CN116388240B - Wind power plant energy storage control method based on PSO (power system on air) optimized double-layer cloud controller - Google Patents

Wind power plant energy storage control method based on PSO (power system on air) optimized double-layer cloud controller Download PDF

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CN116388240B
CN116388240B CN202310668923.3A CN202310668923A CN116388240B CN 116388240 B CN116388240 B CN 116388240B CN 202310668923 A CN202310668923 A CN 202310668923A CN 116388240 B CN116388240 B CN 116388240B
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energy storage
wind power
layer
cloud
cloud controller
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CN116388240A (en
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俞晓冬
张凯
王衡
李道清
郝玲艳
张金烽
于轩舟
姜钊
宋尚庆
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Qilu University of Technology
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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
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Eletrric Generators (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to the technical field of new energy and energy storage, and discloses a wind power plant energy storage control method based on a PSO (power system on demand) optimized double-layer cloud controller, which relates to the technical field of new energy and energy storage and is characterized by comprising the following steps: p1: adopt first layer cloud controller to control wind-powered electricity generation energy storage output, reduce the time of energy storage work in overcharge and overdischarge state, P2: and counting the wind power fluctuation corrected by the first-layer controller, taking the real-time wind power fluctuation as the input of the second-layer cloud controller, and carrying out second correction on the wind power according to the fluctuation requirement, wherein in order to reduce the influence of the second-layer control on the first-layer control, unnecessary energy storage output is prevented, a PSO algorithm is used for optimizing the post-part entropy value of the cloud controller, and the grid-connected requirement is met under the condition of ensuring the minimum secondary energy storage correction power. The non-linear fitting capacity of the invention is stronger, the working dead time of energy storage can be effectively reduced, and the service life of the energy storage is prolonged.

Description

Wind power plant energy storage control method based on PSO (power system on air) optimized double-layer cloud controller
Technical Field
The invention relates to the technical field of new energy and energy storage, in particular to a wind power plant energy storage control method based on a PSO (power system on air) optimized double-layer cloud controller.
Background
Currently, environmental problems are an important issue of global concern at the present stage. Wind power sources play a critical role in improving global environmental problems by virtue of their renewable and clean properties. In the grid connection process of the wind power energy, various influences can be generated on the power grid due to volatility and randomness, so that the energy storage equipment is often combined to enable the energy storage equipment to grid according to the estimated output, and the traditional energy storage output only directly compensates wind power according to the predicted error value. However, for high cost devices for storing energy, this approach tends to reduce the useful life of the energy storage device, thereby increasing the cost of using the energy storage. Thus, control methods for stored energy output are generated.
At present, there are various methods for stabilizing wind power, for example, a filter or wavelet packet decomposition is used to stabilize grid-connected wind power, and then the output of stored energy is reasonably controlled through fuzzy control, so as to reduce the working dead time; and secondly, controlling the output of the stored energy through double-layer fuzzy control, and enabling the wind power to achieve the fluctuation requirement and then be connected with the grid on the basis of ensuring that the state of the stored energy SOC is 0.2-0.8.
Because of the complexity of modeling an energy storage system, the most adopted method for energy storage control is to establish a control model based on a fuzzy controller, and the model establishes a control rule of energy storage equipment through natural rules and experience. However, this control method needs to establish a linear membership function, and mapping is achieved through the relationship between concepts. For wind power, which is a resource with ambiguity and randomness, the method cannot reasonably express the uncertainty of the concept, so that the nonlinear mapping cannot be approximated infinitely;
in order to solve the problems, a wind farm energy storage control method based on a double-layer cloud controller is provided. Firstly, a first layer of cloud controller is adopted to control wind power energy storage output, so that the time of energy storage working in an overcharging state and an overdischarging state is reduced. And then, counting the wind power fluctuation corrected by the first-layer controller, taking the real-time wind power fluctuation as the input of the second-layer cloud controller, and carrying out second correction on the wind power according to the fluctuation requirement, wherein in order to reduce the influence of the second-layer control on the first-layer control, unnecessary energy storage output is prevented, a PSO algorithm is used for optimizing the back-piece entropy value of the cloud controller, and the grid-connected requirement is met under the condition of ensuring the minimum secondary energy storage correction power.
