CN115577925A - Wind power energy storage calling method, device and medium - Google Patents
Wind power energy storage calling method, device and medium Download PDFInfo
- Publication number
- CN115577925A CN115577925A CN202211191581.2A CN202211191581A CN115577925A CN 115577925 A CN115577925 A CN 115577925A CN 202211191581 A CN202211191581 A CN 202211191581A CN 115577925 A CN115577925 A CN 115577925A
- Authority
- CN
- China
- Prior art keywords
- wind power
- output data
- grid
- power generation
- energy storage
- 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
- 238000004146 energy storage Methods 0.000 title claims abstract description 84
- 238000000034 method Methods 0.000 title claims abstract description 62
- 230000006870 function Effects 0.000 claims abstract description 128
- 230000008859 change Effects 0.000 claims abstract description 108
- 238000010248 power generation Methods 0.000 claims abstract description 92
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 28
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 20
- 238000004590 computer program Methods 0.000 claims description 17
- 230000005611 electricity Effects 0.000 claims description 7
- 239000002699 waste material Substances 0.000 abstract description 11
- 238000013468 resource allocation Methods 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000000007 visual effect Effects 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- 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
- 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
-
- 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
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Power Engineering (AREA)
- Wind Motors (AREA)
- Control Of Eletrric Generators (AREA)
Abstract
The invention discloses a wind power energy storage calling method and device, electronic equipment and a storage medium. Wherein, the method comprises the following steps: decomposing the output prediction data of the wind power generation object based on a set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency; determining the maximum active power change limit value of the wind power generation object within a preset time range, and determining the maximum change rate of grid-connected output data of the wind power generation object within the preset time range according to the plurality of eigenmode functions; and under the condition that the maximum change rate is less than or equal to the maximum active power change limit value, determining energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data of each time point, and carrying out energy storage calling based on the energy storage output data, so that energy storage calling is reasonably carried out, resource waste is avoided, and resource allocation is optimized.
Description
Technical Field
The invention relates to the technical field of wind power, in particular to a wind power energy storage calling method, device and medium.
Background
Currently the grid has started to have large scale access to distributed wind generators or centralized wind farms. Aiming at the output fluctuation and randomness of the wind driven generator, the existing control methods for the wind driven generator to participate in the active power of the power system mainly comprise two methods: (1) The method is controlled by using a virtual inertia method, the rotor characteristic of the synchronous generator is simulated by kinetic energy stored by a rotating element in the wind turbine generator, and the inertia response characteristic of the synchronous generator is used for supporting the power grid frequency; (2) The standby power of the unit is used for control, the active power of the reserved part of the load shedding operation of the wind driven generator can be used for supporting the frequency of a power grid, and a pitch control method and an overspeed method are mainly used.
However, both the two methods control the wind driven generator, and because the fluctuation of the wind power output is large, when the actual processing of the wind driven generator does not meet the grid connection requirement, electricity is abandoned, and resources are wasted.
Disclosure of Invention
The invention provides a wind power energy storage calling method, device and medium, which are used for solving the problem of resource waste caused by electricity abandoning of a wind driven generator, reasonably calling energy storage, avoiding resource waste and optimizing resource allocation.
According to one aspect of the invention, a wind power energy storage calling method is provided, which comprises the following steps:
decomposing the output prediction data of the wind power generation object based on a set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency;
determining the maximum active power change limit value of the wind power generation object within a preset time range, and determining the maximum change rate of grid-connected output data of the wind power generation object within the preset time range according to the plurality of eigenmode functions;
and under the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value, determining energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data of each time point, and calling energy storage based on the energy storage output data.
According to another aspect of the present invention, there is provided a wind power storage energy invoking device, including:
the data decomposition module is used for decomposing output prediction data of the wind power generation object based on a set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency;
the change rate determining module is used for determining the maximum active power change limit value of the wind power generation object in a preset time range and determining the maximum change rate of grid-connected output data of the wind power generation object in the preset time range according to the plurality of eigenmode functions;
and the energy storage calling module is used for determining energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data of each time point under the condition that the maximum change rate is less than or equal to the maximum active power change limit value, and calling energy storage based on the energy storage output data.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the wind power storage calling method according to any embodiment of the present invention.
