Wind power integration power smoothing method and system based on model predictive control algorithm

Abstract

The invention discloses a wind power grid-connected power smoothing method and system based on a predictive control algorithm, and the method comprises the steps: constructing a target function J, and taking the weighted sum of the energy storage SOC variation and the grid-connected wind power fluctuation as an optimization target; according to the current energy storage health degree and the wind power fluctuation degree, target function parameters are dynamically adjusted through a fuzzy control rule; and based on the energy storage power constraint, the overall charge state constraint of the energy storage and the wind power fluctuation rate constraint, solving the target function after parameter adjustment to obtain the optimal value of the energy storage charge and discharge power, and adding the energy storage charge and discharge power to the original wind power to obtain the optimal wind power grid-connected power value. According to the method, the parameters of the target function are adjusted by adopting the set fuzzy control rule, and self-adaptive optimization can be carried out under different wind power fluctuation situations.

Description

Technical Field

[0001] The present invention relates to the field of suppressing wind power fluctuations, and in particular to a method and system for smoothing the grid-connected power of wind power based on model predictive control. Background Art

[0002] The wind power of a wind energy storage system is jointly composed of wind power and the charging and discharging power of an energy storage (ES). Given the current wind power, the charging and discharging power of the energy storage system can be calculated and determined by setting a certain energy storage output control strategy to calculate the wind power target, and the wind power fluctuations can be suppressed through the charging and discharging of the energy storage. In terms of control strategies, model predictive control has certain advantages over filtering control and other adaptive controls in terms of response speed and accuracy, solving problems such as difficult parameter setting, mode aliasing, and noise interference. It can predict and control possible future control inputs and perform secondary optimization, and at the same time, by setting the constraints of inputs and outputs, the effectiveness and safety of the system can be ensured. When traditional MPC optimizes the objective function, fixed weight coefficients are usually used to balance the suppression effect. However, these weight coefficients are not dynamically adjusted with the change of the system state, so they cannot be adaptively optimized under different wind power fluctuation scenarios. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for smoothing the grid-connected power of wind power based on model predictive control, aiming to solve the problem of smoothing the grid-connected power of wind power.

[0004] The present invention provides a method for smoothing the grid-connected power of wind power based on model predictive control, including: S1. Construct an objective function based on the model predictive control algorithm; S2. Use the current energy storage health and the degree of wind power fluctuation as the input variables of the controller, and output the parameters of the objective function as the output variables of the controller to adjust the parameters of the objective function; S3. Based on the energy storage power constraint, the overall state of charge constraint of the energy storage, and the wind power volatility constraint, solve the adjusted objective function to obtain the optimal value of the energy storage charging and discharging power, and add the optimal value of the energy storage charging and discharging power to the original wind power to obtain the optimal grid-connected wind power value.

[0005] The present invention also provides a system for suppressing wind power fluctuations based on the model predictive control algorithm, including: Objective function module: used to construct an objective function based on the model predictive control algorithm; Parameter adjustment module: used to use the current energy storage health and the degree of wind power fluctuation as the input variables of the controller, and output the parameters of the objective function as the output variables of the controller to adjust the parameters of the objective function; Solving module: It is used to solve the adjusted objective function based on the energy storage power constraint, the state of charge constraint of the overall energy storage, and the wind power volatility constraint to obtain the optimal value of the energy storage charge and discharge power, and add the optimal value of the energy storage charge and discharge power to the original wind power to obtain the optimal wind power grid connection power value.

[0006] In the embodiment of the present invention, the parameters of the objective function are adjusted by using the set fuzzy control rules, which can achieve adaptive optimization under different wind power fluctuation scenarios and realize the suppression of dynamic wind power fluctuations.

