Self-adaptive wavelet packet wind power stabilizing method based on time scale

Abstract

The invention relates to a self-adaptive wavelet packet wind power stabilizing method based on a time scale, and the method comprises the steps: dividing a time domain based on a set time scale, carrying out the self-adaptive wavelet packet decomposition, and carrying out the stabilizing of fluctuation out-of-limit data in adjacent time domains through employing an arithmetic average method; based on the characteristics of the energy storage equipment and the node energy distribution condition, the correlation between the hybrid energy storage power components is analyzed by using the energy entropy difference, the energy type and power type energy storage power components are obtained, and hybrid energy storage capacity optimization is carried out by combining the maximum charging and discharging power and the energy change; the method comprises the following steps: dynamically partitioning an energy storage SOC, improving a fuzzy control membership function, and optimizing a hybrid energy storage power instruction by adopting two-stage fuzzy control so as to reduce SOC out-of-limit of the energy storage system. According to the method, power fluctuation can be effectively stabilized, the hybrid energy storage component can be more accurately distributed, meanwhile, SOC out-of-limit can be effectively reduced, and the electric energy quality is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power fluctuation smoothing strategies, and in particular relates to a time scale-based adaptive wavelet packet smoothing method for wind power. Background Art

[0002] In the process of achieving the goals of carbon peak and carbon neutrality in the power system, the traditional power system with fossil energy as the core is transforming and upgrading to a new power system with clean and low-carbon energy as the core. However, as one of the representatives of new energy, wind energy has volatility and uncertainty in its output, which will greatly affect the stability of the power system, grid frequency and power quality during the grid connection process. Therefore, how to effectively smooth out grid connection fluctuations has become an urgent problem to be solved. At present, both energy-type energy storage and power-type energy storage have their own shortcomings, and the hybrid energy storage system combines the strengths of energy-type energy storage and power-type energy storage, realizes economic and technical complementarity, and becomes the first choice for smoothing wind power output fluctuations.

[0003] When configuring an energy storage system, it is crucial to reasonably determine the wind power grid-connected power and the coordinated allocation of fluctuating power among different energy storage devices. A wind power fluctuation smoothing strategy is needed that can reasonably allocate power between energy-type storage and power-type storage and extend the service life of the energy storage system. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for stabilizing wind power based on a time scale adaptive wavelet packet, reasonably distribute power between energy type energy storage and power type energy storage, and extend the service life of the energy storage system.

[0005] The present invention provides a method for stabilizing wind power based on a time scale adaptive wavelet packet. The method is directed to a hybrid energy storage system composed of power-type energy storage and energy-type energy storage, and comprises the following steps:

[0006] Step 1: divide the time domain based on the set time scale and perform adaptive wavelet packet decomposition to reduce the local over-suppression phenomenon when processing long-term wind power data. For the possible fluctuation exceeding the limit at the boundary of adjacent time domains, perform volatility analysis on the data without boundary processing to find the time domain boundary where the fluctuation exceeding the limit occurs, and use the arithmetic average method in the adjacent time domain to smooth the data with fluctuation exceeding the limit;

[0007] Step 2: Based on the characteristics of the energy storage equipment and the energy distribution of the nodes, the correlation between the power components of the hybrid energy storage is analyzed using the energy entropy difference. By analyzing the energy entropy difference between the high-frequency components, the node with the largest difference in energy entropy during the change process is selected as the high- and low-frequency dividing point of the hybrid energy storage, the power components of the energy-type and power-type energy storage are obtained, and the hybrid energy storage capacity is optimized in combination with the maximum charge and discharge power and energy change;

[0008] Step 3: By dynamically partitioning the energy storage SOC, improving the fuzzy control membership function, and using two-stage fuzzy control to optimize the hybrid energy storage power command, the SOC of the energy storage system can be reduced.

