Power generation system energy storage capacity regulation and control method and system based on light, wind and power storage complementation

Abstract

The invention discloses an energy storage capacity regulation and control method and system of a power generation system based on complementation of light, wind and electricity, relates to the field of energy storage capacity regulation and control, and ensures that the energy storage capacity influence parameters of various types and energy storage capacity regulation and control strategies are not influenced in the power generation process of the current power generation system by constructing a mapping equation between the energy storage capacity influence parameters and the energy storage capacity regulation and control strategies. Real-time energy storage capacity influence parameters are acquired at intervals, so that a corresponding optimal energy storage capacity regulation and control strategy is mapped; besides, the time interval for acquiring the real-time energy storage capacity influence parameter is adjusted by acquiring the percentage data of the time period in which the power generation capacity of the current power generation system in a period cannot meet the electric quantity required by the electric load in the current energy storage capacity regulation and control strategy adjustment evaluation period; and it is ensured that the percentage data of the time period of the electric quantity required by the electric load in the current energy storage capacity regulation and control strategy adjustment evaluation period meets the requirement.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage capacity regulation, and specifically, it particularly relates to a method and system for regulating the energy storage capacity of a power generation system based on the complementarity of optical, wind, and energy storage. Background Art

[0002] Currently, for the regulation of the energy storage capacity of a power generation system, relevant existing data is often collected, such as weather data; and based on this data, analysis is carried out to regulate the energy storage capacity of the power generation system; however, if relevant existing data is continuously collected, it will lead to an excessive amount of data processing for adjusting the current energy storage capacity regulation strategy data, and then lead to a reduction in the efficiency of adjusting the energy storage capacity regulation strategy data; if the frequency of collecting relevant existing data is too low, the possibility of mutations in the relevant data will be greatly increased, so that the energy storage capacity cannot be regulated in a timely manner in response to these data mutations, and then the power generation of the power generation system cannot be reasonably distributed. Summary of the Invention

[0003] In view of the problems in the related art, the present invention proposes a method and system for regulating the energy storage capacity of a power generation system based on the complementarity of optical, wind, and energy storage to overcome the above-mentioned technical problems existing in the related art.

[0004] To solve the above technical problems, the present invention is realized through the following technical solutions: The present invention is a method for regulating the energy storage capacity of a power generation system based on the complementarity of optical, wind, and energy storage, including the following steps: S1. Set several types of parameters that affect the energy storage capacity of the power generation system and types of energy storage capacity regulation strategies for the power generation system to obtain an energy storage capacity impact parameter type set and an energy storage capacity regulation strategy type set; S2. In coordination with the energy storage capacity impact parameter type set and the energy storage capacity regulation strategy type set, collect multiple sets of various types of energy storage capacity impact parameter data and corresponding energy storage capacity regulation strategy data at multiple time points within various historical collection periods and construct a final energy storage capacity regulation strategy mapping equation; S3. Set the current real-time collection interval to collect the energy storage capacity impact parameters of the current power generation system and input them into the final energy storage capacity regulation strategy mapping equation for mapping to obtain the current energy storage capacity regulation strategy data set; S4. Adjust the energy storage capacity regulation strategy of the current power generation system according to the current energy storage capacity regulation strategy data set; and adjust the current real-time collection interval according to the percentage data of the period when the generated power of the current power generation system after adjustment cannot meet the required power of the electrical load in the current energy storage capacity regulation strategy adjustment evaluation period; By constructing mapping equations between various types of energy storage capacity impact parameters and energy storage capacity regulation strategies, it is ensured that during the power generation process of the current power generation system, by periodically collecting real-time energy storage capacity impact parameters, the corresponding optimal energy storage capacity regulation strategy can be mapped; in addition, by collecting the percentage data of the time period when the power generation of the current power generation system within a certain period cannot meet the power required by the electrical load in the current energy storage capacity regulation strategy adjustment and evaluation period to adjust the time interval for collecting real-time energy storage capacity impact parameters, it is ensured that the percentage data of the time period when the power required by the electrical load meets the requirements in the current energy storage capacity regulation strategy adjustment and evaluation period.

[0005] Preferably, the S1 includes the following steps: S11. Set several types of parameters that affect the energy storage capacity of the power generation system to obtain a set of energy storage capacity impact parameter types; then set several types of acquisition cycle types of photovoltaic-wind power-energy storage data to obtain a set of photovoltaic-wind power-energy storage data acquisition cycle types; S12. In combination with the set of energy storage capacity impact parameter types and the set of photovoltaic-wind power-energy storage data acquisition cycle types, set several types of energy storage capacity regulation strategies for the power generation system to obtain a set of energy storage capacity regulation strategy types; First, by setting several types of parameters that affect the energy storage capacity of the power generation system, it provides a basis for collecting corresponding historical data for constructing the final energy storage capacity regulation strategy mapping equation; secondly, since both short-term data and long-term data have a certain impact on the selection of the energy storage capacity regulation strategy of the power generation system; therefore, by setting different types of acquisition cycle types of photovoltaic-wind power-energy storage data, it provides an acquisition range for collecting energy storage capacity impact parameters in different cycles, considering the selection of the energy storage capacity regulation strategy of the power generation system from both short-term and long-term perspectives, ensuring the comprehensiveness and accuracy of the selected energy storage capacity regulation strategy of the power generation system.