According to another aspect of the present invention, a computer-readable storage medium is provided, where computer instructions are stored, and the computer instructions are configured to enable a processor to implement the wind power energy storage calling method according to any embodiment of the present invention when executed.
According to the technical scheme of the embodiment of the invention, the output prediction data of the wind power generation object is decomposed based on a set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency, signal decomposition is carried out according to the time scale characteristics of the output prediction data, the maximum active power change limit value of the wind power generation object in a preset time range is determined, and the maximum change rate of the grid-connected output data of the wind power generation object in the preset time range is determined according to the plurality of eigenmode functions; under the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value, the energy storage output data of each time point is determined according to the actual output data and the grid-connected output data of the wind power generation object at each time point, energy storage is called based on the energy storage output data, the maximum change rate of the grid-connected output data of the wind power generation object in the seam within the preset time range is determined, the energy storage output data of each time point is determined according to the relation between the maximum change rate and the maximum active power change limit, energy storage is called based on the energy storage output data, the problem of resource waste caused by electricity abandonment of the wind power generator is solved, the energy storage calling is reasonably performed, the resource waste is avoided, and the beneficial effect of resource allocation is optimized.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a wind power storage energy calling method according to an embodiment of the present invention;
fig. 2 is a flowchart of a wind power storage invoking method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a wind power energy storage calling method according to a second embodiment of the present invention
Fig. 4 is a schematic structural diagram of a wind power storage energy invoking device according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the wind power storage energy calling method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a wind power energy storage calling method according to an embodiment of the present invention, which is applicable to an energy storage calling situation in the embodiment of the present invention, and the method may be executed by a wind power energy storage calling device, and the wind power energy storage calling device may be implemented in a form of hardware and/or software, and may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, decomposing the output prediction data of the wind power generation object based on an ensemble empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency.
The ensemble empirical mode decomposition algorithm may be a noise-aided data analysis method, and may be used to suppress mixing between frequency components in the original signal and perform signal decomposition. For example: the EEDM (Ensemble Empirical Mode Decomposition) algorithm is an Ensemble Empirical Mode Decomposition algorithm.
The wind power generation object may be a device that generates power by wind power, and may be understood as a wind power generation device, and may be specifically configured to convert kinetic energy of wind into mechanical kinetic energy, and then convert the mechanical energy into electrical kinetic energy. Illustratively, the wind power object includes a wind farm or a wind power generator.
The output prediction data can be a power signal which is predicted to be output by the wind power generation device within a preset time. The output prediction data of the wind farm can be understood as the total electric power generated by all wind generating sets of the current wind farm, i.e. the active power delivered by the wind farm to the power grid.
In particular, the eigenmode function may be understood as a signal component of the contribution prediction data. The output prediction data may be decomposed into several eigenmode functions, i.e., IMF (Intrinsic Mode Function) components.
Specifically, the high-frequency eigenmode function may be a high-frequency signal, which is a signal frequency with a frequency higher than a preset frequency threshold. The low-frequency eigenmode function may be a low-frequency signal, which is a signal frequency below a preset frequency threshold. Taking an image of ensemble empirical mode decomposition as an example, according to the intensity of change in the image, a high-frequency signal and a low-frequency signal are respectively determined, the high-frequency signal may be an edge of the image with intense intensity change, and the eigenmode function of the low-frequency signal is an area of the image with gentle intensity change.
Optionally, decomposing the output prediction data of the wind power generation object based on a set empirical mode decomposition algorithm may include: and adding the noise signal into the original signal, and taking the added noise signal as a whole according to the upper and lower extreme points of the original signal. And respectively connecting the upper extreme point and the lower extreme point in sequence to respectively obtain an upper envelope line and a lower envelope line, and obtaining a mean envelope line according to the upper envelope line and the lower envelope line. And subtracting the envelope curve of the mean value of the original signal to obtain an intermediate signal. And judging whether the intermediate signal meets the requirement in the whole data segment, wherein the number of the extreme points and the number of the zero-crossing points must be equal or the difference cannot exceed one at most, and the average value of an upper envelope line formed by the local maximum points and a lower envelope line formed by the local minimum points is zero at any moment. If yes, obtaining IMF component, otherwise, re-obtaining the average value according to the original signal for judgment. And after obtaining the IMF component, subtracting the IMF component from the original signal, taking the difference value of the IMF component and the original signal as a new original signal, and repeating the step of averaging to obtain a plurality of eigenmode functions.
