Control method for stabilizing wind power fluctuation of energy storage system

Abstract

The invention relates to a wind power fluctuation stabilizing control method for an energy storage system, and the method comprises the steps: obtaining an active power allowable change limit value of a wind power plant and the installed capacity of the wind power plant, determining a grid-connected power allowable fluctuation range based on the grid-connected power in the power balance equation of the wind storage combined power generation system, a wind power plant active power allowable change limit value and the installed capacity of the wind power plant; determining a target power value based on the grid-connected power allowable fluctuation range and wind power in a power balance equation of the wind-storage combined power generation system; constructing a target function by taking the deviation between the grid-connected power in the power balance equation of the wind storage combined power generation system and a target power value and the minimum deviation of the residual capacity of the energy storage system in the energy storage energy state iteration equation from an ideal value as targets; and determining a wind power fluctuation stabilizing control scheme of the energy storage system based on comparison between the grid-connected power and the grid-connected power allowable fluctuation range. According to the method, the out-of-limit time of the energy storage system is greatly shortened, and the capacity of the energy storage system is fully utilized.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage system control for smoothing wind power fluctuations, and in particular to a method for controlling energy storage system for smoothing wind power fluctuations. Background Art

[0002] Vigorously developing wind power is an effective way to accelerate the construction of a clean, low-carbon, safe and efficient energy system and achieve sustainable energy development and dual carbon goals. However, the volatility and intermittency of wind power have brought huge impacts and influences to the safe and stable operation of the power grid. The rapid development and application of energy storage technology can effectively make up for the shortcomings of wind power generation, effectively smooth the fluctuations in wind power grid-connected power, and increase the penetration rate of wind power in the power grid. With the substantial increase in the scale of energy storage configured in wind farms, improving the smoothing capacity and life of wind farm energy storage has become the focus of attention of all parties. However, existing studies have insufficient analysis of the impact of the current remaining capacity of the energy storage system on the future smoothing capacity. How to effectively balance the contradiction between the ability to smooth wind power fluctuations and the service life of the energy storage system needs further study. Summary of the invention

[0003] Based on this, it is necessary to provide a method for controlling wind power fluctuations by using an energy storage system to address the above issues.

[0004] The present invention provides a method for controlling wind power fluctuations in an energy storage system, the method comprising:

[0005] S1: Construct a model predictive control system model based on the power balance equation of the wind-storage combined power generation system and the energy state iteration equation of the energy storage;

[0006] S2: obtaining the allowable change limit of the active power of the wind farm and the installed capacity of the wind farm, and determining the allowable fluctuation range of the grid-connected power based on the grid-connected power in the power balance equation of the wind-storage combined power generation system, the allowable change limit of the active power of the wind farm, and the installed capacity of the wind farm;

[0007] S3: Determine a target power value based on the grid-connected power allowable fluctuation range and the wind power in the power balance equation of the wind-storage combined power generation system;

[0008] S4: Construct an objective function with the goal of minimizing the deviation between the grid-connected power and the target power value in the power balance equation of the wind-storage combined power generation system and the deviation of the remaining capacity of the energy storage system from the ideal value in the energy storage energy state iteration equation;

[0009] S5: Determine a control scheme for smoothing wind power fluctuations of an energy storage system based on a comparison between the grid-connected power and the allowable fluctuation range of the grid-connected power;

[0010] The energy storage system wind power fluctuation control scheme includes:

[0011] The first control scheme adjusts the parameters of the objective function;

[0012] The second control scheme uses statistical factor analysis theory to construct conflicting factors, and corrects the charging and discharging power of the energy storage system according to the conflicting factors.

[0013] Preferably, the power balance equation of the wind-storage combined power generation system is expressed as:

[0014] P g (i+1)=P b (i)+P w (i);

[0015] The expression of the energy storage state iteration equation is:

[0016] C SOC (i+1)=C SOC (i)-ηP b (i)×T c / C rated ;

[0017] C SOC (i) = SOC(i) × C rated ;

[0018] Among them, P g (i+1) represents the grid-connected power at the i+1th moment, P b (i) represents the output power of the energy storage system at the i-th moment, P w (i) represents the wind power at the i-th moment, C SOC (i+1) represents the remaining capacity of the energy storage system at the i+1th moment, C SOC (i) represents the remaining capacity of the energy storage system at the i-th moment, η represents the conversion efficiency of the energy storage system, T c Represents the energy storage system control cycle, C rated represents the rated capacity of the energy storage system, and SOC(i) represents the state of charge of the energy storage system at the i-th moment.

