A two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology

Abstract

The present application relates to grid-connected photovoltaic power generation system operation technical field, especially a kind of two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology.The HESS implementation adjustment command is obtained by two-stage photovoltaic power fluctuation smoothing method.The photovoltaic output power signal is decomposed using wavelet packet method, the amplitude-frequency information of photovoltaic power signal and the performance characteristics of different types of energy storage are combined, the target power and energy storage charge-discharge power are calculated to achieve the effect of photovoltaic grid-connected power smoothing.

Description

Technical Field

[0001] This invention relates to the field of grid-connected photovoltaic power generation system operation technology, and in particular to a two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology. Background Technology

[0002] In recent years, photovoltaic (PV) power generation, as an environmentally friendly renewable energy source, has received increasing attention and development. The penetration rate of PV power generation systems in the power grid has been growing rapidly. However, the operation of grid-connected PV power generation systems faces many technical challenges. Typically, PV power generation is affected by variations in irradiance or other environmental factors. Due to the strong fluctuations and intermittency of PV power generation, it has a significant impact on the power quality and reliability of the power grid. Furthermore, the State Grid Corporation of China has issued technical requirements for PV power plants, which stipulate that maximum power fluctuations should be mitigated at different times under various limiting conditions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology. This method obtains the HESS (Hyper-Effective Settlement) adjustment command through the two-stage photovoltaic power fluctuation smoothing method.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology includes the following steps:

[0006] S1. Establish a grid-connected photovoltaic-hybrid energy storage system, wherein the photovoltaic-hybrid energy storage system includes a photovoltaic system (PV), a hybrid energy storage system (HESS), and a local controller; wherein the hybrid energy storage system includes batteries and supercapacitors;

[0007] S2. Based on the grid-connected photovoltaic-hybrid energy storage system in S1, use the local controller to collect the real-time power signal of the photovoltaic system and the short-term photovoltaic power output prediction data for 1 minute;

[0008] S3. Based on the State Grid's photovoltaic grid connection standard recommendations, two photovoltaic active power fluctuation standards with different time scales are established, namely 1 minute and 10 minutes.

[0009] S4. Based on the relevant data in S2, a two-stage photovoltaic power fluctuation smoothing method is adopted. The two stages of the two-stage photovoltaic power fluctuation smoothing method include a filtering stage and an adjustment stage. In the filtering stage, wavelet packet decomposition technology is used to process the future short-term photovoltaic power generation prediction data. In the adjustment stage, the local controller generates real-time adjustment commands for HESS based on the photovoltaic active power fluctuation standards at different time scales, thereby achieving the effect of smoothing the fluctuation of photovoltaic power generation.

[0010] Preferably, the grid-connected photovoltaic-hybrid energy storage system described in steps S1 and S2 satisfies the following power balance:

[0011] P g (t)=P o (t)+P bat (t)+P sc (t)

[0012] Among them, P g (t) is the grid-connected photovoltaic power after HESS smoothing; P o (t) represents the original photovoltaic power; P bat (t) and P sc (t) represents the power of the battery and the supercapacitor, respectively; for these two variables, positive values ​​indicate discharge and negative values ​​indicate charging.

[0013] The photovoltaic active power fluctuation standard mentioned in step S3 is as follows: (The standard is missing from the original text.)

[0014] The State Grid's photovoltaic grid connection standard recommends that for a 50MW photovoltaic power generation system, the fluctuation of photovoltaic active power should not exceed 10% of the installed capacity within 1 minute and should not exceed 30% within 10 minutes.

[0015] The photovoltaic power fluctuation rate within 1 minute is R1(t), and the maximum and minimum values ​​of the photovoltaic power fluctuation limit within 1 minute are P1(t) and P2(t), respectively. 1,max (t) and P 1,min (t):

[0016]

[0017]

[0018]

[0019] Where, max{P g (t-Δt)} and min{P g (t-Δt)} represents the maximum and minimum values ​​of grid-connected photovoltaic power over a given period; P r This refers to the installed capacity of the photovoltaic system.

[0020] The photovoltaic power fluctuation rate within 10 minutes is R 10 (t), the maximum and minimum values ​​of the photovoltaic power fluctuation limit within 10 minutes are P. 10,max (t) and P 10,min (t);

[0021]

[0022]

[0023]

[0024] Preferably, step S4 includes the following sub-steps:

[0025] S4-1. Based on the difference in response time between the balancing battery and the supercapacitor, select 1 minute as the marginal charging / discharging response time between the battery and the supercapacitor.

[0026] S4-2. Based on the short-term prediction of photovoltaic power in S2, a six-layer wavelet packet decomposition strategy is used to filter the signal and obtain the first-stage power command of the battery and supercapacitor.

[0027] S4-3. Based on the grid-connected power after filtering in S4-2, adjust the HESS command at 1-minute intervals; use the 1-minute photovoltaic active power fluctuation standard described in S3 to judge whether the grid-connected power after filtering meets the fluctuation standard. If it does not meet the standard, adjust the charging and discharging command of the supercapacitor to adjust the photovoltaic grid-connected power; if it meets the standard, proceed to S4-4.

[0028] S4-4. Based on the grid-connected power after filtering in S4-2, adjust the HESS command at 1-minute intervals; use the 10-minute photovoltaic active power fluctuation standard described in S3 to evaluate whether the grid-connected power after filtering meets the fluctuation standard. If it does not meet the standard, adjust the battery charging and discharging command to adjust the photovoltaic grid-connected power; if it meets the standard, proceed directly to step S4-5.

[0029] S4-5. If the sampling duration has not been reached, the algorithm continues and jumps directly to S4-2 to adjust the HESS command for the next moment. If the sampling duration has been reached, the algorithm terminates.

[0030] Preferably, step S4-2 includes the following sub-steps:

[0031] S4-2-1. The high-frequency components of the short-term photovoltaic power forecast data are decomposed into 26 layers of wavelet packets. n Given a set of different signals, the frequency bandwidth of each signal can be given as f0:

[0032]

[0033] Where n is the number of layers; f s It is the sampling frequency;

[0034] S4-2-2. According to S4-2-1, the signal frequency bandwidth of the six-layer wavelet packet decomposition is 0.0078Hz; the response frequency corresponds to a marginal response time of 0.0167Hz for 1 minute. The signal corresponding to the 1-minute response frequency is the same as that from S in the six-layer wavelet packet decomposition.6,1 -S 6,3 The signal is similar to the low-frequency component; the low-frequency component S obtained through filtering is... 6,0 For grid-connected power; the high-frequency signal S 6,1 -S 6,3 The high-frequency components are allocated to the battery; the remaining high-frequency components are suppressed by the supercapacitor.

[0035] S4-2-3. Through filtering and power processing, the first stage command of HESS can be divided into the first stage battery power command. and the power of the first-stage supercapacitor

[0036]

[0037]

[0038]

[0039] in, The grid-connected power, P, is filtered using wavelet packet decomposition. o (t) represents the original photovoltaic power.

[0040] The beneficial effects of this invention are:

[0041] This invention utilizes wavelet packet decomposition of the photovoltaic (PV) output power signal. Combining the amplitude-frequency information of the PV power signal with the performance characteristics of different types of energy storage, it calculates and smooths the target power and the charging / discharging power of the energy storage, thereby achieving a smoothing effect for PV grid-connected power. To further meet the State Grid's PV grid-connected standards, the two-stage PV power fluctuation smoothing method proposed in this invention can further adjust the charging and discharging commands of batteries and supercapacitors in hybrid energy storage, thereby achieving real-time smoothing control of grid-connected PV power and suppressing large-scale fluctuations in PV grid-connected power.

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. Attached image description:

[0043] Figure 1 This is a schematic diagram of the grid-connected photovoltaic-HESS combined system in this invention;

[0044] Figure 2 This is a flowchart of the two-stage photovoltaic power smoothing method in this invention;

[0045] Figure 3 This is a smoothing result diagram for a sunny day in this invention;

[0046] Figure 4 This is a comparison chart of the original photovoltaic power fluctuation rate and the grid-connected photovoltaic power fluctuation rate over 1 minute on a sunny day in this invention;

[0047] Figure 5 This is a comparison chart of the original photovoltaic power fluctuation rate and the grid-connected photovoltaic power fluctuation rate over 10 minutes on a sunny day in this invention;

[0048] Figure 6 This is a smoothing result diagram for cloudy days in this invention;

[0049] Figure 7 This is a comparison chart of the original power fluctuation rate and the grid-connected power fluctuation rate over a cloudy day for 1 minute in this invention;

[0050] Figure 8 This is a comparison chart of the original power fluctuation rate and the grid-connected power fluctuation rate over a cloudy day for 10 minutes in this invention. Detailed Implementation

[0051] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0052] Example 1: This invention relates to the optimization problem of smoothing grid-connected power in a grid-connected photovoltaic-hybrid energy storage system. It utilizes a two-stage photovoltaic power fluctuation smoothing method to achieve smooth grid-connected power. The grid-connected photovoltaic-hybrid energy storage system is as follows: Figure 1 As shown. The purpose of this invention is to propose a two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology. The specific flowchart is as follows. Figure 2 As shown, this invention effectively suppresses large fluctuations in grid-connected photovoltaic power, thus smoothing out grid-connected photovoltaic power. To illustrate the effectiveness of this invention, the following detailed description uses two typical test days (i.e., sunny and cloudy days) as the subjects of this invention:

[0053] A two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology includes the following steps:

[0054] S1. Establish a grid-connected photovoltaic-hybrid energy storage system, which includes a photovoltaic system (PV), a hybrid energy storage system (HESS), and a local controller. The hybrid energy storage system includes batteries and supercapacitors.

[0055] S2. Based on the grid-connected photovoltaic-hybrid energy storage system in S1, use the local controller to collect the real-time power signal of the photovoltaic system and the short-term photovoltaic power prediction data for the next minute.

[0056] Preferably, the grid-connected photovoltaic-hybrid energy storage system satisfies the following power balance:

[0057] P g (t)=P o (t)+P bat (t)+P sc (t)

[0058] Among them, P g (t) is the grid-connected photovoltaic power after HESS smoothing; P o (t) represents the original photovoltaic power; P bat (t) and P sc (t) represents the power of the battery and the supercapacitor, respectively. For these two variables, positive values ​​indicate discharge and negative values ​​indicate charging.

[0059] S3. Based on the State Grid's photovoltaic grid connection standard recommendations, two photovoltaic active power fluctuation standards with different time scales are established, namely 1 minute and 10 minutes.

[0060] The photovoltaic active power fluctuation standards at the two different time scales are as follows:

[0061] The State Grid's photovoltaic grid connection standard recommends that for a 50MW photovoltaic power generation system, the fluctuation of photovoltaic active power should not exceed 10% of the installed capacity within 1 minute and should not exceed 30% within 10 minutes. Therefore, the photovoltaic power fluctuation rate within 1 minute is R1(t), and the maximum and minimum values ​​of the photovoltaic power fluctuation limit within 1 minute are P... 1,max (t) and P 1,min (t).

[0062]

[0063]

[0064]

[0065] Where, max{P g (t-Δt)} and min{P g (t-Δt)} represents the maximum and minimum values ​​of grid-connected photovoltaic power over a given period; P r This refers to the installed capacity of the photovoltaic system.

[0066] The photovoltaic power fluctuation rate within 10 minutes is R 10 (t), the maximum and minimum values ​​of the photovoltaic power fluctuation limit within 10 minutes are P. 10,max (t) and P 10,min (t).

[0067]

[0068]

[0069]

[0070] S4. Based on the relevant data in S2, a two-stage photovoltaic power fluctuation smoothing method is adopted. The two stages of the two-stage photovoltaic power fluctuation smoothing method include a filtering stage and an adjustment stage. In the filtering stage, wavelet packet decomposition technology is used to process the short-term photovoltaic power generation prediction data for the next minute. In the adjustment stage, the local controller generates real-time adjustment commands for HESS based on the photovoltaic active power fluctuation standards at different time scales, thereby achieving the effect of smoothing the fluctuation of photovoltaic power generation.

[0071] Step S4 includes the following sub-steps:

[0072] S4-1. Based on the difference in response time between the balancing battery and the supercapacitor, select 1 minute as the marginal charge / discharge response time between the battery and the supercapacitor.

[0073] S4-2. Based on the short-term prediction of photovoltaic power in S2, a six-layer wavelet packet decomposition strategy is used to filter the signal and obtain the first-stage power command of the battery and supercapacitor.

[0074] Step S4-2 includes the following sub-steps:

[0075] S4-2-1. The high-frequency components of the short-term forecast data for grid-connected photovoltaic power are decomposed into 26 layers using a six-level wavelet packet decomposition method. n Given a set of different signals, the frequency bandwidth of each signal can be given as f0.

[0076]

[0077] Where n is the number of layers; f s It is the sampling frequency.

[0078] S4-2-2. According to S4-2-1, the signal frequency bandwidth of the six-layer wavelet packet decomposition is 0.0078Hz. (The last part, "S," appears to be a typo and can be omitted.) 6,0 For example, the upper and lower bounds of its signal frequency band can be calculated using the following formula. This means that low-frequency signals (i.e., S...) 6,0 The signal frequency band is from 0Hz to 0.0078Hz.

[0079] f lower =f0×i=0.0078×0=0Hz

[0080] f upper =f0×(i+1)=0.0078×(0+1)=0.0078Hz

[0081] Where flower and f upper These are the upper and lower limits of the frequency. Table 1 below shows S... 6,0 -S 6,7 The signal frequency band.

[0082] Table 1. Signal frequency band S 6,0 -S 6,7

[0083] Signal signal frequency band <![CDATA[S 6,0 ]]> 0 Hz - 0.0078 Hz <![CDATA[S 6,1 ]]> 0.0078 Hz - 0.0156 Hz <![CDATA[S 6,2 ]]> 0.00156 Hz - 0.0234 Hz <![CDATA[S 6,3 ]]> 0.0234 Hz ​​- 0.0313 Hz <![CDATA[S 6,4 ]]> 0.0313 Hz - 0.0391 Hz <![CDATA[S 6,5 ]]> 0.0391 Hz - 0.0469 Hz <![CDATA[S 6,6 ]]> 0.0469 Hz - 0.0547 Hz <![CDATA[S 6,7 ]]> 0.0547 Hz - 0.0628 Hz

[0084] S4-2-3, the marginal response time corresponding to the response frequency in 1 minute is 0.0167Hz, and the signal corresponding to the 1-minute response frequency is derived from S in the 6-layer wavelet packet decomposition. 6,1 -S 6,3 The signal is similar to the low-frequency component. The low-frequency component S obtained through filtering is... 6,0 For grid-connected power; the high-frequency signal S 6,1 -S 6,3 The high-frequency components are allocated to the battery. The remaining high-frequency components are suppressed by the supercapacitor.

[0085] S4-2-4. Through filtering and power processing, the first stage command of HESS can be divided into the first stage battery power command. and the power of the first-stage supercapacitor

[0086]

[0087]

[0088]

[0089] in, The grid-connected power, P, is filtered using wavelet packet decomposition. o (t) represents the original photovoltaic power.

[0090] S4-3. Based on the filtered grid-connected power in S4-2, adjust the HESS command at 1-minute intervals. Use the 1-minute photovoltaic active power fluctuation standard described in S3 to evaluate whether the filtered grid-connected power meets the fluctuation standard. If it does not meet the standard, adjust the supercapacitor's charging and discharging commands to adjust the photovoltaic grid-connected power. If it meets the standard, proceed to S4-4.

[0091] S4-4. Based on the filtered grid-connected power in S4-2, adjust the HESS command at 1-minute intervals. Using the 10-minute photovoltaic active power fluctuation standard described in S3, evaluate whether the filtered grid-connected power meets the fluctuation standard. If it does not meet the standard, adjust the battery charging and discharging commands to adjust the photovoltaic grid-connected power. If it meets the standard, proceed directly to step S4-5.

[0092] S4-5. If the sampling duration has not been reached, the algorithm continues and jumps directly to S4-2 to adjust the HESS command for the next moment. If the sampling duration has been reached, the algorithm terminates.

[0093] On sunny days, both primary photovoltaic power generation and grid-connected photovoltaic power generation are as follows: Figure 3 As shown, compared with the original photovoltaic power, the grid-connected photovoltaic power after HESS compensation is smoother, demonstrating the effectiveness of HESS in smoothing photovoltaic power fluctuations. The photovoltaic power fluctuation rates for 1 minute and 10 minutes are shown below. Figure 4 and Figure 5 As shown in the figure, for a 1-minute fluctuation, the original photovoltaic power does not meet the standard, while the grid-connected photovoltaic power after applying the proposed smoothing method meets the fluctuation standard. For a 10-minute fluctuation, the grid-connected photovoltaic power using the proposed smoothing method has a lower fluctuation compared to the original PV power.

[0094] The fluctuation rate of photovoltaic power is greatest on cloudy days, and the smoothing result for cloudy days is as follows: Figure 5 As shown. Figure 8 As shown, grid-connected photovoltaic power has a smoother curve compared to uncontrolled photovoltaic power generation. The photovoltaic power fluctuation rates for 1 minute and 10 minutes on cloudy days are shown below. Figure 7 and Figure 8 As shown in the figure, the original photovoltaic power fluctuations did not meet the standards for both 1-minute and 10-minute fluctuation rates, while the grid-connected photovoltaic power generation after applying the smoothing method could simultaneously meet the 1-minute and 10-minute fluctuation standards. This demonstrates the effectiveness of HESS in smoothing photovoltaic power fluctuations. The results of the typical test day shown above demonstrate that this method can significantly suppress grid-connected photovoltaic power fluctuations, thus achieving the effect of smoothing grid-connected photovoltaic power.

Claims

1. A two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology, characterized in that, Includes the following steps: S1. Establish a grid-connected photovoltaic-hybrid energy storage system, wherein the photovoltaic-hybrid energy storage system includes a photovoltaic system (PV), a hybrid energy storage system (HESS), and a local controller; wherein the hybrid energy storage system includes batteries and supercapacitors; S2. Based on the grid-connected photovoltaic-hybrid energy storage system in S1, use the local controller to collect the real-time power signal of the photovoltaic system and the short-term photovoltaic power output prediction data for 1 minute; S3. Based on the State Grid's photovoltaic grid connection standard recommendations, two photovoltaic active power fluctuation standards with different time scales are established, namely 1 minute and 10 minutes. S4. Based on the relevant data in S2, a two-stage photovoltaic power fluctuation smoothing method is adopted. The two stages of the two-stage photovoltaic power fluctuation smoothing method include a filtering stage and an adjustment stage. In the filtering stage, wavelet packet decomposition technology is used to process the future short-term photovoltaic power generation prediction data. In the adjustment stage, the local controller generates real-time adjustment HESS commands based on the photovoltaic active power fluctuation standards at different time scales to achieve the effect of smoothing the fluctuation of photovoltaic power generation. Step S4 includes the following sub-steps: S4-1. Based on the difference in response time between the balancing battery and the supercapacitor, select 1 minute as the marginal charging / discharging response time between the battery and the supercapacitor. S4-2. Based on the short-term prediction of photovoltaic power in S2, a six-layer wavelet packet decomposition strategy is used to filter the signal and obtain the first-stage power command of the battery and supercapacitor. S4-3. Based on the grid-connected power after filtering in S4-2, adjust the HESS command at 1-minute intervals; use the 1-minute photovoltaic active power fluctuation standard described in S3 to judge whether the grid-connected power after filtering meets the fluctuation standard. If it does not meet the standard, adjust the charging and discharging command of the supercapacitor to adjust the photovoltaic grid-connected power; if it meets the standard, proceed to S4-4. S4-4. Based on the grid-connected power after filtering in S4-2, adjust the HESS command at 1-minute intervals; use the 10-minute photovoltaic active power fluctuation standard described in S3 to evaluate whether the grid-connected power after filtering meets the fluctuation standard. If it does not meet the standard, adjust the battery charging and discharging command to adjust the photovoltaic grid-connected power; if it meets the standard, proceed directly to step S4-5. S4-5. If the sampling duration has not been reached, the algorithm continues and jumps directly to S4-2 to adjust the HESS command for the next moment. If the sampling duration has been reached, the algorithm terminates.

2. The two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology according to claim 1, characterized in that: The grid-connected photovoltaic-hybrid energy storage system described in steps S1 and S2 satisfies the following power balance: in, This is the grid-connected photovoltaic power after HESS smoothing; It is the original photovoltaic power; and These are the power of the battery and the supercapacitor, respectively; for these two variables, positive values ​​represent discharging and negative values ​​represent charging. The photovoltaic active power fluctuation standard mentioned in step S3 is as follows: (The standard is missing from the original text.) The State Grid's photovoltaic grid connection standard recommends that for a 50MW photovoltaic power generation system, the fluctuation of photovoltaic active power should not exceed 10% of the installed capacity within 1 minute and should not exceed 30% within 10 minutes. The photovoltaic power fluctuation rate within 1 minute is The maximum and minimum values ​​of the photovoltaic power fluctuation limit within 1 minute are respectively and : in, and These are the maximum and minimum values ​​of grid-connected photovoltaic power over a certain period of time; This refers to the installed capacity of the photovoltaic system. Photovoltaic power fluctuation rate within 10 minutes The maximum and minimum values ​​for photovoltaic power fluctuation limits within 10 minutes are and ; 。 3. The two-stage photovoltaic power fluctuation smoothing method based on wavelet packet decomposition technology according to claim 1, characterized in that: Step S4-2 includes the following sub-steps: S4-2-1. The high-frequency components of the short-term photovoltaic power forecast data are decomposed using a six-level wavelet packet decomposition method. Given a set of different signals, the frequency bandwidth of each signal can be given as... : in It refers to the number of floors; It is the sampling frequency; S4-2-2. According to S4-2-1, the signal frequency bandwidth of the six-layer wavelet packet decomposition is 0.0078 Hz; the response frequency corresponds to a marginal response time of 0.0167 Hz for 1 minute. The signal corresponding to the 1-minute response frequency is the same as that from the signal in the six-layer wavelet packet decomposition... - The signals are similar, belonging to the low-frequency range; The low-frequency component obtained through filtering For grid-connected power; to convert high-frequency signals - The high-frequency components are allocated to the battery; the remaining high-frequency components are suppressed by the supercapacitor. S4-2-3. Through filtering and power processing, the first stage command of HESS can be divided into the first stage battery power command. and the power of the first-stage supercapacitor : in, Grid-connected power processed by wavelet packet decomposition. That is the original photovoltaic power.

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