[0006] Preferably, the S2 includes the following steps: S21. In combination with the set of energy storage capacity regulation strategy types, the set of energy storage capacity impact parameter types, and the set of photovoltaic-wind power-energy storage data acquisition cycle types, collect multiple sets of various types of energy storage capacity impact parameter data and corresponding energy storage capacity regulation strategy data at multiple time points within various acquisition cycles of photovoltaic-wind power-energy storage data before the energy storage capacity regulation strategy is adjusted in history, and perform data equation fitting on the collected energy storage capacity impact parameter data to obtain a set of historical energy storage capacity impact parameter change equation matrices and a set of historical energy storage capacity regulation strategy data , a i Indicates the iThe matrix of historical energy storage capacity impact parameter change equations corresponding to the process of adjusting the energy storage capacity regulation strategy in the group's history Represents the total number of groups of the historical process of adjusting the energy storage capacity regulation strategy collected Represents the i Group of energy storage capacity regulation strategy data corresponding to the historical process of adjusting the energy storage capacity regulation strategy a i As follows ; Among them a ijk Represents a i In the j The change equation of the k Type of energy storage capacity impact parameter within the Represents the total number of types of set light-wind-solar-storage data collection periods Represents the total number of types of set energy storage capacity impact parameters S22. Construct the final energy storage capacity regulation strategy mapping equation by using the set of historical energy storage capacity impact parameter change equations and the historical energy storage capacity regulation strategy data set Since there is a strong correlation between the selection of the energy storage capacity regulation strategy and the change trend of the current energy storage capacity impact parameter data; therefore, by collecting and fitting the change equations of various types of energy storage capacity impact parameter data and the corresponding energy storage capacity regulation strategy data of different cycle types, it provides data support for constructing the mapping equation between the energy storage capacity regulation strategy and the change trend of the energy storage capacity impact parameter data in the future; and by constructing the final energy storage capacity regulation strategy mapping equation, it provides a mapping tool for selecting the corresponding reasonable energy storage capacity regulation strategy based on the change trend of various types of current energy storage capacity impact parameter data in the future

[0007] Preferably, the S22 includes the following steps S221. Construct the initial energy storage capacity regulation strategy mapping equation in cooperation with the set of historical energy storage capacity impact parameter change equations and the historical energy storage capacity regulation strategy data set; as follows ; Among them Is the dependent variable of the initial energy storage capacity regulation strategy mapping equation, representing the energy storage capacity regulation strategy data Is the mapping relationship of the initial energy storage capacity regulation strategy mapping equation, representing the numerical combination relationship between its respective independent variables and the dependent variable, such as the dependent variable is proportional to the independent variable, etc ceil Represents the rounding function is the ([ j j × k )-th independent variable of the initial energy storage capacity regulation strategy mapping equation, representing the change equation of the energy storage capacity influence parameter of the [[ j j -th type within the [[ k k -th type of optical, wind, and power energy storage data acquisition period; S222. Input the equation data in each historical energy storage capacity influence parameter change equation matrix in the historical energy storage capacity influence parameter change equation matrix set into the initial energy storage capacity regulation strategy mapping equation for mapping to obtain the initial mapping data set of the historical energy storage capacity regulation strategy[[ , b i represents the mapping data obtained by inputting [[ a a i into the initial energy storage capacity regulation strategy mapping equation for mapping; S223. Adjust the constant coefficients of the initial energy storage capacity regulation strategy mapping equation according to the initial mapping data set of the historical energy storage capacity regulation strategy and the historical energy storage capacity regulation strategy data set. After the adjustment is completed, the final energy storage capacity regulation strategy mapping equation is obtained; By combining various types of energy storage capacity influence parameters in the energy storage capacity influence parameter type set in a mathematical form and cooperating with the corresponding constant coefficients, so that the combined operation is the same as the numerical value of the corresponding historical energy storage capacity regulation strategy data, thus realizing the mapping from various types of energy storage capacity influence parameters to energy storage capacity regulation strategy data, providing a mapping tool for subsequently obtaining the corresponding energy storage capacity regulation strategy data according to the change equations of various types of energy storage capacity influence parameters in different periods; among them, by substituting the collected historical data into the initial energy storage capacity regulation strategy mapping equation and comparing the mapping result with the actual data, the accuracy of the mapping can be known; thus providing a judgment basis for determining whether adjustment is needed, ensuring the mapping accuracy of the obtained final energy storage capacity regulation strategy mapping equation; in addition, since the change equations of various types of energy storage capacity influence parameters are fitted based on the existing data, therefore, mapping the corresponding energy storage capacity regulation strategy data based on the change equations of various types of energy storage capacity influence parameters also takes into account the change factors brought by various types of energy storage capacity influence parameters in the future, thereby improving the comprehensiveness and rationality of the mapped energy storage capacity regulation strategy data.

