Hybrid energy storage capacity configuration method for wind storage combined power generation system

Abstract

The invention is applicable to the technical field of wind-storage combined power generation systems, and provides a hybrid energy storage capacity configuration method for a wind-storage combined power generation system, which comprises the following steps of: searching optimal Q and R parameters by adopting improved Kalman filtering, and stabilizing wind power fluctuation; optimizing VMD decomposition parameters by using a Kepler algorithm to realize power distribution of the storage battery and the super capacitor; and calculating the energy storage rated power and capacity based on the charging and discharging efficiency and the SOC constraint. According to the method, the energy storage configuration cost is effectively reduced by more than 50%, and the method is suitable for energy storage optimization of large-scale wind power integration.

Description

Technical Field

[0001] The invention belongs to the technical field of wind-storage combined power generation systems, and particularly relates to a method for configuring the hybrid energy storage capacity for a wind-storage combined power generation system. Background Art

[0002] Wind power generation is a clean and green energy source, and the continuous development of wind power resources is an important means for the global energy to transform towards sustainable development. However, the randomness and volatility of large-scale wind power grid connection bring various challenges to the stable operation of the power system. In recent years, hybrid energy storage systems have been widely used in suppressing wind power fluctuations due to their flexible operating characteristics, and a large number of studies have been carried out on their control methods. However, in practical engineering applications, the economy of energy storage is often the primary factor to be considered. Therefore, it is of great significance to study the energy storage cost in this scenario. Summary of the Invention

[0003] The purpose of the embodiments of the invention is to provide a method for configuring the hybrid energy storage capacity for a wind-storage combined power generation system, aiming to solve the problems raised in the above background art.

[0004] The embodiments of the invention are implemented as follows. A method for configuring the hybrid energy storage capacity for a wind-storage combined power generation system includes the following steps: Step 1: Use an improved Kalman filter algorithm to suppress fluctuations and obtain the target grid-connected power; Step 2: Use the KOA-VMD algorithm to achieve power distribution considering the characteristics of the battery and supercapacitor energy storage devices; Step 3: Calculate the rated power and capacity of the hybrid energy storage according to the charge and discharge power and the energy storage efficiency, and complete the configuration.

[0005] In a further technical solution, the specific steps of the said Step 1 include: Step 1.1: Establish a time update equation and a state update equation; Step 1.2: Find the optimal Q and R values; Step 1.3: Use the KOA-VMD algorithm to suppress wind power fluctuations. During the filtering process, find the optimal Q and R, and preliminarily suppress the wind power fluctuations. After obtaining the wind power grid-connected power, the target power of the hybrid energy storage system is the difference between the grid-connected power and the original output power of the wind power.

[0006] In a further technical solution, the specific steps of the said Step 1.1 include: Use the Kalman filter algorithm to suppress wind power fluctuations, which includes two parts: prediction and update. First, the time update equation is based on t the posterior state estimate value at time tThe prior state estimate at a moment is used to implement the prediction of the Kalman filter. Subsequently, the measurement update equation combines the prior estimate obtained from the time update equation with the actual measurement value to obtain a more accurate posterior estimate. Based on the research background of suppressing wind power fluctuations using the Kalman filter, the time update equation and the state update equation are established: The time update equation is as follows: ; ; The state update equation is as follows: ; ; ; ; In the formula: is the measurement residual; is t the prior estimate of the state at the t -1 moment; is t the grid-connected power after suppression at the -1 moment, unit: MW; is the prior estimate covariance; t is the covariance estimated at the t -1 moment; is t the grid-connected power at the moment, unit: MW; R is the original wind power at the Q moment, unit: MW; P ( t | t ) is t the estimated covariance at the

[0007] For a further technical solution, step 1.2 includes the following specific steps: Based on the energy storage device, the wind power fluctuations are suppressed. The fluctuation amount and the energy storage capacity are the current focus. Considering the mutual restriction between the two objectives, a target function J including the fluctuation amount and the energy storage capacity is constructed based on the weighting coefficient to better balance the two mutually contradictory fluctuation suppression objectives: ; In the formula: b is the weighting coefficient, and its value is between 0 and 1. The sum of the weighting coefficients of the energy storage capacity and the fluctuation amount is 1. is the maximum throughput energy for energy storage; is the fluctuation amount of the grid-connected power after suppression, which consists of two parts: the 1-minute fluctuation amount and the 10-minute fluctuation amount; ; In the formula: is the original wind power at time t; is the grid-connected power obtained after suppression at time t.