[0007] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it is implemented in accordance with the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically describes the embodiments of the present invention. Description of the Drawings

[0008] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 It is a flowchart of the wind power fluctuation suppression method based on the model predictive control algorithm in the embodiment of the present invention; Figure 2 It is a schematic diagram of the original wind power and the curve after the suppression of the wind power fluctuation suppression method based on the model predictive control algorithm in the embodiment of the present invention; Figure 3 It is a schematic diagram of the distribution of the wind power fluctuation before and after the suppression of the wind power fluctuation suppression method based on the model predictive control algorithm in the embodiment of the present invention; Figure 4 It is a three-dimensional schematic diagram of the fuzzy control rules of the wind power fluctuation suppression method based on the model predictive control algorithm in the embodiment of the present invention; Figure 5 It is a schematic diagram of the energy storage output curve under different health statuses of the wind power fluctuation suppression method based on the model predictive control algorithm in the embodiment of the present invention; Figure 6 It is a schematic diagram of the wind power fluctuation suppression system based on the model predictive control algorithm in the embodiment of the present invention.

[0010] Description of the Reference Numerals: 61: Objective function module; 62: Parameter adjustment module; 63: Solving module. Detailed Embodiments

[0011] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0012] Method Embodiment 1 According to an embodiment of the present invention, a method for suppressing wind power fluctuations based on a model predictive control algorithm is provided. Figure 1 It is a flowchart of the method for suppressing wind power fluctuations based on the model predictive control algorithm according to the embodiment of the present invention, as Figure 1 shown, specifically including: S1. Construct an objective function based on the model predictive control algorithm; The wind power fluctuation amount needs to meet certain requirements to be grid-connected. The wind power condition of the 50MW wind farm of the present invention is that the power fluctuation amount within 1 minute is 1 / 10 of the installed capacity. On the other hand, it is necessary to consider the overall SOC constraint of the energy storage. Under the condition of minimizing the occurrence of too high or too low SOC and frequent charge and discharge as much as possible, the wind power fluctuation is suppressed as much as possible.

[0013] The specific process of S1 is as follows; Construct an optimization model of the model predictive control algorithm; (1) Wind power state space equation Formula 1; In the formula, k is the control time node; is the wind power, is the wind power at the k+1 moment, with the unit of kilowatt; is the total energy storage power at the k moment, with the unit of kilowatt. When its value is positive, it means that the energy storage emits power, and when it is negative, it means that the energy storage absorbs power; is the overall state of charge of the energy storage at the k+1 moment; is the overall state of charge of the energy storage at the k moment; is the rated capacity of the energy storage, with the unit of kilowatt-hour; is the control period, with the unit of minute; (2) Rolling optimization objective Starting from the mutual relationship between wind power and energy storage, when the energy storage has enough suppression margin, the wind power fluctuations should be suppressed as much as possible. When the suppression margin of the energy storage is small, the standard for suppressing wind power fluctuations can be appropriately reduced to restore the suppression margin of the energy storage. Therefore, considering the mutual restriction relationship between the wind power fluctuation suppression effect and the overall SOC of the energy storage, the overall target power of the hybrid energy storage is optimized by rolling.

[0014] Starting from the above two points for design, starting from time k, within N prediction steps, the optimization objective is as follows: Formula 2; In the formula, is the objective function; is the charge and discharge capacity index calculated from the wind power fluctuation degree and its health at time k. The smaller its value, the smaller the fluctuation of wind power relative to the fluctuation of the wind farm power generation, and the greater the corresponding energy storage output will be; is the wind power increment, and the expression is , are the wind power at times k and k - 1 respectively; is the rated wind power of 50000.

[0015] As the health of the energy storage decreases, the amount of electricity that can be discharged when the energy storage reaches the lowest SOC value will decrease, and the corresponding discharge capacity will also decrease. The depth of discharge required to emit the same amount of energy will increase. From the formula, it can be seen that the depth of discharge is an important factor affecting the health of the energy storage. To sum up, under different health states, the parameters of the rolling optimization objective can be adjusted Under the condition of meeting the wind power fluctuation, appropriately increase the wind power fluctuation to reduce the energy storage output, so as to avoid the situation that the health of the energy storage decreases faster in the later stage of use under the condition of meeting the wind power fluctuation.