Disclosure of Invention
The invention aims to provide a wind power plant energy storage control method based on a PSO (power system operation) optimized double-layer cloud controller.
The invention adopts the following technical scheme to realize the aim of the invention:
the wind power plant energy storage control method based on the PSO optimization double-layer cloud controller is characterized by comprising the following steps of:
p1: the first layer cloud controller is adopted to control wind power energy storage output, so that the time of energy storage working in an overcharging and overdischarging state is reduced;
p2: and counting the wind power fluctuation corrected by the first-layer controller, taking the real-time wind power fluctuation as the input of the second-layer cloud controller, and carrying out second correction on the wind power according to the fluctuation requirement, wherein in order to reduce the influence of the second-layer control on the first-layer control, unnecessary energy storage output is prevented, a PSO algorithm is used for optimizing the post-part entropy value of the cloud controller, and the grid-connected requirement is met under the condition of ensuring the minimum secondary energy storage correction power.
Specifically: the specific steps of the P1 are as follows:
step 1: according to the actual power of the wind power plantP a And predicted powerP r Calculating the prediction error of wind powerP e ；
Wherein the prediction errorP e Namely, the initial actual output of the stored energy is calculated according to the relation between the stored energy SOC and the actual output, and the SOC variation value and the SOC of the stored energy at the current moment are expressed as；
Step 2: for the initial actual force obtained in step 1P e The SOC state of energy storage at the current moment t and the initial actual output of energy storage of the wind power plant are combinedP e As the front piece input of the cloud controller, outputting a stabilizing coefficient K according to a cloud control rule table 1 Correcting the initial actual output of the stored energy to obtain the first corrected output of the stored energyP w Ensure that the stored SOC is at SOC down To SOC up Between them;
wherein ,SOCdown Lower limit of energy storage state of charge (SOC) specified for guaranteeing service life of energy storage up An upper limit on the state of charge of the stored energy specified to ensure the useful life of the stored energy;
step 3: first corrected output for stored energy obtained in step 2P w With the actual power of the wind farmP a Adding to obtain the wind power after the first layer correctionP h ；
Step 4: for the corrected wind power obtained in step 3P h Calculating real-time wind power fluctuationP f ；
The specific steps of the P2 are as follows:
step 5: fluctuation of wind power obtained in P1P f As the front-end input of the second-layer cloud controller, according to the fluctuation requirement of the grid connection of the wind power plant, a PSO algorithm is utilized to establish a cloud control rule table, and a stabilizing coefficient K is output according to the control rule table 2 Obtaining the second corrected output of the stored energyP w' ；
Step 6: for the stored energy correction output obtained in step 1 of P2P w' With the actual power of the wind farmP a Adding to obtain final grid-connected wind powerP h' 。
Specifically, the prediction error in step 1P e The calculation formula is as follows:
（1）
at the current timetThe SOC variation value and the SOC of the stored energy are as follows:
（2）
（3）
wherein ,Ecas the value of the capacity of the stored energy,for the sampling period +.>For the stored SOC change value at the current time t +.>And predicting an error for the current moment.
Specifically, the cloud controller described in step 2 is constructed by cloud models of different parameters, whereinExThe desire to characterize the cloud model,Enthe entropy of the cloud model is characterized,Heand (5) representing the super entropy of the cloud model. The membership functions of the cloud model are:
（4）
（5）
wherein ,xas a conceptual value of the input,Norm(En,He 2 ) To generate toEnIn the hope that,He 2 is a random number of variance.
