CN117254498A - Power grid energy storage node capacity pre-calculation method - Google Patents
Power grid energy storage node capacity pre-calculation method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- 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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- 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
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The application provides a power grid energy storage node capacity pre-calculation method, which comprises the following steps: acquiring historical sensing parameters from a database to generate time fitting curves of various parameters, and generating an instantaneous electric energy output curve Q based on the fitting curves i (t); in a preset period [ TS, TE ]]In, find D i Negative interval of (t) [ ZS ] i,k ，ZE i,k ]The D is i (t) is equal to Q i (t) -S (t), S (t) is the time period [ TS, TE ] of the distributed power station of the power grid system]An instantaneous power demand profile within; combining the intervals under each history sample i to obtain F_i negative intervals after combining each history sample; calculating D in F_i intervals of each history sample i Integral value of (t) to obtain LACK i,f Abs (LACK) i,f ) Is set as the capacity requirement of the energy storage node of the distributed power system, and the maximum electric quantity storage of the electric energy periodic stable output of the distributed power station is calculated by quantificationThe gap is prepared, and the gap is provided with corresponding electric energy storage capacity, so that the purpose of reliably providing stable electric energy output with controllable time period is finally realized.
Description
The application is a divisional application, the application number of the main application is 202111283987.9, the application date is 2021.11.01, and the invention is named as a distributed power system energy storage node capacity demand estimation system.
Technical Field
The application belongs to the technical field of electric power energy storage, and particularly relates to a power grid energy storage node capacity pre-calculation method.
Background
The statements in this section merely provide background information related to the present application and may not necessarily constitute prior art.
With the rapid development of human informatization and industrialization, the energy consumption of the human society has a high-speed growth situation for a long time, and the energy consumption brings great challenges to energy supply, and is particularly applicable to the supply of electric energy involving the social civilian and industrial basis.
In order to relieve the power supply pressure, the related policies of national export encourage the distributed power generation grid-connected policy, so that folk resources of each area are mobilized, the energy sources of waterpower, wind power, solar energy, biological energy (biogas) and sea tide energy are widely utilized to generate power, a distributed power grid system is constructed, effective supplement is provided for the power, and finally the purpose of relieving the power supply pressure through widely developing green energy sources is achieved.
The management of the distributed power generation technology in the existing network is rough, the authenticated distributed power supply is mainly subjected to electric energy merging management, the equal characteristics of the distributed power supply output are restrained, but the factors of considerable instability exist in the instantaneous electric energy output of hydraulic power, wind power, solar energy, biological energy (methane) and sea tide energy power generation in consideration of the influence of the natural environment, so that the continuous supply period of electric quantity is not required, the traditional power supply still needs to perform full-scale power generation operation for coping with the uncertainty of the distributed power supply output, and the output is regulated timely according to the state of a power grid, so that the aim of guaranteeing the stable supply of the whole network electric energy is fulfilled, and the power generation waste is caused.
Therefore, the problem that the distributed power system is difficult to provide stable power output with controllable time period is solved, and the problem that the prior art is required to solve is solved.
Disclosure of Invention
In order to solve the problems, the method for pre-calculating the capacity of the energy storage node of the power grid is provided, a maximum electric quantity reserve gap for the electric energy time-period stable output of the distributed power supply station is calculated quantitatively, corresponding electric energy storage capacity is provided for the distributed power supply station, and finally the purpose that the distributed power supply station reliably provides the stable electric energy output with controllable time period is achieved.
The application provides a power grid energy storage node capacity pre-calculation method, which comprises the following steps:
step 1, acquiring historical sensing parameters from a database;
step 2, generating a time fitting curve C of various parameters based on historical sensing parameters ij (t), I represents a history sample number, the value is 1, & I, I is the total number of history samples; j represents the number of the sensing parameter type, the value is 1, the number of the sensing parameters is equal to the total number of the sensing parameters, and then an instantaneous electric energy output curve Q is generated based on the fitting curve i (t)；
Step 3, in a preset period [ TS, TE ]]In, find D i Negative interval of (t) [ ZS ] i,k ，ZE i,k ]K represents a negative value interval number, and takes 1, and D constructed by taking historical samples i as K_i and K_i i The number of negative intervals of (t), said D i (t) is equal to Q i (t) -S (t), S (t) is the time period [ TS, TE ] of the distributed power station of the power grid system]An instantaneous power demand profile within;
step 4, carrying out interval combination on each history sample i to obtain F_i negative intervals after each history sample is combined;
step 5, calculating D in F_i intervals of each history sample in the I history samples i Integral value of (t) to obtain LACK i,f Abs (LACK) i,f ) The maximum value of (2) is set to the capacity requirement of the energy storage node of the distributed power system, where F takes on the values 1.
