CN104283224B - A kind of energy-storage system smooth wind power Poewr control method limiting wind-powered electricity generation stability bandwidth - Google Patents

Abstract

A smooth wind power control method for an energy storage system that limits the fluctuation rate of wind power. Through effective control of the charge and discharge of the battery energy storage system, the SOC (State Of Charge) level of the battery energy storage system and the consideration of the wind power On the basis of the fluctuation rate limit, the purpose of smoothing wind power is achieved through the moving average algorithm, and the impact of wind power grid connection on the grid is reduced. Only when the wind power fluctuation rate is greater than the limit value, the wind power passes through the moving average filter to obtain the grid-connected power reference value, and the grid-connected power reference value minus the wind power value is the total power demand of the battery energy storage system at this time value. At the same time, the SOC of the energy storage system is monitored in real time to protect the battery energy storage system. The control method proposed in this patent can effectively reduce the number of times of energy storage use and energy storage energy, and prolong the life of the energy storage system.

Description

A smooth wind power control method for energy storage systems that limits wind power fluctuations

technical field

The invention relates to a grid-connected application technology of a battery energy storage system and a wind farm, in particular to a method for smoothing wind power under the condition of wind power fluctuation rate limitation.

Background technique

With the increasing installed capacity of renewable energy such as wind power generation and wind power generation, the impact of its output power fluctuations on the power quality, safety and stability of traditional power grids has received more and more attention. At this time, the energy storage system can effectively overcome the adverse impact of suppressing the output fluctuation of renewable energy on the power grid by virtue of its rechargeable and dischargeable operating characteristics, thereby reducing the volatility caused by the renewable energy power generation system and improving the acceptance of renewable energy by the power grid. ability to generate electricity.

According to different storage forms, energy storage can be divided into physical energy storage (including flywheel energy storage, compressed air energy storage and pumped hydro storage), electrochemical energy storage (including lead acid, nickel cadmium, nickel hydrogen, lithium ion, sodium sulfur and battery energy storage such as liquid flow) and electromagnetic energy storage (including superconducting energy storage and supercapacitor energy storage). Among them, battery energy storage has the best economy and the most mature engineering application technology.

The combination of large-scale energy storage system and wind power generation system is an important research direction to solve the difficulty of intermittent energy grid connection. Relying on the battery energy storage system to smooth the wind power, the intermittent and highly volatile renewable energy such as wind power can be transformed into a controllable energy source, thereby improving the grid's ability to accept the wind power system.

Contents of the invention

In order to overcome the defects of the prior art, the purpose of the present invention is to propose a smooth wind power control method for the energy storage system that limits the fluctuation rate of wind power. In this case, the moving average algorithm is used to smooth the demand of wind power, which not only effectively reduces the number of times of energy storage usage and energy storage energy, but also prolongs the life of the energy storage system.

For this reason, the present invention is realized by following method:

A method for smoothing wind power control of an energy storage system that limits wind power fluctuations, the method comprising the following steps:

A) Establish wind power smoothing function;

B) Read relevant data of the wind power generation system and battery energy storage system in real time, including the sampling power of the wind power generation system, sampling power time interval, rated power and rated capacity, and the initial state of charge of the battery energy storage system;

C) Calculate the wind power fluctuation rate at the current moment;

D) Take the sampling power of the wind power generation system as the input value of the smoothing function of wind power, and judge whether the fluctuation rate of wind power at the current moment is greater than the limit value of the fluctuation rate of wind power. The smoothing power of the system; otherwise, no smoothing is performed;

E) Obtain the active power and state of charge of the battery energy storage system at the current moment, and judge whether the state of charge of the battery energy storage system exceeds the preset value at the current moment, and if it exceeds, stop the wind power smoothing control;

F) According to the sign of the active power of the battery energy storage system, it is judged that the battery energy storage system is in the charging or discharging state, and the instructions for the battery energy storage system are set according to the state of the battery energy storage system;

G) Send the sign of the active power of the battery energy storage system and the command to the battery energy storage system to the battery management system, and control the charge and discharge of the battery energy storage system in real time to meet the requirements of smooth wind power.

