Power smoothing control method and system based on wavelet denoising and Kalman filtering

Abstract

The invention discloses a power smoothing control method and system based on wavelet denoising and Kalman filtering, and relates to the technical field of power control, and the method comprises the steps: collecting the new energy power generation power at the current moment in real time, and inputting the new energy power generation power into an algorithm program; taking the data as a wavelet transform power sequence acquisition parameter, and obtaining a de-noising sequence by adopting wavelet decomposition; generating power at the current moment is taken out from the sequence, and filtered power is obtained through Kalman filtering; obtaining the final energy storage action quantity, obtaining the smoothed generation power according to the difference value, and calculating the SOC state at the next moment; and establishing a quantitative index and carrying out fluctuation quantification. Compared with a traditional algorithm, the power smooth control method based on wavelet denoising and Kalman filtering provided by the invention is based on an MATLAB program, utilizes the noise reduction characteristics of wavelet denoising and Kalman filtering to regulate and control a new energy power generation and energy storage system in real time, realizes smooth power output and fluctuation quantification, and has the advantages of high efficiency and high reliability. And the reliability of new energy power generation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power control, and in particular to a power smoothing control method and system based on wavelet denoising and Kalman filtering. Background Art

[0002] There are many factors that affect the power output of renewable energy, among which meteorological factors change relatively quickly. Therefore, the power generation of renewable energy is characterized by large fluctuations and strong intermittency. Direct grid connection will have a huge impact on the power grid and affect the stability and safety of power grid operation.

[0003] Therefore, renewable energy power generation needs to be smoothed before it is connected to the grid. The power difference caused by smoothing needs to be compensated and adjusted by other parts of the system. For the instability of renewable energy power generation, relying solely on the flexibility of thermal power units for adjustment will inevitably cause thermal power units to start and stop frequently or operate in a deep peak-shaving state for a long time, seriously threatening the safety and economy of power grid operation. Therefore, the use of large-scale energy storage technology is an effective means to solve grid dispatching problems and ensure grid stability. Through the complementary use of advanced energy storage technology and clean power generation technology, the utilization rate of clean energy can be improved. The energy storage system is increasingly showing its important role in improving the flexibility of the power system. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing methods for smoothing the drastic fluctuations in the power generation of renewable energy are difficult to effectively smooth out the fluctuations, and how to achieve efficient optimization of smoothing the power generation of renewable energy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a power smoothing control method based on wavelet denoising and Kalman filtering, comprising:

[0007] Collect the current renewable energy power generation in real time and input the corresponding data into the algorithm program;

[0008] The data input into the algorithm program is used as a wavelet transform power sequence to obtain wavelet transform parameters, and the denoised sequence is obtained by wavelet decomposition;

[0009] Take out the current moment power after denoising from the sequence, and use Kalman filtering to obtain the current moment power after filtering;

[0010] The initial action amount of energy storage is obtained according to the difference between the filtered power generation and the original power generation. The initial output of energy storage is substituted into the energy storage output control algorithm to obtain the final action amount of energy storage. The smoothed power generation is obtained according to the difference between the original power generation and the final action amount of energy storage, and the SOC state at the next moment is calculated.

[0011] Determine whether the duration of power smoothing control meets one day. If the condition is met, form a daily power generation curve based on the smoothed power generation obtained at each moment, establish a quantitative index of the fluctuation of the daily power generation curve, and quantify the fluctuation of the smoothed daily power generation curve.

[0012] As a preferred solution of the power smoothing control method based on wavelet denoising and Kalman filtering described in the present invention, wherein: the real-time acquisition of the new energy power generation at the current moment includes obtaining 1440 acquisition points with a resolution of minutes per day, real-time acquisition of the new energy power generation at the current moment, and inputting the corresponding data into the algorithm program; wherein the data input into the algorithm program is the new energy power generation in the previous 120 minutes including the current moment;

[0013] The algorithm program is MATLAB software.