Specifically, for the establishment of the cloud control rule in step 2, when the SOC of the stored energy is lower than the set threshold, but the stored energy is required to continue outputting energy, the cloud controller outputsK 1 Correcting the stored energy, and reducing the output of the stored energy; otherwise, when the SOC of the stored energy exceeds the set value, the stored energy is needed to be stored, the cloud controller outputsK 1 And the energy storage output is corrected, so that the input of energy storage is reduced. Using cloud controlThe rule table of the device carries out nonlinear mapping and calculationK 1 The formula of (2) is:
（6）
wherein ,（x1,µ 1 )、(x2,µ 2 ) Is SOC andP e the outermost two cloud concept values in the activated cloud controller middleware and membership degrees thereof.
First corrected output for stored energyP w The calculation formula is as follows:
（7）
wherein ,P b is the rated power of the energy storage device.
Specifically, the wind power after the first layer correction in step 3P h The calculation formula is as follows:
（8）
specifically, in step 4, the wind power fluctuates at the current timeP f The calculation formula of (2) is as follows:
（9）
wherein ,is thattWind power corrected in the first layer of time, < >>Is thattThe wind power corrected by the first layer at the previous moment.
Specifically, the wind power fluctuation requirement in step 5 is not more than the installed value1/3 of the capacity. When the wind power exceeds the fluctuation requirement, the cloud controller outputsK 2 And correcting the energy storage output.
Specifically, the optimization objective function of the PSO algorithm described in step 5 is:
（10）
wherein ,fas a function of the object to be processed,Enas the entropy value of the back-piece,P c (En) For the sum of the absolute values of the stored energy capacity during the correction of the second-tier controller,R(En) And the number of samples of wind power fluctuation which does not meet the grid-connected condition is set.
The constraint function is:
（11）
calculation ofP c The formula of (2) is:
wherein ,representing from the initial time to the final timetIs added to the sum of the absolute values of the stored energy correction forces.
Calculation ofK 2 The formula of (2) is:
（12）
（13）
（14）
（15）
Ex n 、En n and (3) withHe n The parameters of the front cloud are respectively the parameters of the front cloud,Ex' n 、En' n and (3) withHe' n As a parameter of the back-piece cloud,Norm(a,b) To generate toaIn the hope that,bis a random number of the variance of the values,µ n is a conceptual valuex n Corresponding membership degree;En' nn to take the following measuresEn' n In the hope that,He n '2 random numbers that are variances;En nn to take the following measuresEn n In the hope that,He n 2 is a random number of variance.
Calculation ofR(En) The formula of (2) is:
P ic for the installed capacity of the container,after correction for the second layer controllertGrid-connected wind power at moment>After correction for the second layer controllertGrid-connected wind power at the previous moment, +.>After correction for the second layer controllertWind power fluctuation at the moment.
Specifically, the output force is secondarily corrected in step 5P w 'The calculation formula is as follows:
（16）
specifically, in step 6, final grid-connected wind power is obtainedP h 'The calculation formula is as follows:
（17）
compared with the prior art, the invention has the advantages and positive effects that: (1) Compared with the prior art, the cloud controller uses the cloud model to represent different concepts, has stronger nonlinear fitting capability, can effectively reduce the working dead time of energy storage, and prolongs the service life of the energy storage.
(2) And the PSO algorithm is adopted to optimize and select the back part entropy of the second-layer cloud controller, so that the redundant output of energy storage is reduced and the influence on the control effect of the first-layer cloud controller is reduced on the premise of ensuring the stabilizing effect.
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. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
fig. 1 is a control process of a first layer cloud controller according to an embodiment of the present invention.
Fig. 2 is a control process of a second layer cloud controller according to an embodiment of the present invention.
FIG. 3 is a first layer cloud control membership function of an embodiment of the present invention.
FIG. 4 is a second layer cloud control membership function of an embodiment of the present invention.
