CN111276983A - Photovoltaic smoothing method and system based on fuzzy self-adaptive complete empirical mode decomposition algorithm - Google Patents

Photovoltaic smoothing method and system based on fuzzy self-adaptive complete empirical mode decomposition algorithm Download PDF

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CN111276983A
CN111276983A CN201911381634.5A CN201911381634A CN111276983A CN 111276983 A CN111276983 A CN 111276983A CN 201911381634 A CN201911381634 A CN 201911381634A CN 111276983 A CN111276983 A CN 111276983A
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photovoltaic
power
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王军
陈磊
孙章
吴帆
张朝瑞
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Xihua University
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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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]

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Abstract

The invention discloses a photovoltaic stabilizing method based on a fuzzy self-adaptive complete empirical mode decomposition algorithm, which comprises the following steps of: s1: acquiring photovoltaic actual measurement data and generating a photovoltaic power signal; s2: generating a combined target power by decomposing the photovoltaic power signal; s3: acquiring a power base value and generating a first power compensation instruction; s4: and acquiring photovoltaic prediction maximum data and generating a second power compensation instruction. According to the invention, by using the CEEMDAN algorithm and introducing the fuzzy control algorithm, the photovoltaic power signal can be processed in a self-adaptive manner according to different photovoltaic power fluctuation rates, and the optimal photovoltaic stabilized combined target power can be obtained. Meanwhile, an optimized power basic value model is established through an MADS algorithm and a power basic value is obtained, so that the problem that the service life of a unit is damaged by a first photovoltaic stabilizing unit due to stabilizing photovoltaic fluctuation because the working condition state of the first photovoltaic stabilizing unit is changed frequently and randomly possibly caused by directly using decomposed high-frequency photovoltaic power signals to control the photovoltaic stabilizing unit is solved.

Description

Photovoltaic smoothing method and system based on fuzzy self-adaptive complete empirical mode decomposition algorithm
Technical Field
The invention relates to the technical field of photovoltaic fluctuation stabilization of a power distribution network, in particular to a photovoltaic stabilization method and a system thereof based on a fuzzy self-adaptive complete empirical mode decomposition algorithm.
Background
With the gradual reduction of traditional energy sources and the gradual highlighting of environmental problems, the vigorous development and utilization of renewable energy sources have become the basic national policy of China. Among them, photovoltaic power generation is one of ideal sustainable energy, and has the characteristics of no pollution, no noise, safety, reliability and the like.
However, as the installed photovoltaic capacity in the power grid gradually increases, the random fluctuation of the photovoltaic power generation power will affect the real-time power balance of the power grid, cause the voltage and frequency fluctuation of the power grid, and affect the power quality and stability of the power grid. And meanwhile, the fluctuation is difficult to predict, so that the power grid dispatching becomes difficult. The photovoltaic fluctuation stabilizing method for limiting the photovoltaic power fluctuation within a certain range is a key technology for applying photovoltaic power generation in a large scale, reducing light abandonment and solving the current energy crisis.
In summary, the photovoltaic power generation still has the problem that the photovoltaic power is a strong nonlinear signal due to strong fluctuation and randomness of solar energy, and the conventional filtering algorithm is difficult to process the signal.
Disclosure of Invention
In view of this, the invention provides a photovoltaic stabilizing method and a system thereof based on a fuzzy self-adaptive complete empirical mode decomposition algorithm, and solves the problem that the traditional photovoltaic stabilizing method cannot effectively stabilize the strong nonlinear fluctuation of a photovoltaic power signal by improving the processing method of the photovoltaic fluctuation rate.
In order to solve the technical problems, the technical scheme of the invention is to adopt a photovoltaic stabilizing method based on a fuzzy self-adaptive complete empirical mode decomposition algorithm, which comprises the following steps: s1: acquiring photovoltaic actual measurement data and generating a photovoltaic power signal; s2: calculating the photovoltaic power signal through a CEEMDAN algorithm and a fuzzy control algorithm and generating a combined target power; s3: acquiring a power base value and generating a first power compensation instruction; s4: and acquiring photovoltaic prediction maximum data and generating a second power compensation instruction.
