A method for configuring energy storage capacity to adapt to new energy generation fluctuation

Abstract

The application discloses a kind of energy storage capacity configuration methods suitable for new energy power generation fluctuation, it is related to new energy field, its steps include:1. using clustering method to carry out scene reduction to the historical data of new energy power generation power, obtain the new energy power generation power matrix of typical day;2. establish the damping optimization model suitable for new energy power generation fluctuation;3. the optimal solution matrix of fluctuation damping optimization model is obtained by solving damping optimization model, and then the energy storage power matrix and energy storage state matrix are obtained, and the damping effect evaluation index is calculated;4. according to energy storage power matrix, configure energy storage rated capacity and rated charge-discharge power.The application can effectively reduce the configured energy storage rated capacity and rated charge-discharge power, prolong the service life of energy storage, and be conducive to fully utilizing new energy and reducing carbon emissions.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation and energy storage control, and specifically relates to a method for configuring energy storage capacity to adapt to fluctuations in new energy power generation. Background Technology

[0002] Wind and solar power generation are new, pollution-free power generation methods that are developing rapidly and have the potential to replace traditional energy sources. However, changes in weather conditions can cause unpredictable fluctuations in renewable energy generation, leading not only to localized voltage problems such as static voltage fluctuations, instantaneous voltage drops, and voltage flicker, but also making it more difficult to maintain a balanced power supply and demand. Therefore, researching new technologies to mitigate these fluctuations is imperative. Energy storage systems possess rapid and flexible bidirectional power regulation capabilities. Proper charge and discharge control of these systems can quickly respond to sudden changes in renewable energy power generation, effectively mitigating power fluctuations.

[0003] Currently, energy storage prices remain high, necessitating reasonable allocation of energy storage capacity. Insufficient capacity results in poor performance in mitigating power fluctuations; conversely, excessive capacity incurs high economic costs. Various filtering methods are widely used for power fluctuation mitigation, such as moving average, exponential smoothing, and wavelet transform. These methods utilize filtering algorithms to obtain a reference value for grid-connected power, and plan the configuration of energy storage capacity and charging / discharging power based on the difference between the renewable energy generation power and the reference value. While these methods can mitigate power fluctuations, they have the following drawbacks: 1) The grid-connected renewable energy generation will decrease after mitigation, hindering the full utilization of renewable energy and reducing carbon emissions; 2) The equivalent renewable energy generation power curve after mitigation has poor tracking ability of the original power curve, leading to frequent energy storage adjustments and excessive charging / discharging power, thus affecting the lifespan of energy storage and requiring larger rated capacity and rated charging / discharging power, resulting in excessively high energy storage configuration costs. Summary of the Invention

[0004] This invention addresses the problems of reduced grid-connected renewable energy generation and poor tracking ability of the equivalent renewable energy power curve after smoothing in traditional filtering and fluctuation suppression methods. It proposes an energy storage capacity configuration method adapted to renewable energy generation fluctuations. First, a clustering method is used to reduce the historical renewable energy power generation data to obtain renewable energy power vectors for different typical days. Then, with the objective function of minimizing the average absolute error of renewable energy power generation before and after fluctuation suppression, and incorporating constraints such as meeting fluctuation standards and ensuring that the renewable energy generation before and after suppression is equal to the equivalent renewable energy generation after suppression, a smoothing optimization model adapted to renewable energy generation fluctuations is established. Second, the optimal solution matrix of the fluctuation suppression optimization model is obtained by solving the model, thereby obtaining the energy storage power matrix and energy storage state matrix, and calculating the evaluation index of the smoothing effect. Finally, the rated capacity and rated charge / discharge power of the energy storage are configured based on the energy storage power matrix. This invention provides a reference for more scientific energy storage configuration.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for configuring energy storage capacity to adapt to fluctuations in new energy power generation, characterized by the following steps:

[0007] Step 1. Obtain historical data on renewable energy power generation, represented by matrix P:

[0008]

[0009] In the formula, M represents the number of days; N represents the number of sampling points for renewable energy power generation per day; each column in matrix P represents the renewable energy power generation vector for each day;

[0010] Step 2. Use clustering methods to perform scene reduction on the historical data P of renewable energy power generation, and obtain a renewable energy power generation matrix for L typical days.