FIG. 5 is a flowchart of a PSO optimization algorithm in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions of 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 apparent that the described embodiment is only one embodiment of the present invention, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
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 example embodiments in accordance with the present application. 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.
The specific embodiments of the present invention are as follows:
the embodiment provides a wind power plant energy storage control method based on a PSO (power system on demand) optimized double-layer cloud controller, which comprises the following steps:
first obtaining the actual power of the wind farmP a And predicting powerP r Predicted powerP r Wind power prediction technology (such as extreme learning machine algorithm or IGA-ANFIS prediction model) is adopted to pre-predict wind powerMeasuring to obtain wind power predicted power at the current time t, then constructing a first layer of cloud controller to control wind power energy storage output, and reducing time of energy storage working in overshoot and over-discharge states, wherein the method comprises the following specific steps:
step 1: according to the actual power of the wind power plantP a And predicted powerP r Calculating the prediction error of wind powerP e Prediction errorP e The calculation formula is formula (1):wherein the prediction errorP e Namely, the initial actual output of the stored energy is calculated according to the relation between the stored energy SOC and the actual output, and the SOC variation value and the SOC of the stored energy at the current moment t are expressed asAt the current timetThe SOC change value of the stored energy is represented by formula (2): />The SOC of the stored energy at the current time t is represented by formula (3): />；
Step 2, for the energy storage initial output obtained in step 1P e The SOC state of energy storage at the current moment and the initial actual output of energy storage of the wind power plant are combinedP e As the front-piece input of the cloud controller, outputting the stabilizing coefficient according to the cloud control rule tableK 1 Correcting the initial actual output of the stored energy to obtain the first corrected output of the stored energyP w Ensure that the stored SOC is at SOC down To SOC up Between SOC (State of charge) down 、SOC up The values of (2) are 0.2 and 0.8;
the invention reveals the control process of the first layer cloud controller in fig. 1.
The cloud controller in the step 2 is constructed by cloud models with different parameters, whereinExThe desire to characterize the cloud model,Enthe entropy of the cloud model is characterized,Heand (5) representing the super entropy of the cloud model. The membership functions of the cloud model are represented by the formulas (4) and (5):，/>，xas a conceptual value of the input,Norm(En,He 2 ) To generate toEnIn the hope that,He 2 random numbers that are variances;
the cloud control rule in the step 2 is established according to the following principle:
when the SOC of the stored energy is small but the stored energy is needed to continue outputting energy, the cloud controller outputsK 1 Correcting the stored energy, and reducing the output of the stored energy;
when the SOC of the stored energy is large, the stored energy is needed to continue to store the energy, and the cloud controller outputsK 1 And the energy storage output is corrected, so that the input of energy storage is reduced.
Wherein, the control rule table in the above steps is:
table 1: control rule table
In the above table, { VS, S, B, VB } is used to represent membership functions of small, large and large SOC, and { L, LM, M, MH, H } is used to represent membership functions of small, large and large SOCP e The concept of increasing from negative to positive, { NB, N, Z, P, PB } isK 1 The corresponding output concept has the super entropy of 0.01, and the specific membership function is shown in figure 3.
Mapping non-linearities using a rule table of a cloud controller, computingK 1 Formula (6):；
wherein ,（x1,µ 1 )、(x2,µ 2 ) Is SOC andP e two cloud concept values at the outermost side in the activated cloud controller back part and membership degrees thereof;
first corrected output of stored energy according to the above ruleP w The calculation formula is formula (7), wherein P b Is the rated power of the energy storage device.
