CN113904352A - Power distribution optimization method and terminal for hybrid energy storage system - Google Patents

Power distribution optimization method and terminal for hybrid energy storage system Download PDF

Info

Publication number
CN113904352A
CN113904352A CN202111182218.XA CN202111182218A CN113904352A CN 113904352 A CN113904352 A CN 113904352A CN 202111182218 A CN202111182218 A CN 202111182218A CN 113904352 A CN113904352 A CN 113904352A
Authority
CN
China
Prior art keywords
power
energy storage
type energy
unbalanced
frequency
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
Application number
CN202111182218.XA
Other languages
Chinese (zh)
Inventor
刘文亮
熊军
张颖
彭晖
洪汛
廖晔
李迎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Fujian Electric Power Co Ltd
Xiamen Power Supply Co of State Grid Fujian Electric Power Co Ltd
Original Assignee
State Grid Fujian Electric Power Co Ltd
Xiamen Power Supply Co of State Grid Fujian Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by State Grid Fujian Electric Power Co Ltd, Xiamen Power Supply Co of State Grid Fujian Electric Power Co Ltd filed Critical State Grid Fujian Electric Power Co Ltd
Priority to CN202111182218.XA priority Critical patent/CN113904352A/en
Publication of CN113904352A publication Critical patent/CN113904352A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
    • H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • 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
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • 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
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/60—Arrangements for transfer of electric power between AC networks or generators via a high voltage DC link [HVCD]

Abstract

The invention discloses a power distribution optimization method and a terminal of a hybrid energy storage system, wherein the charge and discharge power of energy type energy storage and the charge and discharge power of power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in a micro-grid system; decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to finish initial power distribution; and performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power respectively to obtain the adjusted final reference power. The invention realizes the optimal distribution of the hybrid energy storage power by an empirical mode decomposition method and a fuzzy controller, so that the power distribution is more accurate and reliable.