[0011]

[0012] In the formula, L is the number of typical days (L is much smaller than M); N is the number of sampling points for the daily renewable energy power generation; each column in the matrix represents the renewable energy power generation vector for each typical day;

[0013] Step 3. For each typical day, establish a smoothing optimization model adapted to the fluctuations in new energy power generation:

[0014] Step 3.1. The objective function is to minimize the average absolute error of the new energy power generation before and after fluctuation smoothing, as shown in equation (3):

[0015]

[0016] In the formula, MAE j The average absolute error of new energy power generation before and after fluctuation smoothing on the j-th typical day; Let N be the renewable energy power generation at the i-th sampling point on the j-th typical day; N is the number of sampling points for renewable energy power generation per day. Let be the equivalent renewable energy power generation after smoothing at the i-th sampling point on the j-th typical day. It is the sum of the renewable energy power generation before smoothing and the energy storage power used for fluctuation smoothing, and is the decision variable of this optimization model.

[0017] Step 3.2. Establish constraints, as shown in equations (4)-(7):

[0018]

[0019]

[0020]

[0021]

[0022] Equation (4) is the initial value constraint for the smoothed equivalent renewable energy power generation. With the initial value of new energy power generation before smoothing out Equal; Equation (5) is the lower bound constraint of the equivalent new energy power generation after smoothing; Equation (6) is the constraint for satisfying the fluctuation standard, where Δt is the threshold for fluctuation; Equation (7) is a constraint that the amount of new energy power generation before the smoothing is equal to the equivalent amount of new energy power generation after the smoothing. This constraint ensures that the amount of new energy grid-connected power generation before and after the smoothing will not decrease. Δt is the sampling interval of new energy power generation.

[0023] Step 4. Use optimization software to solve the fluctuation smoothing optimization model shown in equations (3)-(7), and obtain the optimal solutions for L typical days in sequence: In the formula, T in the upper right corner represents the transpose, and the symbol "*" represents the optimal solution; according to formula (8), the optimal solution of the fluctuation smoothing optimization model for each typical day is obtained. The optimal solution matrix P of the combined fluctuation smoothing optimization model smooth* :

[0024]

[0025] Step 5. Form the energy storage power matrix and energy storage state matrix.

[0026] Step 5.1. Calculate the energy storage power vector used for fluctuation mitigation on each typical day according to equation (9):

[0027]

[0028] In the formula, This is the optimal solution for the j-th typical daily fluctuation smoothing optimization model; The vector of new energy power generation on the j-th typical day before fluctuation smoothing is the matrix shown in equation (2). The j-th column; Let be the energy storage power vector for the j-th typical day, where A value greater than zero indicates energy storage is discharging, a value less than zero indicates energy storage is charging, and a value equal to zero indicates that energy storage is not working.

[0029] Step 5.2. Form the energy storage power matrix P ess and energy storage state matrix I ess

[0030] The energy storage power vector for each typical day is calculated according to equation (10). Combined into an energy storage power matrix P ess :

[0031]

[0032] Let the integers 1, -1, and 0 represent the three states of energy storage discharge, energy storage charge, and energy storage inactive, respectively. According to the energy storage power matrix P shown in equation (10) ess The energy storage state matrix I can be obtained. ess Its dimension is the same as P ess The dimensions are the same, that is, N×L;

[0033] Step 6. Calculate the evaluation index for fluctuation mitigation effect:

[0034] Step 6.1. Calculate the mean absolute error (MAE) of renewable energy power generation before and after the smoothing of fluctuations on all typical days according to formula (11):

[0035]

[0036] In the formula, The optimal solution matrix P shown in equation (8) smooth* The element in the i-th row and j-th column; The matrix shown in equation (2) The element in the i-th row and j-th column; N is the number of new energy power generation sampling points per day; L is the number of typical days;

[0037] Step 6.2. Calculate the charge / discharge state ratio according to formula (12):

[0038]

[0039] In the formula, R c and R d num(I) represents the percentage of energy storage in charging and discharging states, respectively;ess =-1) represents the energy storage state matrix I ess The number of elements with a value of -1, i.e., the number of charging states; num(I ess =1) represents the energy storage state matrix I ess The number of elements with a value of 1 represents the number of discharge states.

