CN114725968A - Wind power fluctuation smooth control method based on dynamic adjustment SOC state - Google Patents

Abstract

The invention provides a wind power fluctuation smooth control method based on a dynamic adjustment SOC state, which comprises the following steps: s1, dividing the time of one day into n moments according to a set time step; s2, collecting an SOC value of an energy storage battery in the wind power system at the kth moment, and determining a charging and discharging saturation capacity value of the energy storage battery; s3, constructing a prediction power sequence of the energy storage battery in the timing domain based on the charging and discharging 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 obtained through prediction as energy storage reference power, and controlling the energy storage battery to charge and discharge based on the reference power; s5, let k equal to k +1, and return to step S2; the method can accurately predict the power in the future setting time domain of the wind power based on the charging and discharging saturation capacity value of the energy storage battery at the current moment, and control the charging and discharging of the energy storage battery based on the predicted value, so that the wind power stabilizing capacity when the energy storage output level is higher is improved.

Description

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

technical field

The invention relates to a wind power control method, in particular to a wind power fluctuation smoothing control method based on dynamic adjustment of SOC state.

Background technique

Because of its relatively mature technology and relatively low cost, wind power has become a new power generation method with the most development scale and commercial development prospects. In recent years, the rapid development of wind power has played a certain role in environmental protection, energy conservation and emission reduction. Significant volatility, with the increase in wind power penetration, has an impact on the stability and security of the grid. Because of its fast response and flexible operation, energy storage technology can flexibly and quickly handle power and quickly adjust electric energy to smooth wind power fluctuations, thereby reducing the impact of wind power fluctuations on the power grid. But on the other hand, the ability of energy storage to stabilize wind power is limited by the capacity of energy storage. During the charging and discharging process, there may be too much electricity that cannot be charged further or too little electricity that cannot be discharged. At present, there is no effective method to solve the above technical problems.

Therefore, in order to solve the above technical problems, it is urgent to propose a new technical means.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a wind power fluctuation smoothing control method based on dynamic adjustment of SOC state, which can accurately predict the power of wind power in the future set time domain based on the current charging and discharging saturation capability value of the energy storage battery. , and control the charge and discharge of the energy storage battery based on the predicted value, improve the wind power suppression capability when the energy storage output level is high, and restore the output level faster when the energy storage output level is low, and can reduce the energy storage battery. Life loss, improve the reliability and economy of the entire wind power system.

A method for smoothing wind power fluctuation based on dynamic adjustment of SOC state provided by the present invention includes the following steps:

S1. Divide the time of the 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 k-th time, and determine the charge and discharge saturation capacity value of the energy storage battery;

S3. Construct the predicted power sequence of the energy storage battery in the set time domain based on the charge-discharge saturation capability value of the energy storage battery at the k-th moment;

S4. Use the first item in the predicted power sequence of the energy storage battery obtained by prediction as the energy storage reference power, and control and control the charging and discharging of the energy storage battery based on the reference power;

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

Further, in step S2, it specifically includes:

The SOC of the energy storage battery is divided into the charge-discharge saturation interval and the charge-discharge unsaturated interval, where:

The charge-discharge unsaturated interval includes [0, a), [a, b] and (b, 1];

When the SOC of the energy storage battery is in the range [0, a), it is the unsaturated range of charge and discharge, and the discharge capacity of the energy storage battery is insufficient at this time;

When the SOC of the energy storage battery is in the [a, b] interval, it is the charge-discharge saturation interval, and the energy storage is sufficient at this time;

When the SOC of the energy storage battery is in the (b, 1] range, it is the unsaturated range of charge and discharge, and the charging capacity of the energy storage battery is insufficient at this time;

Build a calculation model for the charge-discharge saturation capacity value:

其中：γ为当前储能电池的SOC状态，ρ(γ)为储能电池的充放电饱和能力值，a为放电能力饱和运行边界，b为充电能力饱和运行边界，且a+b＝1，a>0，b>0；R为充放电饱和能力临界值；I为充放电饱和能力下降梯度。

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

Further, step S3 specifically includes:

Construct the discrete-time state-space equations:

Among them: 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 power, SOC _ES is the state of charge of the energy storage battery, the time interval between T _s , and Q _ES is the storage battery the capacity of the battery;

Build a rolling optimization function model:

Among them: ρ(k) is the charge-discharge saturation capacity value of the energy storage battery calculated by SOC _ES (k) at time k,

P _{w_rate} is the wind power rated power; ΔP _g is the wind power grid-connected power increment, expressed as ΔP _g =P _g (k)-P _g (k-1);

Transform the rolling optimization function model into a quadratic programming form for solving.

