A method and system for smoothing wind power fluctuations with SOC balance of multiple energy storage units

Abstract

The embodiment of the present invention provides a method and system for smoothing wind power fluctuations with SOC balance of multiple energy storage units, predicting the wind power of the day based on historical data of the wind farm through a BP neural network algorithm; according to the discrete form of a first-order low-pass filter, with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, minimizing the power command of the energy storage device and ensuring that the actual grid-connected power fluctuation rate meets the grid-connected requirements; according to the power command of the entire set of energy storage devices and the collected charge states of different energy storage units, a corresponding method is used to calculate the power command of each energy storage unit; thereby solving the problem of difficulty in selecting the filtering time constant and excessive difference in the charge states of each energy storage unit in the prior art, and ensuring the safe and stable operation of the wind farm; the present invention is also applicable to the power fluctuation problem in the grid-connected photovoltaic energy storage combined system.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of wind-storage combined system access to a power grid, and in particular to a method and system for smoothing wind power fluctuations with SOC balancing of multiple energy storage units. Background Art

[0002] The development and utilization of renewable energy plays a key role in solving energy crises and environmental problems. Given the obvious advantages of wind energy, such as easy access, huge quantity and harmlessness, wind power generation has become one of the main ways to utilize renewable energy.

[0003] Due to seasonal and climatic factors, wind power generation has obvious volatility and uncertainty. As its scale expands, it will have an impact on the power grid after being connected to the power grid, thus affecting the safe and stable operation of the power grid. Energy storage has the function of storing or releasing a certain amount of electric energy. It can store some electric energy during the peak period of power generation and the low period of power consumption, and release it during the low period of power generation and the peak period of power consumption, so as to achieve the purpose of peak shaving and valley filling. Configuring battery energy storage on the wind farm side is an effective way to alleviate the power fluctuation of wind power grid connection and improve the controllability of wind power. This is of great significance for enhancing the friendly grid connection performance of wind power and improving the level of wind power consumption. Summary of the invention

[0004] The embodiments of the present invention provide a method and system for smoothing wind power fluctuations with balanced SOC of multiple energy storage units, which solves the problems in the prior art that the filtering time constant of the first-order low-pass filter is difficult to determine and the SOC differences of each energy storage unit are too large, and achieves the technical effect of ensuring the safe and stable operation of the wind farm and the energy storage device.

[0005] In a first aspect, an embodiment of the present invention provides a method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units, comprising:

[0006] Step S1, obtaining historical data of the wind farm in a set time period before the current day;

[0007] Step S2: predicting the wind power forecast for the current day based on the wind farm historical data and a pre-trained wind power forecasting network;

[0008] Step S3, taking the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, determining the filtering time constant of the first-order filter based on the predicted wind power;

[0009] Step S4, determining a total power command reference value based on the filtering time constant;

[0010] Step S5: determining the power command of each energy storage unit based on the state of charge of each energy storage unit and the power command reference value.

[0011] Preferably, the wind farm historical data includes historical wind speed, wind direction data and wind power data of the wind farm, real-time active power of the wind farm, wind turbine parameters of the wind-storage combined grid-connected system, collection line parameters and length, and transformer parameters;

[0012] The step S1 also includes obtaining parameters of an energy storage device and a charge state of each energy storage unit in the wind-storage combined grid-connected system, wherein the energy storage device includes a plurality of energy storage units.

[0013] As a preferred embodiment, it also includes:

[0014] Obtain wind speed data, wind direction data and wind power data of the wind farm every minute in the day before the current day to construct a training sample set; the wind direction data includes wind direction sine data and wind direction cosine data;

[0015] A neural network training is performed based on the training sample set to obtain a wind power prediction network for predicting a wind power fluctuation curve one day before the current day.

[0016] Preferably, the step S3 specifically includes:

[0017] Determine the discretized first-order low-pass filter:

[0018]

[0019] In the above formula, T is the filtering time constant, Δt is the sampling step; P W (t) is the wind power at time t, P tag (t) is the target power at time t, P tag (t-Δt) is the target power at the previous moment. The calculation starting point is t1=0, the calculation end point is t2, the calculation interval is (t1, t2), and the time is divided into n equal parts, that is, Δt=(t2-t1) / n; the wind power forecast power is used instead of the real-time wind power to obtain the model:

[0020]

[0021] Where P W_ref (t) is the predicted wind power at time t.