Description

Power distribution optimization method and terminal for hybrid energy storage system
Technical Field
The invention relates to the technical field of hybrid energy storage, in particular to a power distribution optimization method and a terminal for a hybrid energy storage system.
Background
In recent years, direct current micro-grids have received wide attention due to the characteristics of high operating efficiency, high reliability and the like. In order to ensure the power balance in the direct-current microgrid, the hybrid energy storage system is an important technical means, complementary advantages of different types of energy storage can be played, and power fluctuation caused by new energy output is stabilized.
According to the characteristic of energy storage, a hybrid energy storage system is formed by the power type energy storage equipment and the energy type energy storage equipment, the characteristics of high power density of the power type energy storage equipment and high energy density of the energy type energy storage equipment are combined, advantage complementation is achieved, and the hybrid energy storage system can play a more effective role in stabilizing the power fluctuation layer. Therefore, the hybrid energy storage is an indispensable key device for the operation of an independent micro-grid, the grid-connected power generation of renewable energy sources, the application of a distributed power supply and the micro-grid, and has the functions of stabilizing power fluctuation, clipping peaks and filling valleys, fault ride-through and the like, so that the hybrid energy storage has very important significance in the power generation of the renewable energy sources.
In order to exert the technical advantages of different energy storage devices in the hybrid energy storage system, unbalanced power in the direct current microgrid needs to be reasonably distributed to different energy storage units. The traditional technology can only realize the distribution of unbalanced power according to a constant proportion, and the unbalanced power is difficult to be distributed to different types of energy storage systems according to frequency characteristics, so that the optimal performance of the hybrid energy storage system cannot be exerted.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a power distribution optimization method and a terminal of a hybrid energy storage system are provided to exert the best performance of the hybrid energy storage system.
In order to solve the technical problems, the invention adopts the technical scheme that:
the power distribution optimization method of the hybrid energy storage system comprises the following steps:
s1, the charge and discharge power of the energy type energy storage and the charge and discharge power of the power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in the microgrid system;
s2, decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to complete initial power distribution;
and S3, performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power to obtain the adjusted final reference power.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
hybrid energy storage system power distribution optimization terminal, including memory, processor and computer program stored on the memory and operable on the processor, the processor when executing the computer program realizes the following steps:
s1, the charge and discharge power of the energy type energy storage and the charge and discharge power of the power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in the microgrid system;
s2, decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to complete initial power distribution;
and S3, performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power to obtain the adjusted final reference power.
The invention has the beneficial effects that: the hybrid energy storage system power distribution optimization method and the terminal apply an empirical mode decomposition method to frequency decomposition of unbalanced signals in a micro-grid system to obtain a relatively accurate primary power distribution instruction, and then a fuzzy controller is designed to carry out secondary optimization on the primary power instruction of the hybrid energy storage system aiming at the problem that SOC (system on chip) line crossing is easily caused by high power density of power type energy storage, so that optimal distribution of hybrid energy storage power is realized. Compared with the traditional hybrid energy storage power distribution method, the method has the advantages that the unbalanced power of the hybrid energy storage system is distributed more accurately and reliably, so that the optimal performance of the hybrid energy storage system is exerted, and the power fluctuation of the hybrid energy storage system is quickly stabilized.
Drawings
Fig. 1 is a schematic flow chart of a power allocation optimization method for a hybrid energy storage system according to an embodiment of the present invention;
FIG. 2 is a block diagram illustrating initial power allocation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a control strategy of a fuzzy controller according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of quadratic optimization according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a hybrid energy storage system power distribution optimization terminal according to an embodiment of the present invention.
Description of reference numerals:
1. a hybrid energy storage system power distribution optimizing terminal; 2. a processor; 3. a memory.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1 to 4, the method for optimizing power distribution of a hybrid energy storage system includes the steps of:
s1, the charge and discharge power of the energy type energy storage and the charge and discharge power of the power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in the microgrid system;
s2, decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to complete initial power distribution;
and S3, performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power to obtain the adjusted final reference power.
From the above description, the beneficial effects of the present invention are: an empirical mode decomposition method is applied to frequency decomposition of unbalanced signals in a micro-grid system to obtain a relatively accurate primary power distribution instruction, and then a fuzzy controller is designed to carry out secondary optimization on the primary power instruction of the hybrid energy storage system aiming at the problem that SOC (system on chip) line crossing is easily caused by high power density of power type energy storage, so that optimal distribution of hybrid energy storage power is realized. Compared with the traditional hybrid energy storage power distribution method, the method has the advantages that the unbalanced power of the hybrid energy storage system is distributed more accurately and reliably, so that the optimal performance of the hybrid energy storage system is exerted, and the power fluctuation of the hybrid energy storage system is quickly stabilized.