[0014] As a preferred solution of the power smoothing control method based on wavelet denoising and Kalman filtering described in the present invention, wherein: the acquisition of wavelet transform parameters includes using the wavelet transform power sequence as an input signal to obtain the wavelet transform parameters using the ddencmp function in MATLAB software; wherein the wavelet transform parameters include a threshold value, a threshold value type, and an allowable approximate retention coefficient;

[0015] The parameters of wavelet transform are input into the wdencmp function in MATLAB software and the db4 wavelet is selected for wavelet decomposition to obtain a one-dimensional sequence with 120 elements after denoising.

[0016] As a preferred solution of the power smoothing control method based on wavelet denoising and Kalman filtering described in the present invention, wherein: the Kalman filtering is expressed as,

[0017]

[0018] in, is the prior state estimate at the current moment, is the posterior state estimate at the previous moment, is the estimated value of the posterior state at the current moment; is the variance between the actual value and the predicted value at the current moment, P t-1 is the variance between the true value and the optimal estimate at the previous moment, P t is the variance between the current true value and the optimal estimated value; Q is the prediction noise, which is inversely proportional to the confidence level of the predicted value; K t is the Kalman gain; R is the measurement noise, which is inversely proportional to the confidence level of the measurement value; z t is the state measurement value at the current moment;

[0019] Set the initial value of the Kalman filter algorithm and P0, adjust the hyperparameters R and Q, extract the denoised current moment power generation from the output result of wavelet decomposition, and use Kalman filtering to obtain the filtered current moment power generation.

[0020] As a preferred solution of the power smoothing control method based on wavelet denoising and Kalman filtering described in the present invention, wherein: the energy storage output control algorithm includes energy storage output control, energy storage over-limit control, energy storage action threshold determination and SOC state evaluation;

[0021] If the energy storage battery is in a discharging state, the loss during the discharging process needs to be compensated in practical applications, so the power of energy storage discharge is P / η;

[0022] If the absolute value of the energy storage output exceeds the maximum charge and discharge power, the output of the energy storage needs to be adjusted to the maximum charge power or the maximum discharge power;

[0023] Set the energy storage action threshold to reduce the number of energy storage actions. The energy storage will only be activated when the absolute value of the energy storage output at the current moment and the energy storage output at the previous moment is greater than the threshold h, otherwise the energy storage output at the current moment is set to zero.

[0024] If the current SOC is greater than the maximum SOC limit and the energy storage is in a charging state, the output of the energy storage at the current moment is set to zero. If the current SOC is less than the minimum SOC limit and the energy storage is in a discharging state, the output of the energy storage at the current moment is set to zero.

[0025] As a preferred solution of the power smoothing control method based on wavelet denoising and Kalman filtering described in the present invention, the initial action amount of energy storage is obtained according to the difference between the filtered power generation and the original power generation, the initial output of energy storage is substituted into the energy storage output control algorithm to obtain the final action amount of energy storage, the smoothed power generation is obtained according to the difference between the original power generation and the final action amount of energy storage, and the SOC state at the next moment is calculated, wherein the SOC state calculated at the next moment is expressed as,

[0026] If P b,t >0, then SOC t+1 =(SOC t *CAP-P b,t *60 / 3600) / CAP;

[0027] If P b,t <0, then SOC t+1 =(SOC t *CAP-P b,t *η*60 / 3600) / CAP

[0028] Among them, SOCt+1 is the SOC state at the next moment, SOC t is the SOC state at the current moment; CAP is the energy storage capacity; P b,t is the final energy storage action at the current moment; η is the energy storage charging and discharging efficiency.

[0029] As a preferred solution of the power smoothing control method based on wavelet denoising and Kalman filtering described in the present invention, the quantitative index of the fluctuation of the daily power generation curve includes the degree of fluctuation severity, the maximum fluctuation amplitude and the fluctuation rate;

[0030] The intensity of the fluctuation is expressed as,

[0031]

[0032] Wherein, P(i) is the i-th power sampling value in the time period T; is the average power value in the T time period;

[0033] The maximum fluctuation range is expressed as,

[0034]

[0035] in, is the maximum power in the T time period; is the maximum power in the T time period;

[0036] The volatility is expressed as,

[0037]

[0038] Among them, λ(t) is the fluctuation rate at time t, P(t) is the output value at time t, P rate For installed capacity.

[0039] Another object of the present invention is to provide a power smoothing control system based on wavelet denoising and Kalman filtering, which can solve the problem of severe power fluctuations in existing new energy power generation systems by constructing an efficient power smoothing control system.

