A multi-objective coordinated frequency optimization method and device for a wind farm containing a flywheel energy storage

Abstract

A kind of wind farm multi-objective coordinated frequency optimization method and device containing flywheel energy storage, the method includes: according to the frequency characteristic requirement of power system, obtain wind storage frequency modulation power;With wind storage frequency modulation power error minimum and wind storage instability risk minimum as objective function, wind turbine speed, flywheel energy storage state of charge and the rated active power of wind turbine, flywheel energy storage respectively as constraint condition, establish multi-objective optimization model;Obtain wind storage state factor and frequency state factor, and obtain adaptive weight coefficient by fuzzy algorithm, the objective function is normalized processing, and then the multi-objective optimization model is converted into single objective optimization model;Solving single objective optimization model, obtain the optimal frequency modulation power of wind turbine and flywheel energy storage;The device includes data acquisition unit, multi-objective optimization model establishment unit, single objective optimization model conversion unit and solving unit.The present application can improve the operation stability when wind storage participates in system frequency response.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, specifically relating to a multi-objective coordinated frequency optimization method and device for wind farms with flywheel energy storage. Background Technology

[0002] Wind power generation is clean, low-carbon, and technologically mature, with its grid-connected capacity increasing year by year. However, the rotor speed of current mainstream wind turbines is decoupled from the system frequency, thus lacking frequency response capability and posing a significant challenge to system frequency operation. Therefore, an improved rotor-side control strategy is considered, introducing additional frequency regulation power to participate in frequency regulation. However, since the rotor speed in steady state is related to wind speed, and wind speed is fluctuating and uncertain, the reliability of this method in participating in system frequency response is poor. Furthermore, when the wind turbine exits frequency regulation, the speed recovery process can easily lead to secondary frequency fluctuations. Based on this, a flywheel energy storage device is considered to be added at the wind farm outlet, treating wind power generation and energy storage as a whole, which greatly improves the operational flexibility of wind power and energy storage and the reliability of frequency regulation.

[0003] By controlling the rotor kinetic energy of wind turbines to adjust their output active power, the system frequency response is achieved. Flywheel energy storage located at the wind farm's outlet participates in the system frequency response by regulating its own charging and discharging. Treating wind and energy storage as a whole, it achieves the same frequency response capability as conventional power sources of the same scale. This is achieved by obtaining the wind-energy storage frequency regulation power based on the frequency characteristics at the wind-energy storage grid connection point through a comprehensive inertia stage, where the comprehensive inertia stage simulates the frequency response characteristics of a conventional power source. Wind power generation utilizes rotor kinetic energy for frequency response, and the wind turbine's rotor speed is limited. Excessive or insufficient speed hinders subsequent speed recovery, affecting the wind turbine's operational stability. Similarly, flywheel energy storage also has operational limits. When wind and energy storage participate in the system frequency response, the frequency regulation power of wind power generation and energy storage needs to be coordinated and allocated. Currently, the main approach to coordinated control is to prioritize the use of rotor kinetic energy, with energy storage as a supplement: the frequency regulation power of wind power generation is obtained based on the current rotor speed, and energy storage activates when wind power generation cannot meet the frequency response requirements. The aforementioned technical solutions mostly treat a wind farm as a single wind turbine unit. However, wind turbine units within a wind farm exhibit different rotational speeds, making it impossible to accurately assess the wind farm's frequency regulation capability. Furthermore, most of these solutions fail to consider coordinating and controlling the frequency regulation power among the various units, resulting in each wind turbine unit being unable to fully utilize its own characteristics to obtain its optimal frequency regulation power. In addition, most of these technical solutions do not consider the risk of wind-storage instability, leading to poor operational stability of wind-storage systems during and after participation in frequency regulation. Summary of the Invention

[0004] The purpose of this invention is to improve the operational stability of the wind-storage system when participating in the frequency response, taking into account the risk of wind-storage instability, and to provide a multi-objective coordinated frequency optimization method and device for wind farms with flywheel energy storage.

