring to fig. 2 to fig. 5 as specific application embodiments:
selecting wind and light resource data of different areas in a certain place, taking the time step as 15min, calculating the time as 8760h all year round, and selecting wind and light historical data of the last 4 years to form 1460 groups of unit wind and light output samples with coupled load time sequence characteristics. And simulating uncertainty of wind and light output through an extreme scene and a clustering scene, and selecting the average historical load of the last 4 years as load data. The specific implementation process is as follows:
step one, setting according to the time sequence change characteristics of the loadDefining coefficients
、
、
、
According to the formula
、
、
、
Respectively calculating to obtain the peak load time
Load trough time
Load rise period
And load down period
As shown in fig. 2.
And step two, calculating a source-load time sequence coupling characteristic index according to the wind and light combined output at different time intervals. According to the formula
Wind-solar combination during peak time of computational loadMaximum power
(ii) a According to the formula
Calculating wind and light combined minimum power of load during low ebb period
(ii) a According to the formula
Calculating the median difference of wind-light combined output in peak-valley period
Wherein
The wind and light combined output median at the peak load time,
the wind and light combined output median is the load valley time period; according to the formula
Calculating the average value of the wind-light joint output change rate in the load rising period
(ii) a According to the formula
Calculating the average value of the wind-light joint output change rate in the load reduction period
。
And thirdly, performing logical operation on the source-load time sequence coupling characteristic indexes, and dividing to obtain an extreme scene set S1, as shown in FIG. 3.
Step fourTo thereby
、
、
、
、
As a clustering feature, a clustering decision chart of the sample is obtained by adopting a density peak clustering method, as shown in fig. 4(a), a clustering center is selected according to the clustering decision chart, a wind-light joint output classification chart is further obtained by calculation, as shown in fig. 4(b), a mean value is obtained for each type of wind power output curve, and a clustering scene set S2 is obtained, as shown in fig. 5.
Step five, under a clustering scene and an extreme scene, the sum of the investment cost of the wind-light power station, the operation cost of the hydropower station, the controllable load adjustment cost and the economic cost after the conversion of the wind-light power generation power is minimum as an upper-layer optimization target, and a wind-light capacity configuration scheme within the acceptance capacity range of the system is obtained; and the lower layer performs operation optimization by taking the minimum load tracking coefficient as a target according to the upper layer configuration scheme. Under the constraint of the upper and lower layer constraint sets, the upper and lower layer models are iterated and circulated, and the obtained optimization results are shown in table 1. As can be seen from Table 1, the method provided by the invention improves the configuration capacity of the wind and light power station under the condition of increasing a small amount of cost, and effectively improves the wind and light power generation permeability.
TABLE 1
According to the method, the source-load time sequence coupling-based capacity configuration method provided by the invention has the advantages that the source-load time sequence coupling characteristics under different load periods are considered to carry out scene classification on the wind-light joint output samples, a multi-scene capacity configuration model is established under different scene sets to describe wind-light uncertainty, the overall optimal solution of the model is taken as the optimal configuration capacity, and the high permeability of wind-light energy can still be realized under the influence of different scenes.
It should be noted that, a certain order does not necessarily exist between the above steps, and those skilled in the art can understand, according to the description of the embodiments of the present invention, that in different embodiments, the above steps may have different execution orders, that is, may be executed in parallel, may also be executed interchangeably, and the like.
Another embodiment of the present invention provides a capacity allocation apparatus based on source-to-load sequential coupling, as shown in fig. 6, the apparatus 1 includes:
the time interval division module 11 is configured to perform time interval division on the obtained daily load curve to obtain a plurality of load time intervals;
the characteristic calculation module 12 is used for calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples;
the scene classification module 13 is configured to perform scene classification on the wind-solar combined output sample based on the source-load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set;
and the capacity configuration module 14 is configured to establish a capacity configuration model in the extreme scene set and the clustering scene set and solve a global optimal solution of the capacity configuration model to obtain an optimal configuration capacity of the wind and photovoltaic power station.
The time interval division module 11, the feature calculation module 12, the scene classification module 13, and the capacity configuration module 14 are sequentially connected, the module referred to in the present invention refers to a series of computer program instruction segments capable of completing a specific function, and is more suitable for describing the execution process of the capacity configuration based on source-load time sequence coupling than a program, and the specific implementation of each module refers to the corresponding method embodiment, and is not described herein again.