[0008] Preferably, S223 includes the following steps: S2231. When there are differences between the initial mapping data in the historical energy storage capacity regulation strategy initial mapping dataset and the corresponding historical energy storage capacity regulation strategy data in the historical energy storage capacity regulation strategy dataset, adjust the constant coefficients of the initial energy storage capacity regulation strategy mapping equation until there are no differences between the initial mapping data in the historical energy storage capacity regulation strategy initial mapping dataset and the corresponding historical energy storage capacity regulation strategy data in the historical energy storage capacity regulation strategy dataset, and then obtain the final energy storage capacity regulation strategy mapping equation; otherwise, no adjustment is required. Preferably, the adjustment of the constant coefficients of the initial energy storage capacity regulation strategy mapping equation in S2231 includes the following steps: S22311. Set the value range of several constant coefficients of the initial energy storage capacity regulation strategy mapping equation to obtain an energy storage capacity regulation strategy mapping constant coefficient value range set ; as follows, ; Among them, , respectively represent the lower limit and upper limit of the value of the i -th constant coefficient of the initial energy storage capacity regulation strategy mapping equation, represents the total number of constant coefficients of the initial energy storage capacity regulation strategy mapping equation; Construct an energy storage capacity regulation strategy mapping adjustment squirrel population; set the maximum number of iterations of the energy storage capacity regulation strategy mapping adjustment squirrel population as and the current number of iterations as , which are respectively recorded as the maximum number of strategy adjustment iterations and the current number of strategy adjustment iterations; the number of search space dimensions of the energy storage capacity regulation strategy mapping adjustment squirrel population is the same as ; S22312. Set the initial position of each squirrel in the energy storage capacity regulation strategy mapping adjustment squirrel population according to the energy storage capacity regulation strategy mapping constant coefficient value range set to obtain a first initial position matrix c 1 ; as follows, ; Among them, c 1ji represents the position component of the initial position of the j -th squirrel in the energy storage capacity regulation strategy mapping adjustment squirrel population on the i -th constant coefficient dimension of the initial energy storage capacity regulation strategy mapping equation, represents the scale of the energy storage capacity regulation strategy mapping adjustment squirrel population; c 1ji The generation formula of ; In the formula, rand 1ji represents a random number between 0 and 1 generated for c 1ji ; S22313. Construct the fitness function of the squirrel population adjusted by the mapping of the energy storage capacity regulation strategy ; as follows, ; In the formula, represents the total number of mapping data that are different from the corresponding historical energy storage capacity regulation strategy data in the mapping dataset obtained by substituting a set of constant coefficients obtained in each iteration process into the initial energy storage capacity regulation strategy mapping equation and then inputting the equation data in each historical energy storage capacity impact parameter change equation matrix in the historical energy storage capacity impact parameter change equation matrix set in S222 into the initial energy storage capacity regulation strategy mapping equation for mapping; is a positive number, representing a protection parameter, used to prevent the denominator of the fitness function from being 0; S22314. Start the iteration. Before the iteration, set the current iteration number of the strategy adjustment to 1; in the first round of iteration, use the fitness function of the squirrel population adjusted by the mapping of the energy storage capacity regulation strategy Calculate the fitness value of the initial position of each squirrel in the first initial position matrix to obtain the first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position of the squirrel as the first global best fitness and the first global best position respectively; update the initial position of each squirrel in the first initial position matrix according to the first global best fitness and the first global best position; after the update is completed, add 1 to the current iteration number of the strategy adjustment and enter the next round of iteration; In each other round of iteration, use the fitness function of the squirrel population adjusted by the mapping of the energy storage capacity regulation strategy Calculate the fitness value of the position of each squirrel in the squirrel population adjusted by the mapping of the energy storage capacity regulation strategy updated in the previous round of iteration to obtain the second fitness value set; take the maximum fitness value in the second fitness value set and the corresponding position of the squirrel as the second global best fitness and the second global best position respectively; update the position of each squirrel in the squirrel population adjusted by the mapping of the energy storage capacity regulation strategy updated in the previous round of iteration according to the second global best fitness and the second global best position; after the update is completed, add 1 to the current iteration number of the strategy adjustment and enter the next round of iteration; S22315. When When it meets the condition, stop the iteration to obtain the first final global best fitness and the first final global best position; otherwise, continue the iteration until it meets the condition; when the value of the first final global best fitness is substitute each position component of the first final global best position into the initial energy storage capacity regulation strategy mapping equation to obtain the final energy storage capacity regulation strategy mapping equation; otherwise, return to S22314 to continue the iteration until the value of the first final global best fitness is it meets the condition; The squirrel optimization algorithm can conduct extensive searches in the search space by simulating the foraging behavior and gliding mechanism of squirrels, and it is not easily trapped in local optimal solutions. It can find the global optimal solution or a position close to the global optimal solution. During the search process, it can conduct detailed searches in local areas, and by adjusting the positions and search strategies of squirrels, improve the accuracy and efficiency of local searches. It is not sensitive to the selection of initial values and parameters, and under different initial conditions and parameter settings, the algorithm can stably converge to better solutions. Based on the above advantages, in this solution, the squirrel optimization algorithm is used to simultaneously perform multiple iterative adjustments on multiple constant coefficients of the initial energy storage capacity regulation strategy mapping equation. At the same time, the mapping accuracy of the initial energy storage capacity regulation strategy mapping equation is used as the fitness function. Therefore, as the iteration progresses, the mapping accuracy of the initial energy storage capacity regulation strategy mapping equation becomes higher and higher, and finally meets the mapping requirements.