[0040] In order to solve the above technical problems, the present invention provides the following technical solutions: a power smoothing control system based on wavelet denoising and Kalman filtering, comprising: a data acquisition module, a wavelet denoising module, a Kalman filtering module, an energy storage control module and a power analysis module; the data acquisition module is used to collect the new energy power generation at the current moment in real time, and input the corresponding data into the algorithm program; the wavelet denoising module is used to obtain the wavelet transform parameters by taking the data input into the algorithm program as the wavelet transform power sequence, and using wavelet decomposition to obtain the denoised sequence; the Kalman filtering module is used to extract the denoised power generation at the current moment from the sequence, and use Kalman filtering to obtain the filtered power. the current moment power generation; the energy storage control module is used to obtain the initial action amount of energy storage according to the difference between the filtered power generation and the original power generation, substitute the initial output of energy storage into the energy storage output control algorithm to obtain the final action amount of energy storage, obtain the smoothed power generation according to the difference between the original power generation and the final action amount of energy storage, and calculate the SOC state at the next moment; the power analysis module is used to determine whether the power smoothing control duration meets one day. If the condition is met, a daily power generation curve is formed according to the smoothed power generation obtained at each moment, a quantitative index of the fluctuation of the daily power generation curve is established, and the fluctuation of the smoothed daily power generation curve is quantified.

[0041] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the power smoothing control method based on wavelet denoising and Kalman filtering are implemented.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the power smoothing control method based on wavelet denoising and Kalman filtering as described above.

[0043] Beneficial effects of the present invention: Compared with the traditional power smoothing algorithm, the power smoothing control method based on wavelet denoising and Kalman filtering provided by the present invention is based on the MATLAB algorithm program, fully utilizes the noise reduction characteristics of wavelet denoising and Kalman filtering, and performs real-time regulation of the output of renewable energy power generation and the output of the energy storage system based on the calculation results of wavelet denoising and Kalman filtering, realizes the complementarity between wind and solar power and energy storage under the condition of satisfying the constraints of energy storage output, thereby realizing the smooth output of renewable energy power generation, and quantifying the fluctuations of the smoothed daily power generation curve, effectively improving the reliability of renewable energy power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 A schematic flow chart of a power smoothing control method based on wavelet denoising and Kalman filtering provided by an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of three-layer wavelet decomposition of a power smoothing control method based on wavelet denoising and Kalman filtering provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of an energy storage output control process algorithm of a power smoothing control method based on wavelet denoising and Kalman filtering provided by an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of the output of each wind farm at each moment in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided by an embodiment of the present invention;

[0049] Figure 5 A schematic diagram of the output of each photovoltaic field at each moment in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided by an embodiment of the present invention;

[0050] Figure 6 A schematic diagram showing the comparison of wind and solar power at each moment before and after smoothing in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided in one embodiment of the present invention;

[0051] Figure 7 A schematic diagram of energy storage output at each moment in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided by an embodiment of the present invention;

[0052] Figure 8 A schematic diagram of the energy storage SOC state at each moment in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided by an embodiment of the present invention;

[0053] Fig. 9 A schematic diagram showing the comparison of the intensity of fluctuations before and after smoothing in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided in one embodiment of the present invention;

[0054] Fig.10A schematic diagram showing the comparison of fluctuation amplitudes before and after smoothing in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided in one embodiment of the present invention;

[0055] Fig.11 A schematic diagram of comparing the volatility before and after smoothing in a basic example of a power smoothing control method based on wavelet denoising and Kalman filtering provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Example 1

[0059] Reference Figure 1-Figure 3 , is an embodiment of the present invention, and provides a power smoothing control method based on wavelet denoising and Kalman filtering, comprising:

[0060] S1: Get 1440 collection points with a resolution of one minute a day, collect the current renewable energy power generation in real time, and input the corresponding data into the algorithm program.

[0061] The data input into the algorithm program is the renewable energy power generation in the previous 120 minutes including the current moment.

[0062] S2: The data input into the algorithm program is used as a wavelet transform power sequence to obtain wavelet transform parameters, and a denoised sequence with 120 elements is obtained by wavelet decomposition.