In the step A, the wind power smoothing function shown in the following formula is established by a moving average algorithm:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; smooth</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; q</span>}}{\overset{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; )</span>$

t ₀ =t-(pq-1)Δt

Among them, P _smooth' (t) is the smooth power of the wind power generation system at the current time t; P _wind (t ₀ +i*Δt) is the sampling power of the wind power generation system at the time (t ₀ +i*Δt);

ω _i is the weight coefficient, and

p, q are any positive integers less than m, i=p, p+1,...q and p+q+1=m, m is the scale of the moving average window; Δt is the sampling power time interval;

In the step C, the wind power fluctuation rate is obtained by the following formula:

$\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}^{\mathrm{<span\; class="notranslate">\; ref</span>}}}$

Among them, δ(t) is the wind power fluctuation rate at the current time t; P _wind (t) is the sampling power of the wind power generation system at the current time t; P _wind (t-Δt) is the sampling power of the wind power generation system at the previous time; Δt is the sampling power time interval; is the rated power of the wind power generation system.

In the step E, the active power of the battery energy storage system is obtained by the following formula:

P _bat (t)=P _wind (t)-P _smooth' (t)

Among them, P _bat (t) is the active power of the battery energy storage system at the current time t; P _wind (t) is the sampling power of the wind power generation system at the current time t; P _smooth' (t) is the smoothing power of the wind power system at the current time t Power, this value is obtained by the smooth function of step A;

Calculate the state of charge of the battery energy storage system at the current moment by the following formula:

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; soc</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; soc</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; bat</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}^{\mathrm{<span\; class="notranslate">\; red</span>}}$

Among them, C _SOC (0) is the initial state of charge of the battery energy storage system; P _bat (t) is the active power of the battery energy storage system at the current time t; Δt is the sampling power time interval; is the rated capacity of the wind power system.

The concrete method of described step F comprises:

If the sign of the active power of the battery energy storage system is positive, it means that the battery energy storage system is in the charging state, then set the command to the battery energy storage system to -1; if the sign of the active power of the battery energy storage system is negative, the battery energy storage system If the system is in a discharge state, set the command to the battery energy storage system to 1.

The above process is processed and calculated by the data acquisition and monitoring control system (SCADA), and the calculated battery energy storage power and charging instructions are sent to the battery energy storage system. The power arithmetic sum, the obtained value is the grid-connected power of the wind-storage combined power generation system.

Compared with prior art, the beneficial effect of the present invention is:

The present invention outputs the power of the battery energy storage and the charge and discharge command through the above-mentioned control method, and realizes the purpose of smoothing the fluctuation of the wind power power; the method takes into account the factors of the SOC of the battery energy storage system and the fluctuation rate of the wind power power at the same time, through automatic Adapting the moving average algorithm to smooth wind power will not only effectively reduce the number of energy storage uses and energy storage energy, but also serve the purpose of prolonging the service life of the energy storage system.

Description of drawings

Fig.1 The block diagram of smooth wind power control based on moving average controller energy storage system;

Figure 2-8 is a simulation result diagram based on this patent, in which:

Fig. 2 is a data curve diagram of the sampling power of the wind power generation system;

Fig. 3 is the data graph of wind power fluctuation rate;

Fig. 4 is a comparison diagram of two filtering methods for smoothing wind power and energy storage output;

Fig. 5 is a data curve diagram of the state of charge Csoc of the battery energy storage system;

Fig. 6 is a comparison chart of smoothing wind power fluctuation rate by two filtering methods;

Fig. 7 is a comparison diagram of energy storage energy changes of two filtering methods;

Fig. 8 is the proportion diagram of the wind power fluctuation rate > 10% of the two kinds of filters.

detailed description

The preferred embodiments will be described in detail below in conjunction with the accompanying drawings. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

The present invention starts from the constraint condition of wind power fluctuation rate, takes the state of charge (SOC) of the battery energy storage system and the wind power power fluctuation rate as constraint conditions, and uses the moving average algorithm to realize the smooth control of wind power power. To achieve the purpose of wind power smoothing.