[0019] Preferably, the process of constructing a model predictive control system model includes:

[0020] S1.1: Based on the power balance equation of the wind-storage combined power generation system and the energy storage energy state iteration equation, and combined with the superposition theorem, a model predictive control system model of the wind-storage combined power generation system is constructed, and its state space equation expression is:

[0021] x(i+1)=Ax(i)+B1u(i)+B2r(i);

[0022] x(i)=[P g (i),C SOC (i)]T ;

[0023] u(i)=P b (i);

[0024] r(i)=P w (i);

[0025] Among them, x(i) represents the state variable, u(i) represents the control variable, r(i) represents the input variable, P g (i) represents the grid-connected power at the i-th moment, C SOC (i) represents the remaining capacity of the energy storage system at the i-th moment, P b (i) represents the output power of the energy storage system at the i-th moment, P w (i) represents the wind power at the i-th moment, η represents the conversion efficiency of the energy storage system, T c Represents the energy storage system control cycle, C rated Indicates the rated capacity of the energy storage system;

[0026] S1.2: According to the model predictive control principle, the output at the future i+k time is calculated using the following formula:

[0027]

[0028] Among them, P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P w (i+k|i) represents the active output increment of wind power in the period [i+(k-1),i+k]; N represents the model predictive control step size; C SOC (i+k|i) represents the remaining capacity of the energy storage system at time i+k predicted at time i; P b (i+k|i) represents the output power of the energy storage system at time i+k; u T (i+k|i) represents the optimal control sequence obtained within a period of time, and the expression is:

[0029] u T (i+k|i)=[u(i+1),u(i+2),···,u(i+N)], k=1,2,···,N;

[0030] Among them, u(i+1) represents the control variable at time i+1, and u(i+N) represents the control variable at time i+N;

[0031] S1.3: Take u(i+1) as the output power of the energy storage system at time i+1, update the state variables, optimize the state space equation of the wind-storage combined power generation system, when i=1440, the iteration terminates, and the optimized state space equation of the wind-storage combined power generation system is the model predictive control system model.

[0032] Preferably, in S2, the grid-connected power allowable fluctuation range is determined based on the grid-connected power in the power balance equation of the wind-storage combined power generation system, the allowable change limit of the active power of the wind farm, and the installed capacity of the wind farm, including:

[0033] Based on the allowable change limit of the active power of the wind farm and the installed capacity of the wind farm, the allowable fluctuation range of the grid-connected power within 1 minute and within 10 minutes from the i-th moment is calculated, and the calculation formula is:

[0034]

[0035]

[0036] Among them, Pg1.min(i)-Pg1.max(i) represents the allowable fluctuation range of grid-connected power within 1 minute from the i-th moment; Pg 10 .min(i)-Pg 10 .max(i) represents the permissible fluctuation range of grid-connected power within 10 minutes from the i-th moment; P g (i) represents the grid-connected power at the i-th moment; n represents the allowable change limit of the wind farm active power of the wind farm with the first installed capacity; m represents the allowable change limit of the wind farm active power of the wind farm with the second installed capacity; C install1 Indicates the first installed capacity; C install2 represents the second installed capacity; Δt represents each moment within 10 minutes from the i-th moment; Δi represents the time interval.

[0037] Preferably, in S3, determining the target power value based on the grid-connected power allowable fluctuation range and the wind power in the power balance equation of the wind-storage combined power generation system includes:

[0038]

[0039] Among them, P a (i) represents the target power value; P g.max (i) represents the upper limit of the permissible fluctuation range of the grid-connected power within 1 minute or 10 minutes from the i-th moment; P g.min (i) represents the lower limit of the grid-connected power fluctuation range allowed within 1 minute or 10 minutes from the i-th moment; P w (i) represents the wind power at the i-th moment.

[0040] Preferably, in S4, the expression of the objective function is:

[0041]

[0042] Where λ(i) represents the dynamic adjustment coefficient; k represents the kth moment, M represents the length of the control instruction sequence; P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P a (i+k|i) represents the target power value at time i+k; C SOC (i+k|i) represents the remaining capacity of the energy storage system at time i+k predicted at time i; C ideal Indicates the ideal value.

[0043] Preferably, S4 also includes energy storage system power constraints and energy storage system capacity constraints;

[0044] The power constraint expression of the energy storage system is:

[0045]

[0046] The energy storage system capacity constraint expression is:

[0047] SOC min ≤SOC(i)≤SOC max ;

[0048] Among them, P rated Indicates the rated power of the energy storage system; SOC min represents the lower limit of the energy storage system's state of charge; SOC(i) represents the energy storage system's state of charge at the i-th moment; C rated Indicates the rated capacity of the energy storage system; P b (i) represents the output power of the energy storage system at the i-th moment; SOC max Indicates the upper limit of the energy storage system's state of charge; T s Indicates the sampling period.

[0049] Preferably, in S5, the first control scheme includes: when the grid-connected power is not within the allowable fluctuation range of the grid-connected power, the output power of the energy storage system in the power balance equation of the wind-storage combined power generation system and the remaining capacity of the energy storage system in the energy storage energy state iteration equation are input into the first-layer fuzzy controller, and the dynamic adjustment coefficient in the objective function is adjusted to maintain the charge state of the energy storage system within the allowable working range;

[0050] The second control scheme includes: when the grid-connected power is within the allowable fluctuation range of the grid-connected power, using statistical factor analysis theory to construct a contradictory factor, inputting the contradictory factor and the output power of the energy storage system in the power balance equation of the wind-storage combined power generation system into the second-layer fuzzy controller, outputting a correction coefficient, and correcting the charging and discharging power of the energy storage system according to the correction coefficient.