[0022] Preferably, the objective function in the model is:

[0023] min f=T

[0024] Assume that the target power fluctuation rate within the sampling time is less than the grid-connected power standard, and the constraint condition is:

[0025] max{P tag (Δt),P tag (2Δt),…P tag(nΔt)}-min{P tag (Δt),P tag (2Δt),…P tag (nΔt)}≤P W_rated γ%

[0026] In the above formula, P W_rated is the rated power of the wind farm, and γ% is the standard power fluctuation rate limit of wind power grid connection.

[0027] Preferably, in step S4, the power command reference value is P B_ref (t) = P tag (t)-P W (t).

[0028] Preferably, the step S5 specifically includes:

[0029] Step S51, determining the state of charge of each energy storage unit, dividing the state of charge into five intervals from low to high: (0.85, 1), (0.7, 0.85), (0.3, 0.7), (0.15, 0.3), and (0, 0.15), and calculating the number of energy storage units in each interval to be a, b, c, d, and e respectively;

[0030] Step S52: Obtain the power command reference value P in step S4 B_ref (t), if P B_ref (t)>0, it enters the discharge mode. In the discharge mode:

[0031] If P B_ref (t)≤(a+b)P i_rate , where P i_rate is the rated power of the i-th energy storage unit; it means that the energy storage units with a state of charge in the interval (0.7, 1) can complete the discharge task, so the energy storage units in the interval (0.7, 1) are arranged in descending order according to the state of charge, and the discharge instructions are assigned to the energy storage units in the interval (0.7, 1) in sequence;

[0032] If (a+b+c)P i_rate ≥P B_ref >(a+b)P i_rate , it means that only calling the energy storage unit with a state of charge in the interval (0.7,1) cannot complete this discharge instruction, but calling the energy storage unit with a state of charge in the interval (0.3,1) can complete the discharge instruction; at this time, the energy storage unit with a state of charge in the interval (0.7,1) is fully discharged according to the rated power, and the energy storage unit with a state of charge in the interval (0.3,0.7) is discharged according to the following formula:

[0033]

[0034] In the formula, SOC iis the state of charge of the i-th energy storage unit, P i_rate is the rated power of the i-th energy storage unit;

[0035] If (a+b+c+d)P i_rate ≥P B_ref >(a+b+c)P i_rate , it means that the energy storage unit with a state of charge in the interval (0.15, 0.3) needs to be urgently called. At this time, the energy storage unit with a state of charge in the interval (0.3, 1) is fully discharged at rated power, and the energy storage unit with a state of charge in the interval (0.15, 0.3) is discharged according to the following formula:

[0036]

[0037] If P B_ref >(a+b+c+d)P i_rate , which means that the power command is large at this time, and all energy storage units with a state of charge in the range of (0.15,1) need to be fully discharged;

[0038] If we judge that if P B_ref (t)<0, it enters charging mode. In charging mode:

[0039] If P B_ref ≥-(d+e)P i_rate , it means that the energy storage units with a state of charge in the interval (0, 0.3) can complete the charging task, so they are arranged in ascending order according to the state of charge, and charging instructions are assigned to the energy storage units in the interval (0, 0.3) in turn;

[0040] If -(d+e)P i_rate ≥P B_ref >-(c+d+e)P i_rate , it means that calling only the energy storage unit with the state of charge in the range (0,0.3) cannot complete this charging instruction, but calling the energy storage unit with the state of charge in the range (0,0.7) can complete it; at this time, the energy storage unit with the state of charge in the range (0,0.3) is fully charged according to the rated power, and the energy storage unit with the state of charge in the range (0.3,0.7) is charged according to the following formula:

[0041]

[0042] If -(c+d+e)P i_rate ≥P B_ref >-(b+c+d+e)P i_rate , it means that the energy storage unit with a state of charge in the range of (0.7, 0.85) needs to be urgently called. At this time, the energy storage unit with a state of charge in the range of (0, 0.7) is fully charged according to the rated power, and the energy storage unit with a state of charge in the range of (0.7, 0.85) is charged according to the following formula:

[0043]

[0044] If P B_ref <-(b+c+d+e)P i_rate , it means that the power command is large at this time, and all energy storage units with a state of charge in the range of (0,0.85) need to be fully charged.

