Wind power fluctuation smoothing control method based on dynamic adjustment of SOC state

Abstract

The present invention provides a method for controlling wind power fluctuations smoothly based on dynamically adjusting the SOC state, comprising the following steps: S1. dividing a day into n moments according to a set time step; S2. collecting the SOC value of the energy storage battery in the wind power system at the kth moment, and determining the charge and discharge saturation capacity value of the energy storage battery; S3. constructing a predicted power sequence of the energy storage battery in a set time domain based on the charge and discharge saturation capacity value of the energy storage battery at the kth moment; S4. taking the first item in the predicted power sequence of the energy storage battery as the energy storage reference power, and controlling the charge and discharge of the energy storage battery based on the reference power; S5. setting k=k+1, and returning to step S2; being able to accurately predict the power of wind power in a future set time domain based on the charge and discharge saturation capacity value of the energy storage battery at the current moment, and controlling the charge and discharge of the energy storage battery based on the predicted value, thereby improving the wind power level suppression capability when the energy storage output level is high.

Description

Technical Field

[0001] The present invention relates to a wind power control method, and in particular to a wind power fluctuation smoothing control method based on dynamically adjusting the SOC state. Background Art

[0002] Wind power generation has become a new type of power generation with the largest development scale and commercial development prospects due to its relatively mature technology and relatively low cost. In recent years, wind power has developed rapidly and has played a certain role in environmental protection and energy conservation and emission reduction. However, wind power shows obvious volatility. With the increase in wind power penetration, it has an impact on the stability and security of the power grid. Energy storage technology has the characteristics of rapid response and flexible operation. It can flexibly and quickly handle power and quickly adjust electric energy to smooth out wind power fluctuations, thereby reducing the impact of wind power fluctuations on the power grid. However, on the other hand, the ability of energy storage to smooth wind power is limited by the capacity of energy storage. During the charging and discharging process, there will be situations where there is too much electricity to charge further or too little electricity to discharge. At present, there is no effective way to solve the above technical problems.

[0003] Therefore, in order to solve the above technical problems, it is urgent to propose a new technical means. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a wind power fluctuation smoothing control method based on dynamically adjusting the SOC state, which can accurately predict the power of wind power in the future set time domain based on the charge and discharge saturation capacity value of the energy storage battery at the current moment, and control the charge and discharge of the energy storage battery based on the predicted value, thereby improving the wind power level suppression capability when the energy storage output level is high, and the speed of faster recovery of the output level when the energy storage output level is low, and can reduce the life loss of the energy storage battery, thereby improving the reliability and economy of the entire wind power system.

[0005] The present invention provides a method for smoothly controlling wind power fluctuations based on dynamically adjusting the SOC state, comprising the following steps:

[0006] S1. Divide a day into n moments according to the set time step;

[0007] S2. Collect the SOC value of the energy storage battery in the wind power system at the kth moment, and determine the charge and discharge saturation capacity value of the energy storage battery;

[0008] S3. Constructing a predicted power sequence of the energy storage battery in a set time domain based on the charge and discharge saturation capacity value of the energy storage battery at the kth moment;

[0009] S4. The first item in the predicted power sequence of the energy storage battery is used as the energy storage reference power, and the charging and discharging of the energy storage battery is controlled based on the reference power;

[0010] S5. Let k=k+1, and return to step S2.

[0011] Further, step S2 specifically includes:

[0012] The SOC of the energy storage battery is divided into a saturated charging and discharging interval and an unsaturated charging and discharging interval, where:

[0013] The unsaturated charge and discharge intervals include [0, a), [a, b] and (b, 1];

[0014] When the SOC of the energy storage battery is in the interval [0, a), it is an unsaturated charging and discharging interval, and the discharge capacity of the energy storage battery is insufficient;

[0015] When the SOC of the energy storage battery is in the interval [a, b], it is the saturated charging and discharging interval, at which time the energy storage energy is sufficient;

[0016] When the SOC of the energy storage battery is in the (b, 1] interval, it is an unsaturated charging and discharging interval, and the charging capacity of the energy storage battery is insufficient;

[0017] Construct a calculation model for charge and discharge saturation capacity value:

[0018] Wherein: γ is the current SOC state of the energy storage battery, ρ(γ) is the charge and discharge saturation capacity value of the energy storage battery, a is the discharge capacity saturation operation boundary, b is the charge capacity saturation operation boundary, and a+b=1, a>0, b>0; R is the charge and discharge saturation capacity critical value; I is the charge and discharge saturation capacity decrease gradient.