[0040] Step 6.3. Calculate the average charging capacity and average discharging capacity according to formula (13):

[0041]

[0042] In the formula, E c and E d These represent the average charging and discharging power required to smooth out fluctuations; sum(P ess <0) indicates that the energy storage power matrix P ess Sum of all elements less than zero in P; sum(P) ess >0) indicates that for the energy storage power matrix P ess Sum all elements greater than zero; Δt is the sampling interval for new energy power generation.

[0043] Step 7. Based on the energy storage power matrix P ess The planned rated charge and discharge power of energy storage is calculated according to formula (14):

[0044]

[0045] In the formula, The planned rated charge / discharge power of the energy storage; max(|P ess |) indicates that the energy storage power matrix P is first processed. ess Take the absolute value of all elements in the formula, and then select the maximum value from them; λ1 is the power configuration coefficient, λ1≥1;

[0046] Step 8. Based on the energy storage power matrix P ess Calculate the planned rated energy storage capacity:

[0047] Step 8.1. Calculate the energy storage power sequence for each typical day according to formula (15).

[0048]

[0049] In the formula, The energy storage capacity value at the i-th sampling point on the j-th typical day; Let be the initial energy storage capacity value for the j-th typical day; Let P be the energy storage power at the k-th sampling point on the j-th typical day, i.e., the energy storage power matrix P shown in equation (10). essThe element value in the k-th row and j-th column; Δt is the sampling interval for new energy power generation.

[0050] Step 8.2. Calculate the difference between the maximum and minimum values ​​of the energy storage power sequence for each typical day according to equation (16):

[0051]

[0052] In the formula, It represents the difference between the maximum and minimum elements of the energy storage power sequence for the j-th typical day;

[0053] Step 8.3. Calculate the planned rated energy storage capacity according to formula (17):

[0054]

[0055] In the formula, λ is the planned rated energy storage capacity; λ2 is the capacity configuration coefficient, λ2≥1.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] 1. This invention takes the minimum average absolute error of new energy power generation before and after fluctuation smoothing as the objective function, ensuring that the equivalent new energy power generation curve after smoothing has the best following ability to the original power curve, thereby reducing the rated capacity and rated charge and discharge power of the configured energy storage, extending the service life of the energy storage, and ultimately reducing the configuration cost of energy storage.

[0058] 2. This invention uses compliance with the new energy power generation fluctuation standard as a constraint condition, which can effectively achieve the purpose of smoothing fluctuations and meeting grid connection standards;

[0059] 3. This invention uses the constraint that the amount of new energy power generation before the smoothing period is equal to the equivalent amount of new energy power generation after the smoothing period, which is conducive to making full use of new energy and reducing carbon emissions. Attached Figure Description

[0060] Figure 1 A flowchart of a method for configuring energy storage capacity to adapt to fluctuations in new energy power generation. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1In this embodiment of the invention, a method for configuring energy storage capacity to adapt to fluctuations in new energy power generation is specifically carried out according to the following steps:

[0063] Step 1. Obtain historical data on renewable energy power generation, represented by matrix P:

[0064]

[0065] In the formula, M represents the number of days; N represents the number of sampling points for renewable energy power generation per day; each column in matrix P represents the renewable energy power generation vector for each day;

[0066] Step 2. Use clustering methods to perform scene reduction on the historical data P of renewable energy power generation, and obtain a renewable energy power generation matrix for L typical days.