Optionally, before the decomposing the output prediction data of the wind power generation object based on the ensemble empirical mode decomposition algorithm, the method may further include: and determining output prediction data of the wind power generation object according to the wind power associated information, wherein the wind power associated parameters comprise at least one of weather information, wind power generator performance information and historical operation data.
Optionally, the weather information may be weather prediction information, the weather prediction information may predict weather between the current days and/or within a preset time range, and the magnitude of the wind force value may be predicted according to different weather information, for example: the wind power value in sunny weather is small, the wind power in storm weather is extremely large, the wind power value in cloudy weather is small, the wind power value in rainy weather is large, and the like. The performance of a wind turbine may be the properties and performance that the wind turbine has, for example: flow, speed, power, pressure, etc. The historical operation data may be data such as a power value output by the wind turbine generator at a historical time point, and an average rotational speed of the wind turbine generator at a historical time point.
According to the embodiment of the invention, the output prediction data of the wind power generation object is determined according to at least one of the weather information, the wind power generator performance information, the historical operation data and the like, so that the output prediction data of the wind power generation object can be more accurately predicted, and the accuracy of the output prediction data is improved.
S120, determining the maximum active power change limit value of the wind power generation object in a preset time range, and determining the maximum change rate of grid-connected output data of the wind power generation object in the preset time range according to the plurality of eigenmode functions.
Active power is understood to mean, among other things, the conversion of electrical energy into other forms of electrical power. Optionally, the active power may be an average power. The grid-connected output data may be a power signal that delivers wind generated energy to the power transmission network. The active power change rate can be the ratio of the change amount between the maximum value and the minimum value of the wind power plant output active power in unit time to the installed capacity. The maximum active power variation limit may be a maximum value of a preset power variation amount.
S130, under the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value, determining energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data of each time point, and calling energy storage based on the energy storage output data.
The actual output data can be a power signal actually output by the wind power generation object in unit time.
In particular, the stored energy may be stored energy. In the embodiment of the invention, the stored energy can be electric energy, and the electric energy converted from the wind power object is stored and transmitted to the load center. The stored energy output data may be a numerical value of the called stored energy.
Optionally, the determining the energy storage output data at each time point according to the actual output data of the wind power generation object at each time point and the grid-connected output data may include: and subtracting the grid-connected output data from the actual output data of the wind power generation object to obtain energy storage output data aiming at each time point.
In the embodiment of the invention, the maximum change rate is compared with the maximum active power change limit, and the power signal of the electric energy storage at each time point is determined according to the power signal actually output by the wind power generation device at each time point and the power signal output by grid connection, so that the power signal of the electric energy storage is called, and the problems that the active power change exceeds the limit value and the prediction error are solved by effectively utilizing the characteristics of high response speed and flexible adjustment of the energy storage.
According to the technical scheme, output prediction data of the wind power generation object are decomposed through a set empirical mode algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency, the maximum active power change limit value of the wind power generation object in a preset time range is determined, the maximum change rate of grid-connected output data of the wind power generation object in the preset time range is determined according to the eigenmode functions, under the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value, energy storage output data of each time point are determined according to actual output data of the wind power generation object and the grid-connected output data of each time point, energy storage calling is carried out based on the energy storage output data, the problem of resource waste caused by electricity abandoning of the wind power generator is solved, reasonable energy storage calling is achieved, resource waste is avoided, and the beneficial effects of resource configuration are optimized.
Example two
Fig. 2 is a flowchart of a wind power storage invoking method according to a second embodiment of the present invention, where on the basis of the second embodiment of the present invention, how to determine the maximum change rate of grid-connected output data of a wind power generation object within a preset time range according to a plurality of eigenmode functions is further refined, as shown in fig. 2, the method of the second embodiment may specifically include:
s210, decomposing the output prediction data of the wind power generation object based on an ensemble empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency.
The output prediction data of the wind power generation object is decomposed based on a set empirical mode decomposition algorithm, so that n eigenmode functions and residual waves can be obtained, wherein the residual waves can be waves with extremely low frequency.
Specifically, the output prediction data is a value obtained by adding the eigenmode function excluding the highest frequency and the remaining eigenmode functions to the residual wave.