Optionally, the S2 includes: s21: decomposing the photovoltaic power signal through the CEEMDAN algorithm to generate a multi-order eigenmode component; s22: and reconstructing the eigenmode component through the fuzzy control algorithm to generate the joint target power.
Optionally, the S21 includes: s211: substituting a noise component into the photovoltaic power signal and generating a first-order eigenmode component; s212: acquiring a photovoltaic residual signal and decomposing to generate a second-order intrinsic mode component; s213: and repeatedly decomposing the photovoltaic residual signals until the multi-order intrinsic mode components are generated.
Optionally, the S3 includes: s31: acquiring a power basic value model; s32: executing MADS algorithm on the power basic value model to generate the power basic value; s33: generating the first power compensation command based on the power base value, the joint target power, and a photovoltaic real-time power.
Optionally, the S4 includes: s41: extracting the photovoltaic predicted maximum data based on photovoltaic predicted data for a plurality of time periods; s42: generating the second power compensation command based on the photovoltaic predicted maximum data, the joint target power, and the power base value.
Optionally, the photovoltaic stabilization method further comprises: transmitting the first power compensation command and the second power compensation command to a photovoltaic suppression unit.
Correspondingly, the invention provides a photovoltaic suppression system based on a fuzzy self-adaptive complete empirical mode decomposition algorithm, which comprises the following steps: the photovoltaic power generation unit is used for acquiring photovoltaic actual measurement data and photovoltaic prediction data and generating a photovoltaic power signal; the control center calculates the photovoltaic power signal through a CEEMDAN algorithm and a fuzzy control algorithm to generate a combined target power, and acquires a power base value and photovoltaic prediction maximum data to generate a first power compensation instruction and a second power compensation instruction; and the photovoltaic stabilizing unit is used for receiving the first power compensation command and the second power compensation command and inhibiting photovoltaic power fluctuation.
Optionally, the control center includes: the first processing unit is used for receiving the photovoltaic power signal and transmitting the first power compensation instruction and the second power compensation instruction to the photovoltaic stabilizing unit; the second processing unit is used for performing data operation and generating the first power compensation instruction and the second power compensation instruction; and the memory sharing unit is used for storing historical data such as the photovoltaic power signal, the first power compensation instruction and the second power compensation instruction and establishing communication connection between the first processing unit and the second processing unit.
Optionally, the second processing unit comprises: the first data processing module is used for decomposing the photovoltaic power signal to generate multi-order eigenmode components; the second data processing module is used for reconstructing the eigenmode component to generate the joint target power; and the third data processing module is used for acquiring the power base value.
Optionally, the photovoltaic stabilizing unit comprises: the first photovoltaic stabilizing unit is used for receiving the first power compensation command and can rapidly inhibit the photovoltaic power fluctuation in a small range; and the second photovoltaic stabilizing unit is used for receiving the second power compensation command and can inhibit the photovoltaic power fluctuation in a larger range.
The primary improvement of the invention is that the photovoltaic stabilizing method based on the fuzzy self-adaptive complete empirical mode decomposition algorithm is provided, and by using the CEEMDAN algorithm and introducing the fuzzy control algorithm, the photovoltaic power signal can be processed in a self-adaptive manner according to the difference of the photovoltaic power fluctuation rate, and the optimal combined target power after photovoltaic stabilization can be obtained. Meanwhile, an optimized power basic value model is established through an MADS algorithm and a power basic value is obtained, so that the problem that the service life of a unit is damaged by a first photovoltaic stabilizing unit due to stabilizing photovoltaic fluctuation because the working condition state of the first photovoltaic stabilizing unit is changed frequently and randomly possibly caused by directly using decomposed high-frequency photovoltaic power signals to control the photovoltaic stabilizing unit is solved.