[0051] Preferably, in S5, the process of constructing the contradiction factor includes:

[0052] Step 1: Calculate the over-limit power of the energy storage system according to the rated capacity of the energy storage system and the charge state of the energy storage system in the energy storage energy state iteration equation; calculate the power fluctuation according to the grid-connected power and the target power value;

[0053]

[0054] P d (i+k)=P g (i+k|i)-P a (i+k|i);

[0055] Where W(i+k) represents the over-limit power of the energy storage system; SOC(i+k) represents the state of charge of the energy storage system at the i+kth moment; C rated Indicates the rated capacity of the energy storage system; SOC min Indicates the lower limit of the state of charge of the energy storage system; SOC max Indicates the upper limit of the energy storage system's state of charge; P d (i+k) represents power fluctuation; P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P a (i+k|i) represents the target power value at time i+k;

[0056] Step 2: A Z matrix is ​​formed according to the over-limit power of the energy storage system and the power fluctuation; an H matrix is ​​constructed according to the Z matrix, and the expression of the element in the xth row and yth column of the H matrix is:

[0057]

[0058]

[0059]

[0060] Among them, h xy represents the element in the xth row and yth column of the H matrix; q is the number of rows in the Z matrix; l is the number of columns in the Z matrix; d xy represents the Euclidean distance between the xth object and the yth object in the Z matrix; z xrrepresents the element in the xth row and rth column of the Z matrix; z xy Represents the element in the xth row and yth column of the Z matrix;

[0061] Step 3: Perform eigenvalue decomposition on the H matrix. The decomposition formula is:

[0062] H=UVU T ;

[0063] Among them, U is the matrix with the corresponding eigenvectors as columns, and V is the diagonal matrix generated by the eigenvalues ​​of the H matrix;

[0064] Step 4: Construct a contradiction factor based on a diagonal matrix generated from a matrix with the corresponding eigenvectors as columns and the eigenvalues ​​of the H matrix; the expression is:

[0065]

[0066] Among them, F represents the contradiction factor.

[0067] Preferably, in the second control scheme,

[0068] The charging and discharging power correction formula of the energy storage system is:

[0069] P' b (i+1|i)=P b (i+1|i)+Δk×(P w (i+1)-P g (i));

[0070] Among them, P b '(i+1|i) represents the corrected output power of the energy storage system at time i+1, P b (i+1|i) represents the output power of the energy storage system at time i+1, Δk is the correction coefficient; P w (i+1|i) represents the active output increment of wind power in the time period [i,i+1]; P g (i) represents the grid-connected power at the i-th moment.

[0071] Beneficial effects: The method provided by the present invention can significantly reduce the time the energy storage system enters the dead zone while improving the ability to smooth wind power fluctuations, greatly reducing the energy storage system over-limit time, fully utilizing the capacity of the energy storage system, and improving the ability to smooth future wind power. While improving the wind power grid-connected capacity, it also greatly enhances the market competitiveness of the wind storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0073] Figure 1 Flow chart of a method for controlling wind power fluctuations by an energy storage system according to an embodiment of the present application.

[0074] Figure 2 This is a wind-storage combined output curve diagram of an embodiment of the present application.

[0075] Figure 2a for Figure 2 Enlarged view of part (a).

[0076] Figure 2b for Figure 2 Enlarged view of part (b).

[0077] Figure 3 This is a state of charge curve diagram of the energy storage system according to an embodiment of the present application.

[0078] Figure 4 This is a curve diagram of the output power of the energy storage system according to an embodiment of the present application.

[0079] Figure 4a for Figure 4 Enlarged view of part (a).

[0080] Figure 4b for Figure 4 Enlarged view of part (b). DETAILED DESCRIPTION

[0081] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0082] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0083] like Figure 1 As shown, this embodiment provides a method for controlling wind power fluctuations of an energy storage system, the method comprising:

[0084] S1: Construct a model predictive control system model based on the power balance equation of the wind-storage combined power generation system and the energy storage energy state iteration equation.

[0085] Specifically, the power balance equation of the wind-storage combined power generation system is expressed as:

[0086] P g (i+1)=P b (i)+P w (i);

[0087] The expression of the energy storage state iteration equation is:

[0088] C SOC (i+1)=C SOC (i)-ηP b (i)×T c / C rated ;

[0089] C SOC (i) = SOC(i) × C rated ;

[0090] Among them, P g (i+1) represents the grid-connected power at the i+1th moment, P b (i) represents the output power of the energy storage system at the i-th moment, P w (i) represents the wind power at the i-th moment, C SOC (i+1) represents the remaining capacity of the energy storage system at the i+1th moment, C SOC (i) represents the remaining capacity of the energy storage system at the i-th moment, η represents the conversion efficiency of the energy storage system, T c Represents the energy storage system control cycle, C rated represents the rated capacity of the energy storage system, and SOC(i) represents the state of charge of the energy storage system at the i-th moment.