Further, the rolling optimization function model includes the following constraints:

Including energy storage power constraints, energy storage SOC constraints and wind power volatility constraints:

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

Beneficial effects of the present invention: through the present invention, the power in the future set time domain of wind power can be accurately predicted based on the charge and discharge saturation capability 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, It can improve the wind power suppression ability 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 can reduce the life loss of the energy storage battery, and improve the reliability and economy of the entire wind power system.

Description of drawings

Below in conjunction with accompanying drawing and embodiment, the present invention is further described:

FIG. 1 is a flow chart of the present invention.

FIG. 2 is a schematic structural diagram of a combined wind power energy storage system according to the present invention.

FIG. 3 is a diagram showing the smoothing effect of the predictive control method of the present invention.

FIG. 4 is a schematic diagram of the effect of adjusting the energy storage output level of the present invention.

Detailed ways

The present invention is further described in detail below:

Figure 2 is a schematic structural diagram of a combined wind power energy storage system to which the method of the present invention is applicable. The energy storage is connected to the AC bus at the grid-connected wind power through DC/DC and DC/AC converters to realize free power control. AC/DC and DC/AC converters are connected to the AC bus to reduce the clustering effect of wind power.

A method for smoothing wind power fluctuation based on dynamic adjustment of SOC state provided by the present invention includes the following steps:

S1. Divide the time of a day into n moments according to the set time step; among them, generally take a day as a step of 1 minute, and divide a day into 1440 moments;

S2. Collect the SOC value of the energy storage battery in the wind power system at the k-th time, and determine the charge and discharge saturation capacity value of the energy storage battery;

S3. Construct the predicted power sequence of the energy storage battery in the set time domain based on the charge-discharge saturation capability value of the energy storage battery at the k-th moment;

S4. Use the first item in the predicted power sequence of the energy storage battery obtained by prediction as the energy storage reference power, and control and control the charging and discharging of the energy storage battery based on the reference power;

S5. Let k=k+1, and return to step S2. Through the above method, it is possible to accurately predict the power of wind power in the future set time domain based on the current charge-discharge saturation capability value of the energy storage battery, and to control the charge and discharge of the energy storage battery based on the predicted value, so as to improve the output level of the energy storage. The higher wind level suppression capability and the faster recovery of the output level when the energy storage output level is low can reduce the life loss of the energy storage battery and improve the reliability and economy of the entire wind power system.

In this embodiment, step S2 specifically includes:

The SOC of the energy storage battery is divided into the charge-discharge saturation interval and the charge-discharge unsaturated interval, where:

The charge-discharge unsaturated interval includes [0, a), [a, b] and (b, 1];

When the SOC of the energy storage battery is in the range [0, a), it is the unsaturated range of charge and discharge, and the discharge capacity of the energy storage battery is insufficient at this time;

When the SOC of the energy storage battery is in the [a, b] interval, it is the charge-discharge saturation interval, and the energy storage is sufficient at this time;

When the SOC of the energy storage battery is in the (b, 1] range, it is the unsaturated range of charge and discharge, and the charging capacity of the energy storage battery is insufficient at this time;

Build a calculation model for the charge-discharge saturation capacity value:

其中：γ为当前储能电池的SOC状态，ρ(γ)为储能电池的充放电饱和能力值，a为放电能力饱和运行边界，b为充电能力饱和运行边界，且a+b＝1，a>0，b>0；R为充放电饱和能力临界值；I为充放电饱和能力下降梯度。

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

In this embodiment, step S3 specifically includes:

Construct the discrete-time state-space equations:

Among them: 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 power, SOC _ES is the state of charge of the energy storage battery, the time interval between T _s , and Q _ES is the storage battery the capacity of the battery;

Build a rolling optimization function model:

Among them: ρ(k) is the charge-discharge saturation capacity value of the energy storage battery calculated by SOC _ES (k) at time k,

P _w _ _rate is the wind power rated power; ΔP _g is the wind power grid-connected power increment, expressed as ΔP _g =P _g (k)-P _g (k-1);

The rolling optimization function model is converted into a quadratic programming form to solve; wherein, the quadratic programming method is used to solve the problem as an existing algorithm, and its principle and process are not repeated here.