[0016] S2. Use the current energy storage health and wind power fluctuation degree as the input variables of the controller, and output the parameters of the objective function as the output variables of the controller to adjust the parameters of the objective function; The specific process of S2 is as follows: Increase the value after the health decreases, that is, appropriately increase the wind power fluctuation under the condition of meeting the grid connection condition, reduce the energy storage life loss, and prevent the health of the energy storage from decreasing significantly faster in the later stage than in the early stage. Then, according to the wind power fluctuation rate constraint, on the basis of considering the overall SOC of the energy storage, use the model predictive control algorithm MPC smoothing strategy to mitigate the wind power fluctuation. Obtain the energy storage output and wind power.

[0017] When applying MPC, assume that the time node is K (unit: minute), and the total optimization control duration T = 600 minutes.

[0018] The optimization process can be described as follows: Step 1: Initialization. Set the starting time node K = 1; Step 2: Prediction and optimization. At each time node K, according to the current state, predict the system state in the future period of time and optimize the control input to minimize the objective function; Step 3: Control input update. Apply the obtained control input at time node K according to the optimization result.

[0019] Step 4: Time node update. After the control is executed, update the time node, making K = K + 1; Loop: Repeat steps 2 - 4 until the time node K reaches the total duration Ttotal = 600 minutes.

[0020] The embodiment of the present invention provides a high - precision triangular function to construct the membership functions of input and output, and designs the fuzzy language values of each group as follows: Input 1: SOH value, with a continuous domain of [0.8, 1] and a fuzzy domain of {0.8, 0.85, 0.9, 0.95, 1}, corresponding to the fuzzy subsets of {VS, S, M, L, VL}. The number of subsets is {very small, small, medium, large, very large} for the SOH value respectively.

[0021] Input 2: At each moment, the active power change of the wind farm relative to the previous minute has a continuous domain of [−20000, 20000], corresponding to a fuzzy domain of {-50000, −30000, -10000, 10000, 30000, 50000}, and the corresponding fuzzy subsets of {NL, NM, NS, PS, PM, PL}.

[0022] The subsets are the current power fluctuation amounts {negative large, negative medium, negative small, positive small, positive medium, positive large}, which indicate the charging power signal when the discharge power signal is negative.

[0023] Output: Weight coefficient has a continuous domain of [0, 1], a fuzzy domain of {0, 0.0125, 0.0250, 0.0375, 0.0500, 0.0675, 0.0800, 0.0975, 1}, and the corresponding fuzzy subsets of {ES, VS, S, MS, M, ML, L, VL, EL}.

[0024] The subset amounts are the weight coefficient values {extremely small, very small, small, slightly smaller than medium, medium, slightly larger than medium, large, very large, extremely large} respectively. Control the weight coefficient The fuzzy control rule table is shown in Table 1.

[0025] Table 1 Fuzzy control rule table ; S3. Based on the energy storage power constraint, the overall state - of - charge constraint of the energy storage, and the wind power volatility constraint, solve the objective function after adjusting the parameters to obtain the optimal value of the energy storage charge - discharge power, and add the optimal value of the energy storage charge - discharge power to the original wind power to obtain the optimal wind power grid - connection power value.

[0026] S3 specifically includes: Constraint conditions MPC also needs to satisfy the following constraint conditions, including energy storage power constraint, SOC constraint, and wind power volatility constraint: Formula 3; In the formula, are the minimum and maximum limits of the energy storage power respectively, with the unit of kilowatt, which are -8000 and 8000 respectively; are the minimum and maximum limits of the energy storage SOC respectively, which are 0.1 and 0.9 respectively; is the fluctuation limit value per unit time, and in this paper, it is taken as 10%.

[0027] Solution process The wind power state space equation can be rewritten in the following form to obtain the MPC mathematical model: Formula 4; Among them, the state variable x(k) of the system consists of the wind power and the state of charge of the energy storage, and is: Formula 5; The control variable u(k) of the system is the energy storage power: Formula 6; The energy storage power refers to the maximum electric energy that the energy storage system can input or output per unit time, reflecting the charging and discharging speed of the energy storage system, and the unit is usually watt (W), kilowatt (kW), megawatt (MW), etc.