[0045] In a second aspect, an embodiment of the present invention provides a wind power fluctuation smoothing system with SOC balancing of multiple energy storage units, comprising:

[0046] The historical data collection module is used to obtain the historical data of the wind farm in the set time period before the current day;

[0047] A wind power prediction module, used to predict the wind power prediction of the current day based on the historical data of the wind farm and a pre-trained wind power prediction network;

[0048] A first-order filtering module, used to determine the filtering time constant of the first-order filter based on the predicted wind power with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition;

[0049] A power command reference module, used for determining a total power command reference value based on the filtering time constant;

[0050] The energy storage unit power instruction calculation module is used to determine the power instruction of each energy storage unit based on the charge state of each energy storage unit and the power instruction reference value.

[0051] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units as described in the embodiment of the first aspect of the present invention are implemented.

[0052] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units as described in the embodiment of the first aspect of the present invention.

[0053] The embodiment of the present invention provides a method and system for smoothing wind power fluctuations with SOC balance of multiple energy storage units. The method predicts the wind power of the day based on the historical data of the wind farm through a BP neural network algorithm. According to the discrete form of the first-order low-pass filter, the power command of the energy storage device is minimized with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, and the actual grid-connected power fluctuation rate is guaranteed to meet the grid-connected requirements. According to the power command of the entire set of energy storage devices and the charge states of different energy storage units collected, the power command of each energy storage unit is calculated by a corresponding method. Thus, the problems in the prior art that the filtering time constant is difficult to select and the charge states of each energy storage unit are too different, and the safe and stable operation of the wind farm is guaranteed. The present invention is also applicable to the power fluctuation problem in the grid-connected photovoltaic energy storage combined system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flowchart of a method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units according to an embodiment of the present invention;

[0056] Figure 2 A flow chart of a method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units according to an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of SOC partitions provided by an embodiment of the present invention;

[0058] Figure 4 This is a main circuit topology diagram of an actual wind-storage combined grid-connected system in an embodiment of the present invention;

[0059] Figure 5 This is a curve diagram of smoothing wind power fluctuations in an embodiment of the present invention;

[0060] Figure 6 It is an active output curve diagram of the energy storage device in the embodiment of the present invention;

[0061] Figure 7 It is a SOC fluctuation curve diagram of each energy storage unit in an embodiment of the present invention;

[0062] Figure 8 FIG. 4 is a schematic diagram of a physical structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In the embodiments of the present application, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0065] The terms "first" and "second" in the embodiments of the present application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a system, product or device comprising a series of parts or units is not limited to the listed parts or units, but optionally also includes parts or units that are not listed, or optionally also includes other parts or units inherent to these products or devices. In the description of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0066] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] In recent years, new energy generation represented by wind power has developed rapidly. However, the randomness and intermittency of wind power are strong. After being connected to the grid, it increases the uncertainty of the power system and affects the stable operation of the system. In addition, after large-scale wind power is connected to the grid, the grid needs to have enough rotating reserve so that the system can adjust the fluctuating power, which increases the system operation cost. Configuring battery energy storage on the wind farm side is an effective way to alleviate the fluctuation of wind power grid-connected power and improve the controllability of wind power. This is of great significance for enhancing the friendly grid-connected performance of wind power and improving the level of wind power consumption.

[0068] Therefore, the embodiment of the present invention provides a method and system for smoothing wind power fluctuations with balanced SOC of multiple energy storage units, which solves the problem that the filtering time constant of the first-order low-pass filter is difficult to determine and the SOC difference of each energy storage unit is too large in the prior art, and achieves the technical effect of ensuring the safe and stable operation of the wind farm and the energy storage device. The following will be explained and introduced through multiple embodiments.