Wherein: the rolling optimization function model includes the following constraints:

Including energy storage power constraints, energy storage SOC constraints and wind power volatility constraints:

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

As shown in Figure 3: Figure 3 is a display diagram of the effect of the method of the present invention to stabilize wind power fluctuations, wherein the abscissa is time, the unit is minutes, the ordinate is power, the unit is MW, and the dotted line is the wind power system before it is stabilized. Output power, the solid line is the grid-connected power of the entire system after smoothing. It can be seen that the power fluctuation after the smoothing is significantly reduced.

As shown in Figure 4: Figure 4 is a schematic diagram of the effect of adjusting the output level of energy storage by the method of the present invention. It can be seen that under the condition of different initial SOC, the SOC of energy storage can gradually recover to around 0.5 with time, and finally maintain It changes relatively smoothly at around 0.5, indicating that the control strategy in this paper can dynamically adjust the recovery speed of the SOC according to the range of the energy storage SOC, and improve the dynamic output level of the energy storage.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent substitutions without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims (4)

1. A wind power fluctuation smoothing control method based on dynamic adjustment of SOC state, characterized in that: comprising the following steps: S1. Divide the time of the 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 k-th time, and determine the charge and discharge saturation capacity value of the energy storage battery; S3. Construct the predicted power sequence of the energy storage battery in the set time domain based on the charge-discharge saturation capability value of the energy storage battery at the k-th moment; S4. Use the first item in the predicted power sequence of the energy storage battery obtained by prediction as the energy storage reference power, and control and control the charging and discharging of the energy storage battery based on the reference power; S5. Let k=k+1, and return to step S2. 2. The wind power fluctuation smoothing control method based on dynamic adjustment of SOC state according to claim 1, characterized in that: in step S2, it specifically comprises: The SOC of the energy storage battery is divided into the charge-discharge saturation interval and the charge-discharge unsaturated interval, where: The charge-discharge unsaturated interval includes [0, a), [a, b] and (b, 1]; When the SOC of the energy storage battery is in the range [0, a), it is the unsaturated range of charge and discharge, and the discharge capacity of the energy storage battery is insufficient at this time; When the SOC of the energy storage battery is in the [a, b] interval, it is the charge-discharge saturation interval, and the energy storage is sufficient at this time; When the SOC of the energy storage battery is in the (b, 1] range, it is the unsaturated range of charge and discharge, and the charging capacity of the energy storage battery is insufficient at this time; Build a calculation model for the charge-discharge saturation capacity value:

Among them: γ is the SOC state of the current energy storage battery, ρ(γ) is the charge-discharge saturation capacity value of the energy storage battery, a is the discharge capacity saturation operation boundary, b is the charging capacity saturation operation boundary, and a+b=1, a>0, b>0; R is the critical value of charge-discharge saturation capability; I is the decrease gradient of charge-discharge saturation capability. 3. The wind power fluctuation smoothing control method based on dynamic adjustment of SOC state according to claim 2, wherein step S3 specifically comprises: Construct the discrete-time state-space equations:

Among them: 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 power, SOC _ES is the state of charge of the energy storage battery, the time interval between T _s , and Q _ES is the storage battery the capacity of the battery; Build a rolling optimization function model:

Among them: ρ(k) is the charge-discharge saturation capacity value of the energy storage battery calculated by SOC _ES (k) at time k, P _{w_rate} is the wind power rated power; ΔP _g is the wind power grid-connected power increment, expressed as ΔP _g =P _g (k)-P _g (k-1); Transform the rolling optimization function model into a quadratic programming form for solving. 4. The wind power fluctuation smoothing control method based on dynamically adjusting SOC state according to claim 3, wherein the rolling optimization function model includes the following constraints: Including energy storage power constraints, energy storage SOC constraints and wind power volatility constraints:

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

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