Drawings
FIG. 1 is a simplified flow diagram of a photovoltaic smoothing method based on a fuzzy adaptive perfect empirical mode decomposition algorithm of the present invention;
FIG. 2 is a simplified flow diagram of the present invention for generating a joint target power;
FIG. 3 is a simplified flow diagram of a multi-order eigenmode component of the present invention;
FIG. 4 is a simplified flow diagram of the present invention for generating a first power compensation command;
FIG. 5 is a simplified flow diagram of the present invention for generating a second power compensation command;
FIG. 6 is a simplified block diagram of a photovoltaic suppression system of the present invention;
FIG. 7 is a simplified data diagram of the membership function of the fluctuation rate after pumped averaging according to the present invention;
FIG. 8 is a simplified data diagram of the reconstruction order membership function of the present invention;
FIG. 9 is a schematic diagram of the model for obtaining a power contribution of the present invention;
FIG. 10 is a simplified flow chart of the present invention for performing the MADS algorithm;
FIG. 11 is a simplified flow chart of the operation steps of the power limiting device of the present invention; and
fig. 12 is a simplified flow diagram of a preferred embodiment of the control center of the present invention.
List of reference numerals
1: photovoltaic power generation unit 2: the control center 3: first photovoltaic stabilizing unit
4: second photovoltaic stabilizing unit 5: the first processing unit 6: second processing unit
7: memory sharing unit 61: the first data processing module 62: second data processing module
63: third data processing module
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a photovoltaic smoothing method based on a fuzzy self-adaptive complete empirical mode decomposition algorithm includes:
s1: acquiring photovoltaic actual measurement data and generating a photovoltaic power signal; preferably, the photovoltaic power signal is defined as a fluctuating output power of the photovoltaic signal per unit time period.
S2: calculating the photovoltaic power signal through a CEEMDAN algorithm and a fuzzy control algorithm and generating a combined target power; preferably, the combined target power is defined as the target power of the total output of the photovoltaic power generation unit and the first suppression unit, and is denoted as Pm。
S3: a power base value is obtained, and a first power compensation command is generated. Preferably, the first power compensation command is defined as a scheduling command for controlling the first photovoltaic suppression unit, and is used for controlling the pumped storage state and the output power of the first photovoltaic suppression unit.
S4: and acquiring photovoltaic prediction maximum data and generating a second power compensation instruction. Preferably, the second power compensation command is defined as a scheduling command for controlling the second photovoltaic suppression unit for controlling the output power of the second photovoltaic suppression unit.
According to the invention, by using the CEEMDAN algorithm and introducing the fuzzy control algorithm, the photovoltaic power signal can be processed in a self-adaptive manner according to different photovoltaic power fluctuation rates, and the optimal photovoltaic stabilized combined target power can be obtained. Meanwhile, an optimized power basic value model is established through an MADS algorithm and a power basic value is obtained, so that the problem that the service life of a unit is damaged by a first photovoltaic stabilizing unit due to stabilizing photovoltaic fluctuation because the working condition state of the first photovoltaic stabilizing unit is changed frequently and randomly possibly caused by directly using decomposed high-frequency photovoltaic power signals to control the photovoltaic stabilizing unit is solved.