[0009] Furthermore, the adaptive wavelet packet decomposition in step 1 includes: selecting db6 wavelet as the wavelet basis to decompose the original output power P of the wind power w Decomposition is performed, 4h is selected as the time scale, a typical day is divided into 6 time domains T(i), wavelet packet decomposition is performed on each time domain T(i), and the minimum number of decomposition levels that meet the grid-connected fluctuation standard is selected respectively.

[0010] Furthermore, the energy storage system is a hybrid energy storage system composed of flywheel energy storage and lithium iron phosphate batteries.

[0011] Furthermore, in step 1, the fluctuation limit is set according to the national standard and the grid frequency regulation characteristics, and the active power limit is set according to the requirements of the grid dispatching department, and the maximum power fluctuation limit in the time window is used as the evaluation index. The standard is defined as follows:

[0012] △P L =P max (t)-P min (t),t∈T L

[0013] Where ΔP L is the power fluctuation limit; T L is a time window of length L; P max (t), P min (t) are T L The maximum and minimum wind power within the range.

[0014] Furthermore, in step 2, the correlation between the hybrid energy storage power components is analyzed by using the energy entropy difference, and the analysis process is as follows:

[0015] H(i)=-P(i)log2P(i)

[0016] △H(i)=|H(i+1)-H(i)|

[0017] Where (i) is the energy entropy value of the i-th node; P(i) is the proportion of the energy of the i-th node in the total energy; ΔH(i) is the energy entropy difference between the i-th and i+1-th nodes;

[0018]

[0019]

[0020] In the formula, cj,k is the wavelet coefficient of signal x(t); is the wavelet basis function; E m is the energy of each node; E is the energy sum of each node;

[0021] Repeat the analysis process, and take the node k with the largest difference in energy entropy as the power dividing point of low-frequency and high-frequency power components, and decompose the energy type and power type energy storage smoothing power P b and P f As follows:

[0022]

[0023] Furthermore, the hybrid energy storage capacity optimization is performed by combining the maximum charge and discharge power and energy change in step 2, which is specifically expressed as follows:

[0024] Maximum charge and discharge power:

[0025]

[0026] Where P b_max and P f_max are the maximum charge and discharge reference powers of the battery and flywheel energy storage respectively; P b_ref and P f_ref are the reference powers of battery and flywheel energy storage respectively; η b and η f are the charge and discharge efficiencies of battery and flywheel energy storage, respectively;

[0027] Energy changes of energy storage:

[0028]

[0029] The improved energy storage capacity is:

[0030]

[0031] In the formula, S ocu , S ocl分别 The upper and lower limits of SOC are 0.8 and 0.2 for energy storage and 0.9 and 0.1 for power storage respectively. C is the rated capacity of energy storage.

[0032] Furthermore, step 3 includes: dynamically partitioning the SOC of the energy storage based on the hybrid energy storage characteristics, using different fuzzy control membership functions and rules to adjust the power output of the flywheel energy storage and the battery for different partitions, and using the centroid method to defuzzify the membership value to obtain the modified power regulation coefficient K of the energy storage f(t), in order to optimize the overall system performance; the corrected power type energy storage power instruction is:

[0033] P f (t) = K f (t)×P f_ref (t)

[0034] The difference between the power command before and after correction is:

[0035] △P f (t) = (1-K f (t))×P f_ref (t)

[0036] The difference ΔP before and after the power command of power type energy storage is corrected f (t) is compensated by energy storage, and the corrected energy storage power instruction is:

[0037] P b (t) = K b (t)×(P b_ref (t)+△P f (t)).

[0038] The present invention also provides a time-scale-based adaptive wavelet packet wind power stabilization system, comprising a wind power stabilization module, wherein the wind power stabilization module executes the time-scale-based adaptive wavelet packet wind power stabilization method.

[0039] The present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method for stabilizing wind power using a time-scale-based adaptive wavelet packet is implemented.

[0040] The present invention also provides an electronic device, comprising:

[0041] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the time-scale-based adaptive wavelet packet method for smoothing wind power.

