Capacity optimization method and device for low-pass filter-based hybrid energy storage system

Abstract

The application provides a capacity optimization method and device for a hybrid energy storage system based on low-pass filtering. The method comprises the following steps: obtaining original sample data of a wind power station and a photovoltaic power station in a hybrid energy storage system to be optimized; obtaining frequency spectrum characteristics of a plurality of preset filtering modes by fast Fourier transform, determining a cutoff frequency, and performing low-pass filtering on the original sample data to smooth the data waveform; determining a fitness function and a constraint condition of a genetic algorithm; optimizing the energy storage capacity of the hybrid energy storage system by a non-dominated sorting genetic algorithm NSGA-II, and determining an optimal energy storage capacity range; sequentially evaluating the hybrid energy storage system with different energy storage capacities in the optimal energy storage capacity range by preset evaluation indexes, determining a target energy storage capacity, and performing optimization on the filtering mode based on the optimized fitness parameter. The method can reasonably configure the optimal energy storage capacity of the hybrid energy storage system, and improve the economy, stability and safety of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy, in particular to a capacity optimization method and device for a hybrid energy storage system based on low-pass filtering. BACKGROUND

[0002] At present, under the situation of increasing energy demand and gradual consumption of old energy, developing new energy is one of the main ways to solve the energy crisis. Among them, wind power and photovoltaic power generation as the main force of renewable energy have been widely used in areas rich in wind and light resources. Due to the influence of weather and other factors, wind and light energy has the characteristics of randomness and uncertainty, and may fluctuate in actual power supply process, and energy storage devices can solve the power fluctuation problem of wind and light energy and improve the penetration rate of wind and light energy. Therefore, the popularity rate of hybrid energy storage systems such as wind-light-storage combined power generation system is gradually increasing.

[0003] However, the cost of energy storage devices is usually high, and it is necessary to reasonably configure energy storage equipment to ensure the normal operation of the power grid system and the economy of the wind-light-storage combined power generation system. Therefore, how to reasonably configure the energy storage capacity of the wind-light-storage combined power generation system and consider the economy and reliability of the combined system has become a problem to be solved at present. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the related art to some extent.

[0005] To this end, the first object of the present application is to propose a capacity optimization method for a hybrid energy storage system based on low-pass filtering, which proposes an evaluation index for the design effect of energy storage capacity, quantitatively describes the system cost and energy storage efficiency of the wind-light-storage combined power generation system in the design and operation stage, and can reasonably configure the optimal energy storage capacity of the hybrid energy storage system and improve the economy, stability and safety of the system.

[0006] The second object of the present application is to propose a capacity optimization device for a hybrid energy storage system based on low-pass filtering.

[0007] The third object of the present application is to propose a non-transitory computer readable storage medium.

[0008] To achieve the above-mentioned purpose, the first aspect of the present application is to propose a capacity optimization method for a hybrid energy storage system based on low-pass filtering, which comprises the following steps:

[0009] Obtain the original sample data of the wind power station and the photovoltaic power station in the hybrid energy storage system to be optimized;

[0010] obtain spectrum characteristics of a plurality of preset filtering modes by fast Fourier transform, determine a cutoff frequency according to the spectrum characteristics, and perform low-pass filtering on the original sample data by the plurality of filtering modes to smooth the data waveform;

[0011] determine a fitness function and a constraint condition of a genetic algorithm based on an optimization requirement, the original sample data, and parameters of the hybrid energy storage system;

[0012] determine an optimal energy storage capacity range by optimizing the energy storage capacity of the hybrid energy storage system based on the fitness function and the constraint condition through a non-dominated sorting genetic algorithm II (NSGA-II);

[0013] determine a target energy storage capacity of the hybrid energy storage system by sequentially evaluating the hybrid energy storage system configured with different energy storage capacities in the optimal energy storage capacity range through preset evaluation indexes, and performing optimization of the filtering mode based on an optimized fitness parameter.

[0014] Optionally, in an embodiment of the present application, the fitness function includes a first fitness function and a second fitness function, a minimum life cycle cost (LCC) function is taken as the first fitness function, and a minimum fluctuation rate (FR) function is taken as the second fitness function.

[0015] Optionally, in an embodiment of the present application, the first fitness function is calculated by the following formula:

[0016]

[0017] wherein,

[0018] wherein, ICC is an initial capital cost of the hybrid energy storage system, N is a life of the hybrid energy storage project, n is a year of operation of the hybrid energy storage project, d n is an annual depreciation, i is an interest rate, tr is a tax rate, a n is an annual maintenance and operation cost, r is a number of replacements of components, R is a total number of replacements in a cycle of the hybrid energy storage project, ICC C is an investment cost of a component to be replaced, l c is a life of the cth component to be replaced, s is a salvage value, s represents a recycling value of system equipment in the last year of the hybrid energy storage project, penalty is an amount of electricity that does not reach an ideal grid-connected value in a year, M is a penalty electricity price, and the floor() function means rounding a number to the next smallest integer.

[0019] Optionally, in an embodiment of the present application, the fluctuation rate represents a ratio of a grid-connected power fluctuation amplitude to a rated power of the hybrid energy storage system in a preset time, and the second fitness function is calculated by the following formula:

[0020]

[0021] P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P is the rated power of the hybrid energy storage system. max P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P is the rated power of the hybrid energy storage system. min P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P is the rated power of the hybrid energy storage system. n P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P is the rated power of the hybrid energy storage system.

[0022] Optionally, in an embodiment of the present application, the constraint conditions include a state of charge constraint condition, a power balance constraint condition and an energy storage capacity constraint condition, the state of charge constraint condition is used to constrain the state of charge of the energy storage system within an allowable range, the power balance constraint condition is used to constrain the grid-connected power at any time to be equal to the output of the hybrid energy storage system, and the energy storage capacity constraint condition is used to constrain the energy storage capacity in the optimization process to be between the minimum configuration capacity and the maximum configuration capacity of the energy storage system.