Further, the step S2 specifically includes the following steps:
s21, adding white noise c (t) to the original unbalanced power signal l (t) to obtain:
Hi(t)＝l(t)+ci(t),i＝1,2,…m；
in the formula, Hi(t) is the unbalanced power signal after the white noise is added for the ith time, ci(t) is mutually independent white noise, and m is the empirical mode decomposition frequency;
s22, decomposing the unbalanced power signal added with the white noise by an empirical mode decomposition method to obtain n IMF components f decomposed for the ith time1i(t),f2i(t),…,fni(t) and residual G from the i-th decompositioni(t), namely:
Figure BDA0003297751100000041
in the formula fki(t) is the kth IMF component of the ith decomposition;
after m-times empirical mode decomposition, m-component decomposition results are obtained as follows:
Figure BDA0003297751100000042
after m-times empirical mode decomposition, the mean value of all the IMF components and residuals obtained is:
Figure BDA0003297751100000043
obtaining an unbalanced power signal H (t) after empirical mode decomposition as follows:
Figure BDA0003297751100000044
s23, decomposing the f after empirical mode decompositionk(t) obtaining a transformation result x after Hilbert transformationk(t) said fk(t) is as follows:
Figure BDA0003297751100000045
definition resolution signal rk(t)＝fk(t)+jxk(t), then:
Figure BDA0003297751100000046
Figure BDA0003297751100000051
Figure BDA0003297751100000052
Figure BDA0003297751100000053
in the formula, Ak(t) is rk(t) instantaneous amplitude; thetak(t) is rkInstantaneous phase of (t), sk(t) is rk(t) instantaneous frequency;
s24, calculating the energy value of the aliasing part by adopting a mode of summing the absolute values of adjacent IMF components as follows:
Figure BDA0003297751100000054
in the formula (f)k(ti) For the instant frequency s of the ith time atk(t) less than the dividing frequency scPower of fk+1(tj) For the jth Δ t time sk(t) is greater than scΔ t is the sampling time interval;
given said sampling time interval Δ t, the instantaneous frequency obtained when E is at a minimum is scIf frequency s is dividedcIs located between the c, c +1 IMF, then f will be1(t)、f2(t)、…、fc(t) as a high-frequency fluctuation component, and using the high-frequency fluctuation component as a primary reference power instruction P of the power type energy storagew,ck(ii) a Will f isc(t)、fc+1(t)、…、fn(t) as a low-frequency fluctuation component, and using the low-frequency fluctuation component as a primary reference power command P of the energy type energy storagee,ckNamely:
Pw,ck(t)＝f1(t)、f2(t)、…、fc(t)；
Pe,ck(t)＝fc+1(t)、fc+2(t)、…、fn(t)。
from the above description, since the stable unbalanced power of the interference system is nonlinear, an improved fly-through empirical mode decomposition method is obtained after hilbert transform to perform adaptive decomposition on the unbalanced power, so that the obtained initial power allocation command is more accurate, and then the energy value of the aliasing part is calculated by summing the absolute values of adjacent IMF components to determine the frequency division, thereby reducing the aliasing phenomenon between the frequencies, so that the initial frequency allocation is more accurate and reliable.
Further, the step S3 specifically includes the following steps:
s31, and storing the state of charge SOC of the power type energy at the time tw(T) and unbalanced power delta P (T) in the hybrid energy storage system are used as input signals of a fuzzy controller, and output signals of the fuzzy controller are charge and discharge regulating coefficients T of power type energy storagew；
S32 state of charge (SOC) of current power type energy storagew(t) in a predetermined intermediate value range, when the unbalanced power Δ P (t)<0 and SOCw(t) in a preset high interval or when the unbalance power Δ P (t)>0 and SOCw(t) in a preset low value interval, the power type energy storage and the energy type energy storage are charged and discharged according to the current reference power;
when the unbalanced power is Δ P (t)<0 and SOCw(t) in a preset low interval or when the unbalance power Δ P (t)>0 and SOCw(T) in the preset high value interval, controlling the charge-discharge regulation coefficient TwDecreasing, then the reference power of the power type energy storageReduced, and reduced differential power is borne by the energy storage;
s33, the reference power of the power type energy storage and the reference power of the energy type energy storage after being adjusted by the fuzzy controller are respectively as follows:
Pw,ck(t+1)＝Tw(t)Pw,ck(t)；
Pe,ck(t+1)＝ΔP(t+1)-Pw,ck(t+1)。
from the above description, it can be known that the power type energy storage such as super capacitor has the characteristics of fast response speed and long cycle service life, and the energy type energy storage such as storage battery is limited by the number of charging and discharging times, and the service life of the storage battery can be reduced by frequent use. Therefore, in the power distribution process of the hybrid energy storage system, the reference power instruction of the power type energy storage system and the energy type energy storage system is adjusted in a coordinated mode, the possibility that the power type energy storage system reaches the upper limit and the lower limit is reduced, the optimal performance of the hybrid energy storage system is exerted, the loss of the energy type energy storage system is reduced, and the service life of the hybrid energy storage system is prolonged.
Referring to fig. 5, the hybrid energy storage system power distribution optimization terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the following steps:
s1, the charge and discharge power of the energy type energy storage and the charge and discharge power of the power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in the microgrid system;
s2, decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to complete initial power distribution;
and S3, performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power to obtain the adjusted final reference power.
From the above description, the beneficial effects of the present invention are: an empirical mode decomposition method is applied to frequency decomposition of unbalanced signals in a micro-grid system to obtain a relatively accurate primary power distribution instruction, and then a fuzzy controller is designed to carry out secondary optimization on the primary power instruction of the hybrid energy storage system aiming at the problem that SOC (system on chip) line crossing is easily caused by high power density of power type energy storage, so that optimal distribution of hybrid energy storage power is realized. Compared with the traditional hybrid energy storage power distribution method, the method has the advantages that the unbalanced power of the hybrid energy storage system is distributed more accurately and reliably, so that the optimal performance of the hybrid energy storage system is exerted, and the power fluctuation of the hybrid energy storage system is quickly stabilized.
