Electrolytic bath rotation value control method based on wind power prediction error condition distribution

Abstract

The invention discloses an electrolytic bath rotation value control method based on wind power prediction error condition distribution, solves the problem of excessive starting and stopping times of an electrolytic bath or unbalanced time when the electrolytic bath continuously operates in an unhealthy state during control of a renewable energy coupling hydrogen production system, and belongs to the field of electrolytic bath operation control. The method comprises the steps that a joint distribution model of wind power plant output power prediction data based on a Copula function is established and solved, and dynamic distribution of wind power prediction errors is obtained according to the distribution model, wherein a control strategy is divided into upper-layer control and lower-layer control; the upper layer control reads power prediction distribution data and prediction error distribution characteristics of the wind power plant, judges the running state of an electrolytic cell array, and predicts the number of electrolytic cells required in each running state in the next rotation period; and the lower-layer control reads the real-time wind power generation power of the electrolytic bath in the running state in the corresponding time period, and performs power distribution on the running power of the electrolytic bath in the fluctuating state according to the corresponding round value optimization strategy.

Description

Technical Field

[0001] The invention relates to an electrolytic cell rotation control method based on wind power prediction error condition distribution, and belongs to the field of electrolytic cell operation control. Background Art

[0002] With the increasing problems of energy depletion and greenhouse effect, the dominant position of traditional fossil energy in the energy structure has been shaken by renewable energy such as wind and solar. It has become a consensus to reduce the use of traditional fossil energy, reduce carbon emissions and develop renewable energy. However, while clean renewable energy such as wind energy and hydrogen energy are developing rapidly, they have also brought new problems. The inherent randomness and intermittency of wind energy not only lead to a large amount of electricity waste, but also affect the safe and stable operation of the power grid; the current preparation of hydrogen mostly relies on fossil energy, and zero-carbon hydrogen production technologies such as water electrolysis are difficult to popularize due to their high costs. The effective combination of wind power generation and water electrolysis hydrogen production technology can take advantage of the advantages of hydrogen energy in energy storage and rapid power regulation, make up for the shortcomings of wind power in volatility, reduce the instability of high-proportion renewable energy grid connection, and increase the utilization hours of renewable energy power generation. At the same time, it can also make full use of abandoned wind power and improve the economic efficiency of water electrolysis hydrogen production.

[0003] At present, the control strategy of renewable energy coupled hydrogen production system is mostly energy management and regulation between each module and electrolyzer. Since the current single electrolyzer capacity is generally within a few megawatts, when facing a wind farm of up to 100 megawatts in large-scale application, multiple electrolyzers must be combined to form an electrolyzer array to meet the demand. At this time, if the coordinated control between the individual units in the electrolyzer array is ignored, it may lead to problems such as too many electrolyzer starts and stops or uneven continuous operation time in an unhealthy state, which directly affects the service life of the electrolyzer. Summary of the invention

[0004] In order to solve the problem that the electrolyzer is started and stopped too many times or runs continuously in an unhealthy state for an uneven period of time when the renewable energy is coupled to the control of the hydrogen production system, the present invention provides an electrolyzer rotation control method based on the conditional distribution of wind power prediction errors.

[0005] The present invention provides an electrolytic cell rotation control method based on wind power prediction error condition distribution, comprising:

[0006] S1. Establish a joint distribution model of wind farm output power prediction data based on Copula function, and calculate the unknown parameters in the Copula function in the joint distribution model;

[0007] S2. According to the joint distribution model, the dynamic distribution of wind power prediction error is obtained;

[0008] S3. Based on the dynamic distribution of wind power prediction errors, optimal stratified rotation control of electrolyzers in the wind-hydrogen combined system is performed:

[0009] First, the upper layer control of the current rotation cycle is performed, and then the lower layer control is performed. While performing the lower layer control, the upper layer control of the next rotation cycle is also performed;

[0010] The upper control reads the power forecast distribution data and forecast error distribution characteristics of the wind farm, determines the operating status of the electrolyzer array, and predicts the number of electrolyzers required in each operating status of the next rotation cycle;

[0011] The lower-level control reads the real-time wind power generation power of the electrolyzer in the corresponding period of operation, and distributes the power of the electrolyzer in the fluctuating state according to the corresponding rotation optimization strategy. At the same time, the power P of the electrolyzer in the non-rated state is adjusted during the period. ad , to fine-tune the operating power of the electrolyzer operating at non-rated conditions.