[0023] Optionally, in an embodiment of the present application, the preset evaluation index includes a stability index and a safety index, and the stability index is calculated by the following formula:

[0024] STB = Rate_w + Non_fit

[0025] wherein,

[0026] wherein, Rate_w is the wind and light curtailment rate, Non_fit is the non-fitting rate, Non_fit represents the deviation between the actual grid-connected value and the calculated grid-connected value, E is the total power generation of the wind power station and the photovoltaic power station, Grid_ac is the actual power absorbed by the grid, and Grid_aim is the planned grid-connected value.

[0027] Optionally, in an embodiment of the present application, the safety index represents the grid-connected safety of the hybrid energy storage system after low-pass filtering by different filtering methods, and the safety index is calculated by the following formula:

[0028]

[0029] wherein, P max P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P is the rated power of the hybrid energy storage system. min P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P is the rated power of the hybrid energy storage system. n P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time, P is the rated power of the hybrid energy storage system.

[0030] Optionally, in an embodiment of the present application, the optimization of the filtering method is based on the optimized fitness parameter, including:

[0031] The normalized volatility is calculated by the following formula:

[0032]

[0033] Fmin is the minimum value of the volatility in the plurality of filtering methods, min Fmax is the maximum value of the volatility in the plurality of filtering methods, max Fmax is the maximum value of the volatility in the plurality of filtering methods, t Ft is the volatility at time t in the example time period;

[0034] The optimal filtering mode of the hybrid energy storage system in different ranges is determined in combination with the normalized volatility and the life cycle cost.

[0035] To achieve the above purpose, a second aspect of the present application also proposes a low-pass filtering-based capacity optimization device of a hybrid energy storage system, comprising the following modules:

[0036] An acquisition module is configured to acquire original sample data of a wind power station and a photovoltaic power station in a hybrid energy storage system to be optimized.

[0037] A filtering module is configured to obtain frequency spectrum characteristics of a plurality of preset filtering modes by fast Fourier transform, determine a cutoff frequency according to the frequency spectrum characteristics, and perform low-pass filtering on the original sample data by the plurality of filtering modes to smooth the data waveform.

[0038] A determination module is configured to determine a fitness function and a constraint condition of a genetic algorithm based on optimization requirements, the original sample data, and parameters of the hybrid energy storage system.

[0039] An optimization module is configured to optimize energy storage capacity of the hybrid energy storage system by a non-dominated sorting genetic algorithm NSGA-II based on the fitness function and the constraint condition, and determine an optimal energy storage capacity range.

[0040] An evaluation module is configured to evaluate the hybrid energy storage system configured with different energy storage capacities in the optimal energy storage capacity range in sequence by a preset evaluation index, determine a target energy storage capacity of the hybrid energy storage system, and perform optimization of the filtering mode based on an optimized fitness parameter.

[0041] To implement the above-mentioned embodiments, a third aspect of the present application also proposes a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the low-pass filtering-based capacity optimization method of the hybrid energy storage system in the above-mentioned embodiments.

[0042] The embodiments of the present application provide the technical scheme with at least the following beneficial effects: the present application firstly processes the power data through low-pass filtering, and optimizes the design of energy storage capacity based on NSGA-II to determine the optimal energy storage capacity range. Then, through the evaluation index of the design effect of the energy storage capacity, the system cost and energy storage efficiency of the wind-solar-storage hybrid power generation system in the design and operation stages are quantitatively described, and in the optimal energy storage capacity range, the target energy storage capacity most suitable for the hybrid energy storage system is determined, thereby improving the accuracy and rationality of the determination of the energy storage capacity of the hybrid energy storage system. At the same time, the normalized volatility is proposed, which can provide a reference for the optimization of the filtering method of the wind-solar-storage hybrid energy system. Thus, the present application can save the calculation time of capacity optimization, reasonably configure the optimal energy storage capacity of the hybrid energy storage system, improve the economy, stability and safety of the wind-solar-storage hybrid power generation system, and achieve the optimization of the filtering method of the hybrid power generation system.

[0043] Additional aspects and advantages of the present application will be described in the following description, will become apparent from the following description, or will be learned by practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0045] Figure 1 A flowchart of a capacity optimization method of a hybrid energy storage system based on low-pass filtering according to an embodiment of the present application;

[0046] Figure 2 A frequency spectrum characteristic diagram of filtered sample data and original sample data according to an embodiment of the present application;

[0047] Figure 3 A flowchart of a specific energy storage capacity optimization method based on a non-dominated sorting genetic algorithm according to an embodiment of the present application;

[0048] Figure 4 An evaluation index schematic diagram of the optimal energy storage capacity of a hybrid energy storage system according to an embodiment of the present application;

[0049] Figure 5 A principle schematic diagram of filtering method optimization combining normalized volatility and life cycle cost according to an embodiment of the present application;

[0050] Figure 6 A structure schematic diagram of a capacity optimization device of a hybrid energy storage system based on low-pass filtering according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] Embodiments of the present application are described below in detail with reference to the accompanying drawings, examples of which are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0052] It should be noted that for the wind-solar-storage system, the currently commonly used storage capacity optimization design lacks evaluation of the design effect of the storage capacity, resulting in poor economy of the configured storage capacity or inability to guarantee the stable output of the combined system. Therefore, the present application proposes a capacity optimization method for a hybrid storage system based on low-pass filtering, which quantitatively describes the system cost and storage efficiency through evaluation indexes during the design and operation stages of the wind-solar-storage system, and can reasonably configure the optimal storage capacity of the hybrid storage system.

[0053] A capacity optimization method and device for a hybrid storage system based on low-pass filtering according to an embodiment of the present application are described below with reference to the accompanying drawings.

[0054] Figure 1 A flowchart of a capacity optimization method for a hybrid storage system based on low-pass filtering according to an embodiment of the present application is shown in FIG. 1, which includes the following steps: Figure 1

[0055] Step S101: Obtain original sample data of a wind power station and a photovoltaic power station in a hybrid storage system to be optimized.