The moving average smoother is realized by programming the moving average algorithm: for the non-stationary sequence {y _j } composed of measured wind power data, the total length of which is N, m adjacent data are continuously moved one by one as a weighted average to represent the translational data. The general formula is:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; q</span>}}{\overset{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; P</span>}$

Substituting the wind power data into the above formula to get: k=q+1,q+2...,NP where, ω _i is the weight coefficient, and ; p, q are any positive integer less than m, and p+q+1=m.

Figure 1 is a block diagram of wind power control based on moving average algorithm. Through the effective control of the charge and discharge of the battery energy storage system, on the basis of the SOC (State of Charge) level of the battery energy storage system and the limitation of wind power fluctuation rate δ(t), smoothing is achieved through a moving average algorithm The purpose of wind power is to reduce the impact of wind power grid connection on the grid. When the wind power fluctuation rate is greater than the limit value, the wind power P _v passes through the moving average filter to obtain the grid-connected power reference value P _smooth' , and the difference between the two is the throughput power of the battery energy storage system at this time. The SOC of the energy system is used to protect the battery energy storage system. The control method proposed by the present invention can effectively reduce the number of times of energy storage use and energy storage energy, and prolong the service life of the energy storage system.

The real-time sampling power P _wind of the wind power generation system is collected by the data acquisition and monitoring control system (SCADA system), and the power and SOC of the battery energy storage system are collected by the battery management system (BMS). Under the premise of the SOC, P _wind is used as the input of the wind power smoothing function in the moving average controller. Under the conditions of the smoothing function of the moving average controller, the smooth power P _smooth' of the wind power generation system is obtained, and P _smooth' is used as the output of the smoothing function of the wind power in the moving average controller, and the active power of the battery energy storage system is P _bat =P _wind -P _smooth' , the grid-connected power P _smooth of the wind-storage combined power generation system is the sum of the sampling power P _wind of the wind power generation system and the active power P _bat of the battery energy storage power station (as shown in Figure 1).

The detailed steps of control method of the present invention will be further introduced below:

Step A: Establish wind power smoothing function.

For the N non-stationary data {y _j } composed of measured wind power data, it can generally be regarded as close to stable in the small interval of every m adjacent data, that is, its mean value is close to a constant. Therefore, the average value of every m adjacent data can be taken to represent the value of any one of the m data, and it can be regarded as smoothing the output power of the wind power generation system. Usually, the mean value is used to represent the smoothing result of midpoint data or endpoint data. For example, if m is equal to 5, and the mean value is used to replace the middle one of these 5 points, then the formula is as follows:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Similarly, y ₄ =1/5(y ₂ +y ₃ +y ₄ +y ₅ +y ₆ ), and so on, the general expression can be obtained as:

${\mathrm{the\; y}}_k=\frac{1}{2\mathrm{no}+1}\underset{k=-\mathrm{no}}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}{\mathrm{the\; y}}_{k+1}$ k=n+1,n+2...,Nn (2)

In the formula, 2n+1=m. Of course, there is also a more general moving average method, which is to continuously move one by one along the data with a total length of N and take m adjacent data as a weighted average to represent the translation data. The general formula is:

${\mathrm{the\; y}}_k=\underset{i=q}{\overset{p}{\mathrm{\&Sigma;}}}{\mathrm{\&omega;}}_i{\mathrm{the\; y}}_{k+i}$ k=q+1,q+2...,NP (3)

In the formula, ω _i is the weight coefficient, and ; p, q are any positive integer less than m, and p+q+1=m. . Different methods of taking these parameters form different moving average methods, such as p=q=2, and ω _i =1/(2n+1), which is the algorithm of formula (2), called equal weight center translation method. In particular, taking p=0 or q=0 is the common endpoint translation. When ω _i =1/m (for all i), it is equal-weight endpoint translation, and its formula is written as:

${\mathrm{the\; y}}_k=\frac{1}{m}\underset{i=0}{\overset{m-1}{\mathrm{\&Sigma;}}}{\mathrm{the\; y}}_{k+i}$ k=1,2,3...,q