[0005] The present invention is achieved using the following technical solution:

[0006] A multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage includes:

[0007] Based on the frequency characteristics requirements of the power system, obtain the wind-storage frequency regulation power;

[0008] With the objectives of minimizing wind-storage frequency regulation power error and minimizing wind-storage instability risk, and with the constraints of wind turbine speed, flywheel energy storage state of charge, and the rated active power of each wind turbine and flywheel energy storage, a multi-objective optimization model is established.

[0009] By combining the wind and energy storage operating status with the frequency characteristics of the power system, the wind and energy storage state factors and frequency state factors are obtained, and the adaptive weight coefficients are obtained through fuzzy algorithm. Based on obtaining the range of values ​​of the objective function, the objective function is normalized, and then the multi-objective optimization model is converted into a single-objective optimization model.

[0010] The particle swarm optimization algorithm is used to solve the single-objective optimization model to obtain the optimal frequency regulation power of the wind turbine and flywheel energy storage.

[0011] A further improvement of the present invention is that the wind-storage frequency regulation power is obtained by passing the wind-storage grid connection point frequency through a dead zone and then through a comprehensive inertia link. The comprehensive inertia link includes a droop link coefficient and a virtual inertia link coefficient.

[0012] A further improvement of this invention is that the expression for the wind-storage frequency regulation power is as follows:

[0013]

[0014] Where p represents the wind-storage frequency regulation power, K1 represents the droop coefficient, K2 represents the virtual inertia coefficient, and f n denoted as the rated frequency, and f represents the frequency value at the current moment.

[0015] A further improvement of this invention is that the objective function for wind-storage frequency regulation power error is as follows:

[0016]

[0017] Where: p1, p2, ..., p n Let p be the frequency regulation power of each of the n wind turbines in the wind farm. n+1 denoted as flywheel energy storage frequency regulation power, and p as wind-storage frequency regulation power.

[0018] A further improvement of this invention lies in the following objective function for wind-storage instability risk:

[0019]

[0020] Where: n max / min The upper and lower limits of the rotor speed of the wind turbine, SOC max / min n represents the upper and lower limits of the flywheel energy storage state of charge. i The current rotational speed of the wind turbine is given by J, the rotor moment of inertia is given by Δt, and the state of rotation (SOC) is given by SOC. n+1 The current state of charge (SOC) of flywheel energy storage. (n+1)t E represents the state of charge of the flywheel energy storage unit time after participating in frequency regulation. n+1 This refers to the rated capacity of the flywheel energy storage.

[0021] A further improvement of this invention is that the constraints are as follows: wind turbine speed, flywheel energy storage state of charge, and the rated active power of the wind turbine and flywheel energy storage, respectively:

[0022] 0≤p i +p MPPTi ≤p Ni (6)

[0023] -p Nn+1 ≤p n+1 ≤p Nn+1 (7)

[0024]

[0025] SOC min ≤SOC n+1 ≤SOC max (9)

[0026] n min ≤n i ≤n max (10)

[0027] In the formula: p MPPTi Let p be the active power of the i-th wind turbine unit when it does not participate in frequency regulation, i = 1, 2, ..., n; Ni The rated active power of the wind turbine, p Nn+1 Rated active power for flywheel energy storage.

[0028] A further improvement of this invention is that the expression for the wind-storage state factor is as follows:

[0029]

[0030] A further improvement of this invention is that the expression for the frequency state factor is as follows:

[0031]

[0032] In the formula: f is the current frequency value, f' is the current frequency change rate, and f' is the current frequency change rate. nFor the rated frequency, Δf max For the maximum frequency difference, f' max This represents the maximum rate of frequency change.

[0033] A further improvement of this invention is that the expression of the single-objective optimization model is as follows:

[0034]

[0035] In the formula: ω is the weighting coefficient, with a value range of 0-1; y1 is the objective function of wind-storage frequency regulation power error; and y2 is the objective function of wind-storage instability risk.

[0036] A multi-objective coordinated frequency optimization device for wind farms with flywheel energy storage, comprising:

[0037] The data acquisition unit obtains the wind-storage frequency regulation power according to the frequency characteristics requirements of the power system;

[0038] The multi-objective optimization model establishment unit takes the minimum wind-storage frequency regulation power error and the minimum wind-storage instability risk as the objective functions, and uses the wind turbine speed, flywheel energy storage state of charge, and the rated active power of the wind turbine and flywheel energy storage as the constraints to establish a multi-objective optimization model.