[0069] Figure 1 and Figure 2 A method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units provided in an embodiment of the present invention includes:

[0070] Step S1, obtaining historical data of the wind farm in the current set time period; including obtaining the topology of the wind-storage combined grid-connected system, wind turbine parameters, collection line parameters and length, transformer parameters, and energy storage device parameters, where the energy storage device includes multiple energy storage units; the real-time active power of the wind farm, the charge state of each energy storage unit, and the historical wind speed, wind direction, and wind power data of the wind farm;

[0071] Step S2: predicting the wind power forecast for the current day based on the wind farm historical data and the pre-trained wind power forecasting network; predicting the wind power for the current day using the BP neural network according to the acquired wind farm historical wind speed, wind direction and wind power data, wind turbine parameters, collector line parameters and length, transformer parameters, and real-time active power of the wind farm;

[0072] Step S3, taking the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, determining the filtering time constant of the first-order filter based on the predicted wind power;

[0073] In this embodiment, based on the discrete form of the first-order low-pass filter, with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, the optimal solution of the filtering time function in different time periods is calculated to optimize the power instruction of the entire energy storage device, as follows:

[0074] Discretized first-order low-pass filter:

[0075]

[0076] In the above formula, T is the filtering time constant, Δt is the sampling step length; P W (t) is the wind power at time t, P tag (t) is the target power at time t, P tag (t-Δt) is the target power at the previous moment. The calculation starting point is t1=0, the calculation end point is t2, the calculation interval is (t1, t2), and the time is divided into n equal parts, that is, Δt=(t2-t1) / n; the wind power forecast power is used instead of the real-time wind power to obtain the model:

[0077]

[0078] Where P W_ref (t) is the predicted wind power at time t. The objective function in the model is:

[0079] min f=T (3)

[0080] Assume that the target power fluctuation rate within the sampling time is less than the grid-connected power standard, and the constraint condition is: max{P tag (Δt),P tag (2Δt),…P tag (nΔt)}-min{P tag (Δt),P tag (2Δt),…P tag (nΔt)}≤P W_rated γ% (4)

[0081] In the above formula, P W_rated is the rated power of the wind farm, and γ% is the standard power fluctuation rate limit of wind power grid connection.

[0082] Step S4, determining a total power command reference value based on the filtering time constant; the power command reference value is:

[0083] P B_ref (t) = P tag (t)-P W (t) (5)

[0084] Step S5, based on the state of charge of each energy storage unit and the power command reference value, determine the power command of each energy storage unit. Based on the whole set of energy storage device parameters, the state of charge of each energy storage unit, and the power command reference value of the energy storage device at the current moment, the power command is graded and determined. A corresponding number of energy storage units are selected for power distribution according to the SOC function relationship, and the power command of each energy storage unit is calculated to achieve SOC balancing of each energy storage unit and reduce the number of charge and discharge times.

[0085] In this embodiment, the wind power of the day is predicted based on the historical data of the wind farm through the BP neural network algorithm; according to the discrete form of the first-order low-pass filter, the power instruction of the energy storage device is minimized with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, and the actual grid-connected power fluctuation rate is guaranteed to meet the grid-connected requirements; according to the power instruction of the whole set of energy storage devices and the collected charge states of different energy storage units, the power instruction of each energy storage unit is calculated by a corresponding method; thereby solving the problem of difficulty in selecting the filtering time constant and excessive difference in the charge states of each energy storage unit in the prior art, and ensuring the safe and stable operation of the wind farm; the present invention is also applicable to the power fluctuation problem in the grid-connected photovoltaic energy storage combined system.

[0086] On the basis of the above embodiment, it is also necessary to pre-train the wind power prediction network to obtain the wind speed data, wind direction data and wind power data of the wind farm every minute in the day before the current day to construct a training sample set; the wind direction data includes wind direction sine data and wind direction cosine data;

[0087] Based on the training sample set, a neural network is trained to obtain a wind power prediction network for predicting the wind power fluctuation curve of the current day. In this embodiment, the BP neural network is trained by 1440 sets of wind speed, wind direction parameters and wind power values, and the training accuracy is 10 -3 , the number of training times is set to 700.