Preferably, in said step 1, at least one historical sample parameter for a time period [ TS, TE ] is obtained from a database.
Preferably, in the step 2, a time fitting curve C of each type of parameter is fitted based on the historical sensing parameters ij The specific method of (t) is as follows: according to the sensing data of the history sample i, obtaining the preset time period [ TS, TE ] of the history sample i through data fitting based on the sampling interval of the sensing data]Fitting curve C in ij (t)。
Preferably, in the step 2, an instantaneous electric energy output curve Q is generated based on the fitted curve i The specific method of (t) is as follows: according to the adopted power generation method, calculating to obtain an instantaneous power generation curve under the power generation method, and for the condition that one station adopts a plurality of methods to generate power, superposing the instantaneous power generation curves of all the power generation methods to obtain the total instantaneous power generation curve of the station.
Preferably, in the step 4, the step D is performed for each history sample i i (t) performing interval combination to obtain F_i negative intervals after each history sample combination, wherein the specific method comprises the following steps:
step 4.1, i is assigned to 1, and F_i is assigned to 0;
step 4.2, judging whether I is larger than I, if so, jumping to step 4.8; if not, clearing Q_i and jumping to step 4.3;
step 4.3, setting the value of M as M_i, wherein M_i is D i (t) the total number of negative intervals before interval consolidation;
step 4.4, judging whether m is 1, if so, then [ ZS i,m ，ZE i,m ]In the write queue Q_i, F_i+1 is assigned to F_i, and the step 4.7 is skipped; if not, jumping to the step 4.5;
step 4.5, calculate D i (t) at [ ZE i,m-1 ，ZS i,m ]The integral value in the interval is S1, D i (t) in [ ZS ] i,m ，ZE i,m ]The integral value in the interval is S2, if S1 is smaller than S2, the [ ZS i,m-1 ，ZE i,m-1 ]、[ZE i,m-1 ，ZS i,m ]、[ZS i,m ，ZE i,m ]Coverage area interval [ ZS ] after combining three intervals i,m-1 ，ZE i,m-1 ]The method comprises the steps of carrying out a first treatment on the surface of the If S1 is greater than or equal to S2, then [ ZS i,m ，ZE i,m ]In the write queue Q_i, F_i+1 is assigned to F_i;
step 4.6, assigning m-1 to m, and jumping to step 4.4;
step 4.7, i+1 is assigned to i, F_i is assigned to 0, and the step 4.2 is skipped;
step 4.8, outputting f_i, q_i is a negative interval queue of each history sample I, wherein I takes on values of 1.
Compared with the prior art, the beneficial effects of this application are:
according to the method, based on the power generation sensing parameters, a power generation time fitting curve of the site is established, then the power grid system is combined to meet the power output requirement of the distributed power supply grid connection, the maximum power reserve gap of the power period stable output of the distributed power supply system is calculated, so that the energy storage node capacity of the distributed power supply system is determined, the maximum gap power is reserved in advance through the energy storage node, the goal that the distributed power supply system can stably supply corresponding power in a preset period is guaranteed, and finally full-scale stable power integration is provided for the power grid system, so that the traditional power supply system generates power according to needs, and waste is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application.
FIG. 1 is a flow chart of a distributed power system energy storage node capacity demand estimation system method;
FIG. 2 is a schematic diagram of a distributed power system energy storage node capacity demand estimation system;
FIG. 3 is a schematic diagram of an embodiment of a distributed power system energy storage node capacity demand estimation system.