[0063] In practical engineering applications, useful signals are usually low-frequency signals or relatively stable signals, while noise signals are generally high-frequency signals. The denoising process can be processed as follows: First, perform wavelet decomposition on the signal. Take the three-layer decomposition as an example. Figure 2As shown, the noise part is usually contained in cD1, cD2, and cD3. Then, the appropriate threshold is selected for the decomposed wavelet coefficients for processing, and then the processed wavelet coefficients are used to reconstruct the signal, so that the purpose of noise reduction can be achieved. Therefore, the general process of signal wavelet denoising is as follows:

[0064] (1) Wavelet decomposition of the signal: This includes selecting a certain wavelet basis function and wavelet decomposition level N, and performing N-layer wavelet decomposition on the signal f(t).

[0065] (2) Threshold quantization of high-frequency coefficients: Select a threshold quantization criterion and perform threshold quantization on the high-frequency coefficients in each decomposition level.

[0066] (3) Wavelet reconstruction: Perform inverse wavelet transform on the high-frequency coefficients of the 1st to Nth layers and the low-frequency coefficients of the Nth layer after threshold quantization to reconstruct the denoised signal.

[0067] Wherein, step S1 specifically includes:

[0068] (1) The data input into the algorithm program is taken as a wavelet transform power sequence, and a one-dimensional sequence with 120 elements is obtained.

[0069] (2) The wavelet transform power sequence is used as the input value x of the ddencmp function in the MATLAB software. The remaining input values ​​are den for in1 and wv for in2, indicating that the signal decomposition as the input is denoised using wavelet decomposition. The output values ​​thr, sorh, and keepapp of the ddencmp function are obtained, namely, the parameters of the wavelet transform: threshold, threshold type, and approximate retention coefficient allowed.

[0070] (3) The wavelet transform parameters are input into the wdencmp function in MATLAB software, and the db4 wavelet is selected for wavelet decomposition. The remaining input values ​​are 'gbl', X is the wavelet transform power sequence, and N is 5, where 'gbl' means that each layer is processed with the same threshold, and N is the number of layers of wavelet decomposition. The output value XC of the wdencmp function is obtained, which is a one-dimensional sequence with 120 elements after denoising.

[0071] S3: extract the current power generation from the denoised sequence, and use Kalman filtering to obtain the filtered current power generation.

[0072] Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through the system's observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process. For the state estimation algorithm, we can obtain three values ​​of the state quantity: state prediction value Best estimate And the true value (x), the principle of Kalman filtering is to use the Kalman gain K to correct the state prediction value to make it close to the true value and finally get the optimal estimate value. Therefore, the general process of Kalman filtering is as follows:

[0073] (1) State prediction: This process is also called prior state estimation. and the variance between the true value and the optimal estimate (P t-1 ) predicts the prior state estimate at the current moment and the variance between the true value and the predicted value There is prediction noise in this process, which is usually represented by a hyperparameter Q.

[0074] (2) State update: This process is also called posterior estimation. It is based on the variance between the actual value and the predicted value at the current moment. Calculate the Kalman gain K, which actually represents the ratio of prediction error to measurement error in the state optimal estimation process. According to the Kalman gain K and the current state measurement value (z t ) and the prior state estimate The difference between the two values ​​gives the posterior state estimate According to the Kalman gain K and the variance between the true value and the predicted value at the current moment Get the variance (P t ), there is measurement noise in this process, which is usually represented by the hyperparameter R.

[0075] Wherein, step S3 specifically includes:

[0076] (1) The formula for setting the Kalman filter algorithm is:

[0077]

[0078] In the formula, is the prior state estimate at the current moment, is the posterior state estimate at the previous moment, is the estimated value of the posterior state at the current moment; is the variance between the actual value and the predicted value at the current moment, P t-1 is the variance between the true value and the optimal estimate at the previous moment, P t is the variance between the current true value and the optimal estimated value; Q is the prediction noise, which is inversely proportional to the confidence level of the predicted value; K t is the Kalman gain; R is the measurement noise, which is inversely proportional to the confidence level of the measurement value; z t is the state measurement value at the current moment.