Further, the step S2 specifically includes the following steps:
s21, adding white noise c (t) to the original unbalanced power signal l (t) to obtain:
Hi(t)＝l(t)+ci(t),i＝1,2,…m；
in the formula, Hi(t) is the unbalanced power signal after the white noise is added for the ith time, ci(t) is mutually independent white noise, and m is the empirical mode decomposition frequency;
s22, decomposing the unbalanced power signal added with the white noise by an empirical mode decomposition method to obtain n IMF components f decomposed for the ith time1i(t),f2i(t),…,fni(t) and residual G from the i-th decompositioni(t), namely:
Figure BDA0003297751100000071
in the formula fki(t) is the kth IMF component of the ith decomposition;
after m-times empirical mode decomposition, m-component decomposition results are obtained as follows:
Figure BDA0003297751100000072
after m-times empirical mode decomposition, the mean value of all the IMF components and residuals obtained is:
Figure BDA0003297751100000081
obtaining an unbalanced power signal H (t) after empirical mode decomposition as follows:
Figure BDA0003297751100000082
s23, decomposing the f after empirical mode decompositionk(t) obtaining a transformation result x after Hilbert transformationk(t), namely:
Figure BDA0003297751100000083
definition resolution signal rk(t)＝fk(t)+jxk(t), then:
Figure BDA0003297751100000084
Figure BDA0003297751100000085
Figure BDA0003297751100000086
Figure BDA0003297751100000087
in the formula, Ak(t) is rk(t) instantaneous amplitude; thetak(t) is rkInstantaneous phase of (t), sk(t) is rk(t) instantaneous frequency;
s24, calculating the energy value of the aliasing part by adopting a mode of summing the absolute values of adjacent IMF components as follows:
Figure BDA0003297751100000088
in the formula (f)k(ti) For the instant frequency s of the ith time atk(t) less than the dividing frequency scPower of fk+1(tj) For the jth Δ t time sk(t) is greater than scΔ t is the sampling time interval;
given said sampling time interval Δ t, the instantaneous frequency obtained when E is at a minimum is scIf frequency s is dividedcIs located between the c, c +1 IMF, then f will be1(t)、f2(t)、…、fc(t) as a high-frequency fluctuation component, and using the high-frequency fluctuation component as a primary reference power instruction P of the power type energy storagew,ck(ii) a Will f isc(t)、fc+1(t)、…、fn(t) as a low-frequency fluctuation component, and using the low-frequency fluctuation component as a primary reference power command P of the energy type energy storagee,ckNamely:
Pw,ck(t)＝f1(t)、f2(t)、…、fc(t)；
Pe,ck(t)＝fc+1(t)、fc+2(t)、…、fn(t)。
from the above description, since the stable unbalanced power of the interference system is nonlinear, an improved fly-through empirical mode decomposition method is obtained after hilbert transform to perform adaptive decomposition on the unbalanced power, so that the obtained initial power allocation command is more accurate, and then the energy value of the aliasing part is calculated by summing the absolute values of adjacent IMF components to determine the frequency division, thereby reducing the aliasing phenomenon between the frequencies, so that the initial frequency allocation is more accurate and reliable.
Further, the step S3 specifically includes the following steps:
s31, and storing the state of charge SOC of the power type energy at the time tw(t) and unbalanced power Δ P (in hybrid energy storage system:)T) as input signal of fuzzy controller, the output signal of said fuzzy controller is the charge-discharge regulation coefficient T of power type energy storagew；
S32 state of charge (SOC) of current power type energy storagew(t) in a predetermined intermediate value range, when the unbalanced power Δ P (t)<0 and SOCw(t) in a preset high interval or when the unbalance power Δ P (t)>0 and SOCw(t) in a preset low value interval, the power type energy storage and the energy type energy storage are charged and discharged according to the current reference power;
when the unbalanced power is Δ P (t)<0 and SOCw(t) in a preset low interval or when the unbalance power Δ P (t)>0 and SOCw(T) in the preset high value interval, controlling the charge-discharge regulation coefficient TwIf the difference is smaller, the reference power of the power type energy storage is smaller, and the reduced difference power is borne by the energy type energy storage;
s33, the reference power of the power type energy storage and the reference power of the energy type energy storage after being adjusted by the fuzzy controller are respectively as follows:
Pw,ck(t+1)＝Tw(t)Pw,ck(t)；
Pe,ck(t+1)＝ΔP(t+1)-Pw,ck(t+1)。
from the above description, it can be known that the power type energy storage such as super capacitor has the characteristics of fast response speed and long cycle service life, and the energy type energy storage such as storage battery is limited by the number of charging and discharging times, and the service life of the storage battery can be reduced by frequent use. Therefore, in the power distribution process of the hybrid energy storage system, the reference power instruction of the power type energy storage system and the energy type energy storage system is adjusted in a coordinated mode, the possibility that the power type energy storage system reaches the upper limit and the lower limit is reduced, the optimal performance of the hybrid energy storage system is exerted, the loss of the energy type energy storage system is reduced, and the service life of the hybrid energy storage system is prolonged.
Referring to fig. 1 to 4, a first embodiment of the present invention is:
the power distribution optimization method of the hybrid energy storage system comprises the following steps:
s1, the charge and discharge power of the energy type energy storage and the charge and discharge power of the power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in the microgrid system;
wherein the charging and discharging power of the energy type energy storage is PeThe charging and discharging power of the power type energy storage is PwThen, the unbalanced power Δ P (t) P in the microgrid systeme+Pw；
S2, decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to complete initial power distribution;
as can be seen from fig. 2, step S2 specifically includes the following steps:
s21, adding white noise c (t) to the original unbalanced power signal l (t) to obtain:
Hi(t)＝l(t)+ci(t),i＝1,2,…m；