The specific embodiment is as follows:
the present application is further described below with reference to the drawings and examples.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, are merely relational terms determined for convenience in describing structural relationships of the various components or elements of the present disclosure, and do not denote any one of the components or elements of the present disclosure, and are not to be construed as limiting the present disclosure.
As shown in fig. 1 to 3, the energy storage node capacity demand estimation system of the distributed power system provided in the present application is characterized by comprising: the system comprises a sensing parameter management module, a power generation amount estimation module, a negative value interval management module, an interval merging module and an energy storage node capacity calculation module, wherein the purposes of the modules are as follows;
and the sensing parameter management module is used for: the module is responsible for acquiring historical sensing parameters from a database;
the power generation amount estimation module: the module generates a time fitting curve C of various parameters based on historical sensing parameter fitting ij (t), I represents a history sample number, the value is 1, & I, I is the total number of history samples; j represents the number of the sensing parameter type, the value is 1, the number of the sensing parameters is equal to the total number of the sensing parameters, and then an instantaneous electric energy output curve Q is generated based on the fitting curve i (t)；
Negative value interval management module: the module is used for presetting a time period [ TS, TE ]]In, find D i Negative interval of (t) [ ZS ] i,k ，ZE i,k ]K represents a negative value interval number, and takes 1, and D is constructed by taking K_i and K_i as history samples i i Total number of negative intervals of (t), said D i (t) is equal to Q i (t)-S(t)；
The interval merging module is used for: the module is responsible for D under each history sample i i (t) performing interval combination to obtain F_i negative intervals after each history sample combination;
the energy storage node capacity calculation module calculates D in F_i intervals of each history sample in I history samples i Integral value of (t) to obtain LACK i,f Abs (LACK) i,f ) The maximum value of (2) is set to the capacity requirement of the energy storage node of the distributed power system, wherein the F value is 1.
The application also provides a power grid energy storage node capacity pre-calculation method, which comprises the following specific steps:
step 1, a sensing parameter management module acquires historical sensing parameters from a database;
step 2, a generating capacity estimation module fits a time fitting curve C of various parameters based on historical sensing parameters ij (t), I represents a history sample number, the value is 1, & I, I is the total number of history samples; j represents the number of the sensing parameter type, the value is 1, the number of the sensing parameters is equal to the total number of the sensing parameters, and then an instantaneous electric energy output curve Q is generated based on the fitting curve i (t)；
Step 3, the negative interval management module is in a preset time period [ TS, TE ]]In, find D i Negative interval of (t) [ ZS ] i,k ，ZE i,k ]K represents a negative value interval number, and takes 1, and D constructed by taking historical samples i as K_i and K_i i The number of negative intervals of (t), said D i (t) is equal to Q i (t) -S (t), S (t) is the time period [ TS, TE ] of the distributed power station of the power grid system]An instantaneous power demand profile within;
step 4, the section merging module performs D on each history sample i i (t) performing interval combination to obtain F_i negative intervals after each history sample combination;
step 5, the energy storage node capacity calculation module calculates D in F_i intervals of each history sample in the I history samples i Integral value of (t) to obtain LACK i,f Abs (LACK) i,f ) The maximum value of (2) is set to the capacity requirement of the energy storage node of the distributed power system, where F takes on the values 1.
C ij (t)、Q i (t)、D i (t) and S (t) are functions of the relative time parameter t, abs (LACK) i,f ) To calculate LACK i,f Absolute value of D i Negative interval of (t) [ ZS ] i,k ，ZE i,k ]Negative interval D i Time intervals when the result of (t) is negative.
In the step 1, the sensing parameter management module obtains at least one historical sample parameter for a time period [ TS, TE ] from the database. For example, if the period is 3 months in a year, a typical method would be to obtain sample parameters for 3 months in the last and previous years, provided that two historical data were obtained.