[0042] Through the above scheme, the method and system for smoothing wind power based on time scale adaptive wavelet packet can effectively smooth power fluctuations, more accurately allocate hybrid energy storage components, and effectively reduce SOC over-limit and improve power quality.

[0043] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a system structure diagram of lithium iron phosphate and flywheel hybrid energy storage;

[0045] Figure 2 It is a flow chart of the method for stabilizing wind power based on the time scale of the adaptive wavelet packet of the present invention;

[0046] Figure 3 This is a comparison chart of wind power 10-minute volatility processed by different wavelet packet methods;

[0047] Figure 4 This is the power allocation result diagram;

[0048] Figure 5 is the membership function of the flywheel;

[0049] Figure 6 is the membership function of lithium iron phosphate battery;

[0050] Figure 7 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0051] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0052] Wind power output is volatile and uncertain, which will greatly affect the stability of the power system, grid frequency and power quality during the grid connection process. Installing energy storage devices in wind farms can timely smooth out the fluctuations of wind power, reduce the impact on the grid, and improve the quality of wind power. In order to achieve the above purposes, this embodiment proposes a time-scale-based adaptive wavelet packet method for smoothing wind power.

[0053] Ginseng Figure 2 As shown, the specific steps of the stabilization strategy of this embodiment are:

[0054] Firstly, an appropriate time scale is selected to divide the time domain and adaptive wavelet packet decomposition is performed to reduce the local over-suppression phenomenon that occurs when processing long-term wind power data, and to smooth out the fluctuations that may occur at the boundaries of adjacent time domains.

[0055] Secondly, considering the characteristics of energy storage equipment and node energy distribution, the energy entropy difference is used to analyze the correlation between the power components of hybrid energy storage, and the maximum entropy difference point is selected as the hybrid energy storage power dividing point to obtain the flywheel energy storage and lithium iron phosphate battery power components. The hybrid energy storage capacity is optimized by combining the maximum charge and discharge power and energy changes.

[0056] Finally, by dynamically partitioning the energy storage SOC, improving the fuzzy control membership function, and optimizing the hybrid energy storage power command, the SOC limit of the energy storage system can be reduced. The specific technical solution is as follows:

[0057] (1) Select an appropriate time scale to divide the time domain, perform adaptive wavelet packet decomposition, and smooth out the fluctuations that may occur at the boundaries of adjacent time domains. This step includes the following:

[0058] (1.1) Divide the appropriate time scale to reduce the impact of the maximum fluctuation in a certain period on the overall smoothing effect, so as to avoid the poor wind power smoothing effect in other periods, thereby increasing the burden on the hybrid energy storage system.

[0059] In this embodiment, a typical day is divided into six time domains T(i), wavelet packet decomposition is performed on each time domain T(i), and the minimum number of decomposition layers that meets the grid-connected fluctuation standard is selected as follows:

[0060]

[0061] Figure 1 This is the system structure diagram of lithium iron phosphate and flywheel hybrid energy storage. The system is mainly composed of wind turbines, flywheel energy storage system, electrochemical energy storage system, transformer, busbar, etc. In the figure, P b is the charge and discharge reference power of lithium iron phosphate battery, P f P is the charge and discharge reference power of the flywheel energy storage, discharge is positive, charge is negative, G is the reference value of wind power grid-connected power; P HESS is the charge and discharge reference power of the hybrid energy storage system, P W Output power to wind farms in real time.

[0062] The relationship between the above powers is as follows:

[0063]

[0064] (1.2) Perform adaptive wavelet packet decomposition to reduce the local over-smoothing phenomenon that occurs when processing long-term wind power data.

[0065] The 10-minute wind power volatility diagram after wavelet packet processing is as follows Figure 3 shown.

[0066] The decomposition and reconstruction are as follows:

[0067] Decomposition algorithm:

[0068]

[0069] Reconstruction algorithm:

[0070]

[0071] (1.3) Smooth out-of-limit fluctuations that may occur at the boundaries of adjacent time domains.