[0028] The measurable disturbance input r(k) of the system is the wind power: Formula 7; Therefore, from the relationship between the variables in Formula 4, the coefficient matrices in Formula 4 can be obtained as: Formula 8; At time k, x(k) is the known actual state value. Based on Formula 4, at time k + 2, it can be deduced that: Formula 9; Let the state variable sequence, control variable, and disturbance quantity sequence be: Formula 11; Then the extended coefficient matrix is: Formula 12; All state variables in the objective function are represented by the control variable Constant terms are omitted. Based on the control quantity The objective function is transformed into the standard formula 13, and the minimum value of the wind power fluctuation is calculated. The u(k)min in is taken as the optimal value of the energy storage charge and discharge power: Formula 13; In the formula, is the transpose of, H and f are the quadratic term coefficient and the linear term coefficient for transforming the objective function into the standard formula 13. The optimal wind power grid-connected power value is obtained by adding the optimal value of the energy storage charge and discharge power to the original wind power.

[0029] Simulate the above method; To verify the effectiveness of the strategy proposed in the present invention, a model is established and simulated on MATLAB. The wind power data of a wind farm with an installed capacity of 50 MW is used as the input. The sampling interval of the wind power data is 1 min, and the research duration is 10 h. The MPC prediction time domain is set to 5 min, and the control period is 1 min. It is assumed that the energy storage capacity is 1000 kWh, the initial SOC is 0.5, and the upper and lower limits of the SOC are 0.9 and 0.1 respectively.

[0030] The effect of suppressing the wind power fluctuation is as follows: Set the unit time fluctuation limit to 10%, and set the initial energy storage health degree to 0.95. Suppress the original wind power, and the result is as Figure 2 shown. Figure 2 is a schematic diagram of the original wind power curve and the curve after suppressing the wind power fluctuation by the method of suppressing wind power fluctuation based on the model predictive control algorithm in the embodiment of the present invention. It can be seen from Figure 2 that after the wind power curve passes through the fluctuation suppression, the grid-connected wind power is obtained, and the curve is smoother. Table 2 shows the average volatility of the wind power before and after the fluctuation suppression at two time scales of 1 min and 10 min. This volatility is obtained by dividing the maximum fluctuation amplitude by the rated capacity of the wind power. It can be seen that the average volatility at both scales has decreased significantly. The 1 min volatility has decreased by about 2.98% on average, and the 10 min volatility has decreased by about 8.71%. This shows that the control strategy can effectively reduce large fluctuations and improve the volatility of the wind power.

[0031] ; Extract the fluctuation amount of the power and compare the distribution before and after the suppression, Figure 3 is a schematic diagram of the distribution of the fluctuation amount of the wind power before and after the suppression by the method of suppressing wind power fluctuation based on the model predictive control algorithm in the embodiment of the present invention. As Figure 3As shown, it can be seen that the maximum value of the original wind power fluctuation can reach 16 MW. After suppression, the maximum fluctuation is 14 MW. The distribution of the wind power fluctuation after suppression is more concentrated, and the proportion of small fluctuation values is larger, indicating that the control strategy can effectively reduce large fluctuations and improve the volatility of wind power.