[0008] The grid-connected power obtained after suppression is allowed to fluctuate within a reasonable range. Therefore, the fluctuation amount in the objective function has a certain benchmark, and the fluctuation amount exceeding the benchmark is included in the calculation of the objective function as a penalty term.

[0009] Calculation formula for the 1-minute time-scale fluctuation amount: ; Calculation formula for the 10-minute time-scale fluctuation amount: ; ; In the formula: is the 1-minute fluctuation amount benchmark value; is the 10-minute fluctuation amount benchmark value; is the maximum fluctuation amount within the 10-minute time scale at time t; Weighting coefficient b The value of will lead to different optimization tendencies. According to the principle of the Pareto front, different b values are set to form multiple groups of optimal solutions. Based on the minimum of the objective function, the optimal b value is selected, and on this basis, the simulated annealing algorithm is used to solve the optimal Q and R values, which can better balance two conflicting objectives and meet the demand for suppressing wind power fluctuations.

[0010] Further technical solution, step 2 includes the following specific steps: Step 2.1: Optimize the VMD parameters using the Kepler algorithm; VMD adaptively decomposes non-stationary signals by presetting the number of modal decompositions K and the penalty factor , and decomposes the input signal f into K discrete sub-signals with specific sparse characteristics. The Kepler optimization algorithm is introduced to optimize the parameters of VMD to seek the best parameter combination ; Step 2.2: Implement power distribution using the KOA-VMD algorithm; The KOA algorithm optimizes the VMD parameters, sets the maximum number of iterations, and searches for the optimal solution of the parameters; Based on the obtained decomposition parameters Decompose Phess, and decompose the target power of the hybrid energy storage into multiple modal components. Allocate the low-frequency components to the battery and the high-frequency components to the supercapacitor.

[0011] A further technical solution is that the step 3 includes the following specific steps: When performing capacity configuration, only consider configuring the rated power and rated capacity of the hybrid energy storage device; since the energy conversion efficiency of the hybrid energy storage system is not 100% during the charge and discharge process, the following adjustments need to be made when configuring the rated power of the energy storage system: ; In the formula: is the rated power, unit: MW; t 0 is the initial sampling time; T is the sampling period; is the energy storage device t charging power at time, unit: MW; is the energy storage device t discharging power at time, unit: MW; is the charging efficiency of the energy storage device; is the discharging efficiency of the energy storage device.

[0012] After obtaining the charge and discharge power of the energy storage system at each time, calculate the cumulative energy storage capacity at each time; considering the change range of the state of charge of the energy storage, calculate the rated capacity of the hybrid energy storage system within the sampling period: ; ; In the formula: E BN is the rated capacity of the battery; E SN is the rated capacity of the supercapacitor; E ba ( t ) is t the cumulative energy storage capacity of the battery at time; E sc ( t ) is t the cumulative energy storage capacity of the supercapacitor at time; SOC max is the maximum state of charge that the energy storage device is allowed to reach; SOC min is the minimum state of charge that the energy storage device is allowed to reach.

[0013] A hybrid energy storage capacity configuration method for a wind-storage combined power generation system provided by an embodiment of the present invention uses improved Kalman filtering to find the optimal Q, the R parameter is used to suppress the wind power fluctuation; the VMD decomposition parameters are optimized by using the Kepler algorithm to realize the power distribution between the battery and the supercapacitor; the rated power and capacity of the energy storage are calculated based on the charge and discharge efficiency and the SOC constraint. This method can effectively reduce the energy storage configuration cost by more than 50% and is applicable to the energy storage optimization of large-scale wind power grid connection. Description of the Drawings

[0014] Figure 1 are the output curves of the wind farm for 24 hours on two typical days; Figure 2 is the comparison of wind power before and after suppression on Typical Day 1; Figure 3 is the comparison of wind power before and after suppression on Typical Day 2; Figure 4 is the 1-minute fluctuation amount before and after suppression; Figure 5 is the 10-minute fluctuation amount before and after suppression; Figure 6 is the flow chart of KOA-VMD power decomposition; Figure 7 are the IMF components obtained by KOA-VMD decomposition; Figure 8 is the Hilbert marginal spectrum diagram of KOA-VMD decomposition; Figure 9 is the power distribution of the hybrid energy storage; Figure 10 is the flow chart of Step 1.2. Detailed Implementation Manner

[0015] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] The following describes the specific implementation of the present invention in detail in conjunction with specific embodiments.