[0067]

[0068] In the formula, L is the number of typical days (L is much smaller than M); N is the number of sampling points for the daily renewable energy power generation; each column in the matrix represents the renewable energy power generation vector for each typical day;

[0069] This invention does not specify a particular clustering method; various clustering methods, such as K-means clustering, hierarchical clustering, and DBSCAN clustering, are applicable.

[0070] Step 3. For each typical day, establish a smoothing optimization model adapted to the fluctuations in new energy power generation:

[0071] Step 3.1. The objective function is to minimize the average absolute error of the new energy power generation before and after fluctuation smoothing, as shown in equation (3):

[0072]

[0073] In the formula, MAE j The average absolute error of new energy power generation before and after fluctuation smoothing on the j-th typical day; Let N be the renewable energy power generation at the i-th sampling point on the j-th typical day; N is the number of sampling points for renewable energy power generation per day. Let be the equivalent renewable energy power generation after smoothing at the i-th sampling point on the j-th typical day. It is the sum of the renewable energy power generation before smoothing and the energy storage power used for fluctuation smoothing, and is the decision variable of this optimization model.

[0074] By taking the minimum average absolute error of new energy power generation before and after fluctuation smoothing as the objective function, it can be ensured that the equivalent new energy power generation curve after smoothing has the best following ability to the original power curve, thereby reducing the rated capacity and rated charge and discharge power of the configured energy storage and extending the service life of the energy storage.

[0075] Step 3.2. Establish constraints, as shown in equations (4)-(7):

[0076]

[0077]

[0078]

[0079]

[0080] Equation (4) is the initial value constraint for the smoothed equivalent renewable energy power generation. With the initial value of new energy power generation before smoothing out Equal; Equation (5) is the lower bound constraint of the equivalent new energy power generation after smoothing; Equation (6) is the constraint for satisfying the fluctuation standard, where Δt is the threshold for fluctuation; Equation (7) is a constraint that the amount of new energy power generation before the smoothing is equal to the equivalent amount of new energy power generation after the smoothing. This constraint ensures that the amount of new energy grid-connected power generation before and after the smoothing will not decrease. Δt is the sampling interval of new energy power generation.

[0081] The fluctuation standard shown in Equation (6) effectively achieves the purpose of smoothing fluctuations and meeting grid connection standards; the constraint that the new energy power generation before smoothing is equal to the equivalent new energy power generation after smoothing, as shown in Equation (7), avoids the occurrence of a decrease in the grid-connected power generation of new energy, which is conducive to making full use of new energy and reducing carbon emissions. Before fluctuation smoothing, the grid-connected power generation of new energy is the new energy power generation before smoothing; while after fluctuation smoothing, the grid-connected power generation of new energy is the equivalent new energy power generation, which is the sum of the new energy power generation before smoothing and the power generation of energy storage used for fluctuation smoothing. When energy storage is charging, the energy storage power generation is negative; when energy storage is discharging, the energy storage power generation is positive.

[0082] Step 4. Use optimization software to solve the fluctuation smoothing optimization model shown in equations (3)-(7), and obtain the optimal solutions for L typical days in sequence: In the formula, T in the upper right corner represents the transpose, and the symbol "*" represents the optimal solution; according to formula (8), the optimal solution of the fluctuation smoothing optimization model for each typical day is obtained. The optimal solution matrix P of the combined fluctuation smoothing optimization model smooth* :

[0083]

[0084] The fluctuation suppression optimization model established in this invention can be linearized and solved using linear optimization algorithms such as the simplicity method and the interior point method.

[0085] Step 5. Form the energy storage power matrix and energy storage state matrix.

[0086] Step 5.1. Calculate the energy storage power vector used for fluctuation mitigation on each typical day according to equation (9):

[0087]

[0088] In the formula, This is the optimal solution for the j-th typical daily fluctuation smoothing optimization model; The vector of new energy power generation on the j-th typical day before fluctuation smoothing is the matrix shown in equation (2). The j-th column; Let be the energy storage power vector for the j-th typical day, where A value greater than zero indicates energy storage is discharging, a value less than zero indicates energy storage is charging, and a value equal to zero indicates that energy storage is not working.