[0056] The hybrid storage system is a combined system including multiple new energy power generation systems and storage systems, for example, the hybrid storage system can be a wind-solar-storage combined power generation system, the wind-solar-storage is a combined power generation system composed of a wind power generation system, a photovoltaic power generation system and a storage system, and the wind-solar-storage combined power generation system can realize the function of peak clipping and valley filling. Through a large-capacity battery storage system, the combined system output characteristics can be changed within a long time span. The hybrid storage system in the present application can also be a combined power generation system including other new energy power generation systems, which is not limited here.

[0057] The original sample data includes output data of the wind power station and the photovoltaic power station.

[0058] In specific implementation, the present application can sample the historical data stored in the hybrid storage system at a certain sampling time interval to obtain the output data of the wind power station and the photovoltaic power station within a period of time.

[0059] ​As a possible implementation manner, the data acquisition and monitoring control system (SCADA) can be configured in advance for the hybrid energy storage system. Through the SCADA, the devices such as the wind turbine and the photovoltaic component in the running field of the hybrid energy storage system can be monitored and controlled, the related data of the wind power station and the photovoltaic power station can be collected in real time, and the collected data can be stored in the database of the hybrid energy storage system, so as to realize the data acquisition and data storage functions of the wind power station and the photovoltaic power station. Then, when capacity optimization is needed, the historical data in the database are called, 1h is taken as a sampling time interval, and the wind power output data and the photovoltaic output data of one year are selected for simulation.

[0060] In the embodiment of the application, the charge and discharge time interval of the hybrid energy storage system can also be set according to the time interval of the output data. For example, when 1h is taken as the time interval of the output data in the above example, the charge and discharge time interval of the energy storage system is set to 1h.

[0061] 102: Obtain the frequency spectrum characteristics of the preset plurality of filtering modes through fast Fourier transform, determine the cutoff frequency according to the frequency spectrum characteristics, and perform low-pass filtering on the original sample data through the plurality of filtering modes to smooth the data waveform.

[0062] The filtering modes preset in the application can be various low-pass filtering modes. For example, the plurality of preset filtering modes can be Butterworth filtering and Bessel filtering. The Butterworth filtering makes the frequency response curve in the passband as flat as possible, thereby reducing ripple. The Bessel filtering maintains the waveform of the filtered signal in the passband.

[0063] The cutoff frequency refers to the boundary frequency at which the energy of the output signal begins to drop sharply or rises sharply in the filter. For example, when the amplitude of the input signal of the system is kept unchanged, the frequency is changed to make the output signal drop to 0.707 times of the maximum value. The-3dB point is expressed by the frequency response characteristic, which is the cutoff frequency.

[0064] Specifically, the original sample data obtained is subjected to low-pass filtering through the plurality of preset filtering modes, the frequency spectrum characteristics are obtained through fast Fourier transform, and the cutoff frequency is obtained according to the frequency spectrum characteristics to perform low-pass filtering.

[0065] For example, the original sample data is subjected to low-pass filtering through the Butterworth and Bessel filtering modes, the frequency spectrum characteristics are obtained through fast Fourier transform, and the frequency spectrum characteristics after low-pass filtering through the Butterworth and Bessel filtering modes are compared with the frequency spectrum characteristics of the unfiltered original sample data as shown in FIG. 3.Figure 2 As shown, according to the spectrum characteristic diagram, the cut-off frequency can be determined, and the low-pass filtering operation is performed. By comparing Figure 2 As can be seen from the waveforms in the three scenarios, the original sample waveform can be smoothed by using the Butterworth and Bessel low-pass filters.

[0066] Step S103: Based on the optimization requirement, the original sample data and the parameters of the hybrid energy storage system, the fitness function and the constraint condition of the genetic algorithm are determined.

[0067] The fitness function is a function for describing the performance of an individual, and is used to compare the relative advantages and disadvantages of individuals. The earlier the population ranking of an individual is, the better the individual is, that is, the more the individual meets the fitness function, and the easier the individual is selected. The selection of the fitness function affects the convergence speed of the subsequent genetic algorithm for optimization and whether the optimal solution can be found.

[0068] The optimization requirement can be a requirement for ensuring the related performance of the hybrid energy storage system and reasonable configuration of the capacity during the capacity optimization process. The parameters of the hybrid energy storage system can be related parameters such as cost parameters and operation data parameters of the system.

[0069] In an embodiment of the present application, the fitness function can be multiple. Considering the optimization requirements for ensuring the economy and safety of the hybrid energy storage system, a minimum function of the life cycle cost (LCC) is taken as a first fitness function, and a minimum function of the fluctuation ratio (FR) is taken as a second fitness function.

[0070] Specifically, the first fitness function is calculated by the following formula:

[0071]

[0072] Wherein,

[0073] Wherein, ICC is the initial capital cost of the hybrid energy storage system, and the unit is yuan. N is the life of the hybrid energy storage project, and the unit is year. n is the year of operation of the hybrid energy storage project, which refers to the nth year of operation of the project. d n is the annual depreciation, and the unit is yuan. i is the interest rate, and tr is the tax rate. n is the annual maintenance and operation cost, and the unit is yuan. r is the replacement number of components, which refers to the rth replacement of components. R is the total replacement number in the cycle of the hybrid energy storage project, ICC C is the investment cost of the component to be replaced. l cis the life of the cth component to be replaced, in years. s is the salvage value, in yuan, which represents the recycling value of the system equipment in the last year of the hybrid energy storage project. penalty is the amount of electricity that does not reach the ideal grid-connected value in one year, in Wh. M is the penalty electricity price, and the floor() function means rounding a number to the next smallest integer. The total number of replacements R is a function of the life of the number of components to be replaced, and is also a function of the component life, which can be expressed by the above formula.

[0074] Further, the volatility rate is calculated to be the minimum, that is, the second fitness function. Wherein, the volatility rate represents the ratio of the grid-connected power fluctuation amplitude in a preset time t to the rated power of the hybrid energy storage system, and the second fitness function is calculated by the following formula:

[0075]

[0076] Wherein, P' max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time t, P' min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in a preset time t, and P n is the rated power of the hybrid energy storage system.