Another embodiment of the present invention provides a capacity allocation system based on source-load sequential coupling, as shown in fig. 7, the system 10 includes:
one or more processors 110 and a memory 120, where one processor 110 is illustrated in fig. 7, the processor 110 and the memory 120 may be connected by a bus or other means, and where fig. 7 illustrates a bus connection.
The memory 120 is a non-volatile computer-readable storage medium and can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions corresponding to the capacity configuration method based on source-to-charge time-sequential coupling in the embodiment of the present invention. The processor 110 executes various functional applications and data processing of the system 10 by executing nonvolatile software programs, instructions and units stored in the memory 120, that is, implements the capacity configuration method based on source-to-charge-sequential coupling in the above method embodiments.
The memory 120 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the system 10, and the like. Further, the memory 120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 120 optionally includes memory located remotely from processor 110, which may be connected to system 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more units are stored in the memory 120, and when executed by the one or more processors 110, perform the capacity configuration method based on source-to-charge sequential coupling in any of the above-described method embodiments, e.g., perform the above-described method steps S100 to S400 in fig. 1.
Embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer-executable instructions for execution by one or more processors, e.g., to perform method steps S100-S400 of fig. 1 described above.
By way of example, non-volatile storage media can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as Synchronous RAM (SRAM), dynamic RAM, (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The disclosed memory components or memory of the operating environment described herein are intended to comprise one or more of these and/or any other suitable types of memory.
In summary, in the capacity allocation method, device, system and medium based on source-load time sequence coupling disclosed by the present invention, the method obtains a plurality of load time periods by performing time period division on the obtained daily load curve; calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples; carrying out scene classification on the wind and light joint output sample based on the source load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set; and establishing a capacity configuration model under the extreme scene set and the clustering scene set, and solving a global optimal solution of the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station. Scene classification is carried out on the wind and light combined output sample by considering source-load time sequence coupling characteristics under different load periods, a multi-scene capacity configuration model is established under different scene sets to describe wind and light uncertainty, and the overall optimal solution of the model is used as the optimal configuration capacity, so that high permeability of wind and light energy can be still realized under the influence of different scenes.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program, which may be stored in a non-volatile computer-readable storage medium, and the computer program may include the processes of the above method embodiments when executed. The storage medium may be a memory, a magnetic disk, a floppy disk, a flash memory, an optical memory, etc.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (10)
1. A capacity configuration method based on source-load time sequence coupling is characterized by comprising the following steps:
the acquired daily load curve is divided into a plurality of load time periods;
calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples;
carrying out scene classification on the wind and light joint output sample based on the source-load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set;
and establishing a capacity configuration model under the extreme scene set and the clustering scene set, and solving a global optimal solution of the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station.
2. The capacity configuration method based on source-load sequential coupling according to claim 1, wherein the time division of the acquired daily load curve to obtain a plurality of load time periods comprises:
acquiring a daily load curve and a preset defining coefficient for time interval division;
according to the defined coefficients and formula
、
、
And
dividing the daily load curve into time intervals, and respectively calculating to obtain load peak time intervals
Load trough time
Load rise period
And load down period
Wherein
、
、
And
for the purpose of the definition of the coefficients,
is the load value at the moment t of the daily load curve,
the load value at the moment of the daily load curve T + delta T,
is the maximum load value of the daily load curve,
is the minimum load value of the daily load curve, t is the time,
are time intervals.
3. The source-to-charge time-series coupling-based capacity configuration method according to claim 2, wherein the calculating the source-to-charge time-series coupling characteristics of each load period according to the collected wind-solar combined output samples comprises:
respectively obtaining wind and light output forces of a plurality of wind power stations and photovoltaic output forces of a plurality of photovoltaic power stations, and obtaining a plurality of wind and light combined output samples through an adder;
according to the time interval division results of each wind-solar combined output sample and daily load curve, respectively according to formulas
wherein the content of the first and second substances,
for the combined wind and light output at the time t,
is t + DeltatThe wind-light combined output at the moment,
the wind and light combined output median at the peak load time,
the wind-light combined output median of the load valley period,
the number of periods of the load rise period,
for the number of periods of the load reduction period,
is a unit time interval.