[0032] The optimization process of the energy storage power is as follows: The weight coefficient of the MPC objective function is a trade-off between the output of the energy storage system and the severity of the wind power fluctuation. The larger the coefficient, the more energy storage output is required, and the better the suppression effect. Before introducing fuzzy control, the coefficient adopts a fixed value of 0.5. After introducing fuzzy control, the system can dynamically adjust the weight according to the health of the energy storage and the severity of the fluctuation. When the health of the energy storage is low, fuzzy control reduces the weight of the energy storage output, thus extending the system life; while when the fluctuation is severe, fuzzy control increases the output of the energy storage system to better suppress the fluctuation. This adaptive mechanism improves the comprehensive performance of the energy storage system, avoids over-discharge when the health is low, and provides a more effective stable output when the fluctuation is severe. Figure 4 is a three-dimensional schematic diagram of the fuzzy control rules of the wind power fluctuation suppression method based on the model predictive control algorithm in the embodiments of the present invention, which can intuitively display the fuzzy control rules. The three-dimensional response surface diagram of the drawn fuzzy controller takes the energy storage health as the X-axis, the value range of the power fluctuation from -5000 to 5000 as the Y-axis, and both are input variables, and the weight coefficient is used as the output variable. After adding fuzzy control, the weight coefficient decreases with the decrease of the energy storage health, and increases with the increase of the fluctuation, reflecting the characteristics of the system's adaptive adjustment.

[0033] Figure 5 is a schematic diagram of the energy storage output curve under different health states of the wind power fluctuation suppression method based on the model predictive control algorithm in the embodiments of the present invention; Figure 5 shows the energy storage power curves under the fuzzy control adjustment with the initial energy storage health of 0.95 and 0.85 respectively on the premise that the wind power meets the wind power fluctuation.

[0034] Table 3 Comparison of energy storage output and 10-minute wind power volatility under different health states ; When the state of health (SOH) of the energy storage battery decreases from 0.95 to 0.85, some changes occur in the charge-discharge energy of the energy storage and the volatility of wind power. Specifically, when SOH = 0.95, the total charge-discharge energy of the energy storage battery is 1.4228e+05 kWh, while when SOH = 0.85, the total charge-discharge energy of the energy storage battery decreases to 1.0946e+05 kWh, and the percentage decrease is approximately 23.1%. This decrease reflects that when the health of the energy storage battery is low, the control strategy tends to reduce its output.

[0035] At the same time, the decrease in the state of health of the energy storage battery will also lead to an increase in the volatility of wind power. When SOH = 0.95, the volatility of wind power calculated over 10 minutes is 26.42%; while when SOH = 0.85, the volatility of wind power rises to 32.86%. By calculating the ratio of the maximum fluctuation amplitude to the rated capacity of wind power, it can be seen that when the health is low, the output capacity of the energy storage battery decreases, resulting in an increase in the fluctuation of wind power, and the change in the fluctuation amplitude is approximately 24.5%.

[0036] This fuzzy control rule has a significant effect when the state of health of the energy storage battery is low. For an energy storage battery with a small state of health (e.g., SOH = 0.85), the fuzzy control adaptively reduces the output of the energy storage battery, thereby reducing the loss of the battery during the charge-discharge process and reducing the further negative impact on the state of health of the battery. At the same time, the control strategy also balances the relationship between the power smoothing effect and the loss of the life of the energy storage battery. By precisely adjusting the weight between the output of the energy storage battery and the fluctuation of wind power, this rule effectively reduces the additional loss of the energy storage battery with low health.

[0037] The beneficial effects of the present invention are as follows: In order to optimize the control effect of the energy storage system, the present invention formulates a fuzzy control rule, taking the state of health (SOH value) of the energy storage and the change in active power of the wind farm at each moment relative to the previous minute as the input variables of the controller. The weight coefficient of the MPC As the control output of the controller, it is used to adaptively adjust the weight relationship between the output of the energy storage battery and the wind power fluctuation smoothing effect.

[0038] Specifically, under the same state of health of the energy storage battery, the greater the wind power fluctuation, the greater the output of the energy storage system; while when the wind power fluctuation is small, the output of the energy storage battery will also decrease accordingly. On the other hand, under the same wind power fluctuation condition, as the state of health of the energy storage battery decreases, the charge-discharge capacity of the battery will decrease accordingly, resulting in a corresponding reduction in the output of the energy storage system. On the premise of ensuring that the wind power meets the grid connection requirements, this mechanism reduces the energy demand during the charge-discharge process, reduces the negative impact on the state of health of the energy storage, and thus balances the relationship between the smoothing effect and the loss of the life of the energy storage battery.