[0088] Based on the above embodiments, step S5 specifically includes:

[0089] Step S51, determining the state of charge of each energy storage unit, dividing the state of charge into five intervals from low to high: (0.85, 1), (0.7, 0.85), (0.3, 0.7), (0.15, 0.3), and (0, 0.15), and calculating the number of energy storage units in each interval to be a, b, c, d, and e respectively;

[0090] In general:

[0091] n=a+b+c+d+e

[0092] Where n is the total number of energy storage units, a is the number of energy storage units in the interval (0.85, 1), b is the number of energy storage units in the interval (0.7, 0.85), c is the number of energy storage units in the interval (0.3, 0.7), d is the number of energy storage units in the interval (0.15, 0.3), and e is the number of energy storage units in the interval (0, 0.15).

[0093] Figure 3A schematic diagram of SOC partitioning provided for an embodiment of the present invention; wherein energy storage units with a state of charge in the interval (0, 0.15) are only charged but not discharged; energy storage units in the interval (0.15, 0.3) are more charged and less discharged, and the discharge situation only occurs when an emergency call is made to meet the stabilization target demand; energy storage units in the interval (0.3, 0.7) can be charged and discharged normally; energy storage units in the interval (0.7, 0.85) are less charged and more discharged, and the charging situation only occurs when the stabilization target call is met; energy storage units in the interval (0.85, 1) are only discharged but not charged.

[0094] Step S52: Obtain the power command reference value P in step S4 B_ref (t), if P B_ref (t)>0, it enters the discharge mode. In the discharge mode:

[0095] If P B_ref (t)≤(a+b)P i_rate , where P i_rate is the rated power of the i-th energy storage unit; it means that the energy storage units with a state of charge in the interval (0.7, 1) can complete the discharge task, so the energy storage units in the interval (0.7, 1) are arranged in descending order according to the state of charge, and the discharge instructions are assigned to the energy storage units in the interval (0.7, 1) in sequence;

[0096] If (a+b+c)P i_rate ≥P B_ref >(a+b)P i_rate , it means that only calling the energy storage unit with a state of charge in the interval (0.7,1) cannot complete this discharge instruction, but calling the energy storage unit with a state of charge in the interval (0.3,1) can complete the discharge instruction; at this time, the energy storage unit with a state of charge in the interval (0.7,1) is fully discharged according to the rated power, and the energy storage unit with a state of charge in the interval (0.3,0.7) is discharged according to the following formula:

[0097]

[0098] In the formula, SOC i is the state of charge of the i-th energy storage unit, P i_rate is the rated power of the i-th energy storage unit;

[0099] If (a+b+c+d)P i_rate ≥P B_ref >(a+b+c)P i_rate , it means that the energy storage unit with a state of charge in the interval (0.15, 0.3) needs to be urgently called. At this time, the energy storage unit with a state of charge in the interval (0.3, 1) is fully discharged at rated power, and the energy storage unit with a state of charge in the interval (0.15, 0.3) is discharged according to the following formula:

[0100]

[0101] If P B_ref >(a+b+c+d)P i_rate , which means that the power command is large at this time, and all energy storage units with a state of charge in the range of (0.15,1) need to be fully discharged;

[0102] If we judge that if P B_ref (t)<0, it enters charging mode. In charging mode:

[0103] If P B_ref ≥-(d+e)P i_rate , it means that the energy storage units with a state of charge in the interval (0, 0.3) can complete the charging task, so they are arranged in ascending order according to the state of charge, and charging instructions are assigned to the energy storage units in the interval (0, 0.3) in turn;

[0104] If -(d+e)P i_rate ≥P B_ref >-(c+d+e)P i_rate , it means that calling only the energy storage unit with the state of charge in the range (0,0.3) cannot complete this charging instruction, but calling the energy storage unit with the state of charge in the range (0,0.7) can complete it; at this time, the energy storage unit with the state of charge in the range (0,0.3) is fully charged according to the rated power, and the energy storage unit with the state of charge in the range (0.3,0.7) is charged according to the following formula:

[0105]

[0106] If -(c+d+e)P i_rate ≥P B_ref >-(b+c+d+e)P i_rate , it means that the energy storage unit with a state of charge in the range of (0.7, 0.85) needs to be urgently called. At this time, the energy storage unit with a state of charge in the range of (0, 0.7) is fully charged according to the rated power, and the energy storage unit with a state of charge in the range of (0.7, 0.85) is charged according to the following formula:

[0107]

[0108] If P B_ref <-(b+c+d+e)P i_rate , it means that the power command is large at this time, and all energy storage units with a state of charge in the range of (0,0.85) need to be fully charged.