[0009] Preferably, S3 includes the following steps: S31. Set the current energy storage capacity regulation strategy adjustment evaluation period and the current real-time acquisition interval; in cooperation with the energy storage capacity impact parameter type set and the photovoltaic-wind power-energy storage data acquisition cycle type set, during the current energy storage capacity regulation strategy adjustment evaluation period, according to the current real-time acquisition interval, collect the energy storage capacity impact parameters in each type of acquisition cycle corresponding to the current power generation system in real time and perform data fitting to obtain the current energy storage capacity impact parameter change equation matrix set; S32. Input each current energy storage capacity impact parameter change equation matrix in the current energy storage capacity impact parameter change equation matrix set into the final energy storage capacity regulation strategy mapping equation for mapping to obtain the current energy storage capacity regulation strategy data set; By setting the current energy storage capacity regulation strategy adjustment evaluation period, it provides a time range for subsequently evaluating the power generation effects brought by several energy capacity regulation strategy data adjusted currently as a whole. Since various types of energy storage capacity impact parameters change continuously over time, by setting the current real-time acquisition interval, the changing trends of the latest various types of energy storage capacity impact parameters can be obtained at each time interval.

[0010] Preferably, S4 includes the following steps: S41. Regulate the energy storage capacity of the current power generation system during the adjustment and evaluation period of the current energy storage capacity regulation strategy according to the current energy storage capacity regulation strategy data set; after the end of the adjustment and evaluation period of the current energy storage capacity regulation strategy, count the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the adjustment and evaluation period of the current energy storage capacity regulation strategy, and record it as the percentage data of the power shortage time period in the current cycle; S42. Set a threshold for the percentage of the power shortage time period in the cycle; when the percentage data of the power shortage time period in the current cycle is greater than or equal to the threshold for the percentage of the power shortage time period in the cycle, adjust the current real-time acquisition interval until the percentage data of the power shortage time period in the current cycle is less than the threshold for the percentage of the power shortage time period in the cycle, and obtain the current final real-time acquisition interval; otherwise, there is no need to adjust the current real-time acquisition interval; By using the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the adjustment and evaluation period of the current energy storage capacity regulation strategy as an evaluation index, it is ensured that after the adjustment of the energy storage capacity regulation strategy of the current power generation, it can meet the power consumption needs of the corresponding electrical load, thereby ensuring the normal operation of the user load; among them, by setting a threshold for the percentage of the power shortage time period in the cycle, a quantitative determination basis is provided for determining whether the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the adjustment and evaluation period of the current energy storage capacity regulation strategy meets the requirements, and then it is determined whether it is necessary to adjust the current real-time acquisition interval; in addition, since the size of the current real-time acquisition interval determines the usage time length of the adjusted current energy storage capacity regulation strategy data, if the current real-time acquisition interval is too large, it will greatly increase the possibility of mutation of some energy storage capacity influencing parameters during this period, such as sudden weather changes; therefore, by adjusting the current real-time acquisition interval to a reasonable size, on the one hand, it is ensured that the possibility of mutation of some energy storage capacity influencing parameters during this period is not very large, and on the other hand, it is ensured that the frequency of collecting energy storage capacity influencing parameters is not too high, avoiding the situation that the data processing volume for adjusting the current energy storage capacity regulation strategy data is too large, thereby reducing the efficiency of adjusting the energy storage capacity regulation strategy data.