Optionally, the output prediction data satisfies the following conditions:
W(t)＝imf 1 (t)+imf 2 (t)+…+imf n (t)+r(t)
wherein W (t) is output prediction data imf x (t) is an eigenmode function, n is the number of eigenmode functions, and the initial value of n is 0,r (t) is a residual wave. And S220, determining the maximum active power change limit value of the wind power generation object in a preset time range.
And S230, reconstructing a grid-connected output function of the wind power generation object based on the plurality of signal components.
The grid-connected output function may be a value obtained by adding a plurality of eigen-mode functions at a preset time point.
Optionally, the calculation formula of the grid-connected output function is as follows:
sig w (t)＝imf i+1 (t)+imf i+2 (t)+…+imf i+n (t)
wherein, sig w (t) is the grid-connected output function, imf x (t) is the eigenmode function, n is the number of eigenmode functions, and the initial value of n is 0.
Optionally, reconstructing the grid-connected output function of the wind power generation object based on the plurality of eigenmode functions may include: determining an eigenmode function for reconstruction from a plurality of said eigenmode functions; and under the condition that two or more eigenmode functions for reconstruction exist, superposing the eigenmode functions for reconstruction to obtain a grid-connected output function of the wind power generation object.
S240, respectively determining grid-connected output data of the wind power generation object at each time point within a preset time range based on the grid-connected output function.
In the embodiment of the invention, in order to fully ensure that the output data of the wind power generation object meets the grid-connected requirement, the output test data can be processed by adopting different time scales respectively, and the grid-connected output data of the wind power generation object at each time point under different time scales is determined. Optionally, the preset time range is divided into a plurality of time points based on the first preset time scale and the second preset time scale respectively; and respectively determining grid-connected output data of the wind power generation object at each time point based on the grid-connected output function.
The time scale is understood to be a time range. The time scale may be taken as the size of the divided time range, the longer the time scale, the larger the time range. The timescale may include a long timescale and a short timescale, for example, the long timescale is 1 hour and the short timescale is 5 minutes. It is understood that the long time scale and the short time scale are relative, for example, the short time scale is 5 minutes, and the time scale larger than five minutes is understood as the long time scale, and the specific value of the time scale is not particularly limited in the embodiment of the present invention.
Illustratively, the first preset timescale and the second timescale may be different timescales. The first preset timescale may be a short timescale and the second timescale may be a long timescale.
Optionally, dividing the preset time range into a plurality of time points based on the first preset time scale and the second preset time scale respectively may include: the preset time range is divided into a plurality of moments based on the short time scale and the long time scale, respectively.
Illustratively, assume that the predetermined time range is 3 hours, the short time scale is 5 minutes, and the long time scale is 1 hour. And dividing a preset time range based on a short time scale, dividing 3 hours according to 5 minutes to obtain 36 time points. And dividing a preset time range based on a long time scale, dividing 3 hours according to 1 hour to obtain 3 time points. The method has the advantages that the time points are divided according to the length and the length of the time scale respectively, grid-connected output data of each time point are determined, and the result is more accurate.
Optionally, respectively determining grid-connected output data of the wind power generation object at each time point based on the grid-connected output function, including: and determining the grid-connected output data of the wind power generation object at each time point corresponding to the first preset time scale based on the grid-connected output function, and determining the grid-connected output data of the wind power generation object at each time point corresponding to the second preset time scale based on the grid-connected output function.
And S250, determining the maximum change rate of the grid-connected output data within the preset time range according to the grid-connected output data of each time point.
Specifically, grid-connected output data of each time point in the preset time range are obtained, the difference value of the grid-connected output data of every two adjacent time points is calculated, and the maximum difference value is determined as the maximum change rate of the grid-connected output data in the preset time range.
It can be understood that, under the condition that the preset time range is divided into a plurality of time points based on the first preset time scale and the second preset time scale respectively, for each time point corresponding to the first preset time scale, the first maximum change rate of the grid-connected output data within the preset time range is determined according to the grid-connected output data of each time point, and for each time point corresponding to the second preset time scale, the second maximum change rate of the grid-connected output data within the preset time range is determined according to the grid-connected output data of each time point.
And S260, under the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value, determining energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data of each time point, and calling energy storage based on the energy storage output data.