in the formula, Hi(t) is the unbalanced power signal after the white noise is added for the ith time, ci(t) is mutually independent white noise, and m is the empirical mode decomposition frequency;
s22, decomposing the unbalanced power signal added with the white noise by an empirical mode decomposition method to obtain n IMF components f decomposed for the ith time1i(t),f2i(t),…,fni(t) and residual G from the i-th decompositioni(t), namely:
Figure BDA0003297751100000101
in the formula fki(t) is the kth IMF component of the ith decomposition;
after m-times empirical mode decomposition, m-component decomposition results are obtained as follows:
Figure BDA0003297751100000102
after m-times empirical mode decomposition, the mean value of all the IMF components and residuals obtained is:
Figure BDA0003297751100000111
obtaining an unbalanced power signal H (t) after empirical mode decomposition as follows:
Figure BDA0003297751100000112
therefore, the essence of step S22 is to decompose the signal into a series of data sequences with different characteristic time scales, and obtain n IMF (intrinsic mode function) components.
S23, decomposing the f after empirical mode decompositionk(t) obtaining a transformation result x after Hilbert transformationk(t), namely:
Figure BDA0003297751100000113
definition resolution signal rk(t)＝fk(t)+jxk(t), then:
Figure BDA0003297751100000114
Figure BDA0003297751100000115
Figure BDA0003297751100000116
Figure BDA0003297751100000117
in the formula, Ak(t) is rk(t) instantaneous amplitude; thetak(t) is rkInstantaneous phase of (t), sk(t) is rk(t) instantaneous frequency;
s24, calculating the energy value of the aliasing part by adopting a mode of summing the absolute values of adjacent IMF components as follows:
Figure BDA0003297751100000118
in the formula (f)k(ti) For the instant frequency s of the ith time atk(t) less than the dividing frequency scPower of fk+1(tj) For the jth Δ t time sk(t) is greater than scΔ t is the sampling time interval;
given said sampling time interval Δ t, the instantaneous frequency obtained when E is at a minimum is scIf frequency s is dividedcIs located between the c, c +1 IMF, then f will be1(t)、f2(t)、…、fc(t) as a high-frequency fluctuation component, and using the high-frequency fluctuation component as a primary reference power instruction P of the power type energy storagew,ck(ii) a Will f isc(t)、fc+1(t)、…、fn(t) as a low-frequency fluctuation component, and using the low-frequency fluctuation component as a primary reference power command P of the energy type energy storagee,ckNamely:
Pw,ck(t)＝f1(t)、f2(t)、…、fc(t)；
Pe,ck(t)＝fc+1(t)、fc+2(t)、…、fn(t)。
thus, f is obtained in step S23k(t) instantaneous frequency-time characteristic, as k increases, fk(t) increasingly smooth, corresponding to skThe lower the frequency of (t). In order to reduce the aliasing phenomenon between the frequencies, step S24 is required to calculate the energy value of the aliasing part by summing the absolute values of the adjacent IMF components to determine the frequency division to reduce the energy value of the aliasing part, where Δ t is an empirical value and has a value interval between0.00001, 0.0001) second.
And S3, performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power to obtain the adjusted final reference power.
As can be seen from fig. 3 and 4, step S3 specifically includes the following steps:
s31, and storing the state of charge SOC of the power type energy at the time tw(T) and unbalanced power delta P (T) in the hybrid energy storage system are used as input signals of a fuzzy controller, and output signals of the fuzzy controller are charge and discharge regulating coefficients T of power type energy storagew；
S32 state of charge (SOC) of current power type energy storagew(t) in a predetermined intermediate value range, when the unbalanced power Δ P (t)<0 and SOCw(t) in a preset high interval or when the unbalance power Δ P (t)>0 and SOCw(t) in a preset low value interval, charging and discharging the power type energy storage and the energy type energy storage according to the current reference power; if the primary reference power is optimized for the second time, the power type energy storage and the energy type energy storage are charged and discharged by adopting the primary reference power;
when the unbalanced power is Δ P (t)<0 and SOCw(t) in a preset low interval or when the unbalance power Δ P (t)>0 and SOCw(T) in the preset high value interval, controlling the charge-discharge regulation coefficient TwWhen the difference power is reduced, the reference power of the power type energy storage is reduced, and the reduced difference power is borne by the energy type energy storage;
and S33, reference power of the power type energy storage and the energy type energy storage after being adjusted by the fuzzy controller is respectively as follows:
Pw,ck(t+1)＝Tw(t)Pw,ck(t)；
Pe,ck(t+1)＝ΔP(t+1)-Pw,ck(t+1)。
referring to fig. 5, a second embodiment of the present invention is:
the hybrid energy storage system power distribution optimization terminal 1 comprises a memory 3, a processor 2 and a computer program stored on the memory 3 and capable of running on the processor 2, wherein the processor 2 implements the steps of the first embodiment when executing the computer program.
In summary, according to the power allocation optimization method and the terminal for the hybrid energy storage system provided by the invention, because the stable unbalanced power of the interference system is nonlinear, the stable unbalanced power is subjected to adaptive decomposition by adopting improved empirical mode decomposition, and the energy value of the aliasing part is calculated by adopting a mode of summing absolute values of adjacent IMF components to determine the frequency division frequency, so that the aliasing phenomenon between the frequencies is reduced, and the initial frequency allocation is more accurate and reliable; then aiming at the problem that the SOC is easy to cross the line due to high power density of the power type energy storage, a fuzzy controller is designed to carry out secondary optimization on an initial power instruction of the hybrid energy storage system, the possibility that the power type energy storage touches upper and lower limits is reduced, and optimal distribution of the power of the hybrid energy storage is realized. Compared with the traditional hybrid energy storage power distribution method, the method has the advantages that the distribution of the unbalanced power of the hybrid energy storage system is more accurate and reliable, the optimal performance of the hybrid energy storage system is exerted, the power fluctuation of the hybrid energy storage system is rapidly stabilized, the loss of energy type energy storage can be reduced, and the service life of the hybrid energy storage system is prolonged.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.