[0077] It should be noted that according to the above formula, the fitness function needs to be calculated according to various parameters such as the cost parameters and the operation data parameters of the hybrid energy storage system. In obtaining the above parameters, the parameters can be obtained in various ways such as reading the pre-stored data in the database of the hybrid energy storage system, or reading the relevant historical operation data from the original sample data obtained in step S101. As an example, some parameters about cost obtained in the fitness function calculation process of the present application are shown in Table 1:

[0078] Economic analysis parameters

[0079]

[0080]

[0081] Table 1

[0082] Further, the constraint conditions are determined. In an embodiment of the present application, the determined constraint conditions include: state of charge constraint condition, power balance constraint condition and energy storage capacity constraint condition. Wherein, the state of charge constraint condition is used to constrain the state of charge of the energy storage system within the allowed range, the power balance constraint condition is used to constrain the grid-connected power at any time to be equal to the output of the hybrid energy storage system, and the energy storage capacity constraint condition is used to constrain the energy storage capacity in the optimization process to be between the minimum configuration capacity and the maximum configuration capacity of the energy storage system.

[0083] Specifically, in the present embodiment, for the state of charge constraint condition, since overcharging and overdischarging will shorten the service life of the energy storage device and increase the replacement rate of the energy storage device, in order to prolong the service life of the energy storage battery, the present application ensures that the state of charge is within the allowed range through the state of charge constraint condition, and the state of charge constraint condition is as shown in the following formula:

[0084] P min ≤P(t)≤P max

[0085] SOC min ≤SOC(t)≤SOC max

[0086] Wherein, P(t) represents the charging and discharging power of the energy storage system at any time, P max and P min respectively represent the upper limit and lower limit of the charging and discharging power of the energy storage, SOC(t) represents the state of charge of the energy storage system at any sampling time, SOC max and SOC min respectively represent the minimum and maximum state of charge of the energy storage.

[0087] For the power balance constraint condition, through the constraint condition, the active power balance of the system is ensured, that is, the grid-connected power at any time should be equal to the wind-solar-storage output, and the power balance constraint condition is as shown in the following formula:

[0088] P grid (t)=P pv (t)+P wind (t)+P ess (t)

[0089] Wherein, P grid (t), P pv (t), P wind (t) and P ess (t) are respectively the grid-connected power, photovoltaic output, wind power output and energy storage output at the sampling time.

[0090] For the energy storage capacity constraint condition, as shown in the following formula:

[0091] C min ≤C ess (t)≤C max

[0092] Wherein, C ess represents the energy storage capacity in the optimization process, C max and C min respectively represent the minimum and maximum configuration capacity of the energy storage battery.

[0093] Step S104: Based on the fitness function and the constraint condition, the energy storage capacity of the hybrid energy storage system is optimized by the non-dominated sorting genetic algorithm NSGA-II to determine an optimal energy storage capacity range.

[0094] The non-dominated sorting genetic algorithm (NSGA-II) is a multi-objective genetic algorithm, which has the characteristics of reducing the complexity of the non-dominated sorting genetic algorithm, fast operation speed, and good convergence of the solution set. In this application, the non-dominated sorting genetic algorithm NSGA-II is used to optimize the energy storage capacity of the wind-solar-storage combined power generation system, and a relatively optimal energy storage capacity range, i.e., an optimal energy storage capacity range, is determined, which is convenient for subsequent selection of the final optimized target energy storage capacity in the range. When the non-dominated sorting genetic algorithm NSGA-II is used for optimization, the fitness function and the constraint condition determined in step S103 are used for calculation in the related steps to achieve the optimal energy storage capacity range determined to meet the optimization requirements.

[0095] In order to more clearly illustrate the specific implementation process of the energy storage capacity optimization based on the non-dominated sorting genetic algorithm, in an embodiment of the present application, a non-dominated sorting genetic algorithm-based energy storage capacity optimization method is also proposed, as shown in Figure 3 The method comprises the following steps:

[0096] Step S301: Population initialization.

[0097] Specifically, the population is initialized according to the problem range and the constraint condition determined in step S103. In this step, the related calculation parameters of the genetic algorithm are set, and the initial population of the corresponding size is generated, which is convenient for subsequent generation of the offspring population according to the initial population. For example, the population size is set to 50, the crossover probability Pe is set to 0.5, the mutation probability Pm is set to 1.2, and the maximum iteration number is set to 500.

[0098] Step S302: Non-dominated sorting.

[0099] Specifically, the non-dominated sorting process based on the initialized population is performed.

[0100] Step S303: Determining the crowding distance.

[0101] Specifically, after the sorting is completed, the forward distribution of the crowding distance value is performed, which is convenient for subsequent selection of the individuals in the population according to the level and the crowding distance.

[0102] Step S304: Selecting individuals.

[0103] Specifically, the selection of individuals is performed by a binary tournament with the crowding distance comparison operator determined in step S303. In the binary tournament, the individuals are selected by the fitness function determined in the above steps.

[0104] In the present embodiment, the binary tournament selection strategy is used to select a certain number of individuals from the population each time, and then the best one is selected to enter the offspring population. The operation is repeated until the size of the new population reaches the size of the original population. Specifically, the following steps are included: first, determine the number of individuals selected each time, for example, the number of individuals selected can be 2. Second, randomly select a corresponding number of individuals from the population to form a group with the same selection probability, and then calculate the fitness value of each individual in the group by the fitness function determined in the above steps. According to the fitness value of each individual, the individual with the best fitness value is selected to enter the offspring population. Third, repeat the above second step to obtain a new generation of individuals.

[0105] Step S305, crossover and mutation are performed by genetic operators.

[0106] Specifically, the real-coded GA uses simulated binary crossover and polynomial mutation. The crossover probability and mutation probability can be the parameters set in the above example. Thus, after the initial population is sorted, the first generation of offspring population is obtained by the three basic operations of selection, crossover and mutation of the genetic algorithm.

[0107] Step S306, recombination and selection are performed.

[0108] Specifically, the offspring population is combined with the current population, and the next generation of individuals is set by selection. The new generation is then filled by each front until the population size exceeds the current population size.