In the embodiment of the invention, if the maximum change rate is less than or equal to the maximum active power change limit value, determining a plurality of eigenmode functions as eigenmode functions for reconstructing the grid-connected output function. And if the maximum change rate is larger than the maximum active power change limit value, re-determining the eigenmode function for reconstructing the grid-connected output function until the eigenmode function meeting the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value is obtained.
The maximum active power change limit value can be preset empirically, and specific numerical values of the maximum active power change limit value are not limited in the embodiment of the invention.
Optionally, the wind power energy storage calling method may further include: and in the case that the maximum change rate is larger than the maximum active power change limit value, returning to the operation of determining the eigenmode function for reconstruction from the plurality of eigenmode functions. For example, a predetermined number of high-frequency eigenmode functions may be discarded, and the remaining eigenmode functions may be determined as eigenmode functions for reconstruction. In particular, the eigenmode functions of the highest frequencies can be discarded at a time.
Specifically, when the maximum change rate is greater than the maximum active power change limit, the grid-connected output function is as follows:
sig w (t)＝imf i+1 (t)+imf i+2 (t)+…+imf i+n (t)
i＝i+1
wherein, sig w (t) isGrid-connected output function imf x (t) is the eigenmode function, n is the number of eigenmode functions, and the initial value of i is 0.
Optionally, the returning performs an operation of determining an eigenmode function for reconstruction from a plurality of eigenmode functions, including: and judging whether the maximum change rate of the new grid-connected output function in the short time scale and the long time scale is less than or equal to the maximum active power change limit value or not. And if so, determining the energy storage output data required to be called according to the difference value between the actual output data of the wind power generation object at each moment and the grid-connected output data. If not, repeating the steps until obtaining the corresponding eigenmode function when the maximum change rate is less than or equal to the maximum active power change limit value.
In a case where the calculated maximum rate of change includes a first maximum rate of change and a second maximum rate of change, the maximum rate of change being less than or equal to the maximum active power change limit, comprising: the first maximum rate of change and the second maximum rate of change are both less than or equal to the maximum active power change limit.
Optionally, in a case that the first maximum rate of change or the second maximum rate of change is greater than the maximum active power change limit value, returning to perform the operation of determining the eigenmode function for reconstruction from the plurality of eigenmode functions.
Fig. 3 provides a schematic flow diagram of an alternative example of a wind power storage calling method. As shown in fig. 3, the wind power energy storage calling method specifically includes the following steps:
(1) Inputting output prediction data W (t) of a wind power plant or a wind driven generator according to factors such as weather conditions, wind driven generator performance, historical operating data and the like;
(2) Inputting output prediction data W (t), decomposing into n eigenmode functions imf by ensemble empirical mode decomposition method x (t) (x =1,2, … n, the frequency of the eigenmode function decreases with increasing index) and the residual wave r (t), satisfying the following condition:
W(t)＝imf 1 (t)+imf 2 (t)+…+imf n (t)+r(t)
wherein W (t) is output forceTest data, imf x (t) is the eigenmode function, n is the number of eigenmode functions, and the initial value of i is 0,r (t) is the residual wave.
(3) Reading a short time scale t according to the maximum active power change limit value of the wind power generation object 1 Maximum active power variation limit P 1 ；
(4) Reading the long-time scale t according to the maximum active power change limit value of the wind power generation object 2 Maximum active power variation limit P 2 ；
(5) Setting an initial value of i, which is a serial number for dividing high-frequency and low-frequency signal parts, to 0;
(6) Mixing imf i+1 (t)、imf i+2 (t)、...imf i+n (t) cumulative reconstruction as a grid-connected output function sig for a wind farm or a wind turbine w (t) a signal satisfying the following condition:
sig w (t)＝imf i+1 (t)+imf i+2 (t)+…+imf i+n (t)；
wherein sig w (t) is the grid-connected output function, imf x (t) is the eigenmode function, n is the number of eigenmode functions, and the initial value of i is 0.