Claims (6)

1. The hybrid energy storage system power distribution optimization method is characterized by comprising the following steps:
s1, the charge and discharge power of the energy type energy storage and the charge and discharge power of the power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in the microgrid system;
s2, decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to complete initial power distribution;
and S3, performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power to obtain the adjusted final reference power.
2. The hybrid energy storage system power distribution optimization method according to claim 1, wherein the step S2 specifically includes the following steps:
s21, adding white noise c (t) to the original unbalanced power signal l (t) to obtain:
Hi(t)＝l(t)+ci(t),i＝1,2,…m；
in the formula, Hi(t) is the unbalanced power signal after the white noise is added for the ith time, ci(t) is mutually independent white noise, and m is the empirical mode decomposition frequency;
s22, decomposing the unbalanced power signal added with the white noise by an empirical mode decomposition method to obtain n IMF components f decomposed for the ith time1i(t),f2i(t),…,fni(t) and residual G from the i-th decompositioni(t), namely:
Figure FDA0003297751090000011
in the formula fki(t) is the kth IMF component of the ith decomposition;
after m-times empirical mode decomposition, m-component decomposition results are obtained as follows:
Figure FDA0003297751090000012
after m-times empirical mode decomposition, the mean value of all the IMF components and residuals obtained is:
Figure FDA0003297751090000021
obtaining an unbalanced power signal H (t) after empirical mode decomposition as follows:
Figure FDA0003297751090000022
s23, decomposing the f after empirical mode decompositionk(t) obtaining a transformation result x after Hilbert transformationk(t), namely:
Figure FDA0003297751090000023
definition resolution signal rk(t)＝fk(t)+jxk(t), then:
Figure FDA0003297751090000024
Figure FDA0003297751090000025
Figure FDA0003297751090000026
Figure FDA0003297751090000027
in the formula, Ak(t) is rk(t) instantaneous amplitude; thetak(t) is rkInstantaneous phase of (t), sk(t) is rk(t) instantaneous frequency;
s24, calculating the energy value of the aliasing part by adopting a mode of summing the absolute values of adjacent IMF components as follows:
Figure FDA0003297751090000028
in the formula (f)k(ti) For the instant frequency s of the ith time atk(t) less than the dividing frequency scPower of fk+1(tj) For the jth Δ t time sk(t) is greater than scΔ t is the sampling time interval;
given said sampling time interval Δ t, the instantaneous frequency obtained when E is at a minimum is scIf frequency s is dividedcIs located between the c, c +1 IMF, then f will be1(t)、f2(t)、…、fc(t) as a high-frequency fluctuation component, and using the high-frequency fluctuation component as a primary reference power instruction P of the power type energy storagew,ck(ii) a Will f isc(t)、fc+1(t)、…、fn(t) as a low-frequency fluctuation component, and using the low-frequency fluctuation component as a primary reference power command P of the energy type energy storagee,ckNamely:
Pw,ck(t)＝f1(t)、f2(t)、…、fc(t)；
Pe,ck(t)＝fc+1(t)、fc+2(t)、…、fn(t)。
3. the hybrid energy storage system power distribution optimization method according to claim 1 or 2, wherein the step S3 specifically includes the following steps:
s31, and storing the state of charge SOC of the power type energy at the time tw(T) and unbalanced power delta P (T) in the hybrid energy storage system are used as input signals of a fuzzy controller, and output signals of the fuzzy controller are charge and discharge regulating coefficients T of power type energy storagew；
S32 state of charge (SOC) of current power type energy storagew(t) in a predetermined intermediate value range, when the unbalanced power Δ P (t)<0 and SOCw(t) in a preset high interval or when the unbalance power Δ P (t)>0 and SOCw(t) atIn a preset low numerical value interval, the power type energy storage and the energy type energy storage are charged and discharged according to the current reference power;
when the unbalanced power is Δ P (t)<0 and SOCw(t) in a preset low interval or when the unbalance power Δ P (t)>0 and SOCw(T) in the preset high value interval, controlling the charge-discharge regulation coefficient TwIf the difference is smaller, the reference power of the power type energy storage is smaller, and the reduced difference power is borne by the energy type energy storage;
s33, the reference power of the power type energy storage and the reference power of the energy type energy storage after being adjusted by the fuzzy controller are respectively as follows:
Pw,ck(t+1)＝Tw(t)Pw,ck(t)；
Pe,ck(t+1)＝ΔP(t+1)-Pw,ck(t+1)。
4. hybrid energy storage system power allocation optimization terminal, including memory, processor and computer program stored on the memory and operable on the processor, characterized in that the processor implements the following steps when executing the computer program:
s1, the charge and discharge power of the energy type energy storage and the charge and discharge power of the power type energy storage in the hybrid energy storage system are used for compensating unbalanced power in the microgrid system;