To facilitate understanding of how to generate the joint target power PmFurther refining the S2, as shown in fig. 2, includes:
s21: decomposing the photovoltaic power signal through the CEEMDAN algorithm to generate a multi-order eigenmode component;
s22: using the power fluctuation quantity per minute after reconstruction as a constraint, calculating an eigenmode reconstruction order by using a fuzzy algorithm, reconstructing low-frequency eigenmode components meeting the requirements, and generating the photovoltaic-pumped storage combined output reference value Pm。
Specifically, to facilitate understanding of how to perform the CEEMDAN decomposition, S21 is further refined, as shown in fig. 3, including:
collecting photovoltaic power signals of a current time period and a plurality of historical photovoltaic power signals which are closest to the current time period as decomposition basic signals x (t), and taking a noise component epsilon0wj(t), (J ═ 1, 2.., J) is added to x (t), where ei-1In order to solve the white noise adaptive coefficient in the previous eigenmode, J is the number of the added Gaussian white noise;
EMD decomposition of x (t) and definition of the first order eigenmode component imf decomposed after j time of noise additionj1(t), the first-order eigenmode component of the photovoltaic power at the period obtained by CEEMDAN decomposition is
Figure RE-GDA0002455975760000051
And a first photovoltaic power headroom signal r decomposed from CEEMDAN1(t) is noted as: r is1(t)＝x(t)-imf'1(t)；
Continuing to the first photovoltaic power headroom signal r1(t) adding an adaptive noise component ε1E1(wj(t)), then performing EMD decomposition to solve the white noise first-order eigenmode component, and further performing decomposition to obtain the second-order eigenmode component of the photovoltaic power at the time period, which is recorded as:
Figure RE-GDA0002455975760000061
repeatedly decomposing ith photovoltaic power margin signal ri(t) obtaining the ith residual signal and the (i + 1) th order eigenmode component, which is recorded as ri(t)＝ri1t()imfit'()；
Figure RE-GDA0002455975760000062
After the photovoltaic power headroom signal is decomposed based on the CEEMDAN algorithm, a decomposition basic signal formed by multiple orders of intrinsic mode components can be generated and recorded as:
Figure RE-GDA0002455975760000063
where Ei (X) is the operator of the ith order modal component generated by EMD, wj(t) is the zero mean unit variance white noise added at the j-th time, t is the time variable, imfi' (t) is the ith-order photovoltaic power eigenmode component obtained after the signal is decomposed by CEEMDAN.
Further, the condition for terminating the CEEMDAN decomposition is that the photovoltaic power headroom signal is a non-oscillating monotonic function or a constant less than a predetermined value.
To facilitate understanding of how the joint target power P is generated by reconstructing the eigenmode components through the fuzzy control algorithmmFurther refines S22 including: calculating a reconstruction order value by using a fuzzy control algorithm with the photovoltaic power fluctuation rate of the current time period as a constraint condition, reconstructing and calculating specified photovoltaic eigenmode components to obtain a low-frequency power signal of the photovoltaic power, and intercepting the low-frequency power of the current time period as the combined target power P of the next minutem；
The fluctuation frequency of each order of modes of the photovoltaic signal after reconstruction is reduced from low order to high order in sequence, and each order of mode reconstruction with lower frequency is selected as combined target power Pm. Therefore, the value of the reconstruction order K of the photovoltaic modal component has a great influence on the stabilizing effect, and the too large value can cause excessive smoothness, the too small value and insufficient smoothness. Therefore, a single-input single-output fuzzy controller is designed by taking the fluctuation rate after the pumping and accumulation stabilization as a constraint condition. The order is reconstructed according to the adaptive adjustment algorithm of the fluctuation rate, so that the combined target power P with the optimal pumped storage stabilizing effect is obtained in real timemAnd the stabilizing effect is improved.
Further, the fuzzy controller is configured to adopt fuzzy subsets of NB1, ZO1, and PB1 as shown in fig. 7, and respectively indicate that the fluctuation rate of the current reconstruction power is { lower, moderate, and higher }, and the fluctuation rate thereof does not exceed 0.1 installed capacity/min in order to meet the requirements of the photovoltaic grid-connected technology. Thus, its domain of discourse is [0-0.1 ]. As shown in fig. 8, the reconstruction order adopts fuzzy subsets of NB2, ZO2, and PB2, which respectively indicate that the current reconstruction power fluctuation rate is { lower, moderate, and higher }, wherein the value of the initial k is selected according to the normalized modulus cumulative mean.
Further, the reconstruction rule of the fuzzy controller is configured to: when the photovoltaic fluctuation is large, in order to improve the participation degree of pumping and stabilizing the photovoltaic fluctuation, the reconstruction order of the photovoltaic modal component is increased to obtain a smoother combined target power; photovoltaic fluctuation is small, pumping storage stabilizing participation is reduced, reconstruction orders are reduced, and therefore energy utilization rate is improved.