[0012] Preferably, S1 comprises:

[0013] Establish the Copula function C of wind farm output power prediction data;

[0014] The kernel density estimation method is used to obtain the probability density function p(x) of the wind farm output power prediction data. The probability density function p(x) is integrated to obtain the cumulative distribution function P(X) of the wind farm output power.

[0015] Use the cumulative distribution function P(X) to transform the marginal distribution function in the Copula function C into a uniform distribution;

[0016] The marginal distribution function of the uniform distribution is input into the Copula function C, and the maximum likelihood estimation method is used to solve the unknown parameters of the Copula function.

[0017] Preferably, the probability density function p(x):

[0018]

[0019] Among them, the sample points of the random variable X are (x1,x2,…,x N ), N is the number of samples, h is the window width, and K() is the kernel function.

[0020] Preferably, the Copula functions mainly include Gussian-Copula, t-Copula, Gumbel-Copula, Clayton-Copula and Frank-Copula.

[0021] As a preferred embodiment, in S2, according to the joint distribution model, the dynamic distribution of wind power prediction error is obtained:

[0022]

[0023] Where e is the prediction error, is the predicted volatility, θ(t′) is the parameter of the corresponding Copula function C; f E (e) is the probability density function of the prediction error, F E (e) is the cumulative distribution function of the prediction error, is the cumulative distribution function of the predicted volatility.

[0024] Preferably, the number of electrolytic cells required in each operating state includes the number of electrolytic cells k in the overload state, the number of electrolytic cells e in the rated state, the number of electrolytic cells l in the underpower state, the number of electrolytic cells s in the shutdown state and the electrolytic cells b operating in the non-rated state.

[0025] Preferably, the numbers of the electrolytic cells in fluctuating operation are updated in each rotation cycle, rotating from 1 to n, and then back to 1 for cyclic update, where n is the number of electrolytic cells participating in the rotation.

[0026] The beneficial effect of the present invention is that the working mode of the electrolyzer is pre-calculated and pre-allocated based on the conditional distribution of wind power prediction errors, and the switching frequency of the working state of the electrolyzer is reduced by fixing the rotation cycle. At the same time, the classification of the working mode of the electrolyzer is refined, the flexibility of the total power adjustment of the electrolyzer array is increased, and the power fluctuation of the electrolyzer is adjustable. This avoids the reduction of the overall life of the system due to the different lifespans of each monomer after long-term operation in a fluctuating state, and at the same time, smoothes the power fluctuation of a single electrolyzer, reducing the adjustment pressure of the electrolyzer. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic flow chart of the control method of the present invention.

[0028] Figure 2 Schematic diagram of the rotation cycle of hierarchical control.

[0029] Figure 3 This is the operation flow chart of the upper and lower layer controls. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0032] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0033] The electrolytic cell rotation control method based on the wind power prediction error condition distribution of this embodiment is characterized in that the method includes:

[0034] Step 1: Establish a joint distribution model of wind farm output power prediction data based on Copula function, and calculate the unknown parameters in the Copula function in the joint distribution model;

[0035] Establish the Copula function C of wind farm output power prediction data:

[0036] Let the random variables x1, x2, …, x N The joint distribution function is H, and the marginal distributions are F1(x1), F2(x2), …, F N (x N ), then there exists a Copula function C such that

[0037] H(x1,x2,…,x N )=C[F1(x1),F2(x2),…,F N (x N )] (1)

[0038] If the marginal distribution F is continuous, the joint distribution function C is unique. Copula functions mainly include 5 types: Gussian-Copula, t-Copula, Gumbel-Copula, Clayton-Copula, and Frank-Copula.

[0039] The kernel density estimation method is used to obtain the probability density function of the wind farm output power prediction data. The probability density function is integrated to obtain the cumulative distribution function P(X) of the wind farm output power:

[0040] Determine the cumulative distribution function of the wind farm output power. Use the kernel density estimation method to solve it. Suppose the sample points of the random variable X are (x1, x2, …, x N ), N is the number of samples, its probability density function is p(x), and the kernel density estimation formula is

[0041]

[0042] Where: h is the window width; K() is the kernel function; after obtaining p(x), integrating p(x) can obtain the cumulative distribution function P(X) of the wind farm output.

[0043] The marginal distribution function F(X) in the Copula function C is transformed into a uniform distribution U using the cumulative distribution function P(X). For r∈(0,1),

[0044]

[0045] Where r is the function to be transformed and U is the uniform distribution.