[0109] Finally, each energy storage unit charges and discharges according to the power command to smooth out the wind power fluctuations.

[0110] Select as Figure 4The typical wind power energy storage combined grid-connected system shown verifies the effectiveness of the wind power fluctuation smoothing method taking into account the SOC balance of multiple energy storage units provided in the embodiment of the present invention. Among them, the wind farm is composed of 23 double-fed induction wind turbines with a single unit capacity / rated voltage of 2.2MW / 0.69kV. The wind turbine rotor diameter is 115m, the cut-in wind speed is 3m / s, the cut-out wind speed is 20m / s, and the rated wind speed is 9.5m / s. There are two collector lines, each containing 11 and 12 units, with a total installed capacity of 50.6MW. The energy storage equipment consists of 8 energy storage units with a single unit capacity of 0.625MW / 1.25MWh. Each group of 4 energy storage units has a total scale of 5MW / 10MWh. The transformer parameter settings are shown in Table 1. In order to reflect the effectiveness of the control method provided in the embodiment of the present invention, in this simulation experiment, the SOCs of the eight energy storage units are set to 0.2, 0.3, 0.4, 0.5, 0.55, 0.6, 0.7, and 0.8 respectively.

[0111] Table 1 Transformer rated parameters

[0112]

[0113] Depend on Figure 5 It can be seen that after the energy storage device smoothes the wind power, the fluctuation rate and roughness of the grid-connected power are greatly reduced, and each energy storage unit distributes power instructions in an orderly manner according to the power size of the energy storage device. Figure 6 is an active output curve diagram of the energy storage device in the embodiment of the present invention; Figure 7 It can be seen that after 9 hours, the SOC of each energy storage unit tends to be consistent, achieving SOC balance.

[0114] The embodiment of the present invention further provides a wind power fluctuation smoothing system with SOC balance of multiple energy storage units, and the wind power fluctuation smoothing method with SOC balance of multiple energy storage units based on the above embodiments includes:

[0115] The historical data collection module is used to obtain the historical data of the wind farm in the set time period before the current day;

[0116] A wind power prediction module, used to predict the wind power prediction of the current day based on the historical data of the wind farm and a pre-trained wind power prediction network;

[0117] A first-order filtering module, used to determine the filtering time constant of the first-order filter based on the predicted wind power with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition;

[0118] A power command reference module, used for determining a total power command reference value based on the filtering time constant;

[0119] The energy storage unit power instruction calculation module is used to determine the power instruction of each energy storage unit based on the charge state of each energy storage unit and the power instruction reference value.

[0120] Based on the same concept, the embodiment of the present invention also provides a schematic diagram of an entity structure, such as Figure 8 As shown, the server may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the steps of the wind power fluctuation smoothing method with SOC balancing of multiple energy storage units as described in the above embodiments. For example, it includes:

[0121] Step S1, obtaining historical data of the wind farm in a set time period before the current day;

[0122] Step S2: predicting the wind power forecast for the current day based on the wind farm historical data and a pre-trained wind power forecasting network;

[0123] Step S3, taking the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, determining the filtering time constant of the first-order filter based on the predicted wind power;

[0124] Step S4, determining a total power command reference value based on the filtering time constant;

[0125] Step S5: determining the power command of each energy storage unit based on the state of charge of each energy storage unit and the power command reference value.

[0126] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0127] Based on the same concept, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores a computer program, which includes at least one code segment, which can be executed by a main control device to control the main control device to implement the steps of the wind power fluctuation smoothing method with SOC balancing of multiple energy storage units as described in the above embodiments. For example, it includes:

[0128] Step S1, obtaining historical data of the wind farm in a set time period before the current day;

[0129] Step S2: predicting the wind power forecast for the current day based on the wind farm historical data and a pre-trained wind power forecasting network;

[0130] Step S3, taking the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, determining the filtering time constant of the first-order filter based on the predicted wind power;

[0131] Step S4, determining a total power command reference value based on the filtering time constant;

[0132] Step S5: determining the power command of each energy storage unit based on the state of charge of each energy storage unit and the power command reference value.

