Hybrid energy storage power allocation method based on adaptive sliding average

Abstract

The present invention provides a hybrid energy storage power allocation method based on adaptive sliding average, which is characterized by comprising the following steps: obtaining the wind power of the target wind farm, and performing wavelet packet decomposition on the wind power to determine the grid-connected power of the wind power and the hybrid energy storage power; initializing the time window d and the window weight α of the adaptive sliding average, and adjusting the time window d and the window weight α of the adaptive sliding average by using a fuzzy controller to obtain the target time window d and the target window weight α; S3. obtaining the actual power of the battery in the hybrid energy storage system within a set time period; determining the reference power p based on the actual output power of the battery 1 and reference power p 2 ; Calculate the target distribution power of the battery and supercapacitor in the hybrid energy storage system at time t: It can weigh the current state of charge of the supercapacitor and the size of the target power of the hybrid energy storage, adjust the parameters in the sliding average, and adaptively distribute the power of the battery and supercapacitor.

Description

Technical Field

[0001] The invention relates to a wind power system control method, and in particular to a hybrid energy storage power distribution method based on adaptive sliding average. Background Art

[0002] With the advancement of the "dual carbon" strategic goals, the proportion of renewable energy such as wind power continues to increase. Wind power has obvious volatility, and when it is directly connected to the grid, it will have a serious impact on the safety and stable operation of the power system. In order to reduce the impact of direct grid connection of wind power, energy storage is generally configured on the wind farm side to smooth out wind power fluctuations. On the other hand, different types of energy storage have their own advantages in energy density, power density and response speed. A single energy storage device cannot fully meet the needs of smoothing wind power fluctuations in different scenarios. Therefore, power-type energy storage and energy-type energy storage are often configured on site in a combination of power storage and energy storage. The most representative of these is the hybrid energy storage form of battery-supercapacitor.

[0003] Batteries have high energy density and are easy to control, while supercapacitors have high power density and long cycle life. Therefore, battery-supercapacitor hybrid energy storage is widely used in wind power suppression scenarios. However, the problem with this hybrid energy storage is that it is difficult to reasonably allocate the power of the two. Too high a target power will cause overcharging and overdischarging of the energy storage, and at the same time, it will aggravate the attenuation of the energy storage life, thereby reducing the economy and safety of energy storage.

[0004] Therefore, in order to solve the above technical problems, it is urgent to propose a new technical means. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a hybrid energy storage power allocation method based on adaptive sliding average, which can weigh the current state of charge of the supercapacitor and the size of the hybrid energy storage target power, adjust the parameters in the sliding average, and adaptively allocate the power of the battery and the supercapacitor, so as to give full play to the advantages of energy-type and power-type energy storage, thereby reducing the life loss of the energy storage battery and improving the reliability and economy of hybrid energy storage in stabilizing wind power.

[0006] The present invention provides a hybrid energy storage power distribution method based on adaptive sliding average, which is characterized by comprising the following steps:

[0007] S1. Obtain the wind power of the target wind farm, and perform wavelet packet decomposition on the wind power to determine the grid-connected power of the wind power and the hybrid energy storage power;

[0008] S2. Initialize the time window d and window weight α of the adaptive sliding average, and use the fuzzy controller to adjust the time window d and window weight α of the adaptive sliding average to obtain the target time window d and target window weight α;

[0009] S3. Obtaining the actual power of the battery in the hybrid energy storage system within a set time period;

[0010] S4. Determine the reference power p1 and the reference power p2 based on the actual output power of the battery;

[0011] S5. Calculate the target distribution power of the battery and supercapacitor in the hybrid energy storage system at time t:

[0012] P ba (t) = αP1 + (1-α)P2

[0013] P sc (t) = P g (t)-P ba (t);

[0014] Among them, P ba (t) is the target allocated power of the battery at time t, P sc (t) is the target allocated power of the supercapacitor at time t, P g (t) is the wind power grid-connected power at time t.

[0015] Further, in step S4, the reference power p1 and the reference power p2 are determined by the following method:

[0016]

[0017]

[0018] Where: P' ba is the actual power of the battery, t represents time t, P ES It is the hybrid energy storage power.