[0017] A hybrid energy storage capacity configuration method for a wind-storage combined power generation system provided by an embodiment of the present invention includes the following steps: Step 1: Use the improved Kalman filter algorithm to suppress the fluctuation to obtain the target grid-connected power; Step 1.1: Establish a time update equation and a state update equation; The Kalman filter algorithm is used to suppress the wind power fluctuation, which mainly includes two parts: prediction and update. First, the time update equation calculates the current moment ( t -1 moment) based on the posterior state estimation value of the previous moment ( tThe prior state estimate value at a certain moment is used to implement the prediction of the Kalman filter. Subsequently, the measurement update equation combines the prior estimate obtained from the time update equation with the actual measurement value to obtain a more accurate posterior estimate.

[0018] Based on the research background of suppressing wind power fluctuations using the Kalman filter, the time update equation and the state update equation are established: The time update equation is as follows: ; ; The state update equation is as follows: ; ; ; ; In the formula: is the measurement residual; is t the prior estimate of the state at time t obtained at time is t the grid-connected power after suppression at time with the unit of MW; is the prior estimate covariance; t is the covariance estimated at time is t the grid-connected power at time is t the original wind power at time with the unit of MW; R is the Kalman filter gain; Q is the measurement noise covariance matrix; P ( t | t ) is t the estimated covariance at time

[0019] Step 1.2: Find the optimal Q and R values; The R and Q values of the traditional Kalman filter algorithm are set artificially. However, the selection of R and Q in the Kalman filter has an important impact on the filtering result. When there is a sudden change in wind power, such as a sharp rise or fall, the parameters cannot be effectively adjusted with the sudden change in wind power, resulting in the filtering result not converging.

[0020] Based on energy storage devices to suppress wind power fluctuations, the fluctuation amount and energy storage capacity are the current focus. Considering the mutual restriction between the two objectives, an objective function J containing the fluctuation amount and energy storage capacity is constructed based on the weighting coefficient to better balance the two conflicting objectives of suppressing fluctuations: ; In the formula: b is the weighting coefficient, whose value is between 0 and 1, and the sum of the weighting coefficients of the energy storage capacity and the fluctuation amount is 1. is the maximum throughput energy of the energy storage; is the fluctuation amount of the grid-connected power after suppression, which consists of two parts: the 1-minute fluctuation amount and the 10-minute fluctuation amount; ; In the formula: is the original wind power at time t; is the grid-connected power obtained after suppression at time t.

[0021] The grid-connected power obtained after suppression is allowed to fluctuate within a reasonable range. Therefore, the fluctuation amount in the objective function has a certain benchmark, and the fluctuation amount exceeding the benchmark is included in the calculation of the objective function as a penalty term.

[0022] Calculation formula for the 1-minute time-scale fluctuation amount: ; Calculation formula for the 10-minute time-scale fluctuation amount: ; ; In the formula: is the 1-minute fluctuation amount benchmark value; is the 10-minute fluctuation amount benchmark value; is the maximum fluctuation amount within the 10-minute time scale at time t; Weighting coefficient b The value will lead to different optimization tendencies. According to the principle of the Pareto front, different b values are set to form multiple groups of optimal solutions. Based on the minimum of the objective function, the optimal b value is selected, and on this basis, the simulated annealing algorithm is used to solve the optimal Q and R values, which can better balance the two conflicting objectives and meet the requirements of suppressing wind power fluctuations. The specific optimization process is as Figure 10 shown.

[0023] Step 1.3: Conduct preliminary suppression of wind power fluctuations; First, the K-means algorithm is used to perform clustering analysis on the wind farm power data with a sampling step of 1 minute and an installed capacity of 45 MW in a wind farm in the northwest of China in January 2021. The value of the clustering number K of the algorithm is set to 6, and the wind power fluctuation conditions of six typical days are obtained. Since the fluctuations of typical days 1, 3, 4, and 6 are relatively small and the moments when the fluctuation amount exceeds the grid connection standard are few, in order to demonstrate the effectiveness of the proposed fluctuation suppression method, the wind power output curves of typical days 2 and 5 with stronger fluctuations are selected as the suppression targets. The wind power curves of typical days 2 and 5 are shown in Figure 1 .