[0079] (2) Adjust the hyperparameters R and Q by estimate By P t-1 estimate If you are in the initial stage of the algorithm, you need to set the initial value and P0, calculate the Kalman gain K t , extract the denoised current power generation from the output of the wavelet decomposition as the current state measurement value z t , Kalman filtering is used to obtain the posterior state estimate at the current moment That is, the current power generation value after filtering, and calculate P t .

[0080] S4: Obtain the initial action amount of the energy storage according to the difference between the filtered power generation and the original power generation, substitute the initial output of the energy storage into the energy storage output control algorithm to obtain the final action amount of the energy storage, obtain the smoothed power generation according to the difference between the original power generation and the final action amount of the energy storage, and calculate the SOC state at the next moment.

[0081] like Figure 3 As shown, the specific steps of the energy storage output control algorithm are as follows:

[0082] (1) Energy storage output control: If the energy storage battery is in a discharging state, the loss during the discharging process needs to be compensated in practical applications. Therefore, the power of energy storage discharge is P / η.

[0083] (2) Energy storage over-limit control: If the absolute value of the energy storage output exceeds the maximum charge and discharge power, the energy storage output needs to be adjusted to the maximum charge power or maximum discharge power.

[0084] (3) Energy storage action threshold determination: Frequent energy storage actions will shorten the energy storage life, so a threshold needs to be set to reduce the number of energy storage actions. The energy storage will only act when the absolute value of the energy storage output at the current moment and the energy storage output at the previous moment is greater than the threshold h, otherwise the energy storage output at the current moment is set to zero.

[0085] (4) SOC state evaluation: If the current SOC is greater than the maximum SOC limit and the energy storage is in a charging state, the output of the energy storage at the current moment is set to zero; if the current SOC is less than the minimum SOC limit and the energy storage is in a discharging state, the output of the energy storage at the current moment is set to zero.

[0086] The formula for calculating the SOC state at the next moment in step S4 is as follows:

[0087] If P b,t >0, then: SOC t+1 =(SOC t *CAP-P b,t *60 / 3600) / CAP

[0088] If P b,t <0, then: SOC t+1 =(SOC t *CAP-P b,t *η*60 / 3600) / CAP

[0089] In the formula, SOC t+1 is the SOC state at the next moment, SOC t is the SOC state at the current moment; CAP is the energy storage capacity; P b,t is the final energy storage action at the current moment; η is the energy storage charging and discharging efficiency;

[0090] S5: Determine whether the duration of power smoothing control meets one day. If the condition is met, form a daily power curve based on the smoothed power generated at each moment, establish a quantitative index of the fluctuation of the daily power curve, and quantify the fluctuation of the smoothed daily power curve. If the fluctuation does not meet the grid connection requirements, further modify the energy storage output.

[0091] The quantitative indicators of the daily power generation curve fluctuation in step S5 include:

[0092] (1) Fluctuation severity: The fluctuation severity of new energy can reflect the power change of wind power output within a certain period of time. The larger the value, the greater the fluctuation severity within the period of time. Assuming that there are n sampling points in the time T, the power fluctuation severity within the period of time is:

[0093]

[0094] Where P(i) is the i-th power sampling value in the T time period; is the average power value in the T time period;

[0095] (2) Maximum fluctuation range: The maximum fluctuation range is expressed as the difference between the maximum and minimum output values ​​within a period of time. The maximum fluctuation range within a period of time T is defined as:

[0096]

[0097] In the formula, is the maximum power in the T time period; is the maximum power value in the T time period.

[0098] (3) Volatility:

[0099]

[0100] Where λ(t) is the fluctuation rate at time t with a time scale of n, P(t) is the output value at time t, and Prate For installed capacity.

[0101] (4) Fluctuation:

[0102] ΔP n =P(t)-P(tn)

[0103] In the formula, ΔP n is the fluctuation quantity with time scale n at time t.

[0104] If the fluctuation limit value does not meet the grid connection requirement in step S5, the energy storage output is further modified. The specific contents are as follows:

[0105] (1) Referring to the current wind farm grid connection standard GB / T19963.1-2021 as the grid connection requirements for power smoothing of the present invention, as shown in Table 1.