[0019] Further, in step S1, determining the grid-connected power of wind power and the hybrid energy storage power specifically includes:

[0020] S101. Perform n-layer wavelet packet decomposition on wind power to obtain 2 n power components, and 2 n The power components are arranged in order from smallest to largest:

[0021] in: P w Represents wind power, P n,i represents the i-th power component, and P n,1 is the minimum value of the power component;

[0022] S102. Constructing the minimum power component P n,1 Judgment model:

[0023]

[0024]

[0025] Where: m is the wind power collection time interval of the target wind farm; P wR is the installed capacity of wind power, δ 1min , δ 10min They are the maximum power fluctuation rate standards for 1 minute and 10 minutes respectively;

[0026] S103. Determine the minimum value P of the power component n,1 Whether the judgment model is satisfied at the same time, if so, the grid-connected power and hybrid energy storage power are determined as follows:

[0027] P g =P n,1

[0028]

[0029] Where: P g is the wind power grid-connected power, P ES If not, the process returns to step S101 and sets n=n+1.

[0030] Further, the adjustment of the time window d and the window weight α in step S2 specifically includes:

[0031] Calculate the overall state of charge of the hybrid energy storage, and the recursive relationship of the state of charge is as follows:

[0032] SOC ES (t+1) = SOC ES (t)-P ES (t) / Q ES

[0033] In the formula, Q ES is the total capacity of hybrid energy storage; SOC ES (t) is the charge state of the hybrid energy storage system at time t, P ES (t) is the hybrid energy storage power at time t;

[0034] Based on the time window d, estimate the magnitude of the charge state change of the hybrid energy storage system △SOC ES (t):

[0035]

[0036] Determine the state of charge SOC of the supercapacitor at time t-1 sc (t-1), and the parameter SOC sc (t-1) and △SOC ES(t) The time window d and the window weight α are adaptively adjusted in the input fuzzy controller according to the set fuzzy control rules.

[0037] The beneficial effects of the present invention are as follows: Through the present invention, it is possible to weigh the current state of charge of the supercapacitor and the size of the target power of the hybrid energy storage, adjust the parameters in the sliding average, and adaptively allocate the power of the battery and the supercapacitor, so as to give full play to the advantages of energy-type and power-type energy storage, thereby reducing the life loss of the energy storage battery and improving the reliability and economy of the hybrid energy storage in stabilizing the wind power. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0039] Figure 1 It is a flow chart of the present invention.

[0040] Figure 2 Schematic diagram of hybrid energy storage target power obtained by wavelet packet decomposition of the present invention.

[0041] Figure 3 It is a schematic diagram of the hybrid energy storage power division of the present invention.

[0042] Figure 4 This is a schematic diagram of the sliding average calculation of reference power in the present invention.

[0043] Figure 5 This is a diagram showing the power distribution effect of the battery supercapacitor of the present invention.

[0044] Figure 6 This is a graph showing the SOC changes of the battery supercapacitor of the present invention. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below:

[0046] The present invention provides a hybrid energy storage power distribution method based on adaptive sliding average, which is characterized by comprising the following steps:

[0047] S1. Obtain the wind power of the target wind farm, and perform wavelet packet decomposition on the wind power to determine the grid-connected power of the wind power and the hybrid energy storage power;

[0048] S2. Initialize the time window d and window weight α of the adaptive sliding average, and use the fuzzy controller to adjust the time window d and window weight α of the adaptive sliding average to obtain the target time window d and target window weight α;

[0049] S3. Obtaining the actual power of the battery in the hybrid energy storage system within a set time period;

[0050] S4. Determine the reference power p1 and the reference power p2 based on the actual output power of the battery;

[0051] S5. Calculate the target distribution power of the battery and supercapacitor in the hybrid energy storage system at time t:

[0052] P ba (t) = αP1 + (1-α)P2

[0053] P sc (t) = P g (t)-P ba (t);

[0054] Among them, P ba (t) is the target allocated power of the battery at time t, P sc (t) is the target allocated power of the supercapacitor at time t, P g (t) is the wind power grid-connected power at time t. The operation of the battery and supercapacitor is controlled based on the target allocated power. Through the above method, the current state of charge of the supercapacitor and the target power of the hybrid energy storage can be weighed, the parameters in the sliding average can be adjusted, and the power of the battery and supercapacitor can be adaptively allocated to give full play to the advantages of energy-type and power-type energy storage, thereby reducing the life loss of the energy storage battery and improving the reliability and economy of the hybrid energy storage in stabilizing the wind power.