[0091] The process of building a model predictive control system model includes:

[0092] S1.1: Based on the power balance equation of the wind-storage combined power generation system and the energy storage energy state iteration equation, and combined with the superposition theorem, a model predictive control system model of the wind-storage combined power generation system is constructed, and its state space equation expression is:

[0093] x(i+1)=Ax(i)+B1u(i)+B2r(i);

[0094] x(i)=[Pg (i),C SOC (i)] T ;

[0095] u(i)=P b (i);

[0096] r(i)=P w (i);

[0097] Among them, x(i) represents the state variable, u(i) represents the control variable, r(i) represents the input variable, P g (i) represents the grid-connected power at the i-th moment, C SOC (i) represents the remaining capacity of the energy storage system at the i-th moment, P b (i) represents the output power of the energy storage system at the i-th moment, P w (i) represents the wind power at the i-th moment, η represents the conversion efficiency of the energy storage system, T c Represents the energy storage system control cycle, C rated Indicates the rated capacity of the energy storage system;

[0098] S1.2: According to the model predictive control principle, the output at the future i+k time is calculated using the following formula:

[0099]

[0100] Among them, P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P w (i+k|i) represents the active output increment of wind power in the time period [i+(k-1),i+k]; N represents the model predictive control step size; C SOC (i+k|i) represents the remaining capacity of the energy storage system at time i+k predicted at time i; P b (i+k|i) represents the output power of the energy storage system at time i+k; u T (i+k|i) represents the optimal control sequence obtained within a period of time, and the expression is:

[0101] u T (i+k|i)=[u(i+1),u(i+2),···,u(i+N)], k=1,2,···,N;

[0102] Among them, u(i+1) represents the control variable at time i+1, and u(i+N) represents the control variable at time i+N;

[0103] S1.3: Take u(i+1) as the output power of the energy storage system at time i+1, update the state variables, optimize the state space equation of the wind-storage combined power generation system, when i=1440, the iteration terminates, and the optimized state space equation of the wind-storage combined power generation system is the model predictive control system model.

[0104] S2: Obtain the allowable change limit of the active power of the wind farm and the installed capacity of the wind farm, and determine the allowable fluctuation range of the grid-connected power based on the grid-connected power in the power balance equation of the wind-storage combined power generation system, the allowable change limit of the active power of the wind farm, and the installed capacity of the wind farm.

[0105] Specifically, based on the allowable change limit of the active power of the wind farm and the installed capacity of the wind farm, the allowable fluctuation range of the grid-connected power within 1 minute and within 10 minutes from the i-th moment is calculated, and the calculation formula is:

[0106]

[0107]

[0108] Among them, Pg1.min(i)-Pg1.max(i) represents the allowable fluctuation range of grid-connected power within 1 minute from the i-th moment; Pg 10 .min(i)-Pg 10 .max(i) represents the permissible fluctuation range of grid-connected power within 10 minutes from the i-th moment; P g (i) represents the grid-connected power at the i-th moment; n represents the allowable change limit of the wind farm active power of the wind farm with the first installed capacity; m represents the allowable change limit of the wind farm active power of the wind farm with the second installed capacity; C install1 Indicates the first installed capacity; C install2 represents the second installed capacity; Δt represents each moment within 10 minutes from the i-th moment; Δi represents the time interval.

[0109] S3: Determine a target power value based on the allowable fluctuation range of the grid-connected power and the wind power in the power balance equation of the wind-storage combined power generation system.

[0110] Specifically include:

[0111]

[0112] Among them, P a (i) represents the target power value; P g.max (i) represents the upper limit of the permissible fluctuation range of the grid-connected power within 1 minute or 10 minutes from the i-th moment; P g.min (i) represents the lower limit of the grid-connected power fluctuation range allowed within 1 minute or 10 minutes from the i-th moment; Pw (i) represents the wind power at the i-th moment.

[0113] S4: Construct an objective function with the goal of minimizing the deviation between the grid-connected power and the target power value in the power balance equation of the wind-storage combined power generation system and the deviation of the remaining capacity of the energy storage system from the ideal value in the energy storage energy state iteration equation.

[0114] The expression of the objective function is:

[0115]

[0116] Where λ(i) represents the dynamic adjustment coefficient; k represents the kth moment, M represents the length of the control instruction sequence; P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P a (i+k|i) represents the target power value at time i+k; C SOC (i+k|i) represents the remaining capacity of the energy storage system at time i+k predicted at time i; C ideal Indicates the ideal value.

[0117] The energy storage system must also meet the energy storage system power constraints and energy storage system capacity constraints;

[0118] The power constraint expression of the energy storage system is:

[0119]

[0120] The energy storage system capacity constraint expression is:

[0121] SOC min ≤SOC(i)≤SOC max ;

[0122] Among them, P rated Indicates the rated power of the energy storage system; SOC min represents the lower limit of the energy storage system's state of charge; SOC(i) represents the energy storage system's state of charge at the i-th moment; C rated Indicates the rated capacity of the energy storage system; P b (i) represents the output power of the energy storage system at the i-th moment; SOC max Indicates the upper limit of the energy storage system's state of charge; T s Indicates the sampling period.