Step 3: first corrected output for stored energy obtained in step 2P w With the actual power of the wind farmP a Adding to obtain the wind power after the first layer correctionP h ，P h The calculation formula is formula (8):。
step 4: for the corrected wind power obtained in step 3P h Calculating real-time wind power fluctuationP f The calculation formula is formula (9):。
obtaining real-time wind power fluctuation corrected by the controller of the first layer through the steps 1, 2, 3 and 4P f Real-time wind power fluctuationP f As the front piece input of the second layer cloud controller, constructing the second layer cloud controller to control wind power energy storage, and obtaining the second correction output of the energy storage according to the fluctuation requirement of grid connection of the wind power plantP w 'Performing secondary correction on wind power to obtain secondary correction output of energy storageP w 'In order to reduce the influence of the second-layer control on the first-layer control and prevent redundant energy storage output, a PSO algorithm is used for optimizing the entropy value of a back-piece of the cloud controller, and the grid-connected requirement is met under the condition of ensuring the minimum correction power of secondary energy storage, and the method comprises the following specific steps ofThe method comprises the following steps:
step 5: fluctuating wind powerP f As the front-end input of the second-layer cloud controller, according to the fluctuation requirement of the grid connection of the wind power plant, a PSO algorithm is utilized to establish a cloud control rule table, and the stabilizing coefficient is output according to the control rule tableK 2 Obtaining the second corrected output of the stored energyP w '，P w 'The calculation formula is formula (16):, wherein ,P f and correcting the real-time wind power fluctuation for the controller of the first layer. The present invention reveals the control process of the second layer cloud controller in fig. 2.
In step 5, the wind power fluctuation requirement is not more than 1/3 of the installed capacity. When the wind power exceeds the fluctuation requirement, the cloud controller outputsK 2 And correcting the energy storage output.
The control rule of the cloud controller in step 5 is as follows:
table 2: control rule table of cloud controller
Wherein { VS, S, B, VB } representsP f From negative to positive, { PS, ZE, NS } isK 2 The corresponding output concept has super entropy of 0.01, and specific membership functions are shown in fig. 4.
Specifically, the optimization objective function of the PSO algorithm described in step 5 is equation (10):, wherein ,fas a function of the object to be processed,Enas the entropy value of the back-piece,P c for the sum of the absolute values of the stored energy capacity during the correction of the second-tier controller,Rfor the number of samples of wind power fluctuation which does not meet the grid-connected condition, a specific optimization flow is shown in fig. 5.
Constraint function isFormula (11):。
calculation ofP c The formula of (2) is:；
wherein ,representing from the initial time to the final timetIs added to the sum of the absolute values of the stored energy correction forces.
Calculation ofK 2 Formula (12):, wherein ,Ex n 、En n and (3) withHe n The parameters of the front cloud are respectively the parameters of the front cloud,Ex' n 、En' n and (3) withHe' n As a parameter of the back-piece cloud,Norm(En n ,He n ) To generate toEn n In the hope that,He n is a random number of the variance of the values,Norm(En' n ,He' n ) To generate +.>For hope of->Random number as varianceµ n Is a conceptual valuex n Corresponding membership degree.
Step 6: correcting the output force of the stored energy obtained in the stepsP w 'With the actual power of the wind farmP a Adding to obtain final grid-connected wind powerP h '，P h 'The formula is (17)。
The above disclosure is merely illustrative of specific embodiments of the present invention, but the present invention is not limited thereto, and any variations that can be considered by those skilled in the art should fall within the scope of the present invention.