[0011] Preferably, the adjustment of the current real-time acquisition interval in S42 includes the following steps: S421. Set the value range of the current real-time acquisition interval to obtain the current real-time acquisition interval value range , 、 respectively represent the lower limit and the upper limit of the value of the current real-time acquisition interval; Construct a squirrel population for real-time acquisition interval adjustment; set the maximum number of iterations of the squirrel population for real-time acquisition interval adjustment to be and the current number of iterations to be , which are respectively denoted as the maximum number of iterations for interval adjustment and the current number of iterations for interval adjustment; the search space dimension of the squirrel population for real-time acquisition interval adjustment is one-dimensional; S422. Set the initial position of each squirrel in the squirrel population for real-time acquisition interval adjustment according to the current real-time acquisition interval value range to obtain a second initial position set , c 2i represents the initial position of the i th squirrel in the squirrel population for real-time acquisition interval adjustment, represents the size of the squirrel population for real-time acquisition interval adjustment; c 2i The generation formula of is as follows, rand 2i represents a random number between 0 and 1 generated for c 2i ; In the formula, S423. Construct the fitness function of the squirrel population for real-time acquisition interval adjustment ; as follows, ; In the formula, represents the percentage data of the time period when the generated electricity of the current power generation system cannot meet the electricity required by the electrical load after applying the current real-time acquisition interval obtained in each round of iteration to the energy storage capacity impact parameters in each type of acquisition cycle corresponding to the current power generation system, at the end of the current energy storage capacity regulation strategy adjustment evaluation cycle; S424. Start iteration. Before iteration, set the current number of iterations for interval adjustment to 1; in the first round of iteration, use the fitness function of the squirrel population for real-time acquisition interval adjustment to calculate the fitness values of the initial positions of each squirrel in the second initial position set to obtain a third fitness value set; take the maximum fitness value in the third fitness value set and the corresponding initial position of the squirrel as the third global best fitness and the third global best position respectively; update the initial positions of each squirrel in the second initial position set according to the third global best fitness and the third global best position; after the update is completed, add 1 to the current number of iterations for interval adjustment and enter the next round of iteration; In each subsequent round of iteration, use the fitness function of the squirrel population for real-time acquisition interval adjustment Calculate the fitness value of adjusting the position of each squirrel in the squirrel population with the real-time acquisition interval updated in the previous iteration process to obtain the fourth fitness value set; take the maximum fitness value in the fourth fitness value set and the position of the corresponding squirrel as the fourth global best fitness and the fourth global best position respectively; update the position of each squirrel in the squirrel population with the real-time acquisition interval updated in the previous iteration process according to the fourth global best fitness and the fourth global best position; after the update is completed, increment the current iteration number of the interval adjustment by 1 and enter the next iteration; S425. When is satisfied, stop the iteration to obtain the second final global best fitness and the second final global best position; otherwise, continue the iteration until is satisfied; take the second final global best fitness as the current optimized percentage data of the power shortage time period in the cycle. When the current optimized percentage data of the power shortage time period in the cycle is less than the power shortage time period percentage threshold, take the second final global best position as the current final real-time acquisition interval; otherwise, return to S424 to continue the iteration until the current optimized percentage data of the power shortage time period in the cycle is less than the power shortage time period percentage threshold. By using the squirrel optimization algorithm to iteratively adjust the current real-time acquisition interval multiple times, and taking the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the current energy storage capacity regulation strategy adjustment evaluation cycle as the fitness function; therefore, as the iteration progresses, the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the current energy storage capacity regulation strategy adjustment evaluation cycle becomes smaller and smaller, and finally it is controlled within the corresponding threshold, ensuring the normal power consumption and normal operation of the electrical load.

[0012] The energy storage capacity regulation system of the power generation system based on the complementarity of light, wind, and electricity storage includes an energy storage capacity impact acquisition parameter setting module, an energy storage capacity regulation strategy type setting module, an energy storage capacity regulation strategy mapping equation construction module, a current power generation system energy storage capacity impact parameter acquisition module, a current energy storage capacity regulation strategy mapping module, and an acquisition time interval adjustment module.

[0013] The present invention has the following beneficial effects: 1. In the present invention, by adjusting the current real-time acquisition interval to a reasonable size, on the one hand, it ensures that the possibility of sudden changes in some energy storage capacity impact parameters during this period is not very large, and on the other hand, it ensures that the frequency of acquiring energy storage capacity impact parameters is not too high, avoiding the situation where the data processing volume for adjusting the current energy storage capacity regulation strategy data is too large, thereby reducing the efficiency of adjusting the energy storage capacity regulation strategy data.

[0014] 2. In the present invention, the squirrel optimization algorithm is used to perform multiple iterative adjustments on multiple constant coefficients of the mapping equation of the initial energy storage capacity regulation strategy simultaneously. As the iteration progresses, the mapping accuracy of the mapping equation of the initial energy storage capacity regulation strategy becomes higher and higher, and finally meets the mapping requirements.

[0015] 3. In the present invention, the squirrel optimization algorithm is used to perform multiple iterative adjustments on the current real-time acquisition interval, and the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the current energy storage capacity regulation strategy adjustment evaluation period is used as the fitness function. Therefore, as the iteration progresses, the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the current energy storage capacity regulation strategy adjustment evaluation period becomes smaller and smaller, and finally it is controlled within the corresponding threshold, ensuring the normal power consumption and normal operation of the electrical load.