[0123] In this embodiment, the model predictive control system model uses the model predictive control rolling optimization objective function, and the process includes: according to the objective function, the power constraint of the energy storage system and the capacity constraint of the energy storage system, the control instruction sequence of the energy storage system in the rolling cycle is obtained, and the first value of the control instruction sequence is applied to the model predictive control system model. In the next rolling cycle, the state variable is updated and the above process is repeated. The optimization time domain length is 15 minutes;

[0124] S5: Based on the comparison between the grid-connected power and the allowable fluctuation range of the grid-connected power, a control scheme for smoothing wind power fluctuations of the energy storage system is determined.

[0125] The energy storage system wind power fluctuation control scheme includes:

[0126] The first control scheme adjusts the parameters of the objective function; specifically, when the grid-connected power is not within the allowable fluctuation range of the grid-connected power, the output power of the energy storage system in the power balance equation of the wind-storage combined power generation system and the remaining capacity of the energy storage system in the energy storage energy state iteration equation are input into the first-layer fuzzy controller, and the dynamic adjustment coefficient in the objective function is adjusted to maintain the charge state of the energy storage system within the allowable working range.

[0127] In the first control scheme, a first-layer fuzzy control strategy is provided, which is as follows:

[0128] The domain of the remaining capacity of the energy storage system is [0,1], and the fuzzy set of the remaining capacity of the energy storage system is {VS1, S1, M1, B1, VB1}; VS1 represents the "small value" of capacity, S1 represents the "small value" of capacity, M1 represents the "moderate value" of capacity, B1 represents the "large value" of capacity, and VB1 represents the "large value" of capacity;

[0129] The domain of the energy storage system output power is [-1,1], and the fuzzy set of the energy storage system output power is {NB, NS, Z, PS, PB}; NB represents the power "negative large value", NS represents the power "negative small value", Z represents the power "zero", PS represents the power "positive small value", and PB represents the power "positive large value";

[0130] The domain of the dynamic adjustment coefficient is [0,1], and the fuzzy set of the dynamic adjustment coefficient is {VS α ,S α ,M α ,B α ,VB α};VS α Indicates the coefficient "small value", S α Indicates that the coefficient is "small", M α Indicates that the coefficient is "moderate", B α Indicates that the coefficient is "too large", VB α Indicates a "large value" coefficient.

[0131] The second control scheme adopts statistical factor analysis theory to construct the contradiction factor, and corrects the charging and discharging power of the energy storage system according to the contradiction factor; specifically, when the grid-connected power is within the allowable fluctuation range of the grid-connected power, the contradiction factor is constructed by statistical factor analysis theory, the contradiction factor and the output power of the energy storage system in the power balance equation of the wind-storage combined power generation system are input into the second-layer fuzzy controller, and the correction coefficient is output, and the charging and discharging power of the energy storage system is corrected according to the correction coefficient.

[0132] In the second control scheme, a second-layer fuzzy control strategy is provided, which is as follows:

[0133] The domain of the energy storage system output power is [-1,1], and the fuzzy set of the energy storage system output power is {L,LM,M s ,MH,H s}; L indicates the power "negative large value", LM indicates the power "negative small value", M s Indicates "moderate" power, MH indicates "positive small value" power, H s Indicates the "positive maximum value" of power;

[0134] The domain of the contradiction factor is [-0.2, 0.2], and the fuzzy set of the remaining capacity of the energy storage system is {VS2, S2, B2, VB2}; VS2 represents the "small value" of the contradiction factor, S2 represents the "small value" of the contradiction factor, B2 represents the "large value" of the contradiction factor, and VB2 represents the "large value" of the contradiction factor;

[0135] The domain of the correction coefficient is [-1,1], and the fuzzy set of the correction coefficient is {NB3, PB3, N3, Z3, P3, PH3, NH3}; NB3 represents the "minimum value" of the correction coefficient, PB3 represents the "small value" of the correction coefficient, N3 represents the "small value" of the correction coefficient, Z3 represents the "moderate value" of the correction coefficient, P3 represents the "large value" of the correction coefficient, PH3 represents the "large value" of the correction coefficient, and NH3 represents the "maximum value" of the correction coefficient.

[0136] The charging and discharging power correction formula of the energy storage system is:

[0137] P' b (i+1|i)=P b (i+1|i)+Δk×(P w (i+1)-P g (i));

[0138] Among them, P b '(i+1|i) represents the corrected output power of the energy storage system at time i+1, P b(i+1|i) represents the output power of the energy storage system at time i+1, Δk is the correction coefficient; P w (i+1|i) represents the active output increment of wind power in the time period [i,i+1]; P g (i) represents the grid-connected power at the i-th moment.