4. The source-charge time-series coupling-based capacity configuration method of claim 3, wherein the scene classification of the wind-solar joint output samples based on the source-charge time-series coupling characteristics of each load period is performed to obtain an extreme scene set and a clustering scene set, and the method comprises the following steps:
screening the wind and light combined output sample according to a preset extreme condition, and forming a wind and light combined output curve with the source-charge time sequence coupling characteristics meeting the preset extreme condition in the wind and light combined output sample into an extreme scene set;
and clustering the residual samples except the extreme scene set in the wind-light joint output samples to obtain the clustered scene set.
5. The source-charge time-series coupling-based capacity allocation method according to claim 4, wherein the screening of the wind-light joint output sample according to the preset extreme condition, and the forming of the wind-light joint output curve, in the wind-light joint output sample, of which the source-charge time-series coupling characteristics meet the preset extreme condition into the extreme scene set, comprises:
obtaining a screening parameter, wherein the screening parameter comprises a first confidence
The second confidence level
The third confidence
The fourth confidence level
Upper limit of output
Lower limit of output
Upper limit value of output change rate
And lower limit of output rate of change
；
According to the formula
Screening the wind-light combined output sample to obtain an extreme peak regulation scene set meeting a first preset extreme condition, wherein,
for a first wind-solar combined output curve meeting the first preset extreme condition,
is a set of first wind-solar combined output curves,
is the probability of scene occurrence;
according to the formula
Screening the wind-light combined output sample to obtain an extreme climbing scene set meeting a second preset extreme condition, wherein,
for a second wind-solar combined contribution curve meeting said second preset extreme condition,
is a set of second wind-solar combined output curves,
is the probability of the scene occurring.
6. The capacity configuration method based on source-to-charge time-series coupling of claim 4, wherein the clustering the remaining samples excluding the extreme scene set from the wind-solar combined contribution samples to obtain the clustered scene set comprises:
and taking the source-load time sequence coupling characteristic of each sample curve in the residual samples as a clustering characteristic, and clustering the residual samples by a density peak value clustering algorithm to obtain the clustering scene set.
7. The source-charge time-series coupling-based capacity configuration method of claim 1, wherein the establishing a capacity configuration model under the extreme scene set and the clustering scene set and solving a global optimal solution of the capacity configuration model to obtain an optimal configuration capacity of the wind and light power station comprises:
establishing a capacity configuration model under the extreme scene set and the clustering scene set, wherein the capacity configuration model comprises an upper layer model and a lower layer model;
calculating an upper layer objective function of the upper layer model under the constraint of an upper layer constraint set to obtain the configuration capacity, the unit operation power and the controllable load regulation power of the wind-solar power station, and transmitting the unit operation power and the controllable load regulation power to the lower layer model;
optimizing the unit operating power and the controllable load adjusting power of the lower layer objective function of the lower layer model under the constraint of the lower layer constraint set to obtain new unit operating power and controllable load adjusting power, and feeding the new unit operating power and controllable load adjusting power back to the upper layer model for iterative computation;
and the upper layer model and the lower layer model are iterated and circulated for a plurality of times, and the iteration is stopped and the optimal configuration capacity of the wind and light power station is output until a global optimal solution is found.
8. A capacity configuration apparatus based on source-to-charge sequential coupling, the apparatus comprising:
the time interval division module is used for carrying out time interval division on the acquired daily load curve to obtain a plurality of load time intervals;
the characteristic calculation module is used for calculating the source-load time sequence coupling characteristics of each load time period according to the collected wind-solar combined output samples;
the scene classification module is used for carrying out scene classification on the wind-light joint output sample based on the source load time sequence coupling characteristics of each load time period to obtain an extreme scene set and a clustering scene set;
and the capacity configuration module is used for establishing a capacity configuration model under the extreme scene set and the clustering scene set and solving a global optimal solution of the capacity configuration model to obtain the optimal configuration capacity of the wind and light power station.
9. A capacity configuration system based on source-to-load sequential coupling, the system comprising at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for configuring capacity based on source-to-charge sequential coupling of any of claims 1-7.