[0039] The single-objective optimization model conversion unit combines the wind-storage operation status and the power system frequency characteristics to obtain the wind-storage state factor and frequency state factor, and obtains the adaptive weight coefficient through the fuzzy algorithm. Based on obtaining the range of the objective function, the objective function is normalized, and then the multi-objective optimization model is converted into a single-objective optimization model.

[0040] The solution unit uses the particle swarm optimization algorithm to solve the single-objective optimization model and obtain the optimal frequency regulation power of the wind turbine and flywheel energy storage.

[0041] A further improvement of the present invention is that the wind-storage frequency regulation power acquired by the data acquisition unit is obtained by passing the wind-storage grid connection point frequency through a dead zone and then through a comprehensive inertia link. The comprehensive inertia link includes a droop link coefficient and a virtual inertia link coefficient.

[0042] A further improvement of the present invention is that the expression for the wind-storage frequency regulation power acquired by the data acquisition unit is as follows:

[0043]

[0044] Where p represents the wind-storage frequency regulation power, K1 represents the droop coefficient, K2 represents the virtual inertia coefficient, and f n denoted as the rated frequency, and f represents the frequency value at the current moment.

[0045] A further improvement of this invention is that, in the multi-objective optimization model establishment unit, the objective function expression for the wind-storage frequency regulation power error is as follows:

[0046]

[0047] Where: p1, p2, ..., p n Let p be the frequency regulation power of each of the n wind turbines in the wind farm. n+1 denoted as flywheel energy storage frequency regulation power, and p as wind-storage frequency regulation power.

[0048] A further improvement of this invention is that, in the multi-objective optimization model establishment unit, the objective function expression for wind-storage instability risk is as follows:

[0049]

[0050] Where: n max / min The upper and lower limits of the rotor speed of the wind turbine, SOC max / min n represents the upper and lower limits of the flywheel energy storage state of charge. i The current rotational speed of the wind turbine is given by J, the rotor moment of inertia is given by Δt, and the state of rotation (SOC) is given by SOC. n+1 The current state of charge (SOC) of flywheel energy storage. (n+1)t E represents the state of charge of the flywheel energy storage unit time after participating in frequency regulation. n+1 This refers to the rated capacity of the flywheel energy storage.

[0051] A further improvement of this invention is that, in the multi-objective optimization model establishment unit, the constraint conditions for the wind turbine speed, the state of charge of the flywheel energy storage, and the rated active power of the wind turbine and the flywheel energy storage are expressed as follows:

[0052] 0≤p i +p MPPTi ≤p Ni (6)

[0053] -p Nn+1 ≤p n+1 ≤p Nn+1 (7)

[0054]

[0055] SOC min ≤SOC n+1 ≤SOC max (9)

[0056] n min ≤n i ≤n max (10)

[0057] In the formula: pMPPTi Let p be the active power of the i-th wind turbine unit when it does not participate in frequency regulation, i = 1, 2, ..., n; Ni The rated active power of the wind turbine, p Nn+1 Rated active power for flywheel energy storage.

[0058] A further improvement of this invention is that the expression for the wind-storage state factor in the single-objective optimization model conversion unit is as follows:

[0059]

[0060] A further improvement of this invention is that the expression for the frequency state factor in the single-objective optimization model conversion unit is as follows:

[0061]

[0062] In the formula: f is the current frequency value, f' is the current frequency change rate, and f' is the current frequency change rate. n For the rated frequency, Δf max For the maximum frequency difference, f' max This represents the maximum rate of frequency change.

[0063] A further improvement of this invention is that, in the single-objective optimization model conversion unit, the expression of the single-objective optimization model is as follows:

[0064]

[0065] In the formula: ω is the weighting coefficient, with a value range of 0-1; y1 is the objective function of wind-storage frequency regulation power error; and y2 is the objective function of wind-storage instability risk.