[0046] Input the marginal distribution function of the uniform distribution into the Copula function C, and use the maximum likelihood estimation method to solve the unknown parameters of the Copula function:

[0047] The unknown parameter α in the five types of Copula functions is obtained by using the step-by-step maximum likelihood function method. Assume that the output of the two wind farms is a random variable X and Y, whose marginal distribution functions are F1() and F2(), and whose probability density functions are f1() and f2(), respectively. Then the likelihood function of the sample is

[0048]

[0049] Where: θ1, θ2 are unknown parameters in the marginal distribution function, x 1i 、x 2i In the middle, the lower part represents that the variable is a random variable corresponding to the unknown parameters θ1 and θ2; C is the Copula function. Taking the logarithm of both sides of the above formula, we get:

[0050]

[0051] From the above formula, we can see that α can be estimated by distribution.

[0052] Step 1: Obtain the maximum likelihood values ​​of parameters θ1 and θ2:

[0053]

[0054] Step 2: Find the maximum likelihood value of parameter α:

[0055]

[0056] After obtaining the above unknown parameters, they are respectively substituted into the standard form formulas of different Copula functions to generate five types of wind farm joint distribution models fitted by five types of Copula functions.

[0057] Step 2: According to the joint distribution model, the dynamic distribution of wind power prediction error is obtained;

[0058] In the analysis process, the wind power forecast output fluctuation and wind power forecast error can be regarded as two different random variables, and their joint distribution can be characterized by the dynamic Copula form as follows:

[0059]

[0060] Where: e is the prediction error; is the predicted fluctuation; θ(t) is the dynamic parameter obtained by the corresponding dynamic Copula evolution equation; f E (e) are the probability density function of the forecast error, the probability density function of the forecast volatility, and the joint distribution probability density function of the two; F E (e), is the corresponding cumulative distribution function.

[0061] In general dispatching, the predicted output of wind power is often a known quantity. By relying on the dynamic dependency model of the fitted wind power forecast fluctuation level and wind power forecast error, the forecast error distribution under each forecast fluctuation level can be theoretically derived. Assume that the wind power forecast fluctuation at time t = t′ is When , its conditional PDF is characterized as As shown in formula (10).

[0062]

[0063] Thus, the dynamic distribution characteristics of the prediction error can be obtained.

[0064] Step 3: Based on the dynamic distribution of wind power prediction errors, optimal stratified rotation control of electrolyzers in the wind-hydrogen combined system is performed:

[0065] The alkaline electrolyzer rotation control strategy is divided into upper control and lower control;

[0066] The upper control layer reads the wind farm power forecast distribution data and forecast error distribution characteristics to operate the electrolyzer array.

[0067] The upper-level control can be obtained by training a deep learning network or a neural network with training data.

[0068] The lower-level control reads the real-time wind power generation power of the electrolyzer in the corresponding period of operation, and distributes the power of the electrolyzer in the fluctuating state according to the corresponding rotation optimization strategy. At the same time, the power P of the electrolyzer in the non-rated state is adjusted during the period. ad , to fine-tune the operating power of the electrolyzer operating at non-rated conditions.

[0069] Therefore, in actual operation, the upper-level control should be run ahead of the lower-level control. Rotation control strategy: First, the upper-level control of the current rotation cycle is performed, and then the lower-level control is performed. While performing the lower-level control, the upper-level control of the next rotation cycle is also performed; the rotation control strategy needs to run two rotation cycles from the start of the upper-level control to the end of the lower-level control, such as Figure 2 shown.

[0070] The upper-level control does not participate in the real-time power allocation of the electrolyzer, but only performs data pre-calculation and storage. During operation, it only needs to determine the operating conditions of the electrolyzer array, pre-calculate the required number of electrolyzers for each operating mode and the corresponding power, and terminate the operation after completing the calculation and storing the data. The lower-level control needs to read the actual value of wind power in real time and provide real-time power guidance to the electrolyzer, so it needs to run continuously during each rotation cycle. The operation process of the upper and lower-level controls is as follows: Figure 3 That is, the upper control layer is responsible for the operation status distribution of the electrolyzer, and the lower control layer is responsible for the real-time output power of the electrolyzer to match the wind power generation power.

[0071] Assume that the number of electrolytic cells involved in the rotation is n, and the rated power of a single electrolytic cell is P e , the power when overload or underpower operation is P ad , P ad The value remains constant during each rotation cycle and is updated each time the electrolyzer array is rotated. The maximum overload factor of the electrolyzer is Maximum overload power The minimum safe operating power is P s The upper and lower limits of the power during the fluctuating operation of the electrolytic cell are P k and P s .