10. A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method for capacity configuration based on source-to-charge sequential coupling of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111052829.2A CN113496316B (en) | 2021-09-09 | 2021-09-09 | Capacity configuration method, device, system and medium based on source-load time sequence coupling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111052829.2A CN113496316B (en) | 2021-09-09 | 2021-09-09 | Capacity configuration method, device, system and medium based on source-load time sequence coupling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113496316A true CN113496316A (en) | 2021-10-12 |
| CN113496316B CN113496316B (en) | 2022-02-11 |
Family
ID=77997093
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111052829.2A Active CN113496316B (en) | 2021-09-09 | 2021-09-09 | Capacity configuration method, device, system and medium based on source-load time sequence coupling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113496316B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114142472A (en) * | 2021-12-06 | 2022-03-04 | 浙江华云电力工程设计咨询有限公司 | Wind and light capacity configuration method and system based on mixed Gaussian distribution probability density |
| CN115423508A (en) * | 2022-08-29 | 2022-12-02 | 大连川禾绿能科技有限公司 | Strategic bidding method of cascade hydropower in uncertain carbon-electricity coupling market |
| CN115795328A (en) * | 2022-11-08 | 2023-03-14 | 国网能源研究院有限公司 | Method and system for simultaneously generating new energy output conventional scene and extreme scene |
| CN116780643A (en) * | 2023-05-31 | 2023-09-19 | 国网山东省电力公司经济技术研究院 | Confidence output calculation method and system for new energy participation in electric power balance |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0818673A2 (en) * | 1996-07-12 | 1998-01-14 | Bayerische Motoren Werke Aktiengesellschaft, Patentabteilung AJ-3 | Vibration resistance tests with a procedure for adaptive correction of the actual value |
| CN103855718A (en) * | 2014-03-10 | 2014-06-11 | 东南大学 | Scheduling method for pumped storage power station to participate in electric power system with wind power |
| US20140365419A1 (en) * | 2011-12-30 | 2014-12-11 | Thomson Licensing | Adaptation of a power generation capacity and determining of an energy storage unit size |
| CN108494015A (en) * | 2018-02-09 | 2018-09-04 | 中国科学院电工研究所 | The integrated energy system design method of one introduces a collection-lotus-storage coordination and interaction |
| CN109687441A (en) * | 2018-12-27 | 2019-04-26 | 广州穗华能源科技有限公司 | A kind of independent micro-capacitance sensor flexibility resource capacity configuration method based on scene decomposition-coordination |
| CN111126657A (en) * | 2019-11-11 | 2020-05-08 | 西安交通大学 | Electric power transaction mode for mutual compensation of clean electric energy peak-valley and peak-valley between provinces of alternating-current interconnected power grid |
| CN111709109A (en) * | 2020-04-28 | 2020-09-25 | 中国能源建设集团江苏省电力设计院有限公司 | Photovoltaic absorption capacity calculation method and device considering source-load time sequence correlation |
-
2021
- 2021-09-09 CN CN202111052829.2A patent/CN113496316B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0818673A2 (en) * | 1996-07-12 | 1998-01-14 | Bayerische Motoren Werke Aktiengesellschaft, Patentabteilung AJ-3 | Vibration resistance tests with a procedure for adaptive correction of the actual value |
| US20140365419A1 (en) * | 2011-12-30 | 2014-12-11 | Thomson Licensing | Adaptation of a power generation capacity and determining of an energy storage unit size |
| CN103855718A (en) * | 2014-03-10 | 2014-06-11 | 东南大学 | Scheduling method for pumped storage power station to participate in electric power system with wind power |
| CN108494015A (en) * | 2018-02-09 | 2018-09-04 | 中国科学院电工研究所 | The integrated energy system design method of one introduces a collection-lotus-storage coordination and interaction |
| CN109687441A (en) * | 2018-12-27 | 2019-04-26 | 广州穗华能源科技有限公司 | A kind of independent micro-capacitance sensor flexibility resource capacity configuration method based on scene decomposition-coordination |
| CN111126657A (en) * | 2019-11-11 | 2020-05-08 | 西安交通大学 | Electric power transaction mode for mutual compensation of clean electric energy peak-valley and peak-valley between provinces of alternating-current interconnected power grid |
| CN111709109A (en) * | 2020-04-28 | 2020-09-25 | 中国能源建设集团江苏省电力设计院有限公司 | Photovoltaic absorption capacity calculation method and device considering source-load time sequence correlation |
Non-Patent Citations (2)
| Title |
|---|