${\mathrm{the\; y}}_k=\frac{1}{m}\underset{i=-m+1}{\overset{0}{\mathrm{\&Sigma;}}}{\mathrm{the\; y}}_{k+i}$ k=N-p+1,N-p+2...,N (4)

Among them, the former formula is the front point translation method, and the latter formula is the back end point translation method. It should be pointed out that the parameter selection of the moving average method will directly affect the translation effect of the data. For example, if the value of m in formula (4) is larger, the adjacent data of the local average is too much, and the smoothing effect achieved is greater. On the contrary, if If m is made smaller, a more obvious smoothing effect cannot be achieved. Therefore, when we use the moving average algorithm, we should reasonably select the parameters m (and p and q) and {ω _i } of the moving average according to the purpose of translation and the actual change of the data. The most simple 5-11 equal-weight center translation or 2, 3 times weighted center translation is often used in dynamic test data processing. The simulation example is to compare the wind power smoothing control strategy proposed in this patent with the traditional first-order inertial filter to further verify the effectiveness of the method proposed in this patent, which has certain engineering practical value.

Therefore, the present invention establishes the wind power smoothing function as shown in the following formula:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{{\mathrm{<span\; class="notranslate">\; smooth</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; q</span>}}{\overset{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

In the above formula, P _smooth' (t) is the smooth power of the wind power generation system at the current time t; ω _i is the weight coefficient, and P _wind (t ₀ +i*Δt) is the sampling power of the wind power generation system at (t ₀ +i*Δt), and t ₀ =t-(pq-1)Δt, Δt is the sampling power time interval; p,q is any positive integer less than m, i=p,p+1,...q and p+q+1=m, m is the scale of the moving average window.

Step B: Read relevant data of the wind power generation system and the battery energy storage system in real time, the data includes the sampling power P _wind (t-Δt), P _wind (t)..., sampling power time interval of the wind power generation system at each moment Δt, rated power and rated capacity And the initial state of charge C _SOC (0) of the battery energy storage system. In this example, the value of C _SOC (0) can be 0.5. In this example, the sampling power interval Δt of the simulation example is one minute, and a total of 2500 points of sampling power are collected;

Step C: Calculate the wind power fluctuation rate δ(t) at the current moment t by formula (6):

$\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}^{\mathrm{<span\; class="notranslate">\; ref</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

In the above formula, P _wind (t) is the sampling power of the wind power generation system at the current time t; P _wind (t-Δt) is the sampling power of the wind power generation system at the previous time; Δt is the sampling power time interval; is the rated power of the wind power generation system, and all the above values are read through step B.

Step D: Take the actual sampled power of the wind power generation system as the input value of the wind power smoothing function in the moving average controller. The smooth power P _smooth' of the wind power generation system is obtained; otherwise, no smoothing process is performed;

Step E: Obtain the active power P _bat and the state of charge C _SOC of the battery energy storage system at the current moment, and judge whether the state of charge of the battery energy storage system at the current moment exceeds the preset value according to the calculated C _SOC , in this example The value range of the preset value is [0.2, 0.8]. If the C _SOC exceeds the preset range, the wind power smooth control is stopped to protect the battery energy storage system;

The active power and state of charge of the battery energy storage system are calculated by formulas (7) and (8) respectively:

P _bat (t)=P _wind (t)-P _smooth' (t) (7)

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; soc</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; soc</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; bat</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; wind</span>}}^{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

In the above formula, P _bat (t) is the active power of the battery energy storage system at the current time t; P _wind (t) is the sampling power of the wind power generation system at the current time t; P _smooth' (t) is the wind power generation system at the current time t The smooth power of , which is obtained by the smooth function of step A; C _SOC (0) is the initial state of charge of the battery energy storage system; P _bat (t) is the active power of the battery energy storage system at the current time t; Δt is Sampling power time interval; is the rated capacity of the wind power system.