In the step 2, the power generation amount estimation module fits a time fitting curve C of various parameters based on the historical sensing parameters ij The specific method of (t) is as follows: according to the sensing data of the history sample i, obtaining the preset time period [ TS, TE ] of the history sample i through data fitting based on the sampling interval of the sensing data]Fitting curve C in ij (t)。
In the step 2, an instantaneous electric energy output curve Q is generated based on the fitted curve i The specific method of (t) is as follows: according to the adopted power generation method, calculating to obtain an instantaneous power generation curve under the power generation method, and for the condition that one station adopts a plurality of methods to generate power, superposing the instantaneous power generation curves of all the power generation methods to obtain the total instantaneous power generation curve of the station. Examples: for solar power generation, then, based on the formula: calculating an instantaneous power generation curve (N corresponds to Q) i (t)), wherein K is the power production per unit area per unit time; p (t) is the sunlight area time function (P (t) corresponds to C after the sensing parameters are fitted ij (t) And), therefore,
Q i (t)＝K*C ij (t)。
in the step 4, the interval merging module performs D for each history sample i i (t) performing interval combination to obtain F_i negative intervals after each history sample combination, wherein the specific method comprises the following steps:
step 4.1, i is assigned to 1, and F_i is assigned to 0;
step 4.2, judging whether I is larger than I, if so, jumping to step 4.8, and if not, emptying Q_i and jumping to step 4.3;
step 4.3, setting the value of M as M_i, wherein M_i is D i (t) the total number of negative intervals before interval consolidation;
step 4.4, judging whether m is 1, if so, then [ ZS i,m ，ZE i,m ]In the write queue Q_i, F_i+1 is assigned to F_i, and the step 4.7 is skipped, if not, the step 4.5 is skipped;
step 4.5, calculate D i (t) at [ ZE i,m-1 ，ZS i,m ]The integral value in the interval is S1, D i (t) in [ ZS ] i,m ，ZE i,m ]The integral value in the interval is S2, if S1 is smaller than S2, the [ ZS i,m-1 ，ZE i,m-1 ]、[ZE i,m-1 ，ZS i,m ]、[ZS i,m ，ZE i,m ]Coverage area interval [ ZS ] after combining three intervals i,m-1 ，ZE i,m-1 ]The method comprises the steps of carrying out a first treatment on the surface of the If S1 is greater than or equal to S2, then [ ZS i,m ，ZE i,m ]In the write queue Q_i, F_i+1 is assigned to F_i;
step 4.6, assigning m-1 to m, and jumping to step 4.4;
step 4.7, i+1 is assigned to i, F_i is assigned to 0, and the step 4.2 is skipped;
and 4.8, outputting F_i and Q_i, wherein I takes on the value of 1.
Examples: as shown in fig. 3, in this embodiment, the preset time period is shown as [ TS, TE ], that is, the entire horizontal axis coordinate span in fig. 3; in this embodiment, assuming that the sensing parameter management module only obtains a historical sample, the distributed power station adopts solar power generation, and S (t) is a constant value WT, the device performs the following steps:
firstly, a sensing parameter management module acquires a historical sample parameter related to a time period [ TS, TE ] in history, wherein the sample parameter is a sunlight area;
then, the generating capacity estimation module fits a time fitting curve C of the sunlight area based on the historical sensing parameters 1j (t) and calculating Q according to the instantaneous generation formula of solar power generation i (t)＝K*C 1j (t), K is the power output per unit area and per unit time, in this embodiment, K takes on a value of 10, i.e., Q i (t)＝10*C 1j (t)；
Then, the negative interval management module calculates D 1 (t)＝Q 1 (t) -S (t), D is obtained 1 (t)＝Q 1 (t) -WT (constant), then, the negative interval management module performs the following processing for a predetermined period [ TS, TE ]]In, find D 1 Negative interval of (t) [ ZS ] i,k ，ZE i,k ]As shown in FIG. 3, the present embodiment includes four negative intervals before merging, i.e. [ ZS ] 1,1 ，ZE 1,1 ]、[ZS 1,2 ，ZE 1,2 ]、[ZS 1,3 ，ZE 1,3 ]、[ZS 1,4 ，ZE 1,4 ]；
Next, the section merging module completes section merging according to steps 4.1 to 4.8, and the specific implementation process is as follows:
first
Step 4.1, setting the value of i as 1, and assigning F_1 as 0;
step 4.2, I takes a value of 1, I is judged to be not larger than I, then Q_1 is emptied and the step 4.3 is skipped;
step 4.3, setting the value of m as 4 (4 is the total number of negative value intervals before the combination of the D1 (t) intervals);
step 4.4, judging that m is not 1, and then jumping to step 4.5;
step 4.5, calculating D1 (t) as S1 in [ ZE1,4-1, ZS1,4] interval and D1 (t) as S2 in [ ZS1,4, ZE1,4] interval, and combining the three intervals of [ ZS1,4-1, ZE1,4-1], [ ZE1,4-1, ZS1,4], [ ZS1,4, ZE1,4] to obtain coverage area
[ZS1,4-1，ZE1,4-1]；
Step 4.6, assigning m-1 to m, namely setting m to 3, and jumping to the step 4.4;
step 4.4 (second round), determine that m is not 1, then jump to step 4.5;
step 4.5 (second round), calculating that the integral value of D1 (t) is S1 in the interval of [ ZE1,3-1, ZS1,3] and the integral value of D1 (t) is S2 in the interval of [ ZS1,3, ZE1,3], and because S1 is more than or equal to S2, writing the [ ZS1,3, ZE1,3] into the queue Q_1, and assigning F_1+1, namely 1 to F_1;
step 4.6 (second round), assigning m-1 to m, i.e. m etc. 2, and jumping to step 4.4;
step 4.4 (third round), determine that m is not 1, then jump to step 4.5;
step 4.5 (third round), calculating that the integral value of D1 (t) in the interval [ ZE1,2-1, ZS1,2] is S1, and the integral value of D1 (t) in the interval [ ZS1,2, ZE1,2] is S2, and because S1 is greater than or equal to S2, writing the [ ZS1,2, ZE1,2] into the queue Q_1, and assigning F_1+1, namely 2 to F_1;
step 4.6 (third round), assigning m-1 to m, i.e. m and the like 1, and jumping to step 4.4;
step 4.4 (fourth round), if m is 1, writing [ ZS1, ze1,1] into the queue q_1, assigning f_1+1 to f_1, i.e. assigning 3 to f_1, and jumping to step 4.7;
step 4.7, i+1 is assigned to i, i.e. i is equal to 2, F_i is assigned to 0, and then step 4.2 is skipped;
step 4.2, since I is equal to 1 and I is 2 at this time, it is determined that I is greater than I, and then step 4.8 is skipped;
step 4.8, output f_1, q_1, wherein f_1 takes a value of 3, represents that q_1 includes three negative intervals, namely [ ZS1, ze1,1], [ ZS1,2, ze1,2], [ ZS1,3, ze1,3], and it is noted that: [ ZS1,3, ZE1,3] is a new interval obtained by combining three intervals of [ ZS1,4-1, ZE1,4-1], [ ZE1,4-1, ZS1,4], [ ZS1,4, ZE1,4 ].
Finally, the energy storage node capacity calculation module calculates integral values in 3 intervals in 1 historical sample to obtain LACK1, LACK1,2 and LACK1,3, and supposing that the value of abs (LACK 1, 3) is maximum in the embodiment, the capacity requirement of the energy storage node of the distributed power supply system is set to abs (LACK 1, 3), so that the requirement that the grid-connected input instantaneous electric quantity is S (t) can be met in a period [ TS, TE ] after the distributed power supply station is based on the local maximum energy storage cache abs (LACK 1, 3).
According to the embodiment, based on the power generation sensing parameters of the distributed power supply station, the power generation time fitting curve of the station is established, then the power grid system is combined to meet the power output requirement of the distributed power supply, and the maximum power reserve gap for the power period stable output of the distributed power supply system is calculated, so that the capacity of the energy storage node of the distributed power supply system is determined, the maximum gap power is reserved in advance through the energy storage node, the goal that the distributed power supply system can stably supply corresponding power in a preset period is ensured, and finally the power integration with stable full power is provided for the power grid system, so that the traditional power supply system generates power according to needs, and waste is reduced.
The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
While the foregoing description of the embodiments of the present application has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the application, but rather, it is intended to cover all modifications or variations which may be resorted to without undue burden to those skilled in the art, having the benefit of the present application.