Claims (8)

1. The wind power plant energy storage control method based on the PSO optimization double-layer cloud controller is characterized by comprising the following steps of:
p1: the first layer cloud controller is adopted to control wind power energy storage output, so that the time of energy storage working in an overcharging and overdischarging state is reduced;
p2: counting the wind power fluctuation corrected by the first-layer controller, taking the real-time wind power fluctuation as the input of the second-layer cloud controller, and carrying out second correction on the wind power according to the fluctuation requirement, wherein in order to reduce the influence of the second-layer control on the first-layer control, unnecessary energy storage output is prevented, a PSO algorithm is used for optimizing the post-part entropy value of the cloud controller, and the grid-connected requirement is met under the condition of ensuring the minimum secondary energy storage correction power;
the specific steps of the P1 are as follows:
step 1: according to the actual power of the wind power plantP a And predicted powerP r Calculating the prediction error of wind powerP e ；
Wherein the prediction errorP e Namely, the initial actual output of the stored energy is calculated according to the relation between the stored energy SOC and the actual output, and the SOC variation value and the SOC of the stored energy at the current moment are representedIs that；
Step 2: for the initial actual force obtained in step 1P e The SOC state of energy storage at the current moment t and the initial actual output of energy storage of the wind power plant are combinedP e As the front-piece input of the cloud controller, outputting the stabilizing coefficient according to the cloud control rule tableK 1 Correcting the initial actual output of the stored energy to obtain the first corrected output of the stored energyP w Ensure that the stored SOC is at SOC down To SOC up Between them;
wherein ,SOCdown Lower limit of energy storage state of charge (SOC) specified for guaranteeing service life of energy storage up An upper limit on the state of charge of the stored energy specified to ensure the useful life of the stored energy;
step 3: first corrected output for stored energy obtained in step 2P w With the actual power of the wind farmP a Adding to obtain the wind power after the first layer correctionP h ；
Step 4: for the corrected wind power obtained in step 3P h Calculating real-time wind power fluctuationP f ；
The specific steps of the P2 are as follows:
step 5: fluctuation of wind power obtained in P1P f As the front-end input of the second-layer cloud controller, according to the fluctuation requirement of the grid connection of the wind power plant, a PSO algorithm is utilized to establish a cloud control rule table, and the stabilizing coefficient is output according to the control rule tableK 2 Obtaining the second corrected output of the stored energyP w '；
Step 6: for the stored energy correction output obtained in step 1 of P2P w 'With the actual power of the wind farmP a Adding to obtain final grid-connected wind powerP h '；
Specifically, the wind power in step 5The rate fluctuation requirement is not more than 1/3 of the installed capacity; when the wind power exceeds the fluctuation requirement, the cloud controller outputsK 2 Correcting the energy storage output, specifically, the optimization objective function of the PSO algorithm in the step 5 is as follows:
（10）
wherein ,fas a function of the object to be processed,Enas the entropy value of the back-piece,P c (En) For the sum of the absolute values of the stored energy capacity during the correction of the second-tier controller,R(En) The method comprises the steps that the number of samples of wind power fluctuation which does not meet grid connection conditions is counted;
the constraint function is:
（11）
calculation ofP c The formula of (2) is:
wherein ,representing from the initial time to the final timetThe sum of the absolute values of the stored energy correction output forces;
calculation ofK 2 The formula of (2) is:
（12）
（13）
（14）
（15）
Ex n 、En n and (3) withHe n The parameters of the front cloud are respectively the parameters of the front cloud,Ex' n 、En' n and (3) withHe' n As a parameter of the back-piece cloud,Norm(a,b) To generate toaIn the hope that,bis a random number of the variance of the values,µ n is a conceptual valuex n The corresponding degree of membership is determined,En' nn to take the following measuresEn' n In the hope that,He n '2 random numbers that are variances;En nn to take the following measuresEn n In the hope that,He n 2 random numbers that are variances;
specifically, the secondary correction of the output force in step 5 of P2P w 'The calculation formula is as follows:
（16）
calculation ofR(En) The formula of (2) is:
for the installed capacity>After correction for the second layer controllertGrid-connected wind power at moment>After correction for the second layer controllertGrid-connected wind power at the previous moment, +.>After correction for the second layer controllertThe wind power at the moment fluctuates,P h 'and finally, grid-connected wind power is obtained.
2. The PSO-optimized double-layer cloud controller-based wind farm energy storage control method as claimed in claim 1, wherein the method is characterized by comprising the following steps: prediction error described in step 1P e The calculation formula is as follows:
(1)。
3. the PSO-optimized double-layer cloud controller-based wind farm energy storage control method as claimed in claim 1, wherein the method is characterized by comprising the following steps: in the step 2, the SOC variation value and SOC of the stored energy at the current time t are:
(2)
(3)
wherein ,Ecas the value of the capacity of the stored energy,for the sampling period +.>For the stored SOC change value at the current time t +.>And predicting an error for the current moment.