(7) If grid-connected output function sig w (t) at t 1 Maximum rate of change P for short timescales w1 Greater than P 1 If i = i +1, the step (6) is carried out again, otherwise, the next step is carried out;
(8) If grid-connected output data sig w (t) at t 2 Maximum rate of change P over a long time scale w2 Greater than P 2 Then i = i +1. Re-performing the step (6), otherwise, performing the next step;
(9) According to actual output data sig of wind power generation objects at all times a (t) subtracting the grid-connected output data sig w (t) the energy storage output data sig can be obtained st (t) thus, as a value for invoking energy storage, the following condition is satisfied:
sig st (t)＝sig a (t)-sig w (t)
wherein sig st (t) actual force data, sig w (t) is the number of grid-connected outputsAccording to, sig st And (t) is energy storage output data.
According to the technical scheme of the embodiment of the invention, the grid-connected output function of the wind power generation object is reconstructed based on a plurality of eigen mode functions, the eigen mode functions used for reconstruction are superposed to obtain the grid-connected output function of the wind power generation object, grid-connected output data of the wind power generation object at each time point in a preset time range are respectively determined based on the grid-connected output function, the maximum change rate of the grid-connected output data is determined, whether the eigen mode function needs to be reconstructed is judged according to the size relation between the maximum change rate and the maximum active power change limit value, energy storage output data are accurately obtained according to the actual output data of the wind power generation object and the grid-connected output data, the problem that the output part of the wind power generator which does not meet the grid-connected requirement can bring electricity abandon is solved, the resource waste is caused, the resource configuration is optimized, and the resource waste is avoided.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a wind power energy storage calling device provided in the third embodiment of the present invention. As shown in fig. 4, the apparatus includes: a data decomposition module 310, a change rate determination module 320, and an energy storage call module 330.
The data decomposition module 310 is configured to decompose the output prediction data of the wind power generation object based on an ensemble empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from a high frequency to a low frequency; the change rate determining module 320 is configured to determine a maximum active power change limit of the wind power generation object within a preset time range, and determine a maximum change rate of grid-connected output data of the wind power generation object within the preset time range according to the plurality of eigenmode functions; and the energy storage calling module 330 is configured to determine energy storage output data at each time point according to the actual output data of the wind power generation object at each time point and the grid-connected output data when the maximum change rate is less than or equal to the maximum active power change limit value, and call energy storage based on the energy storage output data.
According to the wind power energy storage calling device provided by the embodiment of the invention, the output prediction data of a wind power generation object is decomposed through the data decomposition module based on a set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency, the change rate determination module determines the maximum active power change limit value of the wind power generation object in a preset time range, the maximum change rate of grid-connected output data of the wind power generation object in the preset time range is determined according to the plurality of eigenmode functions, the energy storage calling module determines the energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data at each time point under the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value, and energy storage calling is carried out based on the energy storage output data, so that the problem of resource waste caused by electricity abandoning of a wind power generator is solved, reasonable energy storage calling is achieved, the resource waste is avoided, and the beneficial effects of resource configuration are optimized.
Optionally, the data decomposition module is configured to:
before decomposing the output prediction data of the wind power generation object based on the set empirical mode decomposition algorithm, determining the output prediction data of the wind power generation object according to wind correlation information, wherein the wind correlation parameters comprise at least one of weather information, wind power generator performance information and historical operation data, and the wind power generation object comprises a wind power plant or a wind power generator.
Optionally, the energy storage calling module is specifically configured to: and for each time point, subtracting the grid-connected output data from the actual output data of the wind power generation object to obtain energy storage output data.
Optionally, the change rate determining module 320 includes: the system comprises a grid-connected output function reconstruction unit, a grid-connected output data determination unit and a maximum change rate determination unit. The grid-connected output function reconstruction unit is used for reconstructing a grid-connected output function of the wind power generation object based on a plurality of eigenmode functions;
the grid-connected output data determining unit is used for respectively determining grid-connected output data of the wind power generation object at each time point within a preset time range based on the grid-connected output function reconstructing unit; and the maximum change rate determining unit is used for determining the maximum change rate of the grid-connected output data within the preset time range according to the grid-connected output data determining unit.
Optionally, the grid-connected output function reconstructing unit includes: and the reconstruction function determining subunit and the grid-connected output function calculating subunit. Wherein the reconstruction function determining subunit is configured to determine an eigenmode function for reconstruction from the plurality of eigenmode functions; and the grid-connected output function calculating subunit is used for superposing the eigenmode functions for reconstruction under the condition that two or more eigenmode functions for reconstruction exist to obtain the grid-connected output function of the wind power generation object.