[0109] In the present embodiment, starting from the second generation of offspring population, the parent population and the offspring population are merged, and fast non-dominated sorting is performed. At the same time, the crowding degree of individuals in each non-dominated layer is calculated, and appropriate individuals are selected to form a new parent population according to the non-dominated relationship and the crowding degree of individuals. Then, the basic operations of the genetic algorithm are used to generate a new offspring population, which is iteratively processed in the above manner until the end condition is met, such as the population size exceeding the current population size, the number of generations of offspring population exceeding the maximum number of generations, i.e., reaching the maximum number of iterations 500, etc.

[0110] Thus, the non-dominated sorting genetic algorithm NSGA-II is used to optimize the energy storage capacity of the hybrid energy storage system by using the fitness function and the constraint condition, to solve the optimization problem of how to set the energy storage capacity under the conditions of meeting the economy and safety of the joint system. The optimal energy storage capacity range is determined according to the result of the algorithm.

[0111] Step S105: The hybrid energy storage system with different energy storage capacities in the optimal energy storage capacity range is evaluated in sequence by the preset evaluation index, the target energy storage capacity of the hybrid energy storage system is determined, and the optimization of the filtering mode is performed based on the optimized fitness parameter.

[0112] The preset evaluation index is an index for evaluating the design effect of the energy storage capacity, and the evaluation index can quantitatively describe the cost, energy storage efficiency and safety of the hybrid energy storage system.

[0113] Specifically, after the optimal energy storage capacity range is determined, simulation calculation is performed, each energy storage capacity in the range is sequentially configured for the hybrid energy storage system, and the running data under the configuration of different energy storage capacities are obtained to calculate the preset evaluation index. The target energy storage capacity most suitable for the current hybrid energy storage system is determined by evaluating each energy storage capacity in sequence. The target energy storage capacity can be the one with the highest related performance such as economy and safety after the system is configured with the energy storage capacity. Specifically, the target energy storage capacity can be selected in the optimal energy storage capacity range by comparing the sizes of the evaluation index values corresponding to different energy storage capacities.

[0114] In an embodiment of the present application, the preset evaluation index includes a stability index and a safety index, that is, two indexes of stability and safety are proposed to evaluate the performance of the combined power generation system. Specifically, in implementation, first, the stability of the system is described by the abandoned wind and light rate of the combined system and the deviation (i.e. non-fitting rate) between the actual grid-connected value and the calculated grid-connected value. The stability index is calculated by the following formula:

[0115] STB = Rate_w + Non_fit

[0116] Wherein,

[0117] Wherein, Rate_w is the abandoned wind and light rate, i.e. the energy waste rate, Non_fit is the non-fitting rate, Non_fit represents the deviation between the actual grid-connected value and the calculated grid-connected value, E is the total power generation of the wind power station and the photovoltaic power station, Grid_ac is the actual power absorbed by the grid, and Grid_aim is the planned grid-connected value.

[0118] Then, the safety index is used to represent the grid-connected safety of the hybrid energy storage system after low-pass filtering by different filtering modes. Specifically, the safety index is calculated by the following formula:

[0119]

[0120] Wherein, P max is the maximum value of the actual grid-connected power in the preset time, P min is the minimum value of the actual grid-connected power in the preset time, and P nThe rated power of the hybrid energy storage system.

[0121] Thus, the stability index and the safety index proposed in the embodiment are used to evaluate the performance of the combined power generation system with different energy storage capacities in sequence, and the final target energy storage capacity optimized for the current hybrid energy storage system is determined.

[0122] Further, the application can also perform optimization of the filtering method based on the optimized fitness parameter, that is, when the energy storage capacity is fixed, the hybrid energy storage system selects a filtering method with better performance from the above-mentioned multiple filtering methods. The fitness parameter corresponds to the above-mentioned fitness function, for example, when the minimum function of the fluctuation rate FR is used as the fitness function, the fitness parameter is the fluctuation rate.

[0123] In an embodiment of the application, the optimized fitness parameter includes a normalized fluctuation rate (N(F) for short), and the optimal filtering method of the hybrid energy storage system is determined by combining the normalized fluctuation rate and the life cycle cost. In specific implementation, the normalized fluctuation rate is calculated by the following formula:

[0124]

[0125] Wherein, F min is the minimum value of the fluctuation rate in the multiple filtering methods, F max is the maximum value of the fluctuation rate in the multiple filtering methods, and F t is the fluctuation rate at time t in the example time period, the example time period is a preset time period in the capacity optimization calculation instance of the application, t is any time in the example time period, and t is a positive number belonging to the time period.

[0126] Then, the optimal filtering method of the combined power generation system is optimized by N(F)-LCC, that is, on the basis of the same normalized fluctuation rate N(F), the filtering method with higher life cycle cost LCC of the combined power generation system is found. Thus, after determining the better filtering method, the sample data in the hybrid energy storage system can be filtered according to the filtering method in the subsequent process.

[0127] To sum up, the capacity optimization method of the hybrid energy storage system based on low-pass filtering in the embodiment of the application first processes the power data through low-pass filtering, optimizes the energy storage capacity based on NSGA-II, and determines the optimal energy storage capacity range. Then, through the evaluation index of the energy storage capacity design effect, the system cost and energy storage efficiency of the wind-solar-storage combined power generation system in the design and operation stages are quantitatively described, and in the different energy storage capacities in the optimal energy storage capacity range, the target energy storage capacity most suitable for the hybrid energy storage system is determined, which improves the accuracy and rationality of the determination of the energy storage capacity of the hybrid energy storage system. At the same time, the normalized volatility is proposed, which can provide a reference for the optimization of the filtering method of the wind-solar-storage combined energy system. Therefore, the energy storage capacity optimization method of the application can save the calculation time of capacity optimization, reasonably configure the optimal energy storage capacity of the hybrid energy storage system, improve the economy, stability and safety of the wind-solar-storage combined power generation system, and achieve the optimization of the filtering method of the combined power generation system.

[0128] Based on the above embodiment, in order to more clearly illustrate the specific implementation process and technical effects of the capacity optimization method of the hybrid energy storage system based on low-pass filtering of the application, one specific embodiment is described in detail below.