[0139] This embodiment provides a process of constructing a conflict factor, including:

[0140] Step 1: Calculate the over-limit power of the energy storage system according to the rated capacity of the energy storage system and the charge state of the energy storage system in the energy storage energy state iteration equation; calculate the power fluctuation according to the grid-connected power and the target power value;

[0141]

[0142] P d (i+k)=P g (i+k|i)-P a (i+k|i);

[0143] Where W(i+k) represents the over-limit power of the energy storage system; SOC(i+k) represents the state of charge of the energy storage system at the i+kth moment; C rated Indicates the rated capacity of the energy storage system; SOC min Indicates the lower limit of the state of charge of the energy storage system; SOC max Indicates the upper limit of the energy storage system's state of charge; P d (i+k) represents power fluctuation; P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P a (i+k|i) represents the target power value at time i+k;

[0144] Step 2: A Z matrix is ​​formed according to the over-limit power of the energy storage system and the power fluctuation; an H matrix is ​​constructed according to the Z matrix, and the expression of the element in the xth row and yth column of the H matrix is:

[0145]

[0146]

[0147]

[0148] Among them, h xy represents the element in the xth row and yth column of the H matrix; q is the number of rows in the Z matrix; l is the number of columns in the Z matrix; d xy represents the Euclidean distance between the xth object and the yth object in the Z matrix; z xr represents the element in the xth row and rth column of the Z matrix; z xyRepresents the element in the xth row and yth column of the Z matrix;

[0149] Step 3: Perform eigenvalue decomposition on the H matrix. The decomposition formula is:

[0150] H=UVU T ;

[0151] Among them, U is the matrix with the corresponding eigenvectors as columns, and V is the diagonal matrix generated by the eigenvalues ​​of the H matrix;

[0152] Step 4: Construct a contradiction factor based on a diagonal matrix generated from a matrix with the corresponding eigenvectors as columns and the eigenvalues ​​of the H matrix; the expression is:

[0153]

[0154] Among them, F represents the contradiction factor.

[0155] If the contradiction factor F is very large, it means that the energy storage system will be over-powered during the future rolling optimization period, indicating that the SOC has exceeded the SOC max , the energy storage system needs to be discharged in advance. If the F value is very small, it means that the energy storage system is not capable of smoothing wind power fluctuations during the future rolling optimization period, indicating that the SOC is lower than the SOC min , the energy storage system needs to be charged in advance. If the contradiction F is moderate, the energy storage system charges and discharges according to the original instructions.

[0156] This embodiment provides a model predictive control method that takes into account both the smoothing of wind power fluctuations and the output capacity of the energy storage system. It can significantly reduce the time the energy storage system enters the dead zone while improving the ability to smooth wind power fluctuations. At the same time, the designed double-layer fuzzy control strategy comprehensively considers the multi-task execution of smoothing future wind power fluctuations and safety work goals. It greatly reduces the over-limit time of the energy storage system, fully utilizes the capacity of the energy storage system, improves the ability to smooth future wind power, and greatly improves the market competitiveness of the wind storage system while improving the ability to connect wind power to the grid.

[0157] In this embodiment, a 50MW wind farm in a certain area equipped with a 10MW / 20MW·h electrochemical energy storage system is used as the test system, and the specific parameter settings are shown in Table 1 below;

[0158] Table 1 is the parameter setting table;

[0159] parameter Numeric <![CDATA[Installed capacity C of wind farm install > 50MW Control cycle 1min Energy storage system rated capacity 5MW·h Energy storage system rated power 10MW Sampling period Ts 1min <![CDATA[Upper limit of state of charge (SOC) of energy storage system max > 0.8 <![CDATA[Lower limit of state of charge (SOC) of energy storage system min > 0.2

[0160] The traditional model predictive control method with the minimum output of the energy storage system as the optimization target is compared with the conventional model predictive control method. For the convenience of description, the comparison scheme is referred to as Figure 2 , Figure 3 , Figure 4The method proposed in this embodiment is called this scheme (corresponding to Figure 2 , Figure 3 , Figure 4 Solution 2).

[0161] The wind-storage combined output curves of different control schemes are as follows: Figure 2 , Figure 2a , Figure 2b As shown in the figure, it can be seen that the suppression effect of this scheme is better than that of the comparative scheme. Figure 3 It can be seen from the energy storage charge state change curve that in the time periods of 165min~173min, 203~217min, 254min~259 and 863min~882min, the energy storage system of the comparison scheme is in a high energy state, resulting in a lack of charging capacity. In the time periods of 330min~338min, 345min~355min, 442min~458min, 581min~586min, 712min~723min and 746min~764min, the energy storage system of the comparison scheme is in a low energy state, resulting in a lack of discharge capacity. The dead time of the energy storage system of the comparison scheme is as long as 123min, and it does not have the ability to consider energy storage to smooth out wind power fluctuations in the future. Combined with Figure 4 , Figure 4a , Figure 4b It can be seen from the output power of the energy storage system that this solution improves the energy storage system's ability to smooth future wind power by dynamically adjusting the weight coefficient and charging and discharging in advance. Compared with the comparative solution, the dead time of the energy storage system is reduced by 123 minutes. While improving the smoothing of wind power fluctuations, this solution reduces the dead time and extends the service life of the energy storage system. Therefore, compared with the comparative solution, the method provided in this embodiment effectively balances the contradiction between energy storage over-limit and smoothing wind power fluctuations, and improves the ability to smooth future wind power fluctuations.