s2, decomposing the unbalanced power signal by adopting an empirical mode decomposition method to obtain a decomposed unbalanced power signal, taking a high-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of power type energy storage, and taking a low-frequency component in the decomposed unbalanced power signal as an initial reference power instruction of energy type energy storage to complete initial power distribution;
and S3, performing secondary optimization on power distribution of power type energy storage and energy type energy storage in the hybrid energy storage system through the fuzzy controller, and adjusting the corresponding primary reference power to obtain the adjusted final reference power.
5. The hybrid energy storage system power distribution optimization terminal according to claim 4, wherein the step S2 specifically comprises the following steps:
s21, adding white noise c (t) to the original unbalanced power signal l (t) to obtain:
Hi(t)＝l(t)+ci(t),i＝1,2,…m；
in the formula, Hi(t) is the unbalanced power signal after the white noise is added for the ith time, ci(t) is mutually independent white noise, and m is the empirical mode decomposition frequency;
s22, decomposing the unbalanced power signal added with the white noise by an empirical mode decomposition method to obtain n IMF components f decomposed for the ith time1i(t),f2i(t),…,fni(t) and residual G from the i-th decompositioni(t), namely:
Figure FDA0003297751090000041
in the formula fki(t) is the kth IMF component of the ith decomposition;
after m-times empirical mode decomposition, m-component decomposition results are obtained as follows:
Figure FDA0003297751090000042
after m-times empirical mode decomposition, the mean value of all the IMF components and residuals obtained is:
Figure FDA0003297751090000043
obtaining an unbalanced power signal H (t) after empirical mode decomposition as follows:
Figure FDA0003297751090000044
s23, will be experiencedF after modal decompositionk(t) obtaining a transformation result x after Hilbert transformationk(t), namely:
Figure FDA0003297751090000051
definition resolution signal rk(t)＝fk(t)+jxk(t), then:
Figure FDA0003297751090000052
Figure FDA0003297751090000053
Figure FDA0003297751090000054
Figure FDA0003297751090000055
in the formula, Ak(t) is rk(t) instantaneous amplitude; thetak(t) is rkInstantaneous phase of (t), sk(t) is rk(t) instantaneous frequency;
s24, calculating the energy value of the aliasing part by adopting a mode of summing the absolute values of adjacent IMF components as follows:
Figure FDA0003297751090000056
in the formula (f)k(ti) For the instant frequency s of the ith time atk(t) less than the dividing frequency scPower of fk+1(tj) For the jth Δ t time sk(t) is greater than scAt a power of Δ t ofA sampling time interval;
given said sampling time interval Δ t, the instantaneous frequency obtained when E is at a minimum is scIf frequency s is dividedcIs located between the c, c +1 IMF, then f will be1(t)、f2(t)、…、fc(t) as a high-frequency fluctuation component, and using the high-frequency fluctuation component as a primary reference power instruction P of the power type energy storagew,ck(ii) a Will f isc(t)、fc+1(t)、…、fn(t) as a low-frequency fluctuation component, and using the low-frequency fluctuation component as a primary reference power command P of the energy type energy storagee,ckNamely:
Pw,ck(t)＝f1(t)、f2(t)、…、fc(t)；
Pe,ck(t)＝fc+1(t)、fc+2(t)、…、fn(t)。
6. the hybrid energy storage system power distribution optimization terminal according to claim 4 or 5, wherein the step S3 specifically comprises the following steps:
s31, and storing the state of charge SOC of the power type energy at the time tw(T) and unbalanced power delta P (T) in the hybrid energy storage system are used as input signals of a fuzzy controller, and output signals of the fuzzy controller are charge and discharge regulating coefficients T of power type energy storagew；
S32 state of charge (SOC) of current power type energy storagew(t) in a predetermined intermediate value range, when the unbalanced power Δ P (t)<0 and SOCw(t) in a preset high interval or when the unbalance power Δ P (t)>0 and SOCw(t) in a preset low value interval, the power type energy storage and the energy type energy storage are charged and discharged according to the current reference power;
when the unbalanced power is Δ P (t)<0 and SOCw(t) in a preset low interval or when the unbalance power Δ P (t)>0 and SOCw(T) in the preset high value interval, controlling the charge-discharge regulation coefficient TwIf the difference is smaller, the reference power of the power type energy storage is smaller, and the reduced difference power is borne by the energy type energy storage;
s33, the reference power of the power type energy storage and the reference power of the energy type energy storage after being adjusted by the fuzzy controller are respectively as follows:
Pw,ck(t+1)＝Tw(t)Pw,ck(t)；
Pe,ck(t+1)＝ΔP(t+1)-Pw,ck(t+1)。
CN202111182218.XA 2021-10-11 2021-10-11 Power distribution optimization method and terminal for hybrid energy storage system Pending CN113904352A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111182218.XA CN113904352A (en) 2021-10-11 2021-10-11 Power distribution optimization method and terminal for hybrid energy storage system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111182218.XA CN113904352A (en) 2021-10-11 2021-10-11 Power distribution optimization method and terminal for hybrid energy storage system