[0129] In this embodiment, taking a 350KW photovoltaic power station and a 500KW wind power plant as an example, 1h is taken as the sampling time interval, and the wind power output data and the photovoltaic output data of one year are selected for simulation. The evaluation index diagram of the combined power generation system with the optimal energy storage capacity is as shown in Figure 4 In this embodiment, the scenario based on Butterworth filtering is scenario one (scenario 1, hereinafter referred to as S1), and the scenario based on Bessel filtering is scenario two (scenario 2, hereinafter referred to as S2).

[0130] Ten representative energy storage capacities in the optimal energy storage capacity range determined by the non-dominated sorting genetic algorithm NSGA-II are selected to evaluate the performance of the wind-solar-storage combined system processed by the two types of filtering methods. Among them, the ten energy storage capacities (energy storage capacity, ESC) are ESC1: 0Wh; ESC2: 4000Wh; ESC3: 8000Wh; ESC4: 15000Wh; ESC5: 20000Wh; ESC6: 25000Wh; ESC7: 30000Wh; ESC8: 35000Wh; ESC9: 40000Wh; and ESC10: 45000Wh.

[0131] Figure 4The LCC, FR, Non_fit, Non_SFT and Rate_w of two filtering processes under ten ESCs are shown in the figure, in which the horizontal coordinate is capacity and the vertical coordinate is each parameter. After the energy storage capacity is equipped, the FR, Non_fit, Non_SFT and Rate_w of the two scenes are obviously decreased, indicating that the stability, safety, volatility and curtailment of the combined system are obviously reduced with the increase of the energy storage capacity. However, the LCC gradually increases with the increase of the energy storage capacity. Therefore, considering the economy, the embodiment of the application avoids selecting the maximum value in the optimization range as the target energy storage capacity, and selects the target energy storage capacity according to the evaluation index.

[0132] When the same energy storage capacity is selected for the combined system in the two scenes, the LCC, FR, Non_fit and Rate_w of S1 are better than those of S2, but the grid-connected safety of S1 is lower than that of S2. Considering different market demands, the method of data processing can be optimized to save time cost.

[0133] The application optimizes the FR and proposes a normalized volatility N(F):

[0134]

[0135] The principle of seeking the optimal filtering mode of the combined power generation system through the N(F)-LCC graph is shown in Figure 5 .

[0136] According to the N(F)-LCC curves of S1 and S2, N(F) is divided into a-g 7 performance regions in the range of 0-1, which are a region: 0-0.59; b region: 0.60-0.63; c region: 0.64-0.69; d region: 0.70-0.86; e region: 0.87-0.90; f region: 0.91-0.95; and g region: 0.96-1. The N(F) values of S1 and S2 in each performance region are the same, so the filtering method with better economy is the optimal filtering method.

[0137] In the a, c, e and g regions, the LCC of S1 is lower than that of S2, so in the a, c, e and g regions, S1 is the applicable scene, that is, when the Butterworth filter is used, the combined system has better performance. Similarly, the b, d and f regions are applied in S2 with better performance.

[0138] In order to realize the above-mentioned embodiment, the application further proposes a capacity optimization device of a hybrid energy storage system based on low-pass filtering, Figure 6 a structure diagram of a capacity optimization device of a hybrid energy storage system based on low-pass filtering proposed by the embodiment of the application, as Figure 6As shown, the system comprises an acquisition module 100, a filtering module 200, a determination module 300, an optimization module 400 and an evaluation module 500.

[0139] The acquisition module 100 is configured to acquire original sample data of a wind power station and a photovoltaic power station in a hybrid energy storage system to be optimized.

[0140] The filtering module 200 is configured to acquire frequency spectrum characteristics of a plurality of preset filtering modes by fast Fourier transform, determine a cutoff frequency according to the frequency spectrum characteristics, and perform low-pass filtering on the original sample data by the plurality of filtering modes to smooth the data waveform.

[0141] The determination module 300 is configured to determine a fitness function and a constraint condition of a genetic algorithm based on an optimization requirement, the original sample data and parameters of the hybrid energy storage system.

[0142] The optimization module 400 is configured to optimize energy storage capacity of the hybrid energy storage system by a non-dominated sorting genetic algorithm II (NSGA-II) based on the fitness function and the constraint condition, and determine an optimal energy storage capacity range.

[0143] The evaluation module 500 is configured to evaluate the hybrid energy storage system with different energy storage capacities in the optimal energy storage capacity range in sequence by a preset evaluation index, determine a target energy storage capacity of the hybrid energy storage system, and perform optimization on the filtering mode based on an optimized fitness parameter.

[0144] Optionally, in an embodiment of the present application, the fitness function comprises a first fitness function and a second fitness function, a minimum function of a life cycle cost (LCC) is taken as the first fitness function, and a minimum function of a fluctuation rate (FR) is taken as the second fitness function.

[0145] Optionally, in an embodiment of the present application, the determination module 300 is specifically configured to calculate the first fitness function by the following formula:

[0146]

[0147] wherein,

[0148] wherein, ICC is an initial capital cost of the hybrid energy storage system, N is a life of the hybrid energy storage project, n is a year of operation of the hybrid energy storage project, d n is an annual depreciation, i is an interest rate, tr is a tax rate, a n is an annual maintenance and operation cost, r is a number of replacements of components, R is a total number of replacements in a cycle of the hybrid energy storage project, ICC C is an investment cost of a component to be replaced, l cis the life of the cth component to be replaced, s is the salvage value, s represents the recycling value of the system equipment in the last year of the hybrid energy storage project, penalty is the amount of electricity that does not reach the ideal grid-connected value in a year, M is the penalty electricity price, and the floor() function represents rounding a number to the next smallest integer.

[0149] Optionally, in an embodiment of the present application, the fluctuation rate represents a ratio of a fluctuation amplitude of the grid-connected power in a preset time to a rated power of the hybrid energy storage system, and the determining module 300 is specifically configured to: calculate the second fitness function by the following formula:

[0150]

[0151] wherein P' max is a maximum value of a difference between the ideal grid-connected power and the actual grid-connected power in the preset time, P' min is a minimum value of the difference between the ideal grid-connected power and the actual grid-connected power in the preset time, and P n is the rated power of the hybrid energy storage system.