[0019] Further, step S3 specifically includes:

[0020] Formulate the discrete-time state-space equations:

[0021]

[0022] Where: P ES is the power of the energy storage battery, P g is the wind power grid-connected power, P w is the original wind power, SOC ES is the state of charge of the energy storage battery, T s The time interval between moments, Q ES is the capacity of the energy storage battery;

[0023] Construct a rolling optimization function model:

[0024]

[0025] Where: ρ(k) is the SOC ES(k) the calculated charge and discharge saturation capacity value of the energy storage battery,

[0026] P w_rate is the rated power of wind power; ΔP g is the wind power grid-connected power increment, expressed as ΔP g =P g (k)-P g (k-1);

[0027] The rolling optimization function model is transformed into a quadratic programming form for solution.

[0028] Furthermore, the rolling optimization function model includes the following constraints:

[0029] Including energy storage power constraints, energy storage SOC constraints and wind power fluctuation rate constraints:

[0030]

[0031] Among them, P ES_max is the maximum value of the energy storage battery power; SOC ES_min , SOC ES_max They are the minimum and maximum SOC limits of the energy storage battery respectively; δ is the grid-connected fluctuation limit per unit time.

[0032] The beneficial effects of the present invention are as follows: Through the present invention, the power of wind power in a future set time domain can be accurately predicted based on the charge and discharge saturation capacity value of the energy storage battery at the current moment, and the charge and discharge control of the energy storage battery can be performed based on the predicted value, thereby improving the wind power level suppression capability when the energy storage output level is high, and the speed of restoring the output level faster when the energy storage output level is low, and reducing the life loss of the energy storage battery, thereby improving the reliability and economy of the entire wind power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0034] Figure 1 It is a flow chart of the present invention.

[0035] Figure 2 It is a structural schematic diagram of the wind power energy storage combined system of the present invention.

[0036] Figure 3 This is a diagram showing the smoothing effect of the predictive control method of the present invention.

[0037] Figure 4 This is a schematic diagram of the effect of adjusting the energy storage output level of the present invention. DETAILED DESCRIPTION

[0038] The present invention is further described in detail below:

[0039] like Figure 2 This is a schematic diagram of the structure of a wind power energy storage combined system to which the method of the present invention is applicable. The energy storage is connected to the AC bus at the wind power grid through DC / DC and DC / AC converters to achieve free control of power. The wind turbine is connected to the AC bus through AC / DC and DC / AC converters to reduce the cluster effect of wind power.

[0040] The present invention provides a method for smoothly controlling wind power fluctuations based on dynamically adjusting the SOC state, comprising the following steps:

[0041] S1. Divide a day into n moments according to a set time step; generally, a day is divided into 1440 moments with a step length of 1 minute;

[0042] S2. Collect the SOC value of the energy storage battery in the wind power system at the kth moment, and determine the charge and discharge saturation capacity value of the energy storage battery;

[0043] S3. Constructing a predicted power sequence of the energy storage battery in a set time domain based on the charge and discharge saturation capacity value of the energy storage battery at the kth moment;

[0044] S4. The first item in the predicted power sequence of the energy storage battery is used as the energy storage reference power, and the charging and discharging of the energy storage battery is controlled based on the reference power;

[0045] S5. Let k=k+1, and return to step S2. Through the above method, the power of wind power in the future set time domain can be accurately predicted based on the charge and discharge saturation capacity value of the energy storage battery at the current moment, and the charge and discharge control of the energy storage battery can be performed based on the predicted value, thereby improving the wind power suppression capability when the energy storage output level is high, and the speed of faster recovery of the output level when the energy storage output level is low, and reducing the life loss of the energy storage battery, thereby improving the reliability and economy of the entire wind power system.

[0046] In this embodiment, step S2 specifically includes:

[0047] The SOC of the energy storage battery is divided into a saturated charging and discharging interval and an unsaturated charging and discharging interval, where:

[0048] The unsaturated charge and discharge intervals include [0, a), [a, b] and (b, 1];

[0049] When the SOC of the energy storage battery is in the interval [0, a), it is an unsaturated charging and discharging interval, and the discharge capacity of the energy storage battery is insufficient;

[0050] When the SOC of the energy storage battery is in the interval [a, b], it is the saturated charging and discharging interval, at which time the energy storage energy is sufficient;

[0051] When the SOC of the energy storage battery is in the (b, 1] interval, it is an unsaturated charging and discharging interval, and the charging capacity of the energy storage battery is insufficient;

[0052] Construct a calculation model for charge and discharge saturation capacity value:

[0053] Wherein: γ is the current SOC state of the energy storage battery, ρ(γ) is the charge and discharge saturation capacity value of the energy storage battery, a is the discharge capacity saturation operation boundary, b is the charge capacity saturation operation boundary, and a+b=1, a>0, b>0; R is the charge and discharge saturation capacity critical value; I is the charge and discharge saturation capacity decrease gradient.