[0133] Based on the same technical concept, an embodiment of the present application also provides a computer program, which, when executed by a main control device, is used to implement the above method embodiment.

[0134] The program may be stored in whole or in part on a storage medium packaged together with the processor, or may be stored in whole or in part on a memory not packaged together with the processor.

[0135] Based on the same technical concept, the embodiment of the present application further provides a processor, which is used to implement the above method embodiment. The above processor can be a chip.

[0136] In summary, the embodiment of the present invention provides a method and system for smoothing wind power fluctuations with SOC balance of multiple energy storage units. The method predicts the wind power of the day based on the historical data of the wind farm through the BP neural network algorithm. According to the discrete form of the first-order low-pass filter, the power command of the energy storage device is minimized with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, and the actual grid-connected power fluctuation rate is guaranteed to meet the grid-connected requirements. According to the power command of the entire set of energy storage devices and the charge states of different energy storage units collected, the power command of each energy storage unit is calculated by a corresponding method. Thereby, the problem of difficulty in selecting the filtering time constant and excessive difference in the charge states of each energy storage unit in the prior art is solved, and the safe and stable operation of the wind farm is ensured. The present invention is also applicable to the power fluctuation problem in the grid-connected photovoltaic energy storage combined system.

[0137] The various embodiments of the present invention can be combined arbitrarily to achieve different technical effects.

[0138] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in this application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidStateDisk), etc.

[0139] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units, characterized in that: include: Step S1, obtaining historical data of the wind farm in a set time period before the current day; Step S2: predicting the wind power forecast for the current day based on the wind farm historical data and a pre-trained wind power forecasting network; Step S3, taking the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition, determining the filtering time constant of the first-order filter based on the predicted wind power; Step S4, determining a total power command reference value based on the filtering time constant; Step S5, determining the power command of each energy storage unit based on the charge state of each energy storage unit and the power command reference value; Step S5 specifically includes: Step S51, determining the state of charge of each energy storage unit, dividing the state of charge into five intervals from low to high: (0.85, 1), (0.7, 0.85), (0.3, 0.7), (0.15, 0.3), and (0, 0.15), and calculating the number of energy storage units in each interval to be a, b, c, d, and e respectively; Step S52: Obtain the power command reference value P in step S4 B_ref (t), if P B_ref (t)>0, it enters the discharge mode. In the discharge mode: If P B_ref (t)≤(a+b)P i_rate , where P i_rate is the rated power of the i-th energy storage unit; it means that the energy storage units with a state of charge in the interval (0.7, 1) can complete the discharge task, so the energy storage units in the interval (0.7, 1) are arranged in descending order according to the state of charge, and the discharge instructions are assigned to the energy storage units in the interval (0.7, 1) in sequence; If (a+b+c)P i_rate ≥P B_ref >(a+b)P i_rate , it means that only calling the energy storage unit with a state of charge in the interval (0.7,1) cannot complete this discharge instruction, but calling the energy storage unit with a state of charge in the interval (0.3,1) can complete the discharge instruction; at this time, the energy storage unit with a state of charge in the interval (0.7,1) is fully discharged according to the rated power, and the energy storage unit with a state of charge in the interval (0.3,0.7) is discharged according to the following formula: In the formula, SOC i is the state of charge of the i-th energy storage unit, P i_rate is the rated power of the i-th energy storage unit; If (a+b+c+d)P i_rate ≥P B_ref >(a+b+c)P i_rate , it means that the energy storage unit with a state of charge in the interval (0.15, 0.3) needs to be urgently called. At this time, the energy storage unit with a state of charge in the interval (0.3, 1) is fully discharged at rated power, and the energy storage unit with a state of charge in the interval (0.15, 0.3) is discharged according to the following formula: If P B_ref >(a+b+c+d)P i_rate , which means that the power command is large at this time, and all energy storage units with a state of charge in the range of (0.15,1) need to be fully discharged; If we judge that if P B_ref (t)<0, it enters charging mode. In charging mode: If P B_ref ≥-(d+e)P i_rate , it means that the energy storage units with a state of charge in the interval (0, 0.3) can complete the charging task, so they are arranged in ascending order according to the state of charge, and charging instructions are assigned to the energy storage units in the interval (0, 0.3) in turn; If -(d+e)P i_rate ≥P B_ref >-(c+d+e)P i_rate , it means that calling only the energy storage unit with the state of charge in the range (0,0.3) cannot complete this charging instruction, but calling the energy storage unit with the state of charge in the range (0,0.7) can complete it; at this time, the energy storage unit with the state of charge in the range (0,0.3) is fully charged according to the rated power, and the energy storage unit with the state of charge in the range (0.3,0.7) is charged according to the following formula: If -(c+d+e)P i_rate ≥P B_ref >-(b+c+d+e)P i_rate , it means that the energy storage unit with a state of charge in the range of (0.7, 0.85) needs to be urgently called. At this time, the energy storage unit with a state of charge in the range of (0, 0.7) is fully charged according to the rated power, and the energy storage unit with a state of charge in the range of (0.7, 0.85) is charged according to the following formula: If P B_ref <-(b+c+d+e)P i_rate , it means that the power command is large at this time, and all energy storage units with a state of charge in the range of (0,0.85) need to be fully charged.

2. The method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units according to claim 1 is characterized in that: The wind farm historical data includes historical wind speed, wind direction data and wind power data of the wind farm, real-time active power of the wind farm, wind turbine parameters of the wind-storage combined grid-connected system, collection line parameters and length, and transformer parameters; The step S1 also includes obtaining parameters of an energy storage device and a charge state of each energy storage unit in the wind-storage combined grid-connected system, wherein the energy storage device includes a plurality of energy storage units.

3. The method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units according to claim 1 is characterized in that: Also includes: Obtain wind speed data, wind direction data and wind power data of the wind farm every minute in the day before the current day to construct a training sample set; the wind direction data includes wind direction sine data and wind direction cosine data; A neural network training is performed based on the training sample set to obtain a wind power prediction network for predicting a wind power fluctuation curve one day before the current day.

4. The method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units according to claim 1, characterized in that: The step S3 specifically includes: Determine the discretized first-order low-pass filter: In the above formula, T is the filtering time constant, Δt is the sampling step length P W (t) is the wind power at time t, P tag (t) is the target power at time t, P tag (t-Δt) is the target power at the previous moment. The calculation starting point is t1=0, the calculation end point is t2, the calculation interval is (t1, t2), and the time is divided into n equal parts, that is, Δt=(t2-t1) / n; the wind power forecast power is used instead of the real-time wind power to obtain the model: Where P W_ref (t) is the predicted wind power at time t.

5. The method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units according to claim 4 is characterized in that: The objective function in the model is: min f=T Assume that the target power fluctuation rate within the sampling time is less than the grid-connected power standard, and the constraint condition is: max{P tag (Δt),P tag (2Δt)…P tag (nΔt)}-min{Ptag(Δt),P tag (2Δt)…P tag (nΔt)}≤P W_rated ·c% In the above formula, P W_rated is the rated power of the wind farm, and γ% is the standard power fluctuation rate limit of wind power grid connection.

6. The method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units according to claim 5 is characterized in that: In step S4, the power command reference value is P B_ref (t) = P tag (t)-P W (t).

7. A wind power fluctuation smoothing system with SOC balance of multiple energy storage units used in the wind power fluctuation smoothing method with SOC balance of multiple energy storage units as claimed in any one of claims 1 to 6, characterized in that: include: The historical data collection module is used to obtain the historical data of the wind farm in the set time period before the current day; A wind power prediction module, used to predict the wind power prediction of the current day based on the historical data of the wind farm and a pre-trained wind power prediction network; A first-order filtering module, used to determine the filtering time constant of the first-order filter based on the predicted wind power with the minimum filtering time constant as the goal and the fluctuation rate constraint of the target power as the limiting condition; A power command reference module, used for determining a total power command reference value based on the filtering time constant; The energy storage unit power instruction calculation module is used to determine the power instruction of each energy storage unit based on the charge state of each energy storage unit and the power instruction reference value.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units as described in any one of claims 1 to 6 are implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for smoothing wind power fluctuations with SOC balancing of multiple energy storage units as described in any one of claims 1 to 6 are implemented.

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