[0089] Step 5.2. Form the energy storage power matrix P ess and energy storage state matrix I ess

[0090] The energy storage power vector for each typical day is calculated according to equation (10). Combined into an energy storage power matrix P ess :

[0091]

[0092] Let the integers 1, -1, and 0 represent the three states of energy storage discharge, energy storage charge, and energy storage inactive, respectively. According to the energy storage power matrix P shown in equation (10) ess The energy storage state matrix I can be obtained. ess Its dimension is the same as P ess The dimensions are the same, that is, N×L;

[0093] Step 6. Calculate the evaluation index for fluctuation mitigation effect:

[0094] Step 6.1. Calculate the mean absolute error (MAE) of renewable energy power generation before and after the smoothing of fluctuations on all typical days according to formula (11):

[0095]

[0096] In the formula, The optimal solution matrix P shown in equation (8) smooth* The element in the i-th row and j-th column; The matrix shown in equation (2) The element in the i-th row and j-th column; N is the number of new energy power generation sampling points per day; L is the number of typical days;

[0097] Step 6.2. Calculate the charge / discharge state ratio according to formula (12):

[0098]

[0099] In the formula, R c and R d num(I) represents the percentage of energy storage in charging and discharging states, respectively; ess =-1) represents the energy storage state matrix I ess The number of elements with a value of -1, i.e., the number of charging states; num(I ess =1) represents the energy storage state matrix I ess The number of elements with a value of 1 represents the number of discharge states.

[0100] Step 6.3. Calculate the average charging capacity and average discharging capacity according to formula (13):

[0101]

[0102] In the formula, E c and E d These represent the average charging and discharging power required to smooth out fluctuations; sum(P ess <0) indicates that the energy storage power matrix P ess Sum of all elements less than zero in P; sum(P) ess >0) indicates that for the energy storage power matrix P ess Sum all elements greater than zero; Δt is the sampling interval for new energy power generation.

[0103] Step 7. Based on the energy storage power matrix P ess The planned rated charge and discharge power of energy storage is calculated according to formula (14):

[0104]

[0105] In the formula, The planned rated charge / discharge power of the energy storage; max(|P ess |) indicates that the energy storage power matrix P is first processed. ess Take the absolute value of all elements in the formula, and then select the maximum value from them; λ1 is the power configuration coefficient, λ1≥1;

[0106] Step 8. Based on the energy storage power matrix P ess Calculate the planned rated energy storage capacity:

[0107] Step 8.1. Calculate the energy storage power sequence for each typical day according to formula (15).

[0108]

[0109] In the formula, The energy storage capacity value at the i-th sampling point on the j-th typical day; Let be the initial energy storage capacity value for the j-th typical day; Let P be the energy storage power at the k-th sampling point on the j-th typical day, i.e., the energy storage power matrix P shown in equation (10). ess The element value in the k-th row and j-th column; Δt is the sampling interval for new energy power generation.

[0110] Initial energy storage capacity on the j-th typical day The value of has no effect on the calculation of the rated capacity of energy storage, and can be any value. For example, in the embodiment, it can be zero for the convenience of calculation.

[0111] Step 8.2. Calculate the difference between the maximum and minimum values ​​of the energy storage power sequence for each typical day according to equation (16):

[0112]

[0113] In the formula, It represents the difference between the maximum and minimum elements of the energy storage power sequence for the j-th typical day;

[0114] Step 8.3. Calculate the planned rated energy storage capacity according to formula (17):

[0115]

[0116] In the formula, λ is the planned rated energy storage capacity; λ2 is the capacity configuration coefficient, λ2≥1.