[0152] Optionally, in an embodiment of the present application, the constraint conditions include: a state of charge constraint condition, a power balance constraint condition, and an energy storage capacity constraint condition, the state of charge constraint condition is configured to constrain the state of charge of the energy storage system to be within an allowable range, the power balance constraint condition is configured to constrain the grid-connected power at any moment to be equal to the output of the hybrid energy storage system, and the energy storage capacity constraint condition is configured to constrain the energy storage capacity in the optimization process to be between a minimum configuration capacity and a maximum configuration capacity of the energy storage system.

[0153] Optionally, in an embodiment of the present application, the preset evaluation index includes a stability index and a safety index, and the evaluation module 500 is specifically configured to: calculate the stability index by the following formula:

[0154] STB = Rate_w + Non_fit

[0155] wherein,

[0156] wherein Rate_w is a wind and light curtailment rate, Non_fit is a non-fitting rate, Non_fit represents a deviation of an actual grid-connected value from a calculated grid-connected value, E is a total power generation of a wind power station and a photovoltaic power station, Grid_ac is an actual amount of electricity absorbed by a power grid, and Grid_aim is a planned grid-connected value.

[0157] Optionally, in an embodiment of the present application, the safety index represents a grid-connected safety of the hybrid energy storage system after low-pass filtering through different filtering modes, and the evaluation module 500 is specifically configured to: calculate the safety index by the following formula:

[0158]

[0159] Pmax is the maximum value of the actual grid-connected power in the preset time, Pmin is the minimum value of the actual grid-connected power in the preset time, and P is the rated power of the hybrid energy storage system. max min n Pmax is the maximum value of the actual grid-connected power in the preset time, Pmin is the minimum value of the actual grid-connected power in the preset time, and P is the rated power of the hybrid energy storage system.

[0160] Optionally, in an embodiment of the present application, the evaluation module 500 is further configured to calculate the normalized fluctuation rate according to the following formula:

[0161]

[0162] Fmin is the minimum value of the fluctuation rate in the plurality of filtering methods, Fmax is the maximum value of the fluctuation rate in the plurality of filtering methods, and F is the fluctuation rate at time t in the example time period. min max Fmin is the minimum value of the fluctuation rate in the plurality of filtering methods, Fmax is the maximum value of the fluctuation rate in the plurality of filtering methods, and F is the fluctuation rate at time t in the example time period. t

[0163] The normalized fluctuation rate and the life cycle cost are combined to determine the optimal filtering method of the hybrid energy storage system in different ranges.

[0164] It should be noted that the foregoing description of the embodiment of the capacity optimization method of the hybrid energy storage system based on low-pass filtering also applies to the device of the embodiment, which will not be described here again.

[0165] In summary, the device for optimizing the capacity of the hybrid energy storage system based on low-pass filtering according to the embodiments of the present application first processes the power data by low-pass filtering, optimizes the energy storage capacity based on NSGA-II, and determines the optimal energy storage capacity range. Then, through the evaluation index of the energy storage capacity design effect, the system cost and the energy storage efficiency of the wind-solar-storage combined power generation system in the design and operation stages are quantitatively described, and the target energy storage capacity most suitable for the hybrid energy storage system is determined among different energy storage capacities in the optimal energy storage capacity range, thereby improving the accuracy and rationality of the determination of the energy storage capacity of the hybrid energy storage system. Meanwhile, the normalized fluctuation rate is proposed, which can provide a reference for the filtering method optimization of the wind-solar-storage combined energy system.

[0166] In order to realize the above-mentioned embodiments, the present application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the capacity optimization method of the hybrid energy storage system based on low-pass filtering according to any one of the above-mentioned embodiments.

[0167] ​​​​In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. Moreover, the usage of the terms "first", "second" or "third" does not limit the quantity or order of the specific features, structures, materials or characteristics, but rather the term "first", "second" or "third" can be used to distinguish different features, structures, materials or characteristics, which can be combined in any suitable manner. Furthermore, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.

[0168] Furthermore, the terms "first", "second", or the like, merely denote different instances of a similar feature, structure, material or characteristic, without necessarily implying any relative importance or any particular order. Thus, a feature defined with "first" or "second" can implicitly or explicitly include at least one of the features. The meaning of "a", "an" and "the" includes plural references unless the context clearly dictates otherwise.

[0169] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more steps for implementing specific logic functions or steps, and the terms in the description are used for causing or carrying out or upgrading of an action between other hardware under their control. The description of processes and methods of operations should be considered as merely illustrative of the principles of the application.

[0170] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a product of the manufacturing and / or processing. The computer-readable medium can include, but is not limited to, the following: an electronic connection (an electronic device having one or more wires), a portable computer diskette (a magnetic device), a RAM (random access memory), a ROM (read-only memory), an EPROM (erasable programmable ROM) or a Flash memory, an optical fiber, and a portable CD ROM. In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via the optical scanner of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and stored in a computer memory.

[0171] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. As such, in some embodiments, the steps or methods can be implemented in a combination of hardware and software. If implemented in hardware, as in another embodiment, any of the above techniques can be implemented with or without the use of a programmable digital signal processor (DSP) or other programmable device. In some embodiments, the steps or methods can be implemented using a combination of different hardware devices.