[0066] The present invention has at least the following beneficial technical effects:

[0067] This invention provides a multi-objective coordinated frequency optimization method and apparatus for wind farms with flywheel energy storage. When wind and energy storage participate in frequency response, the method considers the differences in wind turbine speeds within the wind farm and treats the flywheel energy storage as a special wind turbine to coordinate and allocate wind and energy storage frequency regulation power. The optimal frequency regulation power of the wind turbine and flywheel energy storage should accurately respond to system frequency changes and ensure stable operation of wind and energy storage. Therefore, a multi-objective optimization model is established. To facilitate solving, the multi-objective optimization model is transformed into a single-objective optimization model. The larger the weight coefficient, the more attention is paid to the accuracy of frequency regulation power; the smaller the weight coefficient, the more attention is paid to the risk of wind and energy storage instability. Therefore, adaptive weight coefficients are obtained based on wind and energy storage state factors and frequency state factors through a fuzzy algorithm. The optimal frequency regulation power of the wind turbine and flywheel energy storage is obtained by solving the above model using a particle swarm optimization algorithm. This invention considers the differences among the turbines within the wind farm, rationally utilizes frequency regulation resources, improves system frequency characteristics, and ensures stable operation of wind and energy storage. Attached Figure Description

[0068] Figure 1 This is a flowchart of a multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage.

[0069] Figure 2 This is a block diagram of a multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage.

[0070] Figure 3 This is a schematic diagram of the membership function of wind-storage state factor, frequency state factor and weight coefficient based on a fuzzy algorithm.

[0071] Figure 4 This is a schematic diagram of a control rule based on a fuzzy algorithm.

[0072] Figure 5 This is a structural block diagram of a multi-objective coordinated frequency optimization device for wind farms containing flywheel energy storage.

[0073] In the diagram, fs represents the frequency value at the wind-storage grid connection point, fn represents the rated frequency value, K1 and K2 are the droop coefficient and virtual inertia coefficient of the integrated inertia link, respectively, p is the wind-storage frequency regulation power, and n i Let SOC be the rotor speed of the i-th wind turbine in the wind farm. n+1 For flywheel energy storage state of charge, p i Let p be the frequency regulation power of the i-th wind turbine in the wind farm. n+1 denoted as flywheel energy storage frequency regulation power, and w as a weighting coefficient. Detailed Implementation

[0074] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0075] like Figure 1 As shown, the present invention provides a multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage, comprising the following steps:

[0076] Step 1: Obtain the wind-storage frequency regulation power through a comprehensive inertia link based on the power system frequency characteristics requirements; the wind-storage grid connection point frequency, after passing through a dead zone link, is then used to obtain the wind-storage frequency regulation power through a comprehensive inertia link. The comprehensive inertia link includes a droop link coefficient of K1 and a virtual inertia link coefficient of K2, as shown below. Figure 2As shown, the wind-storage frequency regulation power is given by equation (1). The control coefficients K1 and K2 can be obtained based on factors such as the frequency response requirements of the wind-storage system, the grid connection location of the wind-storage system, and the wind-storage penetration rate. These will not be elaborated here.

[0077]

[0078] Where f n denoted as the rated frequency, and f represents the frequency value at the current moment.

[0079] Step 2: The squared error between the sum of the frequency regulation power of each wind turbine and flywheel energy storage unit in the wind farm and the sum of the reference power of the frequency response element is used as the first objective function. The smaller the objective function value, the more accurately the wind and energy storage system responds to frequency changes. The objective function for the wind and energy storage frequency regulation power error is as follows:

[0080]

[0081] Where: p1, p2, ..., p n Let p be the frequency regulation power of each of the n wind turbines in the wind farm. n+1 denoted as flywheel energy storage frequency regulation power, and p as wind-storage frequency regulation power.

[0082] The relationship between the frequency regulation power and the speed of the wind turbine per unit time is shown in equation (3). When the frequency regulation power is positive, the output power increases and the speed decreases. When the frequency regulation power is negative, the speed increases.

[0083]

[0084] Where: n i n is the wind turbine rotational speed. (i)t Δt represents the rotational speed of the wind turbine unit after frequency regulation per unit time, J represents the rotor moment of inertia, and Δt represents the unit time length.