[0072] To smooth out the fluctuations that may occur at the boundaries of adjacent time domains, volatility analysis is performed on the data without boundary processing to find out the time domain boundaries where the fluctuations occur and smooth them out, so as to avoid the influence of boundary effects on the overall effect caused by the boundaries of adjacent time domains when the wavelet basis function is limited in the time domain.

[0073] Since dividing the time domain for wavelet packet decomposition may result in large discontinuities or fluctuations at the boundaries of the time domain, it is necessary to calculate whether the power at the boundaries of adjacent time domains meets the grid-connected fluctuation standard. If not, set the scale of the smoothing window to L, set L = 1, and take the arithmetic mean of the current power in the smoothing window and the previous (L-1) historical values. As the current grid-connected power of wind power, calculate whether it meets the grid-connected fluctuation standard. If it does not meet the grid-connected fluctuation standard, set L = L + 1, continue to take the arithmetic average of the fluctuation limit value and the historical value in the smoothing window, and repeat the steps until the grid-connected fluctuation standard is met. Figure 3 shown.

[0074] Fluctuation limits need to be set according to national standards and the grid frequency regulation characteristics, and according to the requirements of the grid dispatching department. The maximum power fluctuation limit (MPFR) within the time window is used as the evaluation index. The standard is defined as follows:

[0075] △P L =P max (t)-P min (t),t∈T L

[0076] In the formula, ΔP L ——Power fluctuation limit; T L ——time window of length L; P max (t), P min (t)——T L The maximum and minimum wind power within the range.

[0077] (2) Considering the characteristics of energy storage equipment and node energy distribution, the energy entropy difference is used to analyze the correlation between the hybrid energy storage power components, the hybrid energy storage power cutoff point is selected, the power components are obtained, and the hybrid energy storage capacity is optimized in combination with the maximum charge and discharge power and energy change. This step includes the following:

[0078] (2.1) Calculate the energy entropy, and select the maximum entropy difference as the hybrid energy storage power demarcation point through the energy entropy difference between two adjacent nodes. The energy entropy difference calculation results are shown in Table 1:

[0079] Table 1 Calculation results of energy entropy difference

[0080]

[0081] From Table 1, we can see that the maximum value of energy entropy difference is ΔH 2,3 , then S 6,1:2 Composed of low frequency components, S 6,3:63 The high-frequency component is composed, the low-frequency component is distributed as a power signal to the lithium iron phosphate battery, and the high-frequency component is distributed as a power component to the flywheel energy storage. The power distribution result is as follows Figure 4 shown.

[0082] (2.2) Optimize the hybrid energy storage capacity by combining the maximum charge and discharge power and energy variation.

[0083] Considering that the power components of the two parts have been obtained, this section configures the capacity of the hybrid energy storage system in combination with the large charging and discharging power and energy changes. The maximum power and energy changes considered during the configuration are calculated by the following sections.

[0084] Maximum charge and discharge power:

[0085]

[0086] Where P b_max and P f_max is the maximum charge and discharge reference power of the battery and flywheel energy storage; P b_ref and P f_ref is the reference power of battery and flywheel energy storage; η b and η f The charging and discharging efficiency of batteries and flywheel energy storage.

[0087] Energy changes of energy storage:

[0088]

[0089] The configuration results are shown in Table 2:

[0090] Table 2 Configuration results

[0091]

[0092] (3) By dynamically partitioning the energy storage SOC, improving the fuzzy control membership function, and optimizing the hybrid energy storage power command, the SOC limit of the energy storage system can be reduced. This step includes the following:

[0093] (3.1) Dynamically partition the energy storage SOC, improve the fuzzy control membership function, and optimize the power command. This step includes the following:

[0094] The energy storage state of charge is dynamically partitioned, and the energy storage SOC membership function is improved (the membership function of the flywheel and lithium iron phosphate battery is as follows Figure 5 , Figure 6 As shown), it is set into three areas, namely: S OC1 ∈[0.4,0.6], S OC2 ∈[S ocl ,0.4)∪(0.6,S ocu ] and S OC3 ∈(0~S ocl )∪(S ocu ,1), the specific correction rules are as follows: when S OC =S OC1 , the energy storage system maintains the power allocation scheme based on the energy entropy difference, and there is no need to adjust the current power instruction of the energy storage; when S OC OC1 And energy storage still requires high-power discharge or S OC >S OC1 But when the energy storage still needs high-power charging, the fuzzy controller will OC The optimized energy storage SOC range is shown in Table 3:

[0095] Table 3 Optimized energy storage SOC range

[0096]

[0097]

[0098] Finally, the centroid method is used to defuzzify the membership value and obtain the corrected power.

[0099] This time-scale-based adaptive wavelet packet method for smoothing wind power, first, selects a suitable time scale to divide the time domain, performs adaptive wavelet packet decomposition, and uses the arithmetic average method in adjacent time domains to smooth the data that exceeds the limit of fluctuation. Secondly, the characteristics of hybrid energy storage are integrated, and the energy entropy difference is used to analyze the fluctuating power of wind power, and the fluctuating power is reasonably allocated. Finally, considering the state of charge and cycle life of the energy storage equipment, two-stage fuzzy control is applied to optimize the power output instructions of flywheel energy storage and lithium iron phosphate batteries. The above method can effectively smooth power fluctuations, more accurately allocate hybrid energy storage components, and effectively reduce SOC limits and improve power quality.

[0100] ​This embodiment further provides a time-scale-based adaptive wavelet packet wind power stabilization system, including a wind power stabilization module, wherein the wind power stabilization module executes the time-scale-based adaptive wavelet packet wind power stabilization method.

[0101] This embodiment further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the time-scale-based adaptive wavelet packet method for stabilizing wind power is implemented.

[0102] Ginseng Figure 7 As shown, this embodiment also provides an electronic device, including:

[0103] A memory 201 and a processor 202, wherein the memory 201 and the processor 202 are communicatively connected to each other, the memory 201 stores computer instructions, and the processor 202 executes the time-scale-based adaptive wavelet packet wind power smoothing method by executing the computer instructions.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for stabilizing wind power based on adaptive wavelet packets based on time scale, characterized in that: The method is directed to a hybrid energy storage system consisting of power-type energy storage and energy-type energy storage, and comprises the following steps: Step 1: divide the time domain based on the set time scale and perform adaptive wavelet packet decomposition to reduce the local over-suppression phenomenon when processing long-term wind power data. For the possible fluctuation exceeding the limit at the boundary of adjacent time domains, perform volatility analysis on the data without boundary processing to find the time domain boundary where the fluctuation exceeding the limit occurs, and use the arithmetic average method in the adjacent time domain to smooth the data with fluctuation exceeding the limit; Step 2: Based on the characteristics of the energy storage equipment and the energy distribution of the nodes, the correlation between the power components of the hybrid energy storage is analyzed using the energy entropy difference. By analyzing the energy entropy difference between the high-frequency components, the node with the largest difference in energy entropy during the change process is selected as the high- and low-frequency dividing point of the hybrid energy storage, the power components of the energy-type and power-type energy storage are obtained, and the hybrid energy storage capacity is optimized in combination with the maximum charge and discharge power and energy change; Step 3: By dynamically partitioning the energy storage SOC, improving the fuzzy control membership function, and using two-stage fuzzy control to optimize the hybrid energy storage power command, the SOC of the energy storage system can be reduced.

2. The method for stabilizing wind power based on adaptive wavelet packet based on time scale according to claim 1 is characterized in that: The adaptive wavelet packet decomposition in step 1 includes: selecting db6 wavelet as the wavelet basis to decompose the original output power P of wind power w Decomposition is performed, 4h is selected as the time scale, a typical day is divided into 6 time domains T(i), wavelet packet decomposition is performed on each time domain T(i), and the minimum number of decomposition levels that meet the grid-connected fluctuation standard is selected respectively.

3. The method for stabilizing wind power based on adaptive wavelet packet based on time scale according to claim 2 is characterized in that: The energy storage system is a hybrid energy storage system composed of flywheel energy storage and lithium iron phosphate batteries.

4. The method for stabilizing wind power based on adaptive wavelet packet based on time scale according to claim 3 is characterized in that: In step 1, the fluctuation limit is set according to the national standard and the grid frequency regulation characteristics. The active power limit is set according to the requirements of the grid dispatching department, and the maximum power fluctuation limit in the time window is used as the evaluation index. The standard is defined as follows: △P L =P max (t)-P min (t),t∈T L Where ΔP L is the power fluctuation limit; T L is a time window of length L; P max (t), P min (t) are T L The maximum and minimum wind power within the range.

5. The method for stabilizing wind power based on adaptive wavelet packet based on time scale according to claim 4 is characterized in that: In step 2, the correlation between the hybrid energy storage power components is analyzed by using the energy entropy difference, and the analysis process is as follows: H(i)=-P(i)log2P(i) △H(i)=|H(i+1)-H(i)| Where (i) is the energy entropy value of the i-th node; P(i) is the proportion of the energy of the i-th node in the total energy; ΔH(i) is the energy entropy difference between the i-th and i+1-th nodes; In the formula, c j,k is the wavelet coefficient of signal x(t); is the wavelet basis function; E m is the energy of each node; E is the energy sum of each node; Repeat the analysis process, and take the node k with the largest difference in energy entropy as the power dividing point of low-frequency and high-frequency power components, and decompose the energy type and power type energy storage smoothing power P b and P f As follows:

6. The method for stabilizing wind power based on adaptive wavelet packet based on time scale according to claim 5 is characterized in that: The hybrid energy storage capacity optimization described in step 2 is performed by combining the maximum charge and discharge power and energy change, which is specifically expressed as: Maximum charge and discharge power: Where P b_max and P f_max are the maximum charge and discharge reference powers of the battery and flywheel energy storage respectively; P b_ref and P f_ref are the reference powers of battery and flywheel energy storage respectively; η b and η f are the charge and discharge efficiencies of battery and flywheel energy storage, respectively; Energy changes of energy storage: The improved energy storage capacity is: In the formula, S ocu , S ocl分别 The upper and lower limits of SOC are 0.8 and 0.2 for energy storage and 0.9 and 0.1 for power storage respectively. C is the rated capacity of energy storage.

7. The method for stabilizing wind power based on adaptive wavelet packet based on time scale according to claim 6 is characterized in that: The step 3 includes: dynamically partitioning the SOC of the energy storage based on the hybrid energy storage characteristics, using different fuzzy control membership functions and rules to adjust the power output of the flywheel energy storage and the battery for different partitions, and using the centroid method to defuzzify the membership value to obtain the corrected power regulation coefficient K of the energy storage. f (t), in order to optimize the overall system performance; the corrected power type energy storage power instruction is: P f (t)=K f (t)×P f_ref (t) The difference between the power command before and after correction is: △P f (t)=(1-K f (t))×P f_ref (t) The difference ΔP before and after the power command of power type energy storage is corrected f (t) is compensated by energy storage, and the corrected energy storage power instruction is: P b (t)=K b (t)×(P b_ref (t)+△P f (t))。 8. A time-scale-based adaptive wavelet packet wind power system, characterized in that: It comprises a wind power stabilization module, and the wind power stabilization module executes the time scale-based adaptive wavelet packet wind power stabilization method described in any one of claims 1-7.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, a time-scale-based adaptive wavelet packet method for smoothing wind power is implemented as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the time-scale-based adaptive wavelet packet method for smoothing wind power as described in any one of claims 1 to 7 by executing the computer instructions.

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