[0106] Table 1 Grid connection requirements for power smoothing

[0107]

[0108] (2) If the fluctuation limit of the grid-connected power at time t does not meet the grid-connected requirements, the value ranges of the grid-connected power that meet the 1-min and 10-min fluctuation limit values ​​at time t are calculated respectively and the intersection is taken. The upper or lower limit corresponding to the intersection is taken as the grid-connected power at time t, and the energy storage output is further modified according to the difference between the grid-connected power and the original power.

[0109] Example 2

[0110] Reference Figure 2 , which is an embodiment of the present invention, provides a power smoothing control system based on wavelet denoising and Kalman filtering, comprising:

[0111] Data acquisition module, wavelet denoising module, Kalman filter module, energy storage control module and power analysis module;

[0112] The data acquisition module is used to collect the current renewable energy power generation in real time and input the corresponding data into the algorithm program;

[0113] The wavelet denoising module is used to obtain wavelet transform parameters by taking the data input into the algorithm program as a wavelet transform power sequence, and to obtain the denoised sequence by using wavelet decomposition;

[0114] The Kalman filter module is used to extract the current moment power after denoising from the sequence, and use Kalman filtering to obtain the current moment power after filtering;

[0115] The energy storage control module is used to obtain the initial action amount of energy storage according to the difference between the filtered power generation and the original power generation, substitute the initial output of energy storage into the energy storage output control algorithm to obtain the final action amount of energy storage, obtain the smoothed power generation according to the difference between the original power generation and the final action amount of energy storage, and calculate the SOC state at the next moment;

[0116] The power analysis module is used to determine whether the duration of power smoothing control meets one day. If the condition is met, a daily power generation curve is formed according to the smoothed power generation obtained at each moment, a quantitative index of the fluctuation of the daily power generation curve is established, and the fluctuation of the smoothed daily power generation curve is quantified.

[0117] Example 3

[0118] An embodiment of the present invention is different from the first two embodiments in that:

[0119] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0120] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0121] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0122] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0123] Example 4

[0124] like Figure 4-Figure 11 As shown, an embodiment of the present invention provides a power smoothing control method based on wavelet denoising and Kalman filtering. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0125] The basic example was simulated and calculated using MATLAB software. The real-time operating data of five wind farms, including Baihuashan, Baizhang, Gutian, Jiaolianling and Jiuyuanshan, and five photovoltaic power stations, including Jilong, Lansha, Nanjia, Nongtan and Yingxue, from 22:00 on January 11 to 23:59 on January 12 were selected as typical days in winter. The installed capacity and output of each power plant are shown in Table 2.

[0126] Table 2 Installed capacity of each power plant

[0127] Power Plant Baihuashan Wind Power Baizhang Wind Power Gutian Wind Power Jiaolianling Wind Power Jiuyuanshan Wind Power Installed capacity / MW 80 75.8 150 100 100 Power Plant Jilong Photovoltaic Lansha Photovoltaic Nanjia Photovoltaic Nongtan Photovoltaic Yingxue Photovoltaic Installed capacity / MW 150 67.1 47.25 23.27 76.33

[0128] The average daily output rate of each electric field is shown in Table 3.

[0129] Table 3 Output of each power plant

[0130] Power Plant Baihuashan Wind Power Baizhang Wind Power Gutian Wind Power Jiaolianling Wind Power Jiuyuanshan Wind Power Output rate 41.14％ 64.48％ 93.80％ 57.62％ 66.81％ Power Plant Jilong Photovoltaic Lansha Photovoltaic Nanjia Photovoltaic Nongtan Photovoltaic Yingxue Photovoltaic Output rate 7.87％ 3.89％ 27.23％ 47.91％ 4.30％

[0131] The output of each wind farm at each time is Figure 4 As shown, the output of each photovoltaic field at each moment is as follows Figure 5 shown.

[0132] The simulation calculation parameter settings are shown in Table 4.