[0016] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flow chart of the energy storage capacity regulation method for a power generation system based on the complementarity of photovoltaic, wind power, and energy storage of the present invention; Figure 2 It is a schematic flow chart of constructing the mapping equation of the final energy storage capacity regulation strategy of the present invention; Figure 3 It is a schematic flow chart of adjusting the current real-time acquisition interval of the present invention; Figure 4 It is a schematic module diagram of the energy storage capacity regulation system for a power generation system based on the complementarity of photovoltaic, wind power, and energy storage of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions in the embodiments of the invention will be clearly and completely described below with reference to the drawings in the embodiments of the invention. Obviously, the described embodiments are only a part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the invention.

[0020] Embodiment 1

[0021] Please refer to Figures 1 - 3 , this embodiment is a method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind, power, and energy storage, including the following steps: S1. Set several types of parameters that affect the energy storage capacity of the power generation system and types of energy storage capacity regulation strategies for the power generation system to obtain an energy storage capacity influence parameter type set and an energy storage capacity regulation strategy type set; The S1 includes the following steps: S11. Set several types of parameters that affect the energy storage capacity of the power generation system to obtain an energy storage capacity influence parameter type set; then set several types of acquisition cycle types for photovoltaic, wind, power, and energy storage data to obtain a photovoltaic, wind, power, and energy storage data acquisition cycle type set; The energy storage capacity influence parameter type set includes solar power generation power data, wind power generation power data, local meteorological data, such as sunshine duration, intensity, wind speed, wind direction, etc., power demand data of the electrical load, and remaining power data in the energy storage system, etc.; among them, the solar power generation power data and the wind power generation power data can be obtained through power sensors installed on solar panels and wind turbines, and data such as sunshine duration, intensity, wind speed, and wind direction can be obtained by obtaining from the local meteorological station or measuring through meteorological sensors installed near the system; The photovoltaic, wind, power, and energy storage data acquisition cycle set includes hour-level cycle types, day-level cycle types, week-level cycle types, and month-level cycle types, etc.; S12. In combination with the energy storage capacity influence parameter type set and the photovoltaic, wind, power, and energy storage data acquisition cycle type set, set several types of energy storage capacity regulation strategies for the power generation system to obtain an energy storage capacity regulation strategy type set; the energy storage capacity regulation strategy type set includes several types of energy storage system charging strategy types under different charging powers, energy storage system discharging strategy types under different discharging electric powers, and strategy types of neither charging nor discharging, etc., and each energy storage capacity regulation strategy in the energy storage capacity regulation strategy type set is represented by a natural number for numerical value, such as 1: the strategy type of neither charging nor discharging; S2. In combination with the energy storage capacity influence parameter type set and the energy storage capacity regulation strategy type set, collect multiple sets of historical energy storage capacity influence parameter data of various types at multiple time points within various acquisition cycles and the corresponding energy storage capacity regulation strategy data, and construct a final energy storage capacity regulation strategy mapping equation; The S2 includes the following steps: S21. In coordination with the energy storage capacity regulation strategy type set, the energy storage capacity impact parameter type set, and the photovoltaic-wind power-energy storage data acquisition cycle type set, collect various types of energy storage capacity impact parameter data and corresponding energy storage capacity regulation strategy data at multiple time points within various photovoltaic-wind power-energy storage data acquisition cycles before the adjustment of the energy storage capacity regulation strategy in multiple historical groups, and perform data equation fitting on the collected energy storage capacity impact parameter data to obtain a historical energy storage capacity impact parameter change equation matrix set and a historical energy storage capacity regulation strategy data set , a i denotes the i th historical energy storage capacity impact parameter change equation matrix corresponding to the historical energy storage capacity regulation strategy adjustment process in the collected groups, denotes the total number of groups of the historical energy storage capacity regulation strategy adjustment process in the collection, i denotes the energy storage capacity regulation strategy data corresponding to the a i th historical energy storage capacity regulation strategy adjustment process in the collection; ; wherein, a ijk denotes a i the change equation of the j th type of energy storage capacity impact parameter within the k th type of photovoltaic-wind power-energy storage data acquisition cycle, denotes the total number of the set photovoltaic-wind power-energy storage data acquisition cycle types, denotes the total number of the set energy storage capacity impact parameter types; S22. Construct a final energy storage capacity regulation strategy mapping equation by using the historical energy storage capacity impact parameter change equation matrix set and the historical energy storage capacity regulation strategy data set; The S22 includes the following steps: S221. Construct an initial energy storage capacity regulation strategy mapping equation in coordination with the historical energy storage capacity impact parameter change equation matrix set and the historical energy storage capacity regulation strategy data set; as follows, ; wherein, is the dependent variable of the initial energy storage capacity regulation strategy mapping equation, denoting the energy storage capacity regulation strategy data; is the mapping relationship of the initial energy storage capacity regulation strategy mapping equation, representing the numerical combination relationship between its respective independent variables and the dependent variable, such as a proportional relationship between the dependent