[0085] The relationship between the frequency modulation power of flywheel energy storage per unit time and its SOC is shown in Equation (4). The state of charge value decreases when the flywheel energy storage is discharging and increases when it is charging.

[0086]

[0087] Where: SOC n+1 The current state of charge (SOC) of flywheel energy storage. (n+1)t E represents the state of charge of the flywheel energy storage unit time after participating in frequency regulation. n+1 This refers to the rated capacity of the flywheel energy storage.

[0088] By combining equations (3) and (4), the relationship between wind and energy storage frequency regulation power and wind and energy storage status is established. Equation (5) is used to measure the risk of wind and energy storage instability. The squares of the differences between the wind and energy storage status after frequency regulation and its upper and lower limits are taken respectively. When the wind and energy storage status after frequency regulation is close to the limit, the risk of wind and energy storage instability is large and the value of equation (5) is large. Conversely, the risk of instability is small and the value of equation (5) is small.

[0089]

[0090] Where: n max / min The upper and lower limits of the rotor speed of the wind turbine, SOC max / min These are the upper and lower limits of the flywheel energy storage state of charge.

[0091] Equation (2) and Equation (5) are the two objective functions of the multi-objective optimization model, respectively.

[0092] Considering the limits of the active power output of wind turbine and energy storage respectively and using them as constraints of the optimization model, the active power output of wind turbine in response to frequency changes is determined by the frequency regulation power and the active power corresponding to the current wind speed. If the current speed of wind turbine and the state of charge of flywheel energy storage are both within the limits, the limits are determined by the rated active power of wind turbine and flywheel energy storage, and the constraint conditions are shown in equation (6). Similarly, the frequency regulation power of flywheel energy storage satisfies equation (7).

[0093] 0≤p i +p MPPTi ≤p Ni (i = 1, 2, ... n) (6)

[0094] -p Nn+1 ≤p n+1 ≤p Nn+1 (7)

[0095] In the formula: p MPPTi p represents the active power of the i-th wind turbine when it does not participate in frequency regulation. Ni p is the rated active power of the wind turbine. Nn+1 Rated active power for flywheel energy storage.

[0096] If the current speed of the wind turbine or the state of charge of the flywheel energy storage is at the upper limit, then the wind storage can only discharge at this time, and can only charge at the lower limit.

[0097] The sum of the frequency regulation power of the wind turbine and the battery energy storage is limited to a difference of no more than 20% from the frequency regulation power of the wind turbine and the battery energy storage, as shown in equation (8).

[0098]

[0099] SOC min ≤SOC n+1 ≤SOC max (9)

[0100] n min ≤n i ≤n max (10)

[0101] Equations (6) to (10) are the constraints of the multi-objective optimization model.

[0102] Step 3: Based on the objective function in equation (2) and the constraints in equation (8), the minimum value of objective function one is 0, and the maximum value is 0.04p. 2 The objective function in equation (5) has a minimum value of 0, assuming that the wind turbine speed will not exceed the limit under the current control strategy. Therefore, the maximum value is:

[0103]

[0104] Based on obtaining the range of values ​​of the objective function, it is normalized and the multi-objective optimization problem is transformed into a single-objective optimization problem as shown in Equation (12). The larger the weight coefficient in Equation (12), the more attention is paid to the frequency modulation power error compared with the instability risk, and vice versa. The minimum value of Equation (12) corresponds to the wind storage taking into account both accuracy and stability at this time.

[0105]

[0106] In the formula: ω is the weighting coefficient, with a value range of 0-1; y1 is the objective function of wind-storage frequency regulation power error; and y2 is the objective function of wind-storage instability risk. The optimal objective function value is obtained through formula (12), that is, y1 and y2 are obtained. Wind power and flywheel power are variables in y2 and y2, that is, they correspond to the optimal wind-storage power.

[0107] Step 3: The weight coefficients in equation (12) greatly affect the final result of the optimization model. Therefore, the concepts of wind storage state factor and frequency state factor are proposed, and the adaptive weight coefficients are obtained in real time by combining fuzzy algorithm.