To facilitate understanding of how to obtain the power contribution, S3 is further refined, as shown in fig. 4, including:
s31: acquiring a power basic value model;
s32: executing MADS algorithm on the power basic value model to generate the power basic value;
s33: generating a first power compensation command based on the power base value, the joint target power, and the photovoltaic real-time power.
At present, the solving method aiming at the optimal problem comprises algorithms such as a mathematical programming method, a heuristic algorithm, a direct search method and the like. However, the index function of the pumping and stabilizing effect in the power basic value model is obtained by simulating the optical storage combined control strategy, and the analytic expression is difficult to be given and is defined as a black box function. The optimization model has strong nonlinearity, and no derivative information is available, so that a mathematical programming method cannot be used for solving; however, heuristic algorithms such as particle swarm optimization have the disadvantage of insufficient local fine search capability. For these explicitly non-differentiable "black-box functions," the present invention solves by using a direct search algorithm, the grid adaptive direct search (MADS) algorithm. The algorithm is easy to expand and apply; the method is still effective under the conditions of difficult derivation and unreliable finite difference; has global convergence properties comparable to the line search algorithm and the trust domain algorithm. In addition, the convergence rate is higher, the local optimum is not easy to fall into, and a global optimum solution is more likely to be found.
Further, as shown in fig. 9, obtaining the power base value model includes: describing a target function by adopting a pumping and storage stabilizing effect index and an energy waste index, wherein the pumping and storage stabilizing effect is an accumulated error of the combined light-storage output and the combined target power after the pumping and storage stabilization; and taking the pumped storage power base value as an energy waste index. Thus, the model objective function is noted as:
Figure RE-GDA0002455975760000081
wherein, Pdev.pj(t)＝Ppj(t)-Pm(t)，Pl.low＜Pj＜Pl.up；
PminJoint target Power function, P, for optimization modeldev.pj(t) is the pumping-out-of-average effect function, PPj(t) represents the photo-storage combined output at time t, Pm(t) is a photovoltaic-pumped storage combined output reference value at the moment t, PjIs a power base value, T is an optimization period, Pl.lowIs the first photovoltaic suppression unit lower power limit, Pl.upIs the first photovoltaic suppression unit upper power limit.
Further, executing the MADS algorithm includes the steps of: and finding out a global optimal point through an exploration step, and then converging to a local optimal solution through a Poll step. Further, as shown in FIG. 10, the initialization exploration point P is first determinedj0Mesh size parameter Δ0 mAnd screening frame size Δ p0If the optimal point is found in the exploration step, the grid parameters are increased, otherwise, the Poll step is carried out to continue the optimization, if the optimal point is found, the grid parameters are increased and updated, the exploration step is returned, otherwise, the grid parameters are reduced, and the stopping condition is met or the optimal solution is found.
Further, obtaining the joint target power PmCan then be based on the joint target power PmPhotovoltaic power station real-time power PssAnd a power base value PjGenerating a theoretical first power compensation command PdFSCIs denoted by PdFSC＝Pm-Pss+Pj。
Furthermore, to prevent the theoretical first power compensation command PdFSCExceeding the upper limit and the lower limit of the unit operating power, the invention is provided with a power limiting device, as shown in fig. 11, by comparing a theoretical first power compensation instruction PdFSCGenerating a first power compensation command P relative to the relation between the upper limit and the lower limit of the unit operating powerFSC. Wherein, Pl.lowIs the first photovoltaic suppression unit lower power limit, Pl.upIs the first photovoltaic suppression unit upper power limit.
To facilitate understanding of how the second power compensation command is generated, S4 is further refined, as shown in fig. 5, including:
s41: extracting the photovoltaic prediction maximum data in the time period based on the photovoltaic prediction data; preferably, the photovoltaic prediction data is obtained by calculating meteorological prediction data of each prediction point in the prediction unit time period.