[0133] Table 4 Simulation parameters

[0134]

[0135]

[0136] Smoothing starts at 0:00 on January 11th, and the control period is 1440 moments from 0:00 on January 11th to 23:59 on January 11th. The simulation results are as follows:

[0137] Table 5 Statistics of simulation results

[0138] project Numeric Energy storage action times / times 967 Energy storage limit times / times 0 Cumulative regulated charging capacity / MWh 61.0603 Cumulative regulated discharge power / MWh 75.3587

[0139] The daily power generation curve is formed based on the smoothed power generation obtained at each moment. The wind and solar power comparison at each moment before and after smoothing is as follows: Figure 6 As shown, the energy storage output at each moment is Figure 7 As shown, the energy storage SOC state at each moment is as follows Figure 8 As shown. The quantitative index of daily power generation curve fluctuation is established, and the fluctuation of the smoothed daily power generation curve is quantified. When quantifying the intensity of fluctuation and the amplitude of fluctuation, the resolution T = 15min is taken. Thus, the comparison of the intensity of fluctuation before and after smoothing can be obtained. Fig. 9 As shown in the figure, the fluctuation amplitude before and after smoothing is Fig.10 As shown in the figure, the volatility before and after smoothing is Fig.11 shown.

[0140] By comparing the quantitative indicators of the daily power generation curve fluctuations before and after smoothing, it can be seen that the power smoothing algorithm based on wavelet denoising and Kalman filtering provided by the present invention can be used to control the output of renewable energy power generation and the output of the energy storage system in real time, and the complementarity between wind and solar power and energy storage can be achieved under the condition of satisfying the energy storage output constraints, thereby achieving smooth output of renewable energy power generation, and effectively improving the reliability of renewable energy power generation.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power smoothing control method based on wavelet denoising and Kalman filtering, characterized in that: include: Collect the current renewable energy power generation in real time and input the corresponding data into the algorithm program; The data input into the algorithm program is used as a wavelet transform power sequence to obtain wavelet transform parameters, and the denoised sequence is obtained by wavelet decomposition; Take out the current moment power after denoising from the sequence, and use Kalman filtering to obtain the current moment power after filtering; The initial action amount of energy storage is obtained according to the difference between the filtered power generation and the original power generation. The initial output of energy storage is substituted into the energy storage output control algorithm to obtain the final action amount of energy storage. The smoothed power generation is obtained according to the difference between the original power generation and the final action amount of energy storage, and the SOC state at the next moment is calculated. Determine whether the duration of power smoothing control meets one day. If the condition is met, form a daily power generation curve based on the smoothed power generation obtained at each moment, establish a quantitative index of the fluctuation of the daily power generation curve, and quantify the fluctuation of the smoothed daily power generation curve.

2. The power smoothing control method based on wavelet denoising and Kalman filtering as claimed in claim 1, characterized in that: The real-time collection of the renewable energy power generation at the current moment includes obtaining 1440 collection points with a resolution of minutes per day, collecting the renewable energy power generation at the current moment in real time, and inputting the corresponding data into the algorithm program; wherein the data input into the algorithm program is the renewable energy power generation in the previous 120 minutes including the current moment; The algorithm program is MATLAB software.

3. The power smoothing control method based on wavelet denoising and Kalman filtering as claimed in claim 2, characterized in that: The obtaining of wavelet transform parameters includes using the wavelet transform power sequence as an input signal and applying the ddencmp function in MATLAB software to obtain the wavelet transform parameters; wherein the wavelet transform parameters include a threshold value, a threshold value type, and an allowable approximate retention coefficient; The parameters of wavelet transform are input into the wdencmp function in MATLAB software and the db4 wavelet is selected for wavelet decomposition to obtain a one-dimensional sequence with 120 elements after denoising.

4. The power smoothing control method based on wavelet denoising and Kalman filtering as claimed in claim 3, characterized in that: The Kalman filter is expressed as, in, is the prior state estimate at the current moment, is the posterior state estimate at the previous moment, is the estimated value of the posterior state at the current moment; is the variance between the actual value and the predicted value at the current moment, P t-1 is the variance between the true value and the optimal estimate at the previous moment, P t is the variance between the current true value and the optimal estimated value; Q is the prediction noise, which is inversely proportional to the confidence level of the predicted value; K t is the Kalman gain; R is the measurement noise, which is inversely proportional to the confidence level of the measurement value; z t is the state measurement value at the current moment; Set the initial value of the Kalman filter algorithm and P0, adjust the hyperparameters R and Q, extract the denoised current moment power generation from the output result of wavelet decomposition, and use Kalman filtering to obtain the filtered current moment power generation.