[0055] In this embodiment, in step S4, the reference power p1 and the reference power p2 are determined by the following method:

[0056]

[0057]

[0058] Where: P' ba is the actual power of the battery, t represents time t, P ES is the hybrid energy storage power; Figure 4 As shown, Figure 4 is calculated based on the time window d before the current time t and the time window d after the current time t, where P after time t ES The hybrid energy storage power is the estimated value for the wind power system. Each wind power system can use existing methods to predict power by simply obtaining it directly from the wind power system.

[0059] In this embodiment, in step S1, determining the grid-connected power of wind power and the hybrid energy storage power specifically includes:

[0060] S101. Perform n-layer wavelet packet decomposition on wind power to obtain 2 n power components, and 2 nThe power components are arranged in order from smallest to largest:

[0061] in: P w Represents wind power, P n,i represents the i-th power component, and P n,1 is the minimum value of the power component;

[0062] S102. Constructing the minimum power component P n,1 Judgment model:

[0063]

[0064]

[0065] Where: m is the wind power collection time interval of the target wind farm; P wR is the installed capacity of wind power, δ 1min , δ 10min They are the maximum power fluctuation rate standards for 1 minute and 10 minutes respectively;

[0066] S103. Determine the minimum value P of the power component n,1 Whether the judgment model is satisfied at the same time, if so, the grid-connected power and hybrid energy storage power are determined as follows:

[0067] P g =P n,1

[0068]

[0069] Where: P g is the wind power grid-connected power, P ES If not, the process returns to step S101 and sets n=n+1.

[0070] In this embodiment, the adjustment of the time window d and the window weight α in step S2 specifically includes:

[0071] Calculate the overall state of charge of the hybrid energy storage, and the recursive relationship of the state of charge is as follows:

[0072] SOC ES (t+1) = SOC ES (t)-P ES (t) / Q ES

[0073] In the formula, Q ES is the total capacity of hybrid energy storage; SOC ES (t) is the charge state of the hybrid energy storage system at time t, P ES (t) is the hybrid energy storage power at time t;

[0074] Based on the time window d, estimate the magnitude of the charge state change of the hybrid energy storage system △SOC ES (t):

[0075] k represents the kth time point;

[0076] Determine the state of charge SOC of the supercapacitor at time t-1 sc (t-1), and the parameter SOC sc (t-1) and △SOC ES (t) Input the fuzzy controller to adaptively adjust the time window d and window weight α according to the set fuzzy control rules. Figure 3 As shown:

[0077] According to △SOC ES The size of the hybrid energy storage target power can be divided into 4 categories.

[0078] ①If △SOC ES Very small, that is, the supercapacitor can work alone, then the hybrid energy storage power is classified as type 1;

[0079] ②If △SOC ES Smaller, but the supercapacitor can only work alone when the smoothing margin is higher, so the hybrid energy storage power is classified as type 2;

[0080] ③If △SOC ES The power consumption is relatively large, and the supercapacitor cannot complete the power consumption alone, and needs to work with the battery. In this case, the hybrid energy storage power is classified as type 3;

[0081] ④ If △SOC ES Very large, the battery can only work alone when the smoothing margin is high, then the hybrid energy storage power is classified as type 4.

[0082] Based on the above division, the working status and load capacity of the supercapacitor and the battery can be clearly identified. The fuzzy control rules can be set using existing methods. This is the prior art. A fuzzy control rule used in this embodiment is shown in the following table:

[0083] Table 1 a Fuzzy control rules table

[0084]

[0085] Table 2d Fuzzy control rules table

[0086]

[0087] Among them, the values ​​in the above table are as follows Figure 4 shown.