Optionally, the grid-connected output data determining unit includes: a time division subunit and a time division data determination subunit. The time dividing unit is used for dividing a preset time range into a plurality of time points based on a first preset time scale and a second preset time scale respectively; and the time-sharing data determining subunit is used for respectively determining the grid-connected output data of the wind power generation object at each time point based on the grid-connected output function.
Optionally, the wind power energy storage calling device further includes: and reconstructing the eigenmode function and returning to the module. Wherein the module for returning the reconstructed eigenmode function is configured to return to perform the operation of determining the eigenmode function for reconstruction from the plurality of eigenmode functions if the maximum rate of change is greater than the maximum active power change limit value.
The wind power energy storage calling device provided by the embodiment of the invention can execute the wind power energy storage calling method provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
Example four
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the method wind power storage call method.
In some embodiments, the method wind electrical energy storage calling method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method wind energy storage calling method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method wind electrical energy storage calling method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A wind power energy storage calling method is characterized by comprising the following steps:
decomposing the output prediction data of the wind power generation object based on a set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency;
determining the maximum active power change limit value of the wind power generation object within a preset time range, and determining the maximum change rate of grid-connected output data of the wind power generation object within the preset time range according to the plurality of eigenmode functions;
and under the condition that the maximum change rate is smaller than or equal to the maximum active power change limit value, determining energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data of each time point, and calling energy storage based on the energy storage output data.
2. The method of claim 1, wherein said determining a maximum rate of change of grid-connected output data of said wind power object over said preset time range from a plurality of said eigenmode functions comprises:
reconstructing a grid-connected output function of the wind power generation object based on the plurality of eigenmode functions;
respectively determining grid-connected output data of the wind power generation object at each time point within a preset time range based on the grid-connected output function;
and determining the maximum change rate of the grid-connected output data within the preset time range according to the grid-connected output data of each time point.
3. The method of claim 2, wherein said reconstructing a grid-tied output function of said wind power object based on a plurality of said eigenmode functions comprises:
determining an eigenmode function for reconstruction from a plurality of said eigenmode functions;
and under the condition that two or more eigenmode functions for reconstruction exist, superposing the eigenmode functions for reconstruction to obtain a grid-connected output function of the wind power generation object.
4. The method of claim 3, further comprising:
and in the case that the maximum change rate is larger than the maximum active power change limit value, returning to the operation of determining the eigenmode function for reconstruction from the plurality of eigenmode functions.
5. The method of claim 2, wherein the determining grid-connected output data of the wind power generation object at each time point within a preset time range respectively based on the grid-connected output function comprises:
dividing a preset time range into a plurality of time points based on a first preset time scale and a second preset time scale respectively;
and respectively determining grid-connected output data of the wind power generation object at each time point based on the grid-connected output function.
6. The method of claim 1, wherein determining the stored energy output data at each time point from the actual output data of the wind power object and the grid-connected output data at each time point comprises:
and subtracting the grid-connected output data from the actual output data of the wind power generation object to obtain energy storage output data aiming at each time point.
7. The method of claim 1, wherein prior to decomposing the forecast output data for the wind power plant based on the ensemble empirical mode decomposition algorithm, further comprising:
and determining output prediction data of a wind power generation object according to the wind power related information, wherein the wind power related parameters comprise at least one of weather information, wind power generator performance information and historical operation data, and the wind power generation object comprises a wind power plant or a wind power generator.