[0172] Those of skill in the art would understand that information and signals can be represented using any of a variety of technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0173] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0174] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A capacity optimization method for a low-pass filter-based hybrid energy storage system, characterized in that, The method comprises the following steps: Obtaining original sample data of a wind power station and a photovoltaic power station in a hybrid energy storage system to be optimized; Obtaining frequency spectrum characteristics of a plurality of preset filtering modes by fast Fourier transform, determining a cutoff frequency according to the frequency spectrum characteristics, and performing low-pass filtering on the original sample data by the plurality of filtering modes to smooth the data waveform; Determining a fitness function and a constraint condition of a genetic algorithm based on optimization requirements, the original sample data, and parameters of the hybrid energy storage system; Optimizing energy storage capacity of the hybrid energy storage system based on the fitness function and the constraint condition by a non-dominated sorting genetic algorithm II (NSGA-II) to determine an optimal energy storage capacity range; the fitness function comprises a first fitness function and a second fitness function, a minimum function of a life cycle cost (LCC) is taken as the first fitness function, and a minimum function of a fluctuation rate (FR) is taken as the second fitness function; Evaluating the hybrid energy storage system configured with different energy storage capacities in the optimal energy storage capacity range by preset evaluation indexes in sequence to determine a target energy storage capacity of the hybrid energy storage system, and performing optimization of a filtering mode based on an optimized fitness parameter; The preset evaluation indexes comprise a stability index and a safety index, and the optimization of the filtering mode based on the optimized fitness parameter comprises: calculating a normalized fluctuation rate, determining an optimal filtering mode of the hybrid energy storage system in different ranges in combination of the normalized fluctuation rate and the LCC, and calculating the first fitness function by the following formula: wherein where ICC is the initial capital cost of the hybrid energy storage system, N is the lifetime of the hybrid energy storage project, n is the year of operation of the hybrid energy storage project, d n is the annual depreciation, i is the interest rate, tr is the tax rate, a n is the annual maintenance and operation cost, r is the number of replacements of the components, R is the total number of replacements in the lifetime of the hybrid energy storage project, ICC C is the investment cost of the component to be replaced, l c is the lifetime of the cth component to be replaced, s is the salvage value, s represents the recycling value of the system equipment in the last year of the hybrid energy storage project, penalty is the amount of electricity that does not reach the ideal grid-connected value in a year, M is the penalty electricity price, and the floor() function means rounding a number to the next smallest integer.

2. The capacity optimization method of claim 1, wherein, The fluctuation rate represents a ratio of a grid-connected power fluctuation amplitude in a preset time to a rated power of the hybrid energy storage system, and the second fitness function is calculated by the following formula: P max is the maximum value of the difference between the ideal grid-connected power and the actual grid-connected power within the preset time, P min is the minimum value of the difference between the ideal grid-connected power and the actual grid-connected power within the preset time, P n is the rated power of the hybrid energy storage system.

3. The capacity optimization method of claim 1, wherein, The constraint condition comprises a state of charge constraint condition, a power balance constraint condition, and an energy storage capacity constraint condition; the state of charge constraint condition is used to constrain a state of charge of the energy storage system to be within an allowable range, the power balance constraint condition is used to constrain grid-connected power at any moment to be equal to output of the hybrid energy storage system, and the energy storage capacity constraint condition is used to constrain the energy storage capacity in the optimization process to be between minimum configuration capacity and maximum configuration capacity of the energy storage system.

4. The capacity optimization method of claim 1, wherein, The stability index is calculated by the following formula: STB=Rate_w+Non_fit wherein Wherein, Rate_w is a wind power curtailment rate, Non_fit is a non-fit rate, Non_fit represents a deviation of an actual grid-connected value from a calculated grid-connected value, E is total power generation of the wind power station and the photovoltaic power station, Grid_ac is an actual power absorbed by a power grid, and Grid_aim is a planned grid-connected value.

5. The capacity optimization method of claim 4, wherein, The safety index represents grid-connected safety of the hybrid energy storage system after low-pass filtering by different filtering modes, and is calculated by the following formula: Wherein, P max is the maximum value of the actual grid-connected power in the preset time, P min is the minimum value of the actual grid-connected power in the preset time, P n is the rated power of the hybrid energy storage system.

6. The capacity optimization method of claim 1, wherein, The normalized fluctuation rate is calculated by the following formula: wherein F min is the minimum value of volatility among the plurality of filtering methods, F max is the maximum value of volatility among the plurality of filtering methods, F t is the volatility at time t in the example time period.

7. A capacity optimization device for a low-pass filter-based hybrid energy storage system, characterized by, The method comprises the following steps: An obtaining module is configured to obtain original sample data of a wind power station and a photovoltaic power station in a hybrid energy storage system to be optimized; The filter module is configured to obtain spectral characteristics of a plurality of preset filter modes by a fast Fourier transform, determine a cutoff frequency according to the spectral characteristics, and perform low-pass filtering on the original sample data by the plurality of filter modes to smooth the data waveform. The determination module is configured to determine a fitness function and a constraint condition of a genetic algorithm based on an optimization requirement, the original sample data, and parameters of the hybrid energy storage system; the fitness function includes a first fitness function and a second fitness function; the first fitness function is a minimum function of a life cycle cost (LCC); and the second fitness function is a minimum function of a fluctuation rate (FR). The optimization module is configured to optimize an energy storage capacity of the hybrid energy storage system by a non-dominated sorting genetic algorithm II (NSGA-II) based on the fitness function and the constraint condition, and determine an optimal energy storage capacity range. The evaluation module is configured to evaluate the hybrid energy storage system configured with different energy storage capacities in the optimal energy storage capacity range in sequence by a preset evaluation index, determine a target energy storage capacity of the hybrid energy storage system, and perform optimization of a filter mode based on an optimized fitness parameter. The preset evaluation index includes a stability index and a safety index; the optimization of the filter mode based on the optimized fitness parameter includes: calculating a normalized fluctuation rate, and determining an optimal filter mode of the hybrid energy storage system in different ranges in combination of the normalized fluctuation rate and the LCC. The first fitness function is calculated by the following formula: wherein where ICC is the initial capital cost of the hybrid energy storage system, N is the lifetime of the hybrid energy storage project, n is the year of operation of the hybrid energy storage project, d n is the annual depreciation, i is the interest rate, tr is the tax rate, a n is the annual maintenance and operation cost, r is the number of replacements of the components, R is the total number of replacements in the lifetime of the hybrid energy storage project, ICC C is the investment cost of the components to be replaced, l c is the lifetime of the cth component to be replaced, s is the salvage value, s represents the recycling value of the system equipment in the last year of the hybrid energy storage project, penalty is the amount of electricity that does not reach the ideal grid- connected value in a year, M is the penalty electricity price, and the floor() function means rounding a number to the next smallest integer.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the capacity optimization method of the hybrid energy storage system based on low-pass filtering according to any one of claims 1-7.

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