[0072] The variable parameters involved include the input and output variables of the upper-level control. The input is the maximum predicted value P of the wind power interval in the next rotation cycle. windmax And the minimum predicted value P windmin , the output includes the electrolytic cell operating state distribution of the next rotation cycle, including the number of electrolytic cells in overload state k, the number of electrolytic cells in rated state e, the number of electrolytic cells in underpower state l, the number of electrolytic cells in shutdown state s and the electrolytic cells b operating in non-rated state;.

[0073] The row rotation rules of the electrolytic cell array are formulated based on the rotation of the fluctuating electrolytic cells. In order to avoid the same electrolytic cell from continuously running in a fluctuating state, the number of the fluctuating electrolytic cell will be updated in each rotation cycle, rotating from 1 to n, and then back to 1 for cyclic update.

[0074] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. The electrolytic cell rotation control method based on the wind power prediction error condition distribution is characterized by: The method comprises: S1. Establish a joint distribution model of wind farm output power prediction data based on Copula function, and calculate the unknown parameters in the Copula function in the joint distribution model; S2. According to the joint distribution model, the dynamic distribution of wind power prediction error is obtained; S3. Based on the dynamic distribution of wind power prediction errors, optimal stratified rotation control of electrolyzers in the wind-hydrogen combined system is performed: First, the upper layer control of the current rotation cycle is performed, and then the lower layer control is performed. While performing the lower layer control, the upper layer control of the next rotation cycle is also performed; The upper control reads the power forecast distribution data and forecast error distribution characteristics of the wind farm, determines the operating status of the electrolyzer array, and predicts the number of electrolyzers required in each operating status of the next rotation cycle; The lower-level control reads the real-time wind power generation power of the electrolyzer in the corresponding period of operation, and distributes the power of the electrolyzer in the fluctuating state according to the corresponding rotation optimization strategy. At the same time, the power P of the electrolyzer in the non-rated state is adjusted during the period. ad , to fine-tune the operating power of the electrolyzer operating at non-rated conditions.

2. The electrolytic cell rotation control method based on wind power prediction error condition distribution according to claim 1 is characterized in that: S1 includes: Establish the Copula function C of wind farm output power prediction data; The kernel density estimation method is used to obtain the probability density function p(x) of the wind farm output power prediction data. The probability density function p(x) is integrated to obtain the cumulative distribution function P(X) of the wind farm output power. Use the cumulative distribution function P(X) to transform the marginal distribution function in the Copula function C into a uniform distribution; The marginal distribution function of the uniform distribution is input into the Copula function C, and the maximum likelihood estimation method is used to solve the unknown parameters of the Copula function.

3. The electrolytic cell rotation control method based on wind power prediction error conditional distribution according to claim 2 is characterized in that: Probability density function p(x): Among them, the sample points of the random variable X are (x1,x2,…,x N ), N is the number of samples, h is the window width, and K() is the kernel function.

4. The electrolytic cell rotation control method based on wind power prediction error condition distribution according to claim 1 is characterized in that: Copula functions mainly include Gussian-Copula, t-Copula, Gumbel-Copula, Clayton-Copula and Frank-Copula.

5. The electrolytic cell rotation control method based on wind power prediction error condition distribution according to claim 1, characterized in that: In S2, according to the joint distribution model, the dynamic distribution of wind power forecast error is obtained Where e is the prediction error, is the predicted volatility, θ(t′) is the parameter of the corresponding Copula function C; f E (e) is the probability density function of the prediction error, F E (e) is the cumulative distribution function of the prediction error, is the cumulative distribution function of the predicted volatility.

6. The electrolytic cell rotation control method based on wind power prediction error condition distribution according to claim 1, characterized in that: The number of electrolytic cells required in each operating state includes the number of electrolytic cells k in the overload state, the number of electrolytic cells e in the rated state, the number of electrolytic cells l in the underpower state, the number of electrolytic cells s in the shutdown state and the electrolytic cells b operating in the non-rated state.

7. The electrolytic cell rotation control method based on wind power prediction error condition distribution according to claim 1 is characterized in that: The numbers of the electrolytic cells in fluctuating operation are updated in each rotation cycle, from 1 to n, and then back to 1 for cyclic update, where n is the number of electrolytic cells participating in the rotation.

8. A computer-readable storage device storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the electrolytic cell rotation control method based on the conditional distribution of wind power prediction errors as claimed in any one of claims 1 to 7 are implemented.

9. An electrolytic cell rotation control device based on wind power prediction error condition distribution, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of the electrolytic cell rotation control method based on the conditional distribution of wind power prediction errors as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the electrolytic cell rotation control method based on the conditional distribution of wind power prediction errors as described in any one of claims 1 to 7 are implemented.

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