Claims (5)
1. The power grid energy storage node capacity pre-calculation method is characterized by comprising the following steps of:
step 1, acquiring historical sensing parameters from a database;
step 2, generating a time fitting curve C of various parameters based on historical sensing parameters ij (t), i represents historySample number, 1..and i., I is the total number of historical samples; j represents the number of the sensing parameter type, the value is 1, the number of the sensing parameters is equal to the total number of the sensing parameters, and then an instantaneous electric energy output curve Q is generated based on the fitting curve i (t)；
Step 3, in a preset period [ TS, TE ]]In, find D i Negative interval of (t) [ ZS ] i,k ，ZE i,k ]K represents a negative value interval number, and takes 1, and D constructed by taking historical samples i as K_i and K_i i The number of negative intervals of (t), said D i (t) is equal to Q i (t) -S (t), S (t) is the time period [ TS, TE ] of the distributed power station of the power grid system]An instantaneous power demand profile within;
step 4, carrying out interval combination on each history sample i to obtain F_i negative intervals after each history sample is combined;
step 5, calculating D in F_i intervals of each history sample in the I history samples i Integral value of (t) to obtain LACK i,f Abs (LACK) i,f ) The maximum value of (2) is set to the capacity requirement of the energy storage node of the distributed power system, where F takes on the values 1.
2. The power grid energy storage node capacity pre-calculation method according to claim 1, wherein:
in said step 1, at least one historical sample parameter for a time period [ TS, TE ] is obtained from a database.
3. The power grid energy storage node capacity pre-calculation method according to claim 1, wherein:
in the step 2, a time fitting curve C for fitting various parameters based on the history sensing parameters ij The specific method of (t) is as follows: according to the sensing data of the history sample i, obtaining the preset time period [ TS, TE ] of the history sample i through data fitting based on the sampling interval of the sensing data]Fitting curve C in ij (t)。
4. A method of pre-calculating the capacity of a power grid energy storage node according to claim 3, characterized in that:
in the step 2, an instantaneous electric energy output curve Q is generated based on the fitted curve i The specific method of (t) is as follows: according to the adopted power generation method, calculating to obtain an instantaneous power generation curve under the power generation method, and for the condition that one station adopts a plurality of methods to generate power, superposing the instantaneous power generation curves of all the power generation methods to obtain the total instantaneous power generation curve of the station.
5. A method of pre-calculating the capacity of an energy storage node of a power grid according to any one of claims 1-4, wherein:
in the step 4, the step D is performed on each history sample i i (t) performing interval combination to obtain F_i negative intervals after each history sample combination, wherein the specific method comprises the following steps:
step 4.1, i is assigned to 1, and F_i is assigned to 0;
step 4.2, judging whether I is larger than I, if so, jumping to step 4.8; if not, clearing Q_i and jumping to step 4.3;
step 4.3, setting the value of M as M_i, wherein M_i is D i (t) the total number of negative intervals before interval consolidation;
step 4.4, judging whether m is 1, if so, then [ ZS i,m ，ZE i,m ]In the write queue Q_i, F_i+1 is assigned to F_i, and the step 4.7 is skipped; if not, jumping to the step 4.5;
step 4.5, calculate D i (t) at [ ZE i,m-1 ，ZS i,m ]The integral value in the interval is S1, D i (t) in [ ZS ] i,m ，ZE i,m ]The integral value in the interval is S2, if S1 is smaller than S2, the [ ZS i,m-1 ，ZE i,m-1 ]、[ZE i,m-1 ，ZS i,m ]、[ZS i,m ，ZE i,m ]Coverage area interval [ ZS ] after combining three intervals i,m-1 ，ZE i,m-1 ]The method comprises the steps of carrying out a first treatment on the surface of the If S1 is greater than or equal to S2, then [ ZS i,m ，ZE i,m ]In the write queue Q_i, F_i+1 is assigned to F_i;
step 4.6, assigning m-1 to m, and jumping to step 4.4;
step 4.7, i+1 is assigned to i, F_i is assigned to 0, and the step 4.2 is skipped;
step 4.8, outputting f_i, q_i is a negative interval queue of each history sample I, wherein I takes on values of 1.
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