4. The PSO-optimized double-layer cloud controller-based wind farm energy storage control method as claimed in claim 1, wherein the method is characterized by comprising the following steps: wherein the cloud controller in the step 2 is constructed by cloud models with different parameters, whereinExThe desire to characterize the cloud model,Enthe entropy of the cloud model is characterized,Hecharacterizing the super entropy of the cloud model;
the membership function of the cloud model is as follows:
（4）
（5）
wherein ,xas a conceptual value of the input,Norm(En, He 2 ) To generate toEnIn the hope that,He 2 and (3) a random number of variance, wherein [ mu ] is a membership degree corresponding to the conceptual value x.
5. The PSO-optimized double-layer cloud controller-based wind farm energy storage control method as claimed in claim 1, wherein the method is characterized by comprising the following steps: specifically, for the establishment of the cloud control rule in step 2, when storing energy, the following principle is adoptedThe SOC of (2) is lower than the set threshold, but when the stored energy is needed to continue outputting energy, the cloud controller outputsK 1 Correcting the stored energy, and reducing the output of the stored energy; otherwise, when the SOC of the stored energy exceeds the set value, the stored energy is needed to be stored, the cloud controller outputsK 1 Correcting the energy storage output to reduce the input of energy storage; mapping non-linearities using a rule table of a cloud controller, computingK 1 The formula of (2) is:
（6）
wherein ,（x1, µ 1 )、(x2, µ 2 ) Is SOC andP e two cloud concept values at the outermost side in the activated cloud controller back part and membership degrees thereof;
first corrected output for stored energyP w The calculation formula is as follows:
（7）
wherein ,P b is the rated power of the energy storage device.
6. The PSO-optimized double-layer cloud controller-based wind farm energy storage control method as claimed in claim 1, wherein the method is characterized by comprising the following steps: specifically, the wind power after the first layer correction in step 3P h The calculation formula is as follows:
（8）。
7. the PSO-optimized double-layer cloud controller-based wind farm energy storage control method as claimed in claim 1, wherein the method is characterized by comprising the following steps: specifically, the current time wind in step 4Fluctuation of electric powerP f The calculation formula of (2) is as follows:
（9）
in the formula ,is the wind power after the first layer correction at the current moment,/>Is thattThe wind power corrected by the first layer at the previous moment.
8. The PSO-optimized double-layer cloud controller-based wind farm energy storage control method as claimed in claim 1, wherein the method is characterized by comprising the following steps: specifically, the final grid-connected wind power in the step P2 and the step 6P h 'The calculation formula is as follows:
（17）。
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108233430A (en) * 2018-02-05 2018-06-29 三峡大学 A kind of alternating current-direct current mixing microgrid optimization method of meter and system energy fluctuation
US10958211B1 (en) * 2019-10-04 2021-03-23 The Florida International University Board Of Trustees Systems and methods for power management
CN115912342A (en) * 2022-11-21 2023-04-04 中原工学院 Regional flexible load low-carbon scheduling method based on cloud model

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108233430A (en) * 2018-02-05 2018-06-29 三峡大学 A kind of alternating current-direct current mixing microgrid optimization method of meter and system energy fluctuation
US10958211B1 (en) * 2019-10-04 2021-03-23 The Florida International University Board Of Trustees Systems and methods for power management
CN115912342A (en) * 2022-11-21 2023-04-04 中原工学院 Regional flexible load low-carbon scheduling method based on cloud model

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
风水气互补发电优化的云模型自适应粒子群优化算法;吴杰康;熊焰;;中国电机工程学报(第S1期);全文 *

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