8. The utility model provides a wind-powered electricity generation energy storage calling device which characterized in that includes:
the data decomposition module is used for decomposing the output prediction data of the wind power generation object based on a set empirical mode decomposition algorithm to obtain a plurality of eigenmode functions from high frequency to low frequency;
the change rate determining module is used for determining the maximum active power change limit value of the wind power generation object in a preset time range and determining the maximum change rate of grid-connected output data of the wind power generation object in the preset time range according to the plurality of eigenmode functions;
and the energy storage calling module is used for determining energy storage output data of each time point according to the actual output data of the wind power generation object and the grid-connected output data of each time point under the condition that the maximum change rate is less than or equal to the maximum active power change limit value, and calling energy storage based on the energy storage output data.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the wind power storage call method of any one of claims 1-7.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for causing a processor to implement the wind power energy storage calling method according to any one of claims 1 to 7 when executed.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211191581.2A CN115577925A (en) | 2022-09-28 | 2022-09-28 | Wind power energy storage calling method, device and medium |
| PCT/CN2023/079020 WO2024066196A1 (en) | 2022-09-28 | 2023-03-01 | Stored-energy calling method and apparatus for wind power, and device and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211191581.2A CN115577925A (en) | 2022-09-28 | 2022-09-28 | Wind power energy storage calling method, device and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115577925A true CN115577925A (en) | 2023-01-06 |
Family
ID=84582389
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211191581.2A Pending CN115577925A (en) | 2022-09-28 | 2022-09-28 | Wind power energy storage calling method, device and medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115577925A (en) |
| WO (1) | WO2024066196A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024066196A1 (en) * | 2022-09-28 | 2024-04-04 | 广东电网有限责任公司 | Stored-energy calling method and apparatus for wind power, and device and medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104600728B (en) * | 2014-12-29 | 2017-05-10 | 国网新疆电力公司经济技术研究院 | Optimizing method of mixed energy accumulation capacity configuration for stabilization wind power fluctuation |
| CN112072655B (en) * | 2020-09-10 | 2022-05-27 | 天津大学 | Hybrid energy storage optimal configuration method for grid-connected wind energy storage power generation system |
| CN113595157B (en) * | 2021-08-03 | 2022-03-22 | 国家电投集团电站运营技术（北京）有限公司 | Coordination control method, device, equipment and storage medium for active power of wind power plant |
| CN115577925A (en) * | 2022-09-28 | 2023-01-06 | 广东电网有限责任公司 | Wind power energy storage calling method, device and medium |
-
2022
- 2022-09-28 CN CN202211191581.2A patent/CN115577925A/en active Pending
-
2023
- 2023-03-01 WO PCT/CN2023/079020 patent/WO2024066196A1/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024066196A1 (en) * | 2022-09-28 | 2024-04-04 | 广东电网有限责任公司 | Stored-energy calling method and apparatus for wind power, and device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2024066196A1 (en) | 2024-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115577925A (en) | Wind power energy storage calling method, device and medium | |
| CN114709878A (en) | Light storage cooperative configuration method, device and medium applied to light storage straight and flexible building | |
| CN116931438B (en) | Method, device, equipment and medium for determining parameters of speed regulator of water turbine | |
| WO2024108953A1 (en) | Load shedding test method and apparatus for pumped storage unit, and device and medium | |
| CN109872248B (en) | Wind power plant cluster output calculation method and system | |
| CN115117943A (en) | Charging and discharging scheduling method, device, equipment and medium | |
| CN116307511A (en) | Energy storage configuration method, device, equipment and medium for park power grid | |
| CN115511453A (en) | Data processing method, device, equipment and system | |
| CN115441513A (en) | Phase modulator determining method and device suitable for new energy station | |
| CN117117904B (en) | Frequency modulation contribution evaluation method and device and electronic equipment | |
| CN116432478B (en) | Energy determination method, device, equipment and medium for electric power system | |
| CN112803396B (en) | Capacity measurement method and device of frequency modulation unit and electronic equipment | |
| CN116646994B (en) | Optimal scheduling method and device for power system | |
| CN117595275A (en) | Method, device, equipment and storage medium for clearing | |
| CN116014759A (en) | New energy-oriented multistage self-adaptive edge intelligent control method and device | |
| CN116093985A (en) | Energy storage system configuration method and device based on energy balance method | |
| CN113988648A (en) | Method and device for calculating risk value of wind power flexible-direct system | |
| CN117559498A (en) | Rural energy station regulation and control method, device, equipment and storage medium | |
| CN115065079A (en) | AGC instruction response method and device based on hybrid energy storage system | |
| CN117217495A (en) | Energy scheduling method and device, electronic equipment and storage medium | |
| CN115955189A (en) | Method, device, equipment and medium for detecting power generation abnormity | |
| CN115375186A (en) | Distributed renewable energy dynamic polymerization method, device, equipment and medium | |
| CN117526380A (en) | Charging power adjustment method, device, control system and storage medium | |
| CN117767353A (en) | Power support cooperative control method and device for virtual synchronous power distribution network | |
| CN115149557A (en) | Power system scheduling method and device, electronic equipment and storage medium |
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 |