[0117] In summary, this invention differs from traditional filtering-based energy storage capacity configuration methods. It establishes an optimization model adapted to fluctuations in renewable energy generation for energy storage capacity configuration, overcoming the shortcomings of traditional methods such as reduced grid-connected renewable energy generation and poor ability to follow the equivalent renewable energy power curve after smoothing. This provides a more scientific reference for energy storage configuration. This invention utilizes clustering methods to reduce the historical renewable energy power data to obtain a renewable energy power matrix for typical days. Using the minimum average absolute error of renewable energy power generation before and after fluctuation smoothing as the objective function, it incorporates the constraint that renewable energy generation before smoothing meets the fluctuation standard and that the equivalent renewable energy power generation after smoothing is equal to the smoothed value, establishing a smoothing optimization model adapted to renewable energy power generation fluctuations. Solving the smoothing optimization model yields the optimal solution matrix, which in turn yields the energy storage power matrix and energy storage state matrix, and calculates the smoothing effect evaluation index. The rated energy storage capacity and rated charge / discharge power are configured based on the energy storage power matrix.

[0118] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0119] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for configuring energy storage capacity to adapt to fluctuations in new energy power generation, characterized in that, The steps include the following: Step 1. Obtain the historical data matrix of new energy power generation; Step 2. Use clustering methods to reduce the historical data matrix of new energy power generation to obtain... A typical day's new energy power generation matrix; Step 3. Establish a smoothing optimization model to adapt to fluctuations in new energy power generation for each typical day; Step 4. Solve the smoothing optimization model using optimization software to obtain the following results sequentially. The optimal solution under a typical day; Step 5. Establish the energy storage power matrix and energy storage state matrix; Step 6. Calculate the evaluation index for fluctuation mitigation effect; Step 7. Calculate the planned rated charge and discharge power of the energy storage based on the energy storage power matrix; Step 8. Calculate the planned rated energy storage capacity based on the energy storage power matrix; Step 3, which involves establishing a smoothing optimization model to adapt to fluctuations in new energy power generation for each typical day, follows these steps: Step 3.

1. The objective function is to minimize the average absolute error of the new energy power generation before and after fluctuation smoothing, as shown in equation (3): (3) In the formula, For the first The average absolute error of new energy power generation before and after the typical daily fluctuation smoothing; For the first A typical day The power generation capacity of new energy at each sampling point; This represents the number of sampling points for the daily renewable energy power generation. For the first A typical day The equivalent renewable energy power generation after smoothing at each sampling point is the sum of the renewable energy power generation before smoothing and the energy storage power used for fluctuation smoothing, and is the decision variable of the smoothing optimization model. Step 3.

2. Establish constraints, as shown in equations (4)-(7): (4) (5) (6) (7) Equation (4) is the initial value constraint for the smoothed equivalent renewable energy power generation. With the initial value of new energy power generation before smoothing out Equal; Equation (5) is the lower bound constraint of the equivalent new energy power generation after smoothing; Equation (6) is the constraint for satisfying the fluctuation standard, where The threshold value is the fluctuation amount; Equation (7) is the constraint that the new energy power generation before the smoothing is equal to the equivalent new energy power generation after the smoothing. The sampling interval for new energy power generation.

2. The energy storage capacity configuration method for adapting to fluctuations in new energy power generation according to claim 1, characterized in that, Step 1 involves acquiring historical data on new energy power generation, presented in a matrix format. express: (1) In the formula, For the number of days; The number of sampling points for daily renewable energy power generation; matrix Each column in the vector represents the daily power vector of new energy generation.

3. The energy storage capacity configuration method for adapting to fluctuations in new energy power generation according to claim 2, characterized in that, Step 2 involves using clustering methods to analyze historical data on new energy power generation. By reducing the scene size, we can obtain... A typical day's new energy power generation matrix : (2) In the formula, For typical days, L is much smaller than ; This represents the number of sampling points for daily renewable energy power generation; each column in the matrix represents the renewable energy power generation vector for each typical day.

4. The energy storage capacity configuration method for adapting to fluctuations in new energy power generation according to claim 3, characterized in that, Step 4 specifically involves: using optimization software to solve the fluctuation smoothing optimization model shown in equations (3) to (7), and sequentially obtaining... The optimal solution under typical conditions: In the formula, the T in the upper right corner represents transpose, and the symbol " “ represents the optimal solution; according to equation (8), the optimal solution of the fluctuation smoothing optimization model for each typical day is obtained.” The optimal solution matrix of the combined fluctuation smoothing optimization model : (8)。 5. The energy storage capacity configuration method adapting to fluctuations in new energy power generation according to claim 4, characterized in that, Step 5, the steps for forming the energy storage power matrix and the energy storage state matrix, are as follows: Step 5.