[0162] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0163] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for controlling wind power fluctuations by using an energy storage system, characterized in that: include: S1: Construct a model predictive control system model based on the power balance equation of the wind-storage combined power generation system and the energy state iteration equation of the energy storage; S2: obtaining the allowable change limit of the active power of the wind farm and the installed capacity of the wind farm, and determining the allowable fluctuation range of the grid-connected power based on the grid-connected power in the power balance equation of the wind-storage combined power generation system, the allowable change limit of the active power of the wind farm, and the installed capacity of the wind farm; S3: Determine a target power value based on the grid-connected power allowable fluctuation range and the wind power in the power balance equation of the wind-storage combined power generation system; S4: Construct an objective function with the goal of minimizing the deviation between the grid-connected power and the target power value in the power balance equation of the wind-storage combined power generation system and the deviation of the remaining capacity of the energy storage system from the ideal value in the energy storage energy state iteration equation; S5: Determine a control scheme for smoothing wind power fluctuations of an energy storage system based on a comparison between the grid-connected power and the allowable fluctuation range of the grid-connected power; The energy storage system wind power fluctuation control scheme includes: The first control scheme adjusts the parameters of the objective function; The second control scheme uses statistical factor analysis theory to construct conflicting factors, and corrects the charging and discharging power of the energy storage system according to the conflicting factors.

2. The method for controlling wind power fluctuations by using an energy storage system according to claim 1, characterized in that: The power balance equation of the wind-storage combined power generation system is expressed as: P g (i+1)=P b (i)+P w (i); The expression of the energy storage state iteration equation is: C SOC (i+1)=C SOC (i)-ηP b (i)×T c / C rated ; C SOC (i)=SOC(i)×C rated ; Among them, P g (i+1) represents the grid-connected power at the i+1th moment, P b (i) represents the output power of the energy storage system at the i-th moment, P w (i) represents the wind power at the i-th moment, C SOC (i+1) represents the remaining capacity of the energy storage system at the i+1th moment, C SOC (i) represents the remaining capacity of the energy storage system at the i-th moment, η represents the conversion efficiency of the energy storage system, T c Represents the energy storage system control cycle, C rated represents the rated capacity of the energy storage system, and SOC(i) represents the state of charge of the energy storage system at the i-th moment.

3. The method for controlling wind power fluctuations by using an energy storage system according to claim 2, characterized in that: The process of building a model predictive control system model includes: S1.1: Based on the power balance equation of the wind-storage combined power generation system and the energy storage energy state iteration equation, and combined with the superposition theorem, a model predictive control system model of the wind-storage combined power generation system is constructed, and its state space equation expression is: x(i+1)=Ax(i)+B1u(i)+B2r(i); x(i)=[P g (i),C SOC (i)] T ; u(i)=P b (i); r(i)=P w (i); Among them, x(i) represents the state variable, u(i) represents the control variable, r(i) represents the input variable, P g (i) represents the grid-connected power at the i-th moment, C SOC (i) represents the remaining capacity of the energy storage system at the i-th moment, P b (i) represents the output power of the energy storage system at the i-th moment, P w (i) represents the wind power at the i-th moment, η represents the conversion efficiency of the energy storage system, T c Represents the energy storage system control cycle, C rated Indicates the rated capacity of the energy storage system; S1.2: According to the model predictive control principle, the output at the future i+k time is calculated using the following formula: Among them, P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P w (i+k|i) represents the active output increment of wind power in the period [i+(k-1),i+k]; N represents the model predictive control step size; C SOC (i+k|i) represents the remaining capacity of the energy storage system at time i+k predicted at time i; P b (i+k|i) represents the output power of the energy storage system at time i+k; u T (i+k|i) represents the optimal control sequence obtained within a period of time, and the expression is: u T (i+k|i)=[u(i+1),u(i+2),…,u(i+N)],k=1,2,…,N; Among them, u(i+1) represents the control variable at time i+1, and u(i+N) represents the control variable at time i+N; S1.3: Take u(i+1) as the output power of the energy storage system at time i+1, update the state variables, optimize the state space equation of the wind-storage combined power generation system, and when i=1440, the iteration terminates. The optimized state space equation of the wind-storage combined power generation system is the model predictive control system model.

4. The method for controlling wind power fluctuations by using an energy storage system according to claim 1, characterized in that: In S2, the grid-connected power allowable fluctuation range is determined based on the grid-connected power in the power balance equation of the wind-storage combined power generation system, the allowable change limit of the active power of the wind farm, and the installed capacity of the wind farm, including: Based on the allowable change limit of the active power of the wind farm and the installed capacity of the wind farm, the allowable fluctuation range of the grid-connected power within 1 minute and within 10 minutes from the i-th moment is calculated, and the calculation formula is: Among them, P g1.min (i)-P g1.max (i) represents the permissible fluctuation range of the grid-connected power within 1 minute from the i-th moment; P g10.min (i)-P g10.max (i) represents the permissible fluctuation range of the grid-connected power within 10 minutes from the i-th moment; P g (i) represents the grid-connected power at the i-th moment; n represents the allowable change limit of the wind farm active power of the wind farm with the first installed capacity; m represents the allowable change limit of the wind farm active power of the wind farm with the second installed capacity; C install1 Indicates the first installed capacity; C install2 represents the second installed capacity; Δt represents each moment within 10 minutes from the i-th moment; Δi represents the time interval.