[0088] like Figure 5 As shown: Figure 5 This is a diagram showing the power distribution effect of the battery supercapacitor in the method of the present invention. It can be seen that in the entire process of smoothing wind power fluctuations, the battery distributes large-amplitude fluctuating power, while the supercapacitor distributes more frequent small-amplitude fluctuating power, which is in line with the performance characteristics of the two energy storage devices.

[0089] like Figure 6 As shown: Figure 6 This is a display diagram of the SOC change of the battery and supercapacitor of the present invention. It can be seen that the SOC operating range of the battery and supercapacitor is normal, without overcharge or over discharge. Among them, the SOC operating range of the battery is relatively narrow, and the SOC changes greatly only in the time period with large power demand in some intervals. Figure 6 It can be seen that the power allocation method of the present invention can dynamically adjust the power output of the battery and the supercapacitor according to the target power requirements of the hybrid energy storage, increase the output of the battery when the power demand is large, and reduce the power pressure of the supercapacitor, thereby improving the coordination and cooperation ability of the two energy storages.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A hybrid energy storage power allocation method based on adaptive sliding average, characterized in that: The following steps are involved: S1. Obtain the wind power of the target wind farm, and perform wavelet packet decomposition on the wind power to determine the grid-connected power of the wind power and the hybrid energy storage power; S2. Initialize the time window d and window weight α of the adaptive sliding average, and use the fuzzy controller to adjust the time window d and window weight α of the adaptive sliding average to obtain the target time window d and target window weight α; S3. Obtaining the actual power of the battery in the hybrid energy storage system within a set time period; S4. Determine the reference power p1 and the reference power p2 based on the actual output power of the battery; S5. Calculate the target distribution power of the battery and supercapacitor in the hybrid energy storage system at time t: P ba (t)=αP1+(1-α)P2 P sc (t)=P g (t)-P ba (t); Among them, P ba (t) is the target allocated power of the battery at time t, P sc (t) is the target allocated power of the supercapacitor at time t, P g (t) is the wind power grid-connected power at time t; In step S4, the reference power p1 and the reference power p2 are determined by the following method: Where: P' ba is the actual power of the battery, t represents time t, P ES It is the hybrid energy storage power.

2. The hybrid energy storage power allocation method based on adaptive sliding average according to claim 1 is characterized in that: In step S1, determining the grid-connected power of wind power and the hybrid energy storage power specifically includes: S101. Perform n-layer wavelet packet decomposition on wind power to obtain 2 n power components, and 2 n The power components are arranged in order from smallest to largest: in: P w Represents wind power, P n,i represents the i-th power component, and P n,1 is the minimum value of the power component; S102. Constructing the minimum power component P n,1 Judgment model: Where: m is the wind power collection time interval of the target wind farm; P wR is the installed capacity of wind power, δ 1min , δ 10min They are the maximum power fluctuation rate standards for 1 minute and 10 minutes respectively; S103. Determine the minimum value P of the power component n,1 Whether the judgment model is satisfied at the same time, if so, the grid-connected power and hybrid energy storage power are determined as follows: P g =P n,1 Where: P g is the wind power grid-connected power, P ES If not, the process returns to step S101 and sets n=n+1.

3. The hybrid energy storage power allocation method based on adaptive sliding average according to claim 1 is characterized in that: The adjustment of the time window d and the window weight α in step S2 specifically includes: Calculate the overall state of charge of the hybrid energy storage, and the recursive relationship of the state of charge is as follows: SOCIETY ES (t+1)=SOC ES (t)-P ES (t) / Q ES In the formula, Q ES is the total capacity of hybrid energy storage; SOC ES (t) is the charge state of the hybrid energy storage system at time t, P ES (t) is the hybrid energy storage power at time t; Based on the time window d, estimate the magnitude of the charge state change of the hybrid energy storage system ΔSOC ES (t): Determine the state of charge SOC of the supercapacitor at time t-1 sc (t-1), and the parameter SOC sc (t-1) and ΔSOC ES (t) The time window d and the window weight α are adaptively adjusted in the input fuzzy controller according to the set fuzzy control rules.

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