[0108] Based on the current speed of the wind turbine, the current state of charge of the flywheel energy storage, and their respective upper and lower limits, the wind-storage state factor is obtained according to equation (5) as shown in equation (13). The wind-storage state factor represents the risk of wind-storage instability at the current moment. The larger the value, the greater the risk of instability and the worse the wind-storage state. Equation (13) has a value range of 0-1.

[0109]

[0110] The frequency state factor is obtained by combining the current frequency state of the wind-storage grid connection point with the maximum frequency difference and the maximum frequency change rate, as shown in Equation (14). The larger the value of Equation (14), the worse the current frequency characteristics.

[0111]

[0112] In the formula: f is the current frequency value, f' is the current frequency change rate, and f' is the current frequency change rate. n For the rated frequency, Δf max For the maximum frequency difference, f' max This represents the maximum rate of frequency change.

[0113] By combining the instruction range, the membership functions of the input variables wind-storage state factor and frequency state factor of the fuzzy algorithm are obtained, and the membership functions of the output variable weight coefficients are obtained, such as... Figure 3 As shown.

[0114] Table 1 shows the fuzzy logic reasoning rules: A larger wind-storage state factor indicates a greater risk of instability; in this case, the focus on the accuracy of wind-storage frequency regulation power should be reduced, i.e., the weighting coefficient should be appropriately decreased; conversely, the weighting coefficient should be increased. A larger frequency state factor indicates a worse frequency state; in this case, the relationship between the accuracy of wind-storage frequency regulation power and the frequency state factor should be increased, i.e., the weighting coefficient should be appropriately increased; conversely, the weighting coefficient should be decreased. When the wind-storage state factor and the frequency state factor have inverse requirements on the weighting coefficient, the two should be compared to obtain the weighting coefficient. The fuzzy logic reasoning results are shown in the figure below. Figure 4 As shown, adaptive weighting coefficients are obtained online in real time by combining wind storage state factors and frequency state factors.

[0115] Table 1. Fuzzy Logic Reasoning Rules

[0116]

[0117]

[0118] Step 4: Combine the adaptive weighting factor with the particle swarm optimization algorithm to solve equation (12) to optimize the model, and use equation (12) as the fitness function. First: Set the population size to N, and adjust the value of N according to the value of n. Initialize the position and velocity under the constraints to obtain equation (15); then, calculate the y value and obtain the historical optimal position X of each individual. BESTg,i With the global optimal position X BESTg In the subscript g, g represents the algebra; then, it is determined whether the exit condition is met. The exit condition is set as the change of fitness function between two adjacent generations being less than the error value and the iteration reaching the maximum number of iterations. If the condition is met, the operation is exited. The decision variables of the current generation are the wind turbine and the optimal frequency regulation power of the energy storage. Otherwise, the speed and position are updated according to Equation (16), and the out-of-bounds position is corrected. Finally, the fitness is calculated again, and the above steps are repeated until the exit condition is met.

[0119]

[0120] In the formula: X1(D,NP) represents the first generation population, V(m)i,j represents the velocity of the m-th element of the j-th individual in the i-th generation population, and X(m)i,j represents the position of the m-th element of the j-th individual in the i-th generation population.

[0121]

[0122] In the formula: c1 and c2 are learning factors, which are generally taken as 2, and k is the inertia factor, which is generally taken as 0.5.

[0123] like Figure 5 As shown, this invention provides a multi-objective coordinated frequency optimization device for wind farms with flywheel energy storage, comprising: a data acquisition unit, which acquires the wind-storage frequency regulation power according to the frequency characteristics requirements of the power system; a multi-objective optimization model establishment unit, which takes minimizing the wind-storage frequency regulation power error and minimizing the wind-storage instability risk as the objective functions, and uses the wind turbine speed, flywheel energy storage state of charge, and the rated active power of each wind turbine and flywheel energy storage as constraints to establish a multi-objective optimization model; a single-objective optimization model conversion unit, which combines the wind-storage operating state and the power system frequency characteristics to obtain the wind-storage state factor and frequency state factor, and obtains adaptive weight coefficients through a fuzzy algorithm, normalizes the objective function based on the obtained value range of the objective function, and then converts the multi-objective optimization model into a single-objective optimization model; and a solution unit, which uses the particle swarm optimization algorithm to solve the single-objective optimization model to obtain the optimal frequency regulation power of the wind turbine and flywheel energy storage. This device considers the differences among the units in the wind farm, rationally utilizes frequency regulation resources, improves the system frequency characteristics, and ensures the stable operation of wind and energy storage.