[0024] To verify the effectiveness of the proposed method, simulation analysis is carried out for different schemes. Method 1: Traditional Kalman filtering is used to suppress wind power fluctuations, Q and R values are randomly set; Method 2: Improved Kalman filtering is used to suppress wind power fluctuations, and the optimal Q and R are found during the filtering process to make it adapt to sudden changes in wind power.

[0025] The wind power curve graphs before and after suppression of typical days 1 and 2 are locally magnified, as shown in Figure 2 and Figure 3 . It can be seen that both Method 1 and Method 2 effectively suppress wind power. However, the grid-connected power obtained after suppression by Method 2 is more in line with the original power operation trend, can better cope with sudden changes in wind power, effectively reduce the energy storage output, and relieve the energy storage burden.

[0026] Taking the 1-minute and 10-minute fluctuation constraints specified by the national standard as the foothold and the basis for formulating the control method, taking typical day 1 as an example, the 1-minute and 10-minute fluctuation amounts are calculated after the original wind power is suppressed by Method 1 and Method 2, as shown in Figure 4 and Figure 5 . Figure 4 and Figure 5 In, the fluctuation sequence 1 is the fluctuation amount of the original wind power curve, the fluctuation sequence 2 is the fluctuation amount of the grid-connected power curve obtained after suppression by Method 2, and the fluctuation sequence 3 is the fluctuation amount of the grid-connected power curve obtained after suppression by Method 1. Among them, the maximum fluctuation powers of typical day 1 and typical day 2 reach 8.93 MW and 7.726 MW respectively. The maximum fluctuation powers in 10 minutes reach 21.25 MW and 18.33 MW respectively. After suppressing wind power fluctuations by different methods, the maximum fluctuation amounts of different methods are shown in Table 1.

[0027] Table 1 Fluctuation suppression amount index

[0028] Comparative analysis Figure 4 and Figure 5As shown in Table 1, the wind power curves obtained by the two filtering methods both meet the grid connection constraint conditions. The grid connection constraints are shown in Table 2. However, the fluctuation of Method 2 is significantly greater than that of Fluctuation 1, which indicates that the Q and R two parameters of the improved Kalman filter can effectively cope with the sudden change of wind power, avoid excessive smoothing, and reduce the energy storage operation cost.

[0029] Table 2 Power Fluctuation Limit of Wind Farm

[0030] After obtaining the grid-connected wind power, the target power of the hybrid energy storage system is the difference between the grid-connected power and the original output power of the wind power.

[0031] Step 2: Implement power distribution considering the characteristics of the battery and supercapacitor energy storage devices using the KOA-VMD algorithm; Step 2.1: Optimize the VMD parameters using the Kepler algorithm; VMD is a signal processing technology. Its core lies in constructing and solving a variational problem, aiming to decompose complex non-stationary signals into a series of intrinsic mode functions. VMD adaptively decomposes non-stationary signals by presetting the number of mode decompositions K and the penalty factor , and decomposes the input signal f into K discrete sub-signals with specific sparse characteristics. To avoid the subjectivity of manually setting parameter combinations and reducing the decomposition accuracy, the Kepler optimization algorithm is introduced to optimize the VMD parameters and seek the best parameter combination . The optimization process is as Figure 6 shown.

[0032] Step 2.2: Implement power distribution using the KOA-VMD algorithm Taking Example 1 of a typical day as an example, the KOA algorithm optimizes the VMD parameters, sets the maximum number of iterations to 50, and searches for the optimal solution of the parameters. Based on the obtained decomposition parameters , decompose Phess to obtain 6 modal components with decreasing frequency ranges, as Figure 7 shown. The frequency characteristics of the signal after the target power of the hybrid energy storage is processed by VMD and Hilbert transform are as Figure 8 .

[0033] It can be seen from the marginal spectrum diagram that IMF1 and IMF2 have lower frequencies and higher amplitudes; IMF3 - IMF6 have higher frequencies and lower amplitudes. Reconstruct IMF1 and IMF2 into the reference power of the battery to reduce the charge and discharge times of the battery and slow down its life loss; the supercapacitor has a high power density, and reconstruct IMF3 - IMF6 into the reference power of the supercapacitor to avoid overcharging or over-discharging. The power that the battery and supercapacitor need to bear after component reconstruction is as Figure 9 shown.