variable and the independent variables, etc.; ceil denotes the rounding function; is the ([ j j × k )-th independent variable of the initial energy storage capacity regulation strategy mapping equation, representing the change equation of the [[ j j -th type of energy storage capacity influence parameter within the [[ k k -th type of photovoltaic, wind power, and energy storage data acquisition period; S222. Input the equation data in each historical energy storage capacity influence parameter change equation matrix in the historical energy storage capacity influence parameter change equation matrix set into the initial energy storage capacity regulation strategy mapping equation for mapping to obtain the initial mapping dataset of the historical energy storage capacity regulation strategy , b i represents the mapping data obtained by inputting [[ a a i into the initial energy storage capacity regulation strategy mapping equation for mapping; S223. Adjust the constant coefficients of the initial energy storage capacity regulation strategy mapping equation according to the initial mapping dataset of the historical energy storage capacity regulation strategy and the historical energy storage capacity regulation strategy dataset. After the adjustment is completed, the final energy storage capacity regulation strategy mapping equation is obtained; The S223 includes the following steps: S2231. When there are initial mapping data in the initial mapping dataset of the historical energy storage capacity regulation strategy that are different from the corresponding historical energy storage capacity regulation strategy data in the historical energy storage capacity regulation strategy dataset, adjust the constant coefficients of the initial energy storage capacity regulation strategy mapping equation until there are no initial mapping data in the initial mapping dataset of the historical energy storage capacity regulation strategy that are different from the corresponding historical energy storage capacity regulation strategy data in the historical energy storage capacity regulation strategy dataset. Then, the final energy storage capacity regulation strategy mapping equation is obtained; otherwise, no adjustment is required; The adjustment of the constant coefficients of the initial energy storage capacity regulation strategy mapping equation in S2231 includes the following steps: S22311. Set the value range of several constant coefficients of the initial energy storage capacity regulation strategy mapping equation to obtain the value range set of the energy storage capacity regulation strategy mapping constant coefficients ; as follows, ; where, , respectively represent the lower limit and upper limit of the value of the [[ i i -th constant coefficient of the initial energy storage capacity regulation strategy mapping equation, represents the total number of constant coefficients of the initial energy storage capacity regulation strategy mapping equation; Construct an energy storage capacity regulation strategy mapping to adjust the squirrel population; set the maximum number of iterations of the energy storage capacity regulation strategy mapping to adjust the squirrel population to be and the current number of iterations to be , which are respectively denoted as the maximum number of iterations for strategy adjustment and the current number of iterations for strategy adjustment; the number of search space dimensions of the energy storage capacity regulation strategy mapping to adjust the squirrel population is the same as ; S22312. Set the initial position of each squirrel in the energy storage capacity regulation strategy mapping to adjust the squirrel population according to the set of constant coefficient value intervals of the energy storage capacity regulation strategy mapping, and obtain the first initial position matrix c 1 ; as follows, ; where, c 1ji represents the position component of the initial position of the j -th squirrel in the energy storage capacity regulation strategy mapping to adjust the squirrel population on the i -th constant coefficient dimension of the initial energy storage capacity regulation strategy mapping equation, represents the scale of the energy storage capacity regulation strategy mapping to adjust the squirrel population; c 1ji The generation formula of is as follows, rand 1ji represents a random number generated between 0 and 1 for c 1ji ; S22313. Construct the fitness function of the energy storage capacity regulation strategy mapping to adjust the squirrel population ; as follows, ; In the formula, represents the total number of mapping data that are different from the corresponding historical energy storage capacity regulation strategy data in the mapping dataset obtained by substituting a set of constant coefficients obtained in each iteration process into the initial energy storage capacity regulation strategy mapping equation and then inputting the equation data in each historical energy storage capacity impact parameter change equation matrix in the historical energy storage capacity impact parameter change equation matrix set in S222 into the initial energy storage capacity regulation strategy mapping equation for mapping; is a positive number, representing a protection parameter, used to prevent the denominator of the fitness function from being 0; S22314. Start the iteration. Before the iteration, set the current number of iterations for strategy adjustment to 1; in the first round of iteration, use the fitness function of the energy storage capacity regulation strategy mapping to adjust the squirrel population Calculate the fitness values of the initial positions of each squirrel in the first initial position matrix to obtain a first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position of the squirrel as the first global best fitness and the first global best position respectively; update the initial positions of each squirrel in the first initial position matrix according to the first global best fitness and the first global best position; after the update is completed, increment the current iteration count of the strategy adjustment by 1 and enter the next iteration; In each other iteration process, use the energy storage capacity regulation strategy mapping to adjust the fitness function of the squirrel population Calculate the fitness values of the positions of each squirrel in the squirrel population adjusted by the energy storage capacity regulation strategy mapping updated in the previous iteration process to obtain a second fitness value set; take the maximum fitness value in the second fitness value set and the corresponding position of the squirrel as the second global best fitness and the second global best position respectively; update the positions of each squirrel in the squirrel population adjusted by the energy storage capacity regulation strategy mapping updated in the previous iteration process according to the second global best fitness and the second global