[0124] The wind-storage frequency regulation power acquired by the data acquisition unit is obtained by passing the wind-storage grid connection point frequency through a dead zone and then through a comprehensive inertia link. The comprehensive inertia link includes a droop link coefficient and a virtual inertia link coefficient.

[0125] The expression for the wind-storage frequency regulation power acquired by the data acquisition unit is as follows:

[0126]

[0127] Where p represents the wind-storage frequency regulation power, K1 represents the droop coefficient, K2 represents the virtual inertia coefficient, and f n denoted as the rated frequency, and f represents the frequency value at the current moment.

[0128] In the multi-objective optimization model establishment unit, the objective function expression for the wind-storage frequency regulation power error is as follows:

[0129]

[0130] Where: p1, p2, ..., p nLet p be the frequency regulation power of each of the n wind turbines in the wind farm. n+1 denoted as flywheel energy storage frequency regulation power, and p as wind-storage frequency regulation power.

[0131] In the multi-objective optimization model establishment unit, the objective function expression for wind-storage instability risk is as follows:

[0132]

[0133] Where: n max / min The upper and lower limits of the rotor speed of the wind turbine, SOC max / min n represents the upper and lower limits of the flywheel energy storage state of charge. i The current rotational speed of the wind turbine is given by J, the rotor moment of inertia is given by Δt, and the state of rotation (SOC) is given by SOC. n+1 The current state of charge (SOC) of flywheel energy storage. (n+1)t E represents the state of charge of the flywheel energy storage unit time after participating in frequency regulation. n+1 This refers to the rated capacity of the flywheel energy storage.

[0134] In the multi-objective optimization model establishment unit, the constraint conditions, namely the wind turbine speed, the flywheel energy storage state of charge, and the rated active power of the wind turbine and the flywheel energy storage, are expressed as follows:

[0135] 0≤p i +p MPPTi ≤p Ni (6)

[0136] -p Nn+1 ≤p n+1 ≤p Nn+1 (7)

[0137]

[0138] SOC min ≤SOC n+1 ≤SOC max (9)

[0139] n min ≤n i ≤n max (10)

[0140] In the formula: p MPPTi Let p be the active power of the i-th wind turbine unit when it does not participate in frequency regulation, i = 1, 2, ..., n; Ni The rated active power of the wind turbine, p Nn+1 Rated active power for flywheel energy storage.

[0141] In the single-objective optimization model transformation unit, the expression for the wind-storage state factor is as follows:

[0142]

[0143] In the single-objective optimization model conversion unit, the expression for the frequency state factor is as follows:

[0144]

[0145] In the formula: f is the current frequency value, f' is the current frequency change rate, and f' is the current frequency change rate. n For the rated frequency, Δf max For the maximum frequency difference, f' max This represents the maximum rate of frequency change.

[0146] In the single-objective optimization model transformation unit, the expression of the single-objective optimization model is as follows:

[0147]

[0148] In the formula: ω is the weighting coefficient, with a value range of 0-1; y1 is the objective function of wind-storage frequency regulation power error; and y2 is the objective function of wind-storage instability risk.

[0149] The present invention also provides a multi-objective coordinated frequency optimization system for wind farms with flywheel energy storage, comprising: a processor and a memory coupled to the processor, the memory storing a computer program, wherein the computer program, when executed by the processor, implements the steps of the multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage.