[0039] System Embodiment 1 According to an embodiment of the present invention, a wind power fluctuation suppression system based on a model predictive control algorithm is provided. Figure 6 It is a schematic diagram of the wind power fluctuation suppression system based on the model control algorithm of the embodiment of the present invention, as Figure 6 shown, specifically including: Objective Function Module 61: Used to construct an objective function for solving the minimum value of wind power fluctuation; The objective function module 61 is specifically used for: Construct a wind power state space equation, the formula is as follows: Formula 1; In the formula, k is the kth moment; is the wind power, is the wind power at the (k + 1)th moment, with the unit of kilowatt; is the total energy storage power at the kth moment, with the unit of kilowatt. When its value is positive, it means the energy storage emits power, and when it is negative, it means the energy storage absorbs power; is the state of charge of the overall energy storage at the (k + 1)th moment; is the state of charge of the overall energy storage at the kth moment; the rated capacity of the energy storage, with the unit of kilowatt-hour; is the control period, with the unit of minute; Based on the wind power state space equation, construct an objective function, the formula is as follows: Formula 2; In the formula, k is the kth moment, N is within N prediction step lengths, 1 ≤ i ≤ N, is the minimum target value of the fluctuation power, is the wind power increment, and the expression is , ; is the rated wind power, is the charge and discharge capacity index calculated from the wind power fluctuation degree and its health at the kth moment, is the state of charge of the overall energy storage at the

[0040] Adjustment Parameter Module 62: Used to take the current energy storage health and wind power fluctuation degree as the input variables of the controller, and output the parameters of the objective function as the output variables of the controller to adjust the parameters of the objective function; Solution Module 63: Used to solve the adjusted objective function based on the energy storage power constraint, the state of charge constraint of the overall energy storage, and the wind power volatility constraint to obtain the minimum value of wind power fluctuation, The formulas for energy storage power constraint, the overall state of charge constraint of the energy storage, and wind power volatility constraint are as follows: Formula 3; In the formula, are the minimum and maximum limits of the energy storage power respectively, with the unit of kilowatt; are the minimum and maximum limits of the energy storage SOC respectively; is the fluctuation limit value per unit time.

[0041] The solving module 63 is specifically used for: Rewrite the state space equation into Formula 4 to obtain the mathematical model of the model predictive control algorithm: Formula 4; Among them, the state variable x(k) of the system consists of the wind power and the state of charge of the energy storage, and is: Formula 5; The control variable u(k) of the system is the energy storage power: Formula 6; The measurable disturbance input r(k) of the system is the wind power: Formula 7; Based on the relationships between the variables in Formula 4, the coefficient matrices in Formula 4 are obtained as: Formula 8; At time k, x(k) is the known actual state value. Based on Formula 4, at time k + 2: Formula 9; And so on, the expression of the state variable at each moment is obtained, denoted as , and Formula 4 is expanded to: Formula 10; Assume that the state variable sequence, the control variable, and the disturbance quantity sequence are: Formula 11; Then the expansion coefficient matrix is: Formula 12; Express all the state variables in the objective function using the control variable , and omit the constant terms. Based on the control quantity , the objective function is transformed into the standard Formula 13, calculate the minimum value of the wind power fluctuation, and take the u(k)min in the calculated Formula 13; In the formula, is transpose of, H and f are the quadratic term coefficient and the linear term coefficient for converting the objective function into the standard Formula 13, and the optimal wind power grid connection power value is obtained by adding the optimal value of the energy storage charge and discharge power to the original wind power.

[0042] The embodiment of the present invention is a system embodiment corresponding to the above method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, and will not be elaborated here.

[0043] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the technical solutions of the embodiments of the present invention deviate from the scope of the present solution.