[0054] In this embodiment, step S3 specifically includes:

[0055] Formulate the discrete-time state-space equations:

[0056]

[0057] Where: P ES is the power of the energy storage battery, P g is the wind power grid-connected power, P w is the original wind power, SOC ES is the state of charge of the energy storage battery, T s The time interval between moments, Q ES is the capacity of the energy storage battery;

[0058] Construct a rolling optimization function model:

[0059]

[0060] Where: ρ(k) is the SOC ES (k) the calculated charge and discharge saturation capacity value of the energy storage battery,

[0061] P w _ rate is the rated power of wind power; ΔP g is the wind power grid-connected power increment, expressed as ΔP g =P g (k)-P g (k-1);

[0062] The rolling optimization function model is converted into a quadratic programming form for solving; wherein, the quadratic programming method is used for solving as an existing algorithm, and its principle and process are not described in detail here.

[0063] Wherein: the rolling optimization function model includes the following constraints:

[0064] Including energy storage power constraints, energy storage SOC constraints and wind power fluctuation rate constraints:

[0065]

[0066] Among them, P ES_max is the maximum value of the energy storage battery power; SOC ES_min , SOC ES_max They are the minimum and maximum SOC limits of the energy storage battery respectively; δ is the grid-connected fluctuation limit per unit time.

[0067] like Figure 3 As shown: Figure 3 This is a diagram showing the effect of the method of the present invention on smoothing wind power fluctuations, where the horizontal axis is time in minutes, the vertical axis is power in megawatts, the dotted line is the output power of the wind power subsystem before smoothing, and the solid line is the grid-connected power of the entire system after smoothing. It can be seen that the power fluctuation is significantly reduced after smoothing.

[0068] like Figure 4 As shown: Figure 4 This is a schematic diagram of the effect of the method of the present invention on adjusting the energy storage output level. It can be seen that under different initial SOC conditions, the SOC of the energy storage can gradually recover to around 0.5 over time, and eventually maintain a relatively stable change around 0.5, indicating that the control strategy of this paper can dynamically adjust the SOC recovery speed according to the range of the energy storage SOC, thereby improving the dynamic output level of the energy storage.

[0069] 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 wind power fluctuation smoothing control method based on dynamic adjustment of SOC state, characterized by: The following steps are involved: S1. Divide a day into n moments according to the set time step; S2. Collect the SOC value of the energy storage battery in the wind power system at the kth moment, and determine the charge and discharge saturation capacity value of the energy storage battery; S3. Constructing a predicted power sequence of the energy storage battery in a set time domain based on the charge and discharge saturation capacity value of the energy storage battery at the kth moment; S4. The first item in the predicted power sequence of the energy storage battery is used as the energy storage reference power, and the charging and discharging of the energy storage battery is controlled based on the reference power; S5. Let k = k + 1, and return to step S2; Step S2 specifically includes: The SOC of the energy storage battery is divided into a saturated charging and discharging interval and an unsaturated charging and discharging interval, where: The unsaturated charge and discharge intervals include [0, a), [a, b] and (b, 1]; When the SOC of the energy storage battery is in the interval [0, a), it is an unsaturated charging and discharging interval, and the discharge capacity of the energy storage battery is insufficient; When the SOC of the energy storage battery is in the interval [a, b], it is the saturated charging and discharging interval, at which time the energy storage energy is sufficient; When the SOC of the energy storage battery is in the (b, 1] interval, it is an unsaturated charging and discharging interval, and the charging capacity of the energy storage battery is insufficient; Construct a calculation model for charge and discharge saturation capacity value: Where: γ is the current SOC state of the energy storage battery, ρ(γ) is the charge and discharge saturation capacity value of the energy storage battery, a is the discharge capacity saturation operation boundary, b is the charge capacity saturation operation boundary, and a+b=1, a>0, b>0; R is the charge and discharge saturation capacity critical value; I is the charge and discharge saturation capacity decrease gradient; Step S3 specifically includes: Formulate the discrete-time state-space equations: Where: is the power of the energy storage battery, is the wind power grid-connected power, P w is the original wind power, SOC ES is the state of charge of the energy storage battery, T s is the time interval between moments, Q ES is the capacity of the energy storage battery; Construct a rolling optimization function model: Where: is the time when SOC ES (k) the calculated charge and discharge saturation capacity value of the energy storage battery, P w _ rate is the wind power rated power; is the wind power grid-connected power increment, expressed as ΔPg=Pg(k)-Pg(k-1). The rolling optimization function model is transformed into a quadratic programming form for solution.

2. The method for smoothing wind power fluctuation based on dynamic adjustment of SOC state according to claim 1 is characterized in that: The rolling optimization function model includes the following constraints: Including energy storage power constraints, energy storage SOC constraints and wind power fluctuation rate constraints: Among them, P ES _ max is the maximum value of the energy storage battery power; SOC ES _min, SOC ES _max are the minimum and maximum SOC limits of the energy storage battery; δ is the grid-connected fluctuation limit per unit time.

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