5. The power smoothing control method based on wavelet denoising and Kalman filtering as claimed in claim 4, characterized in that: The energy storage output control algorithm includes energy storage output control, energy storage over-limit control, energy storage action threshold determination and SOC state evaluation; If the energy storage battery is in a discharging state, the loss during the discharging process needs to be compensated in practical applications, so the power of energy storage discharge is P / η; If the absolute value of the energy storage output exceeds the maximum charge and discharge power, the output of the energy storage needs to be adjusted to the maximum charge power or the maximum discharge power; Set the energy storage action threshold to reduce the number of energy storage actions. The energy storage will only be activated when the absolute value of the energy storage output at the current moment and the energy storage output at the previous moment is greater than the threshold h, otherwise the energy storage output at the current moment is set to zero. If the current SOC is greater than the maximum SOC limit and the energy storage is in a charging state, the output of the energy storage at the current moment is set to zero. If the current SOC is less than the minimum SOC limit and the energy storage is in a discharging state, the output of the energy storage at the current moment is set to zero.

6. The power smoothing control method based on wavelet denoising and Kalman filtering as claimed in claim 5, characterized in that: The initial action amount of energy storage is obtained according to the difference between the filtered power generation and the original power generation. The initial output of energy storage is substituted into the energy storage output control algorithm to obtain the final action amount of energy storage. The smoothed power generation is obtained according to the difference between the original power generation and the final action amount of energy storage, and the SOC state at the next moment is calculated. The SOC state at the next moment is expressed as: If P b,t >0, then SOC t+1 =(SOC t *CAP-P b,t *60 / 3600) / CAP; If P b,t <0, then SOC t+1 =(SOC t *CAP-P b,t *η*60 / 3600) / CAP Among them, SOC t+1 is the SOC state at the next moment, SOC t is the SOC state at the current moment; CAP is the energy storage capacity; P b,t is the final energy storage action at the current moment; η is the energy storage charging and discharging efficiency.

7. The power smoothing control method based on wavelet denoising and Kalman filtering as claimed in claim 6, characterized in that: The quantitative indicators of the daily power generation curve fluctuation include the severity of fluctuation, the maximum fluctuation amplitude and the fluctuation rate; The intensity of the fluctuation is expressed as, Wherein, P(i) is the i-th power sampling value in the time period T; is the average power value in the T time period; The maximum fluctuation range is expressed as, in, is the maximum power in the T time period; is the maximum power in the T time period; The volatility is expressed as, Among them, λ(t) is the fluctuation rate at time t, P(t) is the output value at time t, P rate For installed capacity.

8. A system using the power smoothing control method based on wavelet denoising and Kalman filtering as claimed in any one of claims 1 to 7, characterized in that: include: Data acquisition module, wavelet denoising module, Kalman filter module, energy storage control module and power analysis module; The data acquisition module is used to collect the power generated by renewable energy at the current moment in real time and input the corresponding data into the algorithm program; The wavelet denoising module is used to obtain wavelet transform parameters by using the data input into the algorithm program as a wavelet transform power sequence, and to obtain a denoised sequence by using wavelet decomposition; The Kalman filter module is used to extract the current moment power after denoising from the sequence, and use Kalman filtering to obtain the current moment power after filtering; The energy storage control module is used to obtain the initial action amount of energy storage according to the difference between the filtered power generation and the original power generation, substitute the initial output of energy storage into the energy storage output control algorithm to obtain the final action amount of energy storage, obtain the smoothed power generation according to the difference between the original power generation and the final action amount of energy storage, and calculate the SOC state at the next moment; The power analysis module is used to determine whether the power smoothing control duration meets one day. If the condition is met, a daily power generation curve is formed according to the smoothed power generation obtained at each moment, a quantitative index of the fluctuation of the daily power generation curve is established, and the fluctuation of the smoothed daily power generation curve is quantified.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power smoothing control method based on wavelet denoising and Kalman filtering according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power smoothing control method based on wavelet denoising and Kalman filtering according to any one of claims 1 to 7 are implemented.

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