Publications (1)

Publication Number Publication Date
CN113904352A true CN113904352A (en) 2022-01-07

Family

ID=79191310

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111182218.XA Pending CN113904352A (en) 2021-10-11 2021-10-11 Power distribution optimization method and terminal for hybrid energy storage system

Country Status (1)

Country Link
CN (1) CN113904352A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116599023A (en) * 2023-05-17 2023-08-15 中国人民解放军海军工程大学 Marine gas turbine power grid hybrid energy storage power distribution method and equipment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105226694A (en) * 2015-10-16 2016-01-06 中国电力科学研究院 The level and smooth generation of electricity by new energy control method of energy storage based on fuzzy empirical mode decomposition
CN106998072A (en) * 2017-05-15 2017-08-01 国网江苏省电力公司电力科学研究院 A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network
CN109659970A (en) * 2018-10-23 2019-04-19 国网甘肃省电力公司电力科学研究院 Energy-storage system based on variation mode decomposition stabilizes the control method of wind power swing
CN111313446A (en) * 2020-03-25 2020-06-19 上海电机学院 Wind power plant energy storage control method
US20210245607A1 (en) * 2020-02-11 2021-08-12 Caterpillar Inc. Hybrid energy storage system optimization strategy with intelligent adaptive control

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105226694A (en) * 2015-10-16 2016-01-06 中国电力科学研究院 The level and smooth generation of electricity by new energy control method of energy storage based on fuzzy empirical mode decomposition
CN106998072A (en) * 2017-05-15 2017-08-01 国网江苏省电力公司电力科学研究院 A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network
CN109659970A (en) * 2018-10-23 2019-04-19 国网甘肃省电力公司电力科学研究院 Energy-storage system based on variation mode decomposition stabilizes the control method of wind power swing
US20210245607A1 (en) * 2020-02-11 2021-08-12 Caterpillar Inc. Hybrid energy storage system optimization strategy with intelligent adaptive control
CN111313446A (en) * 2020-03-25 2020-06-19 上海电机学院 Wind power plant energy storage control method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
胡永夺: "基于复合储能的多源互补微电网协调控制策略研究", 中国优秀硕士学位论文全文数据库 电子数据库 工程II辑, no. 1, pages 29 - 40 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116599023A (en) * 2023-05-17 2023-08-15 中国人民解放军海军工程大学 Marine gas turbine power grid hybrid energy storage power distribution method and equipment
CN116599023B (en) * 2023-05-17 2024-02-27 中国人民解放军海军工程大学 Marine gas turbine power grid hybrid energy storage power distribution method and equipment

Similar Documents

Publication Publication Date Title
US9921602B2 (en) Methods of computing steady-state voltage stability margins of power systems
Zhang et al. Power‐frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system
CN108448565B (en) Power distribution method for direct-current micro-grid composite energy storage system
Babaei et al. Salp swarm algorithm‐based fractional‐order PID controller for LFC systems in the presence of delayed EV aggregators
CN111799844B (en) Virtual synchronous generator control method and device and terminal equipment
US10985572B2 (en) Optimal charging and discharging control for hybrid energy storage system based on reinforcement learning
Djalal et al. Small-Signal-Stability Enhancement using a Power-System Stabilizer based on the Cuckoo-Search Algorithm against Contingency N-1 in the Sulselrabar 150-kV System
Alam et al. Energy management in DC microgrid with an efficient voltage compensation mechanism
Guo et al. Two‐stage optimal MPC for hybrid energy storage operation to enable smooth wind power integration
CN112952862A (en) Hybrid energy storage frequency division coordination controller for stabilizing wind power fluctuation and implementation method
CN113904352A (en) Power distribution optimization method and terminal for hybrid energy storage system
CN111478344B (en) Energy microgrid load frequency control method and system and related products
Dai et al. Multi-VSG-based frequency regulation for uninterruptible power AC micro-grid with distributed electric vehicles
CN103246207A (en) On-line reactive power optimization control method based on real-time simulation system
CN111651878A (en) Large power grid static voltage stability optimization decision method and system considering situation assessment
CN109474027B (en) Droop control method and device and converter
CN106684914A (en) Adaptive PI control method for improving electric power spring pressure regulating performance
Zhang et al. Optimization strategy for hybrid energy storage power distribution based on fuzzy control
Ritt et al. A distribution system state estimation for an efficient integration of electric vehicle charging infrastructure into low-voltage grids
Yazdanian et al. Small signal stability of washout filter–based power sharing for an inverter‐based microgrid
CN113241788B (en) Asynchronous sampling real-time peak clipping and valley filling control method and device for wind power system and storage medium
CN111313478A (en) Renewable energy storage optimization configuration method based on power smoothing
CN110797884B (en) Method and device for determining energy storage capacity by analog AGC frequency modulation
Ralhan et al. Comparative Analysis of FOPI Controller using Eagle Perching Optimization for Fuel Cell connected Distributed Generation System
Wang et al. Entropy weight and grey relational analysis based virtual capacitance collaborative control for multiple energy storage units

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