5. The method for controlling wind power fluctuations by using an energy storage system according to claim 2, characterized in that: In S3, determining the target power value based on the grid-connected power allowable fluctuation range and the wind power in the power balance equation of the wind-storage combined power generation system includes: Among them, P a (i) represents the target power value; P g.max (i) represents the upper limit of the permissible fluctuation range of the grid-connected power within 1 minute or 10 minutes from the i-th moment; P g.min (i) represents the lower limit of the grid-connected power fluctuation range allowed within 1 minute or 10 minutes from the i-th moment; P w (i) represents the wind power at the i-th moment.

6. The method for controlling wind power fluctuations by using an energy storage system according to claim 1, characterized in that: In S4, the expression of the objective function is: Where λ(i) represents the dynamic adjustment coefficient; k represents the kth moment, M represents the length of the control instruction sequence; P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P a (i+k|i) represents the target power value at time i+k; C SOC (i+k|i) represents the remaining capacity of the energy storage system at time i+k predicted at time i; C ideal Indicates the ideal value.

7. The method for controlling wind power fluctuations by using an energy storage system according to claim 6, characterized in that: In S4, it also includes energy storage system power constraints and energy storage system capacity constraints; The power constraint expression of the energy storage system is: The energy storage system capacity constraint expression is: SOC min ≤SOC(i)≤SOC max ; Among them, P rated Indicates the rated power of the energy storage system; SOC min represents the lower limit of the energy storage system's state of charge; SOC(i) represents the energy storage system's state of charge at the i-th moment; C rated Indicates the rated capacity of the energy storage system; P b (i) represents the output power of the energy storage system at the i-th moment; SOC max Indicates the upper limit of the energy storage system's state of charge; T s Indicates the sampling period.

8. The method for controlling wind power fluctuations by using an energy storage system according to claim 6, characterized in that: In S5, the first control scheme includes: when the grid-connected power is not within the allowable fluctuation range of the grid-connected power, the output power of the energy storage system in the power balance equation of the wind-storage combined power generation system and the remaining capacity of the energy storage system in the energy storage energy state iteration equation are input into the first-layer fuzzy controller, and the dynamic adjustment coefficient in the objective function is adjusted to maintain the charge state of the energy storage system within the allowable working range; The second control scheme includes: when the grid-connected power is within the allowable fluctuation range of the grid-connected power, using statistical factor analysis theory to construct a contradictory factor, inputting the contradictory factor and the output power of the energy storage system in the power balance equation of the wind-storage combined power generation system into the second-layer fuzzy controller, outputting a correction coefficient, and correcting the charging and discharging power of the energy storage system according to the correction coefficient.

9. The method for controlling wind power fluctuations by using an energy storage system according to claim 1, characterized in that: In S5, the process of constructing the contradiction factor includes: Step 1: Calculate the over-limit power of the energy storage system according to the rated capacity of the energy storage system and the charge state of the energy storage system in the energy storage energy state iteration equation; calculate the power fluctuation according to the grid-connected power and the target power value; P d (i+k)=P g (i+k|i)-P a (i+k|i); Where W(i+k) represents the over-limit power of the energy storage system; SOC(i+k) represents the state of charge of the energy storage system at the i+kth moment; C rated Indicates the rated capacity of the energy storage system; SOC min Indicates the lower limit of the state of charge of the energy storage system; SOC max Indicates the upper limit of the energy storage system's state of charge; P d (i+k) represents power fluctuation; P g (i+k|i) indicates the grid-connected power predicted at time i+k at time i; P a (i+k|i) represents the target power value at time i+k; Step 2: A Z matrix is ​​formed according to the over-limit power of the energy storage system and the power fluctuation; an H matrix is ​​constructed according to the Z matrix, and the expression of the element in the xth row and yth column of the H matrix is: Among them, h xy represents the element in the xth row and yth column of the H matrix; q is the number of rows in the Z matrix; l is the number of columns in the Z matrix; d xy represents the Euclidean distance between the xth object and the yth object in the Z matrix; z xr represents the element in the xth row and rth column of the Z matrix; z xy Represents the element in the xth row and yth column of the Z matrix; Step 3: Perform eigenvalue decomposition on the H matrix. The decomposition formula is: H=RED T 4 Among them, U is the matrix with the corresponding eigenvectors as columns, and V is the diagonal matrix generated by the eigenvalues ​​of the H matrix; Step 4: Construct a contradiction factor based on a diagonal matrix generated from a matrix with the corresponding eigenvectors as columns and the eigenvalues ​​of the H matrix; the expression is: Among them, F represents the contradiction factor.

10. The method for controlling wind power fluctuations by using an energy storage system according to claim 8, characterized in that: In the second control scheme, The charging and discharging power correction formula of the energy storage system is: P′ b (i+1|i)=P b (i+1|i)+Δk×(P w (i+1)-P g (i)); Among them, P b '(i+1|i) represents the corrected output power of the energy storage system at time i+1, P b (i+1|i) represents the output power of the energy storage system at time i+1, Δk is the correction coefficient; P w (i+1|i) represents the active output increment of wind power in the time period [i,i+1]; P g (i) represents the grid-connected power at the i-th moment.

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