[0034] As Figure 9 can be seen, in the hybrid energy storage system, there are significant differences in the power allocated to the battery and the supercapacitor. The battery mainly bears the smooth low-frequency power components, and the remaining high-frequency power components are borne by the supercapacitor. Zoom in on the power distribution within the range of 08:00 - 12:00 in Figure 9 , it can be seen that the charge and discharge cycle times of the supercapacitor are more, while those of the battery are relatively less. This power distribution method reduces the charge and discharge conversion times of the battery and reduces the life loss. By adopting a reasonable power distribution strategy, the advantages of different energy storage devices in the hybrid energy storage system can be fully utilized.

[0035] Step 3: Configure the capacity of the hybrid energy storage system; When configuring the capacity, only consider configuring the rated power and rated capacity of the hybrid energy storage device. Since the energy conversion efficiency of the hybrid energy storage system is not 100% during the charge and discharge process, the following adjustments need to be made when configuring the rated power of the energy storage system: ; In the formula: is the rated power, unit: MW; t 0 is the initial sampling time; T is the sampling period; is the charging power of the energy storage device at t time, unit: MW; is the discharging power of the energy storage device at t time, unit: MW; is the charging efficiency of the energy storage device; is the discharging efficiency of the energy storage device.

[0036] After obtaining the charge and discharge powers of the energy storage system at each moment, the cumulative energy storage capacity at each moment can be calculated. Considering the change range of the state of charge of the energy storage, calculate the rated capacity of the hybrid energy storage system within the sampling period.

[0037] ; ; In the formula: E BN is the rated capacity of the battery;E SN is the rated capacity of the supercapacitor; E ba ( t ) is the t accumulative energy storage capacity of the battery at time E sc ( t ) is the t accumulative energy storage capacity of the supercapacitor at time SOC max is the maximum state of charge that the energy storage device is allowed to reach; SOC min is the minimum state of charge that the energy storage device is allowed to reach; SOC max 、SOC min .

[0038] To compare the advantages and disadvantages of the two methods, the capacity of different energy storage devices is configured. The capacity configuration parameters of the energy storage system are shown in Table 3. The hybrid energy storage target power obtained by different methods for typical days 1 and 2 is configured, and the configuration results are shown in Table 4.

[0039] Table 3 Related parameters of energy storage system capacity configuration

[0040] Table 4 Capacity configuration results

[0041] As can be seen from Table 4, the energy storage cost for suppressing wind power fluctuations using Method 1 on typical day 1 is 86.263 million yuan, while the cost using Method 2 is reduced to 32.484 million yuan, with a cost reduction of 53.779 million yuan; the cost using Method 2 on typical day 2 is reduced by 50.590 million yuan compared to Method 1, proving that Method 2 is superior to Method 1.

[0042] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for configuring the hybrid energy storage capacity of a wind-storage combined power generation system, characterized in that, It includes the following steps: Step 1: Use the improved Kalman filter algorithm to suppress fluctuations and obtain the target grid-connected power; Step 2: Use the KOA-VMD algorithm to achieve power distribution considering the characteristics of the battery and the supercapacitor energy storage device; Step 3: Calculate the rated power and capacity of the hybrid energy storage according to the charge and discharge power and the energy storage efficiency, and complete the configuration.

2. The method for configuring the hybrid energy storage capacity for the wind and energy storage combined power generation system according to claim 1, wherein The said Step 1 includes the following specific steps: Step 1.1: Establish the time update equation and the state update equation; Step 1.2: Search for the optimal Q and R value; Step 1.3: Adopt the KOA-VMD algorithm to suppress wind power fluctuations. During the filtering process, find the optimal Q and R to preliminarily suppress wind power fluctuations. After obtaining the grid-connected wind power, the target power of the hybrid energy storage system is the difference between the grid-connected power and the original output power of the wind power.

3. The method for configuring the hybrid energy storage capacity for the wind and energy storage combined power generation system according to claim 2, wherein The said Step 1.1 includes the following specific steps: The Kalman filter algorithm is used to suppress the wind power fluctuation, which includes two parts: prediction and update. First, the time update equation calculates the prior state estimate value at t the posterior state estimate value at time -1 to deduce the prior state estimate value at t time, realizing the prediction of the Kalman filter. Subsequently, the measurement update equation combines the prior estimate obtained from the time update equation with the actual measurement value, so as to obtain a more accurate posterior estimate; Based on the research background of suppressing wind power fluctuations by Kalman filter, establish the time update equation and the state update equation: The time update equation is as follows: ； ； The state update equation is as follows: ； ； ； ； In the formula: is the measurement residual; is t the prior estimate of the state at time t -1; is t the grid-connected power after suppression at time -1, unit: MW; is the prior estimate covariance; is t the covariance estimated at time -1; is t the grid-connected power at time is t the original wind power at time is the Kalman filter gain; R is the measurement noise covariance matrix; Q is the process noise covariance matrix; P ( t | t ) is t the estimated covariance at time.