S42: generating the second power compensation command P based on the photovoltaic predicted maximum data, the joint target power, and the power base valuec。
Further, the maximum power P is predicted by the photovoltaic in the whole daypred.mAnd the target power is finally combined with the grid connection of the system. According to a joint target power PmPhotovoltaic power station daily long-term prediction power maximum value Ppred.mAnd a power base value PjBy the formula
Figure RE-GDA0002455975760000091
Calculating a second power compensation command Pc。
Correspondingly, as shown in fig. 6, the present invention provides a photovoltaic suppression system based on a fuzzy self-adaptive perfect empirical mode decomposition algorithm, including: the photovoltaic power generation unit 1 is used for acquiring photovoltaic prediction data and generating a photovoltaic power signal; the control center 2 calculates the photovoltaic power signal through a CEEMDAN algorithm and a fuzzy control algorithm to generate a combined target power, and acquires a power base value and photovoltaic prediction maximum data to generate a first power compensation instruction and a second power compensation instruction; and the photovoltaic stabilizing unit is used for receiving the first power compensation command and the second power compensation command and inhibiting photovoltaic power fluctuation.
Further, the control center 2 includes: the first processing unit 5 is configured to receive the photovoltaic power signal, and transmit the first power compensation instruction and the second power compensation instruction to the photovoltaic stabilizing unit; the second processing unit 6 is configured to perform data operation and generate the first power compensation instruction and the second power compensation instruction; and the memory sharing unit 7 is configured to store historical data such as the photovoltaic power signal, the first power compensation instruction, the second power compensation instruction, and the like, and establish a communication connection between the first processing unit 5 and the second processing unit 6. The first processing unit 5 may be formed by an FPGA processor, the second processing unit 6 may be formed by a DSP processor, and both the first processing unit 5 and the second processing unit 6 may establish a communication connection with the memory sharing unit 7 through a serial high-speed interconnection interface (SRIO).
Further, as shown in fig. 12, the second processing unit 6 includes: the first data processing module 61 is configured to decompose the photovoltaic power signal to generate multiple orders of eigenmode components; a second data processing module 62 for reconstructing the eigenmode components to generate the joint target power; and a third data processing module 63, configured to obtain a power base value. Preferably, the first data processing module 61 may be implanted with a CEEMDAN algorithm program to decompose the photovoltaic raw data acquired from the FPGA to obtain each order of eigenmode components of the data, the second data processing module 62 may be implanted with a fuzzy control algorithm program to calculate a reconstruction order, and reconstruct the eigenmode components according to the calculated order to obtain a photovoltaic-pumped-storage combined output reference value, and the third data processing module 63 performs online rolling optimization on the power base value by using a pumped-storage power base value optimization model and an MADS algorithm program to obtain an optimal power base value at each time interval.
For guaranteeing the photovoltaic stabilizing effect, the photovoltaic stabilizing unit includes: the first photovoltaic stabilizing unit 3 is configured to receive the first power compensation command, and can quickly suppress the photovoltaic power fluctuation within a small range; and the second photovoltaic stabilizing unit 4 is used for receiving the second power compensation command and can inhibit the photovoltaic power fluctuation in a larger range. The first photovoltaic stabilizing unit 3 can be an FSC variable-speed pumped storage power station unit capable of rapidly suppressing power fluctuation in a small range, and the second photovoltaic stabilizing unit 4 can be a cascade hydropower station unit capable of suppressing power fluctuation in a large range. Preferably, the FSC variable-speed pumped storage power station and the cascade hydropower station are arranged to stabilize the output power fluctuation of the photovoltaic power station, the respective advantages are utilized, the defects are made up in a complementary mode, and the phenomena of light abandonment and water abandonment can be reduced while a good stabilizing effect is achieved.
In particular, the central control centre of the cascade hydropower station can be based on the second power compensation command PcThe output of each stage of cascade hydropower station is controlled to carry out cascade stabilization on the photovoltaic power station, and finally the photoelectric output is kept at a certain constant value change in a working period of a longer time period. The control center of the FSC variable-speed pumped-storage power station can compensate the instruction P based on the first powerFSCAnd controlling the output or absorption power of the power station to perform pumped storage stabilization on the photovoltaic power station, and finally obtaining the smooth photovoltaic power in a short time.