Step F: Determine the sign of the active power P _bat (t) of the battery energy storage system. If P _bat (t)>0, it means that the battery energy storage system is in a charging state; if P _bat (t)<0, it means that the battery energy storage system in a state of discharge;

Step G: Send the P _bat (t) command to the battery management system (BMS) to control the charging and discharging of the battery energy storage system in real time to meet the requirements of smooth wind power.

Figure 2-Figure 8 is based on the simulation results of this patent (the rated power of the wind farm is 99MW, the energy storage configuration is 20MW h, t=1min is the sampling time, and the fluctuation rate limit is 10min. If the wind power fluctuation rate is greater than 10%, the moving average power smooth). It can be seen from Figure 2-8 that the moving average algorithm can achieve the purpose of smoothing the output power of wind power, greatly reducing the fluctuation rate of wind power grid connection, effectively reducing the number of times of energy storage use and energy storage energy, fully verified The effectiveness of this patent control strategy.

The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present invention can easily think of changes or Replacement should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims (4)

1. the energy-storage system smooth wind power Poewr control method limiting wind-powered electricity generation stability bandwidth, it is characterised in that the method comprises the steps:

A) the wind power smooth function being shown below by the foundation of rolling average algorithm:

t₀=t-(p-q-1) Δ t

Wherein, P_smooth'T () is the smooth power of current t wind generator system；P_wind(t₀+ i* Δ t) is (t₀The sampled power of+i* Δ t) moment wind-powered electricity generation electricity generation system；

ω_iFor weight coefficient, and

P, q are the arbitrary positive integer less than m, i=p, p+1 ... q and p+q+1=m, m are the yardstick of rolling average window；Δ t is sampled power time interval；

B) reading wind generator system and the related data of battery energy storage system in real time, these data include the initial state-of-charge of the sampled power of wind generator system, sampled power time interval, rated power and rated capacity and battery energy storage system；

C) the wind power stability bandwidth of current time is asked for；

D) using the sampled power of wind generator system as the input value of wind power smooth function, and judge whether the wind power stability bandwidth of current time is more than the limits value of wind power stability bandwidth, if being more than, then obtained the smooth power of wind generator system by the smooth function of step A；Otherwise, it is not smoothed；

E) asking for active power and the state-of-charge of current time battery energy storage system, and judge whether the state-of-charge of current time battery energy storage system exceedes preset value, if exceeding, then stopping the smooth control of wind power；

F) judge that battery energy storage system is in charge or discharge state according to the symbol of battery energy storage system active power, and the instruction to battery energy storage system is set according to battery energy storage system status；

G) symbol of battery energy storage system active power and the instruction to battery energy storage system are sent to battery management system, in real time battery energy storage system are carried out charge and discharge control, to reach to meet the requirement that wind power is smooth 。

Method the most according to claim 1, it is characterised in that ask for wind power stability bandwidth by following formula in described step C:

Wherein, δ (t) is current t wind power stability bandwidth；P_windT () is the sampled power of current t wind generator system；P_wind(t-Δ t) is the sampled power of a upper moment wind-powered electricity generation electricity generation system；Δ t is sampled power time interval；Rated power for wind generator system.

Method the most according to claim 1 and 2, it is characterised in that ask for battery energy storage system active power by following formula in described step E:

P_bat(t)=P_wind(t)-P_smooth'(t)

Wherein, P_batT () is the active power of current t battery energy storage system；P_windT () is the sampled power of current t wind generator system；P_smooth'T () is the smooth power of current t wind generator system, this value is drawn by the smooth function of step A；

The state-of-charge of described current time battery energy storage system is asked for by following formula:

Wherein, C_SOC(0) it is the initial state-of-charge of battery energy storage system；P_batT () is the active power of current t battery energy storage system；Δ t is sampled power time interval；Rated capacity for wind generator system.

Method the most according to claim 1, it is characterised in that the concrete grammar of described step F includes:

If the symbol of battery energy storage system active power is just, represents that battery energy storage system is in charged state, then the instruction of battery energy storage system will be set to-1；If the symbol of battery energy storage system active power is negative, battery energy storage system is in discharge condition, then the instruction of battery energy storage system will be set to 1.