4. The method for configuring the hybrid energy storage capacity for the wind and energy storage combined power generation system according to claim 3, wherein, The said Step 1.2 includes the following specific steps: Based on energy storage devices to suppress wind power fluctuations, considering the mutual restriction between the fluctuation amount and the energy storage capacity, a target function containing the fluctuation amount and the energy storage capacity is constructed based on the weighting coefficient J : ； In the formula: b is the weighting coefficient, and its value is between 0 and 1; the sum of the weighting coefficients of the energy storage capacity and the fluctuation amount is 1; is the maximum throughput energy of the energy storage; is the fluctuation amount of the grid-connected power after smoothing, which consists of two parts: the 1-minute time-scale fluctuation amount and the 10-minute time-scale fluctuation amount; ； Wherein: is t the original wind power at time is t the grid-connected power obtained after suppression at time The grid-connected power obtained after suppression is allowed to fluctuate, so the fluctuation amount in the objective function has a benchmark, and the fluctuation amount exceeding the benchmark is included as a penalty term in the calculation of the objective function; Fluctuation quantity on a 1-min time scale Calculation formula: ； Fluctuation quantity on a 10-minute time scale Calculation formula: ； ； In the formula: is the reference value of the fluctuation amount in 1 minute; is the reference value of the fluctuation amount in 10 minutes; is the maximum fluctuation amount within the 10-minute time scale at time t; According to the principle of the Pareto front, different b values are set to form multiple groups of optimal solutions. Based on the minimum of the objective function, the optimal b value is selected, and on this basis, the simulated annealing algorithm is used to solve the optimal Q and R values.

5. The method for configuring the hybrid energy storage capacity for a wind and energy storage combined power generation system according to claim 2, wherein The said Step 2 includes the following specific steps: Step 2.1: Optimize the VMD parameters by the Kepler algorithm; VMD adaptively decomposes non-stationary signals by presetting the number of modal decompositions K and the penalty factor , and decomposes the input signal f into K discrete sub-signals with specific sparse characteristics. The Kepler optimization algorithm is introduced to optimize the parameters of VMD to seek the best parameter combination ; Step 2.2: Use the KOA-VMD algorithm to achieve power distribution; The KOA algorithm optimizes the VMD parameters, sets the maximum number of iterations, and finds the optimal solution of the parameters; based on the decomposed parameters obtained by optimization , decompose Phess, decompose the target power of the hybrid energy storage into multiple modal components, allocate the low-frequency components to the battery, and allocate the high-frequency components to the supercapacitor.

6. The method for configuring the hybrid energy storage capacity of the wind-storage combined power generation system according to claim 2, wherein The said Step 3 includes the following specific steps: When performing capacity configuration, only consider configuring the rated power and rated capacity of the hybrid energy storage device; since the energy conversion efficiency of the hybrid energy storage system is not 100% during the charge and discharge process, the following adjustments need to be made when configuring the rated power of the energy storage system: ； In the formula: is the rated power, unit: MW; t 0 is the initial sampling time; T is the sampling period; is the energy storage device t Charging power at time, unit: MW; is the energy storage device t Discharging power at time, unit: MW; is the charging efficiency of the energy storage device; is the discharging efficiency of the energy storage device; After obtaining the charge and discharge power of the energy storage system at each moment, calculate the cumulative energy storage capacity at each moment; considering the change range of the state of charge of the energy storage, calculate the rated capacity of the hybrid energy storage system within the sampling period: ； ； Wherein: E BN is the rated capacity of the storage battery; E SN is the rated capacity of the super capacitor; E ba ( t ) is t the cumulative energy storage capacity of the storage battery at time E sc ( t ) is t the cumulative energy storage capacity of the super capacitor at time SOC max is the maximum state of charge that the energy storage device is allowed to reach; SOC min is the minimum state of charge that the energy storage device is allowed to reach.

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