[0150] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage, characterized in that, include: Based on the frequency characteristics requirements of the power system, obtain the wind-storage frequency regulation power; With the objectives of minimizing wind-storage frequency regulation power error and minimizing wind-storage instability risk, and with the constraints of wind turbine speed, flywheel energy storage state of charge, and the rated active power of each wind turbine and flywheel energy storage, a multi-objective optimization model is established. The objective function for wind-storage frequency regulation power error is as follows: （2） In the formula: p 1, p 2, ..., p n Let n be the frequency regulation power of each of the n wind turbines in the wind farm. p n+1 For flywheel energy storage frequency regulation power, p For wind power storage frequency regulation power; The objective function for the risk of wind-storage instability is as follows: （5） In the formula: n max / min These are the upper and lower limits of the rotor speed of the wind turbine. SOC max / min These are the upper and lower limits of the state of charge (SOC) of flywheel energy storage. n i This is the current rotational speed of the wind turbine. J The moment of inertia of the rotor. The unit of time length SOC n+1 The current state of charge for flywheel energy storage. SOC （n+1）t This refers to the state of charge of the flywheel energy storage unit after participating in frequency regulation per unit time. E n+1 Rated capacity for flywheel energy storage; By combining the wind and energy storage operating status with the frequency characteristics of the power system, the wind and energy storage state factors and frequency state factors are obtained, and the adaptive weight coefficients are obtained through fuzzy algorithm. Based on obtaining the range of values ​​of the objective function, the objective function is normalized, and then the multi-objective optimization model is converted into a single-objective optimization model. The particle swarm optimization algorithm is used to solve the single-objective optimization model to obtain the optimal frequency regulation power of the wind turbine and flywheel energy storage.

2. The multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage according to claim 1, characterized in that, The wind-storage frequency regulation power is obtained by passing the wind-storage grid connection point frequency through a dead zone and then through a comprehensive inertia link. The comprehensive inertia link includes a droop link coefficient and a virtual inertia link coefficient.

3. The multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage according to claim 2, characterized in that, The expression for wind-storage frequency regulation power is as follows: （1） in, Indicates the frequency regulation power of wind and energy storage. K 1 represents the droop coefficient. K 2 represents the virtual inertia element coefficient. Indicates the rated frequency. This represents the frequency value at the current moment.

4. The multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage according to claim 1, characterized in that, The constraints are as follows: wind turbine speed, flywheel energy storage state of charge, and the rated active power of each wind turbine and flywheel energy storage. （6） （7） （8） （9） （10） In the formula: For the first i Active power of typhoon generators when they are not involved in frequency regulation i =1,2,…,n; The rated active power of the wind turbine unit. Rated active power for flywheel energy storage.

5. The multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage according to claim 4, characterized in that, The expression for the wind-storage state factor is as follows: （13） 。 6. The multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage according to claim 5, characterized in that, The expression for the frequency state factor is as follows: （14） In the formula: The frequency value at the current moment. The current rate of change of frequency. For the rated frequency, For the maximum frequency difference, This represents the maximum rate of frequency change.

7. The multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage according to claim 6, characterized in that, The expression for the single-objective optimization model is as follows: （12） In the formula: This is the weighting coefficient, with a value range of 0-1. Let be the objective function for wind-storage frequency regulation power error. The objective function is the risk of wind storage instability.

8. A multi-objective coordinated frequency optimization device for wind farms containing flywheel energy storage, characterized in that, The device is based on the multi-objective coordinated frequency optimization method for wind farms with flywheel energy storage as described in claim 1, and includes: The data acquisition unit obtains the wind-storage frequency regulation power according to the frequency characteristics requirements of the power system; The multi-objective optimization model establishment unit takes the minimum wind-storage frequency regulation power error and the minimum wind-storage instability risk as the objective functions, and uses the wind turbine speed, flywheel energy storage state of charge, and the rated active power of the wind turbine and flywheel energy storage as the constraints to establish a multi-objective optimization model. The single-objective optimization model conversion unit combines the wind-storage operation status and the power system frequency characteristics to obtain the wind-storage state factor and frequency state factor, and obtains the adaptive weight coefficient through the fuzzy algorithm. Based on obtaining the range of the objective function, the objective function is normalized, and then the multi-objective optimization model is converted into a single-objective optimization model. The solution unit uses the particle swarm optimization algorithm to solve the single-objective optimization model and obtain the optimal frequency regulation power of the wind turbine and flywheel energy storage.

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