| PENG XIE等: "Microgrid System Energy Storage Capacity Optimization Considering Multiple Time Scale Uncertainty Coupling", 《IEEE TRANSACTIONS ON SMART GRID》 * |
| 苏康博 等: "考虑多类型水电协调的风光电站容量优化配置方法", 《电力系统保护与控制》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114142472A (en) * | 2021-12-06 | 2022-03-04 | 浙江华云电力工程设计咨询有限公司 | Wind and light capacity configuration method and system based on mixed Gaussian distribution probability density |
| CN114142472B (en) * | 2021-12-06 | 2023-08-08 | 浙江华云电力工程设计咨询有限公司 | Wind-solar capacity configuration method and system based on mixed Gaussian distribution probability density |
| CN115423508A (en) * | 2022-08-29 | 2022-12-02 | 大连川禾绿能科技有限公司 | Strategic bidding method of cascade hydropower in uncertain carbon-electricity coupling market |
| CN115423508B (en) * | 2022-08-29 | 2023-07-18 | 大连川禾绿能科技有限公司 | Strategy bidding method for cascade hydropower in uncertain carbon-electricity coupling market |
| CN115795328A (en) * | 2022-11-08 | 2023-03-14 | 国网能源研究院有限公司 | Method and system for simultaneously generating new energy output conventional scene and extreme scene |
| CN115795328B (en) * | 2022-11-08 | 2023-09-01 | 国网能源研究院有限公司 | Method and system for simultaneously generating new energy output conventional scene and extreme scene |
| CN116780643A (en) * | 2023-05-31 | 2023-09-19 | 国网山东省电力公司经济技术研究院 | Confidence output calculation method and system for new energy participation in electric power balance |
| CN116780643B (en) * | 2023-05-31 | 2024-04-02 | 国网山东省电力公司经济技术研究院 | Confidence output calculation method and system for new energy participation in electric power balance |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113496316B (en) | 2022-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113496316B (en) | Capacity configuration method, device, system and medium based on source-load time sequence coupling | |
| CN107688879B (en) | Active power distribution network distributed power supply planning method considering source-load matching degree | |
| CN108306303B (en) | Voltage stability evaluation method considering load increase and new energy output randomness | |
| WO2020063144A1 (en) | Method and system for evaluating energy delivery capacity in flexible dc electrical grid | |
| Apostolopoulou et al. | Robust optimization for hydroelectric system operation under uncertainty | |
| Junior et al. | On the use of maximum likelihood and input data similarity to obtain prediction intervals for forecasts of photovoltaic power generation | |
| JP6693330B2 (en) | Solar power system operation | |
| CN110348695B (en) | Flexibility evaluation method, device, equipment and storage medium of power system | |
| CN113256033B (en) | Power station site selection and volume fixing method, device, system and medium based on mode complementation | |
| CN115425680B (en) | Power prediction model construction and prediction method of multi-energy combined power generation system | |
| CN110212551B (en) | Micro-grid reactive power automatic control method based on convolutional neural network | |
| US20220029424A1 (en) | Hybrid power plant | |
| Nitisanon et al. | Solar power forecast with weather classification using self-organized map | |
| CN113991640B (en) | Thermal power-based multi-energy complementary energy base energy configuration planning method | |
| CN113904382A (en) | Multi-energy power system time sequence operation simulation method and device, electronic equipment and storage medium | |
| CN108683211B (en) | Virtual power plant combination optimization method and model considering distributed power supply volatility | |
| Zhanga et al. | Optimized operation of cascade reservoirs considering complementary characteristics between wind and photovoltaic based on variational auto-encoder | |
| CN109193772B (en) | Energy storage optimal configuration system and method based on wind-solar micro-grid | |
| CN116316828A (en) | Electric discarding simulation method and device for water-wind-solar complementary system and electronic equipment | |
| CN109934450A (en) | More scene active distribution network planning appraisal methods based on Demand Side Response | |
| CN110768294B (en) | Random scheduling method and device for distributed power supply | |
| CN109066797B (en) | Operation control method and device for power system containing photovoltaic power generation | |
| CN110717694A (en) | Energy storage configuration random decision method and device based on new energy consumption expected value | |
| CN116776613B (en) | Wind-light output scene reconstruction system | |
| Mϋller et al. | Wavelet-based ESS sizing strategy to enable power peak-shaving in PV systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |