A Photovoltaic Power Fluctuation Smoothing Method Based on the Adaptive Rotating Door Algorithm

Abstract

The present invention provides a method for smoothing photovoltaic power fluctuations based on an adaptive rotating door algorithm, belonging to the technical field of power systems, and comprising the following steps: obtaining the actual photovoltaic power, compressing the actual photovoltaic power by using the rotating door algorithm with an initial door width, calculating the compression error and the compression rate, and adjusting the door width of the rotating door algorithm based on the compression error and the compression rate to obtain the optimal door width; compressing the actual photovoltaic power by using the rotating door algorithm with the optimal door width, extracting and processing the photovoltaic power characteristic data points to obtain a photovoltaic characteristic curve; obtaining the photovoltaic grid-connected command power, allocating the power deviation between the photovoltaic characteristic curve and the actual photovoltaic power to a super capacitor for compensation, and allocating the power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve to a battery energy storage system for compensation. The present invention effectively reduces the photovoltaic grid-connected power fluctuations, reduces the action times of the BESS, and improves the operation life of the BESS.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a method for smoothing photovoltaic power fluctuations based on an adaptive rotating door algorithm. Background Art

[0002] HESS, short for hybrid energy storage system, is a hybrid energy storage system.

[0003] BESS, short for battery energy storage system, is a battery energy storage system.

[0004] With the low-carbon demand for electricity, new energy sources such as photovoltaic and wind power will be connected to the power grid on a larger scale. However, affected by natural factors such as temperature and solar irradiance, photovoltaic power generation is random and intermittent, resulting in large fluctuations in its grid-connected power, which has an adverse impact on the safe and stable operation of the power grid. To address these issues, configuring a certain capacity of energy storage system in a photovoltaic power station is a common method to suppress the fluctuations of photovoltaic grid-connected power, which can give full play to the advantages of photovoltaic power generation.

[0005] There are two types of energy storage systems: power-type energy storage and energy-type energy storage. The power-type energy storage is characterized by high specific power and is suitable for instantaneous high-power input and output scenarios; the energy-type energy storage is characterized by high specific energy and is mainly used for high-energy input and output scenarios. Therefore, HESS, which combines both fast response and high charge-discharge energy characteristics, is an important means for current photovoltaic grid connection assistance.

[0006] In HESS, the power-type energy storage generally uses supercapacitors, and the energy-type energy storage generally uses BESS. However, the cycle service life of BESS is limited. Frequent charge and discharge and high-rate charge and discharge will sharply reduce the service life of BESS, thus increasing the replacement frequency during the entire life cycle, and further affecting the economy of HESS. Currently, there is no effective way to reduce the operation life loss of BESS and suppress the fluctuations of photovoltaic grid connection.

[0007] This is the deficiency of the existing technology. Therefore, in view of the above-mentioned defects in the existing technology, it is very necessary to provide a method for smoothing photovoltaic power fluctuations based on an adaptive rotating door algorithm. Summary of the Invention

[0008] In view of the defect that in the existing method of using HESS to suppress the fluctuations of photovoltaic grid connection, the operation life loss of BESS is high, which affects the performance of HESS and further affects the stability of photovoltaic grid connection, the present invention provides a method for smoothing photovoltaic power fluctuations based on an adaptive rotating door algorithm to solve the above technical problems. The present invention provides a method for smoothing photovoltaic power fluctuations based on an adaptive rotating door algorithm, including the following steps:

[0009] S1. Obtain the actual photovoltaic power, compress the actual photovoltaic power using the rotating door algorithm with the initialized gate width, calculate the compression error and compression rate, and adaptively adjust the gate width of the rotating door algorithm based on the compression error and compression rate to obtain the optimal gate width;

[0010] S2. Compress the actual photovoltaic power using the rotating door algorithm with the optimal gate width, extract the photovoltaic power characteristic data points, and process the characteristic data points to obtain the photovoltaic characteristic curve;

[0011] S3. Obtain the photovoltaic grid-connected command power, allocate the power deviation between the photovoltaic characteristic curve and the actual photovoltaic power to the supercapacitor for compensating small fluctuations in power, and allocate the power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve to the battery energy storage system for compensating large fluctuations in power.

[0012] Furthermore, the specific steps of step S1 are as follows:

[0013] S11. Obtain the actual photovoltaic power and mark the power corresponding points in the plane coordinate system;

[0014] S12. Initialize the gate width, the upper fulcrum gate slope, and the lower fulcrum gate slope of the rotating door algorithm;

[0015] S13. Use the rotating door algorithm and adjust the upper fulcrum gate slope and the lower fulcrum gate slope based on the gate width, and frame the power points until the relationship between the upper and lower fulcrum gate slopes meets the requirements to obtain the compressed photovoltaic power;

[0016] S14. Calculate the compression error and compression rate of the compressed photovoltaic power with respect to the actual photovoltaic power, compare the relationship between the compression error and the error threshold, and compare the relationship between the compression rate and the compression rate threshold;

[0017] When the compression error exceeds the error threshold, go to step S15;

[0018] When the compression rate exceeds the compression rate threshold, go to step S16;

[0019] When the compression error is less than or equal to the error threshold and the compression rate is less than or equal to the compression rate threshold, go to step S17;

[0020] S15. Increase the gate width using the preset gate width increase algorithm, and return to step S13;

[0021] S16. Decrease the gate width using the preset gate width decrease algorithm, and return to step S13;

[0022] S17. Record the currently used gate width of the rotating door algorithm as the optimal gate width.

[0023] Further, in step S12, the gate width E, the upper fulcrum gate slope k 1d and the lower fulcrum gate slope k 2d ;

[0024] Then, it is obtained that: wherein, t0 and x0 are the initial time and the corresponding power data value respectively, and t1 and x1 are the first time and the corresponding power data value respectively.

[0025] Further, the specific steps of step S13 are as follows:

[0026] S131. Use the rotating door algorithm and adjust the upper support gate and the lower fulcrum gate based on the gate width, and calculate the upper fulcrum gate slope and the lower fulcrum gate slope in the following way:

[0027]

[0028] wherein, t j and x j are the j-th time and the corresponding power data value respectively; t k and x k are the k-th time and the corresponding power data value respectively;

[0029] S132. Judge whether the upper fulcrum gate slope k1 calculated in real time and the lower fulcrum gate slope k2 satisfy k1≥k2;

[0030] If so, go to step S133;

[0031] If not, go to step S134;

[0032] S133. Record the data value x j-1 at the previous time t j-1 as the power feature data point to obtain the compressed photovoltaic power, and enter S14;

[0033] S134. Compare the upper fulcrum gate slope k1 calculated in real time with the saved upper fulcrum gate slope k 1b and update the upper fulcrum gate slope when the upper fulcrum gate slope k1 calculated in real time is greater than the saved upper fulcrum slope k 1b ; compare the lower fulcrum gate slope k2 calculated in real time with the saved lower fulcrum gate slope k 2b and update the lower fulcrum gate slope when the lower fulcrum gate slope k2 calculated in real time is greater than the saved lower fulcrum slope k 2b ;

[0034]

[0035] Return to step S131.

[0036] Further, in step S14, the compression error and compression rate of the compressed photovoltaic power with respect to the actual photovoltaic power are calculated through the following two formulas:

[0037]

[0038] F CR = N2 / N1

[0039] where F CE represents the compression error of the actual photovoltaic power, and F CR represents the compression rate of the actual photovoltaic power. N1 is the total number of power point samples, s i is the sample data value, y i is the characteristic trend value after linear interpolation of the characteristic data points, and N2 is the number of extracted characteristic power data points.

[0040] Further, in step S15, the following gate width increase algorithm is preset based on the Sigmoid function to increase the gate width:

[0041]

[0042] where n is the number of loops for performing the gate width increase process, E(n) and E(n - 1) respectively represent the gate widths at the nth and (n - 1)th loops, and T CE is the set compression error threshold;

[0043] In step S16, the following gate width decrease algorithm is preset based on the Sigmoid function to decrease the gate width:

[0044]

[0045] where m is the number of loops for performing the gate width decrease process, E(m) and E(m - 1) respectively represent the gate widths at the mth and (m - 1)th loops, and T CR is the set compression rate threshold.

[0046] Further, the specific steps of step S2 are as follows:

[0047] S21. Obtain the optimal gate width of the rotating gate algorithm and initialize the upper fulcrum gate slope and the lower fulcrum gate slope;

[0048] S22. Use the rotating gate algorithm and adjust the upper fulcrum gate slope and the lower fulcrum gate slope based on the optimal gate width, frame the power points until the relationship between the upper and lower fulcrum gate slopes meets the requirements, and extract the framed photovoltaic power characteristic data points; S23. Perform linear interpolation on the extracted photovoltaic characteristic data points to obtain a photovoltaic characteristic curve representing the change trend of the photovoltaic power.

[0049] Further, the specific steps of step S3 are as follows:

[0050] S31. Assign the first power deviation between the photovoltaic characteristic curve and the actual photovoltaic power to the supercapacitor, and control the supercapacitor to compensate for the first power deviation;

[0051] S32. Obtain the photovoltaic grid-connected command power, and assign the second power deviation between the photovoltaic grid-connected command power and the actual photovoltaic power to the battery energy storage system, and control the battery energy storage system to compensate for the second power deviation.

[0052] Further, the specific steps of step S31 are as follows:

[0053] S311. Calculate the power deviation between the photovoltaic characteristic curve and the actual photovoltaic power, and set it as the first power deviation; S312. Calculate the first command regulation power of the supercapacitor through the following formula, and send the first command regulation power to the supercapacitor;

[0054] P S (t) = P f (t) - P v (t)

[0055] where, P S (t) is the first command regulation power of the supercapacitor at time t, P f (t) is the photovoltaic characteristic curve at time t, P v (t) is the actual photovoltaic power at time t;

[0056] S313. The supercapacitor distributes the first command regulation power P S (t) to each supercapacitor unit according to the SOC consistency principle;

[0057] S314. The change in the state of charge SOC of each supercapacitor unit is calculated in the following way:

[0058] S sn (t) = S sn (t - 1) + P sn (t)·Δt / C sn

[0059] where, S sn (t) is the SOC of the nth supercapacitor unit at time t, S sn (t - 1) is the SOC of the nth supercapacitor unit at time t - 1, P sn (t) is the charge and discharge power of the nth supercapacitor unit at time t, taking a negative value during charging and a positive value during discharging, C sn is the maximum energy storage capacity of the nth supercapacitor unit, and Δt is the scheduling interval;

[0060] The output of each supercapacitor unit under the maximum charge-discharge power limit is calculated as follows:

[0061]

[0062] Among them, P smaxn is the maximum charging power of the nth supercapacitor unit, and P sminn is the maximum discharging power of the nth supercapacitor unit.

[0063] Furthermore, the specific steps of step S32 are as follows:

[0064] S321. Obtain the photovoltaic grid-connected command power, calculate the power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve, and set it as the second power deviation;

[0065] S322. Calculate the second command regulation power of the battery energy storage system through the following formula, and send the second command regulation power to the battery energy storage system;

[0066] P B (t) = P G (t) - P f (t)

[0067] Among them, P B (t) is the second command regulation power of the battery energy storage system at time t, and P G (t) is the photovoltaic grid-connected command power issued by the power dispatching center at time t;

[0068] S323. The battery energy storage system distributes the second command regulation power P B (t) to each battery unit according to the SOC consistency principle;

[0069] S324. The change in the state of charge SOC of each battery unit is calculated as follows:

[0070] S bn (t) = S bn (t - 1) + P bn (t)·Δt / C bn

[0071] Among them, S bn (t) is the SOC of the nth battery unit at time t, S bn (t - 1) is the SOC of the nth battery unit at time t - 1, P bn (t) is the charge-discharge power of the nth battery unit at time t, taking a negative value during charging and a positive value during discharging, and C bn is the maximum energy storage capacity of the nth battery unit, and Δt is the scheduling interval;

[0072] S316. The output of each battery unit under the maximum charge-discharge power limit is calculated as follows:

[0073]

[0074] where P bmaxn is the maximum charging power of the nth battery unit, and P bminn is the maximum discharging power of the nth battery unit;

[0075] S317. The output of each battery unit is also restricted by the following charge-discharge constraint conditions:

[0076]

[0077] where S bmaxn represents the upper limit of the SOC of the nth battery unit, and S bminn represents the lower limit of the SOC of the nth battery unit.

[0078] The beneficial effects of the present invention are as follows:

[0079] The photovoltaic power fluctuation smoothing method based on the adaptive rotating door algorithm provided by the present invention can obtain a photovoltaic characteristic curve representing the actual change of photovoltaic power by processing photovoltaic power data using the adaptive rotating door algorithm and the linear interpolation method; on this basis, a super capacitor is used to make up for the power deviation with high fluctuation frequency and small amplitude between the photovoltaic characteristic curve and the actual photovoltaic power, and a battery energy storage system is used to make up for the power deviation with low fluctuation frequency and large amplitude between the photovoltaic grid-connected command power and the photovoltaic characteristic curve. For a photovoltaic power station, it can effectively suppress the photovoltaic power fluctuation, so that it can be safely and stably connected to the grid; for the HESS, it effectively avoids overcharging and over-discharging of the battery energy storage system, reduces the action times of the battery energy storage system, reduces the operation life loss of the battery energy storage system, ensures the longer-term use of the battery energy storage system, and thus improves the economy of the HESS operation.

[0080] In addition, the design principle of the present invention is reliable and the structure is simple, and it has a very wide application prospect.

[0081] It can be seen that compared with the prior art, the present invention has prominent substantive features and significant progress, and the beneficial effects of its implementation are also obvious. Description of the Drawings

[0082] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

[0083] Figure 1 It is a schematic flowchart of an embodiment of the photovoltaic power fluctuation smoothing method based on the adaptive rotating door algorithm of the present invention.

[0084] Figure 2 It is a schematic flowchart of another embodiment of the photovoltaic power fluctuation smoothing method based on the adaptive rotating door algorithm of the present invention.

[0085] Figure 3 They are the photovoltaic power characteristic data points extracted by the present invention using the adaptive rotating door algorithm.

[0086] Figure 4 They are the power adjustment instructions and response results of the supercapacitor of the present invention.

[0087] Figure 5 It is the SOC change of the supercapacitor of the present invention.

[0088] Figure 6 They are the power adjustment instructions and response results of the BESS of the present invention.

[0089] Figure 7 It is the SOC change of the BESS of the present invention.

[0090] Figure 8 It is the photovoltaic grid-connected volatility before and after the HESS of the present invention suppresses fluctuations. Detailed implementation manners

[0091] In order to enable those skilled in the art of the present technology to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0092] Embodiment 1:

[0093] As Figure 1 shown, the present invention provides a photovoltaic power fluctuation smoothing method based on an adaptive rotating door algorithm, including the following steps:

[0094] S1. Obtain the actual photovoltaic power, compress the actual photovoltaic power using the initialized gate width through the rotating door algorithm, calculate the compression error and compression rate, and adaptively adjust the gate width of the rotating door algorithm based on the compression error and compression rate to obtain the optimal gate width;

[0095] S2. Compress the actual photovoltaic power using the optimal gate width through the rotating door algorithm, extract the characteristic data points of the photovoltaic power, process the characteristic data points, and obtain the photovoltaic characteristic curve;

[0096] S3. Obtain the photovoltaic grid-connected command power, allocate the power deviation between the photovoltaic characteristic curve and the actual photovoltaic power to the super capacitor for compensating small fluctuations in power, and allocate the power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve to the battery energy storage system for compensating large fluctuations in power.

[0097] Embodiment 2:

[0098] As Figure 2 shown, the present invention provides a method for smoothing photovoltaic power fluctuations based on an adaptive rotating door algorithm, including the following steps:

[0099] S1. Obtain the actual photovoltaic power, compress the actual photovoltaic power using the initial gate width through the rotating door algorithm, calculate the compression error and compression rate, and adaptively adjust the gate width of the rotating door algorithm based on the compression error and compression rate to obtain the optimal gate width; The specific steps of step S1 are as follows:

[0100] S11. Obtain the actual photovoltaic power and mark the power corresponding points in the plane coordinate system;

[0101] S12. Initialize the gate width, the upper fulcrum gate slope, and the lower fulcrum gate slope of the rotating door algorithm; In step S12, initialize the gate width E of the rotating door algorithm, the upper fulcrum gate slope k 1d and the lower fulcrum gate slope k 2d ;

[0102] Then obtain wherein, t0 and x0 are the initial time and the corresponding power data value respectively, and t1 and x1 are the first time and the corresponding power data value respectively;

[0103] S13. Use the rotating door algorithm and adjust the upper fulcrum gate slope and the lower fulcrum gate slope based on the gate width, frame the power points until the relationship between the upper and lower fulcrum gate slopes meets the requirements, and obtain the compressed photovoltaic power; The specific steps of step S13 are as follows:

[0104] S131. Use the rotating door algorithm and adjust the upper support gate and the lower fulcrum gate based on the gate width, and calculate the upper fulcrum gate slope and the lower fulcrum gate slope in the following manner:

[0105]

[0106] wherein, tj and xj are the jth time and the corresponding power data value respectively; t k and x k are the kth time and the corresponding power data value respectively;

[0107] S132. Determine whether the slope k1 of the upper fulcrum gate and the slope k2 of the lower fulcrum gate calculated in real time satisfy k1≥k2;

[0108] If so, go to step S133;

[0109] If not, go to step S134;

[0110] S133. Record the data value x j-1 at the previous moment t j-1 as a power characteristic data point to obtain the compressed photovoltaic power, and enter S14;

[0111] S134. Compare the slope k1 of the upper fulcrum gate calculated in real time with the slope k 1b of the upper fulcrum gate saved, and update the slope of the upper fulcrum gate when the slope k1 of the upper fulcrum gate calculated in real time is greater than the saved slope k 1b of the upper fulcrum; compare the slope k2 of the lower fulcrum gate calculated in real time with the slope k 2b of the lower fulcrum gate saved, and update the slope of the lower fulcrum gate when the slope k2 of the lower fulcrum gate calculated in real time is greater than the saved slope k 2b of the lower fulcrum;

[0112]

[0113] Return to step S131;

[0114] S14. Calculate the compression error and compression rate of the compressed photovoltaic power with respect to the actual photovoltaic power, and compare the relationship between the compression error and the error threshold, and compare the relationship between the compression rate and the compression rate threshold;

[0115] When the compression error exceeds the error threshold, go to step S15;

[0116] When the compression rate exceeds the compression rate threshold, go to step S16;

[0117] When the compression error is less than or equal to the error threshold and the compression rate is less than or equal to the compression rate threshold, go to step S17;

[0118] In step S14, the compression error and compression rate of the compressed photovoltaic power with respect to the actual photovoltaic power are calculated through the following two formulas:

[0119]

[0120] F CR = N2 / N1

[0121] where F CE represents the compression error of the actual photovoltaic power, F CRIndicates the compression ratio of the actual photovoltaic power. N1 is the total number of power point samples, s i is the sample data value, y i is the characteristic trend value after linear interpolation processing of the characteristic data points. N2 is the number of extracted characteristic power data points;

[0122] S15. Increase the gate width using a preset gate width increase algorithm, and return to step S13;

[0123] In step S15, the following gate width increase algorithm based on the Sigmoid function is used to increase the gate width:

[0124]

[0125] where n is the number of loops for the gate width increase process, E(n) and E(n - 1) represent the gate widths at the nth and (n - 1)th loops respectively, and T CE is the set compression error threshold;

[0126] S16. Decrease the gate width using a preset gate width decrease algorithm, and return to step S13;

[0127] In step S16, the following gate width decrease algorithm based on the Sigmoid function is used to decrease the gate width:

[0128]

[0129] where m is the number of loops for the gate width decrease process, E(m) and E(m - 1) represent the gate widths at the mth and (m - 1)th loops respectively, and T CR is the set compression ratio threshold;

[0130] S17. Record the currently used gate width of the rotating door algorithm as the optimal gate width;

[0131] S2. Compress the actual photovoltaic power using the optimal gate width through the rotating door algorithm, extract the photovoltaic power characteristic data points, and process the characteristic data points to obtain the photovoltaic characteristic curve. The specific steps of step S2 are as follows: S21. Obtain the optimal gate width of the rotating door algorithm, and initialize the upper support gate slope and the lower support gate slope;

[0132] S22. Use the rotating door algorithm and adjust the upper support gate slope and the lower support gate slope based on the optimal gate width, select power points until the relationship between the upper and lower support gate slopes meets the requirements, and extract the selected photovoltaic power characteristic data points; Use the actual photovoltaic power of a certain photovoltaic power station for simulation, and the extracted photovoltaic power characteristic data points are as shown in the appendix Figure 3 as follows;

[0133] S23. Perform linear interpolation on the extracted photovoltaic characteristic data points to obtain a photovoltaic characteristic curve representing the change trend of photovoltaic power;

[0134] S3. Obtain the photovoltaic grid - connected command power, allocate the power deviation between the photovoltaic characteristic curve and the actual photovoltaic power to the supercapacitor for compensating small - fluctuation power, and allocate the power deviation between the photovoltaic grid - connected command power and the photovoltaic characteristic curve to the battery energy storage system for compensating large - fluctuation power; The specific steps of step S3 are as follows:

[0135] S31. Allocate the first power deviation between the photovoltaic characteristic curve and the actual photovoltaic power to the supercapacitor, and control the supercapacitor to compensate the first power deviation; The specific steps of step S31 are as follows:

[0136] S311. Calculate the power deviation between the photovoltaic characteristic curve and the actual photovoltaic power, and set it as the first power deviation; S312. Calculate the first command regulation power of the supercapacitor through the following formula, and send the first command regulation power to the supercapacitor;

[0137] P S (t) = P f (t) - P v (t)

[0138] where, P S (t) is the first command regulation power of the supercapacitor at time t, P f (t) is the photovoltaic characteristic curve at time t, P v (t) is the actual photovoltaic power at time t;

[0139] S313. The supercapacitor distributes the first command regulation power P S (t) to each supercapacitor unit according to the SOC consistency principle;

[0140] S314. The change in the state of charge SOC of each supercapacitor unit is calculated in the following way:

[0141] S sn (t) = S sn (t - 1)+P sn (t)·Δt / C sn

[0142] where, S sn (t) is the SOC of the nth supercapacitor unit at time t, S sn (t - 1) is the SOC of the nth supercapacitor unit at time t - 1, P sn (t) is the charge - discharge power of the nth supercapacitor unit at time t, taking a negative value during charging and a positive value during discharging, C snis the maximum energy storage capacity of the nth supercapacitor unit, and Δt is the scheduling interval;

[0143] S315. The output of each supercapacitor unit under the maximum charge and discharge power limit is calculated as follows:

[0144]

[0145] Among them, P smaxn is the maximum charging power of the nth supercapacitor unit, and P sminn is the maximum discharging power of the nth supercapacitor unit;

[0146] S32. Obtain the photovoltaic grid-connected command power, and distribute the second power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve to the battery energy storage system, and control the battery energy storage system to make up the second power deviation; The specific steps of step S32 are as follows:

[0147] S321. Obtain the photovoltaic grid-connected command power, calculate the power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve, and set it as the second power deviation;

[0148] S322. Calculate the second command regulation power of the battery energy storage system through the following formula, and send the second command regulation power to the battery energy storage system;

[0149] P B (t) = P G (t) - P f (t)

[0150] Among them, P B (t) is the second command regulation power of the battery energy storage system at time t, and P G (t) is the photovoltaic grid-connected command power issued by the power dispatching center at time t;

[0151] S323. The battery energy storage system distributes the second command regulation power P B (t) to each battery unit according to the SOC consistency principle;

[0152] S324. The change in the state of charge SOC of each battery unit is calculated as follows:

[0153] S bn (t) = S bn (t - 1) + P bn (t)·Δt / C bn

[0154] Among them, S bn (t) is the SOC of the nth battery unit at time t, and S bn (t - 1) is the SOC of the nth battery unit at time t - 1, and Pbn (t) is the charging and discharging power of the nth battery cell at time t, taking a negative value during charging and a positive value during discharging, C bn is the maximum energy storage capacity of the nth battery cell, and Δt is the scheduling interval;

[0155] S316. The output of each battery cell under the maximum charging and discharging power limit is calculated as follows:

[0156]

[0157] where P bmaxn is the maximum charging power of the nth battery cell, and P bminn is the maximum discharging power of the nth battery cell;

[0158] S317. The output of each battery cell is also restricted by the following charging and discharging constraint conditions:

[0159]

[0160] where S bmaxn represents the upper limit of the SOC of the nth battery cell, and S bminn represents the lower limit of the SOC of the nth battery cell.

[0161] To further understand the present invention and verify the effectiveness of the photovoltaic power fluctuation smoothing method based on the adaptive rotating door algorithm of the present invention, the photovoltaic power fluctuation smoothing method is simulated using the photovoltaic grid-connected power command issued by a certain power dispatching center to a photovoltaic power station; among them, the relevant parameters of the hybrid energy storage system are shown in Table 1 below.

[0162] According to the photovoltaic characteristic curve and the actual photovoltaic power, calculate the command regulation power of the supercapacitor, and distribute the command regulation power of the supercapacitor to the supercapacitor units according to the SOC consistency principle. The supercapacitor units respond under the limitation of meeting the operation constraint conditions; the response result of the supercapacitor to its command regulation power is as Figure 4 shown. It can be seen that the supercapacitor can accurately track its command regulation power; the change of its SOC during the operation of the supercapacitor is as Figure 5 shown. It can be seen that there is no situation of SOC over-limit during the response process;

[0163] According to the photovoltaic grid-connected power command and the photovoltaic characteristic curve, calculate the command regulation power of the BESS, and distribute the command regulation power of the BESS to the battery cells according to the SOC consistency principle. The battery cells are under the condition of meeting the operation constraint

[0164] Table 1

[0165]

[0166] Respond under the limitation of conditions; The response result of the BESS to adjust its power according to the instruction is as Figure 6 shown. It can be seen that at most times, the BESS can accurately track its power regulation instruction, and the tracking error is only 0.012MW, indicating that the BESS can accurately track its power regulation instruction; The change of its SOC during the operation of the BESS is as Figure 7 shown. It can be seen that there is no situation where the SOC exceeds the limit during the response process, thereby reducing the loss of the BESS operation life and ensuring the longer-term operation of the BESS.

[0167] The change of the grid-connected power fluctuation before and after the HESS suppresses is as Figure 8 shown. It can be seen that the average value of the volatility before suppression is 1.92%, and the maximum value is 11%. After the HESS suppresses, the average value of the volatility is only 0.18%, and the maximum value is only 1.97%. Thus, it is verified that the HESS under the present invention can effectively suppress the photovoltaic power fluctuation.

[0168] To further highlight the superiority of the present invention, the photovoltaic power fluctuation smoothing method based on the adaptive rotation gate algorithm of the present invention is compared with another three photovoltaic power fluctuation smoothing methods. Among them, Method 1 is the photovoltaic power fluctuation smoothing method based on wavelet decomposition, Method 2 is the photovoltaic power fluctuation smoothing method based on empirical mode decomposition, and Method 3 is the photovoltaic power fluctuation smoothing method based on variational mode decomposition. The photovoltaic fluctuation smoothing effects under different methods are shown in Table 2 below. It can be seen that the average value and the maximum value of the grid-connected volatility of the method designed in the present invention are the lowest, thereby verifying the superiority of the present invention for suppressing the photovoltaic power fluctuation.

[0169] Table 2

[0170]

[0171] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all be within the scope covered by the present invention. / Any person skilled in the art in the technical field disclosed by the present invention can easily think of changes or substitutions within the technical scope disclosed by the present invention, and they should all be covered within the protection scope of the present invention.

Claims

1. A photovoltaic power fluctuation smoothing method based on an adaptive revolving door algorithm, characterized in that: The steps include: S1. Obtain the actual photovoltaic power, compress the actual photovoltaic power using the initial gate width through the revolving door algorithm, calculate the compression error and compression rate, and adaptively adjust the gate width of the revolving door algorithm based on the compression error and compression rate to obtain the optimal gate width; S2. Compress the actual photovoltaic power using the optimal gate width through the rotating gate algorithm, extract the photovoltaic power characteristic data points, process the characteristic data points, and obtain the photovoltaic characteristic curve; S3. Obtain the photovoltaic grid-connected command power, allocate the power deviation between the photovoltaic characteristic curve and the photovoltaic actual power to the supercapacitor for small power fluctuation compensation, and allocate the power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve to the battery energy storage system for large power fluctuation compensation; The specific steps of step S1 are as follows: S11. Obtain the actual photovoltaic power and mark the corresponding points of the power in the plane coordinate system; S12. Initialize the door width, upper pivot door slope and lower pivot door slope of the revolving door algorithm; S13. Use the revolving door algorithm and adjust the upper and lower pivot gate slopes based on the gate width, select the power point until the relationship between the upper and lower pivot gate slopes meets the requirements, and obtain the compressed photovoltaic power; S14. Calculate the compression error and compression rate of the compressed photovoltaic power to the actual photovoltaic power, and compare the relationship between the compression error and the error threshold, and compare the relationship between the compression rate and the compression rate threshold; when the compression error exceeds the error threshold, proceed to step S15; When the compression rate exceeds the compression rate threshold, proceed to step S16; When the compression error is less than or equal to the error threshold, and the compression rate is less than or equal to the compression rate threshold, proceed to step S17; S15. Use the preset door width increase algorithm to increase the door width, and return to step S13; S16. Use a preset door width reduction algorithm to reduce the door width, and return to step S13; S17. Record the door width currently used by the revolving door algorithm as the optimal door width; The specific steps of step S2 are as follows: S21. Obtain the optimal door width of the revolving door algorithm and initialize the upper pivot door slope and the lower pivot door slope; S22. Use the revolving door algorithm and adjust the upper support door slope and the lower support door slope based on the optimal door width, select the power point until the relationship between the upper and lower support door slopes meets the requirements, and extract the selected photovoltaic power characteristic data points; S23. Perform linear interpolation processing on the extracted photovoltaic characteristic data points to obtain a photovoltaic characteristic curve representing the photovoltaic power change trend.

2. The photovoltaic power fluctuation smoothing method based on the adaptive revolving door algorithm according to claim 1, characterized in that: In step S12, the door width E and the upper support door slope k of the revolving door algorithm are initialized. 1d and the slope k of the lower pivot door 2d ; Then we get Among them, t0 and x0 are the initial moment and the corresponding power data value respectively, and t1 and x1 are the first moment and the corresponding power data value respectively.

3. The photovoltaic power fluctuation smoothing method based on the adaptive revolving door algorithm according to claim 2, characterized in that: The specific steps of step S13 are as follows: S131. Use the revolving door algorithm and adjust the upper and lower pivot doors based on the door width, and calculate the upper and lower pivot door slopes as follows: Among them, t j and x j are the jth moment and the corresponding power data value; t k and x k are the kth moment and the corresponding power data value respectively; S132. Determine whether the upper support door slope k1 and the lower support door slope k2 calculated in real time satisfy k1≥k2; If yes, go to step S133; If not, proceed to step S134; S133. The previous moment t j-1 The data value x j-1 Record as power characteristic data points to obtain compressed photovoltaic power, and enter S14; S134. The upper support door slope k1 calculated in real time is compared with the saved upper support door slope k 1b The upper support point slope k1 calculated in real time is greater than the saved upper support point slope k 1b When , the upper pivot door slope is updated; The real-time calculated lower support door slope k2 is compared with the saved lower support door slope k 2b The comparison is performed and the lower support point door slope k2 calculated in real time is greater than the saved lower support point slope k 2b When , the slope of the lower support door is updated; Return to step S131.

4. The photovoltaic power fluctuation smoothing method based on the adaptive revolving door algorithm according to claim 1, characterized in that: In step S14, the compression error and compression rate of the compressed photovoltaic power to the actual photovoltaic power are calculated by the following two formulas: F CR =N2 / N1 Among them, F CE Indicates the compression error of the actual photovoltaic power, F CR represents the compression rate of the actual photovoltaic power, N1 is the total number of power point samples, s i is the sample data value, y i is the characteristic trend value after linear interpolation of the characteristic data points, and N2 is the number of characteristic power data points extracted.

5. The photovoltaic power fluctuation smoothing method based on the adaptive revolving door algorithm according to claim 1, characterized in that: In step S15, the door width is increased by using the door width increasing algorithm preset as follows based on the Sigmoid function: Where n is the number of cycles of the gate width increase process, E(n) and E(n-1) represent the gate widths at the nth and n-1th cycles respectively, and T CE is the set compression error threshold; In step S16, the following gate width reduction algorithm is preset based on the Sigmoid function to reduce the gate width: Where m is the number of cycles of the gate width reduction process, E(m) and E(m-1) represent the gate widths at the mth and m-1th cycles respectively, and T CR is the compression ratio threshold.

6. The photovoltaic power fluctuation smoothing method based on the adaptive revolving door algorithm according to claim 1, characterized in that: The specific steps of step S3 are as follows: S31. Allocating a first power deviation between the photovoltaic characteristic curve and the actual photovoltaic power to the supercapacitor, and controlling the supercapacitor to compensate for the first power deviation; S32. Obtain the photovoltaic grid-connected command power, and distribute the second power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve to the battery energy storage system, and control the battery energy storage system to compensate for the second power deviation.

7. The photovoltaic power fluctuation smoothing method based on the adaptive revolving door algorithm according to claim 6, characterized in that: The specific steps of step S31 are as follows: S311. Calculate the power deviation between the photovoltaic characteristic curve and the actual photovoltaic power, and set it as the first power deviation; S312. Calculate the first instruction regulating power of the supercapacitor by the following formula, and send the first instruction regulating power to the supercapacitor; P S (t)=P f (t)-P v (t) Among them, P S (t) is the first command adjustment power of the supercapacitor at time t, P f (t) is the photovoltaic characteristic curve at time t, P v (t) is the actual photovoltaic power at time t; S313. The supercapacitor adjusts the power P of the first instruction according to the SOC consistency principle. S (t) allocating to each supercapacitor unit; S314. The state of charge (SOC) change of each supercapacitor unit is calculated as follows: S sn (t)=S sn (t-1)+P sn (t)·Δt / C sn Among them, S sn (t) is the SOC of the nth supercapacitor unit at time t, S sn (t-1) is the SOC of the nth supercapacitor unit at time t-1, P sn (t) is the charge and discharge power of the nth supercapacitor unit at time t, which takes a negative value when charging and a positive value when discharging. sn is the maximum energy storage capacity of the nth supercapacitor unit, and Δt is the scheduling interval; S315. The output of each supercapacitor unit under the maximum charge and discharge power limit is calculated as follows: Among them, P smaxn is the maximum charging power of the nth supercapacitor unit, P sminn is the maximum discharge power of the nth supercapacitor unit.

8. The photovoltaic power fluctuation smoothing method based on the adaptive revolving door algorithm according to claim 6, characterized in that: The specific steps of step S32 are as follows: S321. Obtaining the photovoltaic grid-connected command power, calculating the power deviation between the photovoltaic grid-connected command power and the photovoltaic characteristic curve, and setting it as the second power deviation; S322. Calculate the second instruction adjustment power of the battery energy storage system by the following formula, and send the second instruction adjustment power to the battery energy storage system System sends; P B (t) = P G (t)-P f (t) Among them, P B (t) is the second instruction adjustment power of the battery energy storage system at time t, P G (t) is the photovoltaic grid-connected command power issued by the power dispatching center at time t; S323. The battery energy storage system adjusts the power P of the second instruction according to the SOC consistency principle. B (t) allocating to each battery cell; S324. The SOC change of each battery cell is calculated by: S bn (t)=S bn (t-1)+P bn (t)·Δt / C bn Among them, S bn (t) is the SOC of the nth battery cell at time t, S bn (t-1) is the SOC of the nth battery cell at time t-1, P bn (t) is the charge and discharge power of the nth battery cell at time t, which takes a negative value during charging and a positive value during discharging. bn is the maximum energy storage capacity of the nth battery unit, Δt is the scheduling interval; S316. The output of each battery unit under the maximum charge and discharge power limit is calculated as follows: Among them, P bmaxn is the maximum charging power of the nth battery cell, P bminn is the maximum discharge power of the nth battery cell; S317. The output of each battery unit is also subject to the following charging and discharging constraints: Among them, S bmaxn represents the upper limit of the SOC of the nth battery cell, S bminn Indicates the lower limit of the SOC of the nth battery cell.

Images