1. Calculate the energy storage power vector used for fluctuation mitigation on each typical day according to equation (9): (9) In the formula, For the first The optimal solution of a typical daily fluctuation smoothing optimization model; To smooth out fluctuations before the first The vector of new energy power generation on a typical day is the matrix shown in equation (2). The List; For the first The energy storage power vector for a typical day, where A value greater than zero indicates energy storage is discharging, a value less than zero indicates energy storage is charging, and a value equal to zero indicates that energy storage is not working. Step 5.

2. Forming the energy storage power matrix and energy storage state matrix The energy storage power vector for each typical day is calculated according to equation (10). Combined into an energy storage power matrix : (10) Let the integers 1, -1, and 0 represent the three states of energy storage discharge, energy storage charge, and energy storage inactive, respectively. According to the energy storage power matrix shown in equation (10) Obtain the energy storage state matrix Its dimension and The dimensions are the same, that is, .

6. The energy storage capacity configuration method for adapting to fluctuations in new energy power generation according to claim 5, characterized in that, Step 6, the step of calculating the evaluation index for fluctuation mitigation effect, is as follows: Step 6.

1. Calculate the average absolute error of renewable energy power generation before and after smoothing out fluctuations for all typical daily periods using formula (11). : (11) In the formula, The optimal solution matrix shown in equation (8) The Middle line, number Column elements; The matrix shown in equation (2) The Middle line, number Column elements; The number of sampling points for new energy power generation per day; Typical number of days; Step 6.

2. Calculate the charge / discharge state ratio according to formula (12): (12) In the formula, and These represent the percentage of energy storage in charging and discharging states, respectively. Represents the energy storage state matrix The number of elements with a value of -1 represents the number of charging states. Represents the energy storage state matrix The number of elements with a value of 1 represents the number of discharge states. Step 6.

3. Calculate the average charging capacity and average discharging capacity according to formula (13): (13) In the formula, and These are the average charging capacity and average discharging capacity of energy storage required to smooth out fluctuations, respectively. Represents the energy storage power matrix Sum all elements less than zero in the set; Represents the energy storage power matrix Sum all elements greater than zero in the given set. The sampling interval for new energy power generation.

7. The energy storage capacity configuration method for adapting to fluctuations in new energy power generation according to claim 6, characterized in that, Step 7 specifically involves: based on the energy storage power matrix The planned rated charge and discharge power of energy storage is calculated according to formula (14): (14) In the formula, The planned rated charge and discharge power for energy storage; This indicates that the energy storage power matrix should be considered first. Take the absolute value of all elements in the set, and then select the maximum value from them; For power configuration factor, .

8. The energy storage capacity configuration method for adapting to fluctuations in new energy power generation according to claim 7, characterized in that, Step 8 specifically involves: based on the energy storage power matrix Calculate the planned rated energy storage capacity: Step 8.

1. Calculate the energy storage capacity sequence for each typical day according to Equation (15). : (15) In the formula, For the first A typical day Energy storage capacity at each sampling point; For the first Initial energy storage capacity on a typical day; For the first A typical day The energy storage power at each sampling point is represented by the energy storage power matrix shown in equation (10). The Middle line, number The element values ​​of the column; The sampling interval for new energy power generation; Step 8.

2. Calculate the difference between the maximum and minimum values ​​of the energy storage power sequence for each typical day using equation (16): (16) In the formula, For the first The difference between the maximum and minimum values ​​of a typical daily energy storage power sequence; Step 8.

3. Calculate the planned rated energy storage capacity according to formula (17): (17) In the formula, The planned rated energy storage capacity; This is the capacity configuration factor. .

Images