The above is only a preferred embodiment of the present invention, and it should be noted that the above preferred embodiment should not be considered as limiting the present invention, and the protection scope of the present invention should be subject to the scope defined by the claims. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the spirit and scope of the invention, and these modifications and adaptations should be considered within the scope of the invention.

Claims (10)

1. A photovoltaic smoothing method based on a fuzzy self-adaptive complete empirical mode decomposition algorithm is characterized by comprising the following steps:
s1: acquiring photovoltaic actual measurement data and generating a photovoltaic power signal;
s2: calculating and generating a joint target power by the photovoltaic power signal;
s3: acquiring a power base value and generating a first power compensation instruction;
s4: and acquiring photovoltaic prediction maximum data and generating a second power compensation instruction.
2. The photovoltaic smoothing method according to claim 1, wherein the S2 includes:
s21: generating a plurality of orders of eigenmode components by decomposing the photovoltaic power signal;
s22: reconstructing the eigenmode components to generate the joint target power.
3. The photovoltaic smoothing method according to claim 2, wherein the S21 includes:
s211: substituting a noise component into the photovoltaic power signal and generating a first-order eigenmode component;
s212: acquiring a photovoltaic residual signal and decomposing to generate a second-order intrinsic mode component;
s213: and repeatedly decomposing the photovoltaic residual signals until the multi-order intrinsic mode components are generated.
4. The photovoltaic smoothing method according to claim 3, wherein the S3 includes:
s31: acquiring a power basic value model;
s32: calculating the power basic value model and generating the power basic value;
s33: generating the first power compensation command based on the power base value, the joint target power, and a photovoltaic real-time power.
5. The photovoltaic smoothing method according to claim 4, wherein the S4 includes:
s41: extracting the photovoltaic predicted maximum data based on photovoltaic predicted data;
s42: generating the second power compensation command based on the photovoltaic predicted maximum data, the joint target power, and the power base value.
6. The photovoltaic smoothing method of claim 1, further comprising: transmitting the first power compensation command and the second power compensation command to a photovoltaic suppression unit.
7. A photovoltaic suppression system based on a fuzzy self-adaptive complete empirical mode decomposition algorithm is characterized by comprising the following steps:
the photovoltaic power generation unit is used for acquiring photovoltaic actual measurement data and photovoltaic prediction data and generating a photovoltaic power signal;
the control center calculates the photovoltaic power signal and generates a combined target power, and acquires a power base value and photovoltaic prediction maximum data to generate a first power compensation instruction and a second power compensation instruction;
and the photovoltaic stabilizing unit is used for receiving the first power compensation command and the second power compensation command and inhibiting photovoltaic power fluctuation.
8. The photovoltaic calming system of claim 7, wherein the control center comprises:
the first processing unit is used for receiving the photovoltaic power signal and transmitting the first power compensation instruction and the second power compensation instruction to the photovoltaic stabilizing unit;
the second processing unit is used for performing data operation and generating the first power compensation instruction and the second power compensation instruction;
and the memory sharing unit is used for storing historical data such as the photovoltaic power signal, the first power compensation instruction and the second power compensation instruction and establishing communication connection between the first processing unit and the second processing unit.
9. The photovoltaic calming system of claim 8, wherein the second processing unit comprises:
the first data processing module is used for decomposing the photovoltaic power signal to generate multi-order eigenmode components;
the second data processing module is used for reconstructing the eigenmode component to generate the joint target power;
and the third data processing module is used for acquiring the power base value.
10. The photovoltaic stabilizing system according to claim 7, wherein said photovoltaic stabilizing unit comprises:
the first photovoltaic stabilizing unit is an FSC variable-speed pumped storage power station, is used for receiving the first power compensation instruction and can rapidly inhibit the photovoltaic power fluctuation in a smaller range;
and the second photovoltaic stabilizing unit is a cascade hydropower station and is used for receiving the second power compensation command and inhibiting the photovoltaic power fluctuation in a larger range.
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