Claims

1. A wind power grid-connected power smoothing method based on model predictive control algorithm, characterized in that: include: S1. Construct objective function based on model predictive control algorithm; S2. The current energy storage health and wind power fluctuation degree are used as input variables of the controller, and the parameters of the output objective function are used as the output variables of the controller to adjust the parameters of the objective function; S3. Based on the energy storage power constraint, the overall state of charge constraint of the energy storage and the wind power fluctuation constraint, the objective function after adjusting the parameters is solved to obtain the optimal value of the energy storage charging and discharging power, and the optimal wind power grid-connected power value is obtained by adding the original wind power to the optimal value of the energy storage charging and discharging power.

2. The method according to claim 1, characterized in that: The S1 specifically includes: Construct the wind power state space equation, the formula is as follows: Formula (1): Where k is the kth moment; is the wind power at time k+1, in kilowatts; is the wind power, is the total energy storage power at time k, in kilowatts. A positive value indicates that the energy storage emits power, and a negative value indicates that the energy storage absorbs power. is the charge state of the overall energy storage at time k+1; is the overall state of charge of the energy storage at time k; is the rated capacity of energy storage, in kilowatt-hours; is the control cycle, in minutes; The objective function is constructed based on the wind power state space equation, and the formula is as follows: Formula 2: Where N is the period of N prediction steps, 1≤i≤N, is the objective function, is the wind power increment, and the expression is , are the wind power at time k and k-1 respectively, is the wind power rated power, is the charging and discharging capacity index calculated from the wind power fluctuation degree and its health at time k, that is, the parameter of the objective function, for The overall state of charge of the energy storage at all times.

3. The method according to claim 1, characterized in that The energy storage power constraint, the overall energy storage state of charge constraint and the wind power fluctuation rate constraint formula are as follows: Formula (3): In the formula, are the minimum and maximum limits of energy storage power, respectively, in kilowatts; They are the minimum and maximum limits of energy storage SOC, respectively; is the fluctuation limit per unit time.

4. The method according to claim 2, characterized in that: The objective function after solving the adjustment parameters is obtained to obtain the optimal value of the energy storage charging and discharging power, and the optimal wind power grid-connected power value is obtained by adding the original wind power to the optimal value of the energy storage charging and discharging power. Specifically, the following steps are performed: Rewrite the wind power state space equation formula 1 into formula 4: Formula 4: Among them, the state variables of the system x ( k ) consists of the state of charge of wind power and energy storage, which is: Formula 5: The control variable u (k) of the system is the energy storage power at time k: Formula 6: The measurable disturbance input r(k) of the system is the wind power at time k: Formula 7: Based on the relationship between the variables in formula 4, the coefficient matrix in formula 4 is obtained as follows: Formula 8: At time k, x(k) is the known actual state value, and the k+2 time is derived based on formula 4: Formula 9: By analogy, we can get the expression of the state variable at each moment, record , expand formula 4 to: Formula 10: Assume that the sequence of state variables, control variables and disturbance variables are: Formula 11: Then the expansion coefficient matrix is: Formula 12; All state variables in the objective function Using control variables Indicates that the constant term is omitted and based on the control quantity Convert the objective function into the standard formula 13 and calculate the minimum value of wind power fluctuation. u(k) in min As the optimal value of energy storage charging and discharging power: Formula 13: In the formula, for , H and f are the coefficients of the quadratic term and the linear term of the standard formula 13 that transforms the objective function. The optimal wind power grid-connected power value is obtained by adding the original wind power to the optimal value of the energy storage charging and discharging power.

5. A wind power fluctuation smoothing system based on model predictive control algorithm, characterized in that: include: Objective function module: used to construct the objective function based on the model predictive control algorithm; Parameter adjustment module: used to take the current energy storage health and wind power fluctuation as the input variables of the controller, and output the parameters of the objective function as the output variables of the controller to adjust the parameters of the objective function; Solution module: It is used to solve the objective function after adjusting the parameters based on the energy storage power constraint, the overall state of charge constraint of the energy storage and the wind power fluctuation constraint to obtain the optimal value of the energy storage charging and discharging power, and add the original wind power to the optimal value of the energy storage charging and discharging power to obtain the optimal wind power grid-connected power value.

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