best position; after the update is completed, increment the current iteration count of the strategy adjustment by 1 and enter the next iteration; S22315. When the condition is met, stop the iteration to obtain the first final global best fitness and the first final global best position; otherwise, continue the iteration until the condition is met; when the value of the first final global best fitness is substitute each position component of the first final global best position into the initial energy storage capacity regulation strategy mapping equation to obtain the final energy storage capacity regulation strategy mapping equation; otherwise, return to S22314 to continue the iteration until the value of the first final global best fitness is the condition is met; S3. Set the current real-time acquisition interval to collect the energy storage capacity influence parameters of the current power generation system and input them into the final energy storage capacity regulation strategy mapping equation for mapping to obtain the current energy storage capacity regulation strategy data set; S3 includes the following steps: S31. Set the current energy storage capacity regulation strategy adjustment evaluation period and the current real-time acquisition interval; cooperate with the energy storage capacity influence parameter type set and the photovoltaic, wind power, and energy storage data acquisition cycle type set, and in the current energy storage capacity regulation strategy adjustment evaluation period, collect the energy storage capacity influence parameters in the acquisition cycle of each type corresponding to the current power generation system according to the current real-time acquisition interval and perform data fitting to obtain the current energy storage capacity influence parameter change equation matrix set; S32. Input each current energy storage capacity influence parameter change equation matrix in the current energy storage capacity influence parameter change equation matrix set into the final energy storage capacity regulation strategy mapping equation for mapping to obtain a current energy storage capacity regulation strategy data set; S4. Adjust the energy storage capacity regulation strategy of the current power generation system according to the current energy storage capacity regulation strategy data set; and adjust the current real-time acquisition interval according to the percentage data of the time period when the generated power of the current power generation system after adjustment cannot meet the power required by the electrical load in the current energy storage capacity regulation strategy adjustment evaluation period; The S4 includes the following steps: S41. Regulate the energy storage capacity of the current power generation system during the current energy storage capacity regulation strategy adjustment evaluation period according to the current energy storage capacity regulation strategy data set; after the end of the current energy storage capacity regulation strategy adjustment evaluation period, count the percentage data of the time period when the generated power of the current power generation system cannot meet the power required by the electrical load in the current energy storage capacity regulation strategy adjustment evaluation period, and record it as the percentage data of the insufficient power time period in the current cycle; S42. Set a threshold for the percentage of the insufficient power time period in the cycle; when the percentage data of the insufficient power time period in the current cycle is greater than or equal to the threshold for the percentage of the insufficient power time period in the cycle, adjust the current real-time acquisition interval until the percentage data of the insufficient power time period in the current cycle is less than the threshold for the percentage of the insufficient power time period in the cycle to obtain the current final real-time acquisition interval; otherwise, there is no need to adjust the current real-time acquisition interval; The adjustment of the current real-time acquisition interval in S42 includes the following steps: S421. Set the value range of the current real-time acquisition interval to obtain the current real-time acquisition interval value range , , respectively represent the lower limit and the upper limit of the value of the current real-time acquisition interval; Construct a squirrel population for real-time acquisition interval adjustment; set the maximum number of iterations of the squirrel population for real-time acquisition interval adjustment to be and the current number of iterations to be , which are respectively recorded as the maximum number of iterations for interval adjustment and the current number of iterations for interval adjustment; the search space dimension of the squirrel population for real-time acquisition interval adjustment is one-dimensional; S422. Set the initial position of each squirrel in the squirrel population for real-time acquisition interval adjustment according to the current real-time acquisition interval value range to obtain a second initial position set , c 2i represents the initial position of the i rd squirrel in the squirrel population for real-time acquisition interval adjustment, indicating the scale of the squirrel population adjusted by the real-time acquisition interval; c 2i The generation formula is as follows, ; where, rand 2i represents a random number between 0 and 1 generated for c 2i ; S423. Construct the fitness function of the squirrel population adjusted by the real-time acquisition interval ; as follows, ; where, represents the percentage data of the time period when the generated electricity of the current power generation system cannot meet the electricity required by the electrical load after the current energy storage capacity regulation strategy adjustment evaluation period ends during the process of applying the current real-time acquisition interval obtained in each round of iteration to the energy storage capacity influence parameters in each type of acquisition cycle corresponding to the current power generation system; S424. Start iteration. Before iteration, set the current iteration number of the interval adjustment to 1; in the first round of iteration, use the fitness function of the squirrel population adjusted by the real-time acquisition interval Calculate the fitness values of the initial positions of each squirrel in the second initial position set to obtain the third fitness value set; take the maximum fitness value in the third fitness value set and the corresponding initial position of the squirrel as the third global best fitness and the third global best position respectively; update the initial positions of each squirrel in the second initial position set according to the third global best fitness and the third global best position; after the update is completed, add 1 to the current iteration number of the interval adjustment and enter the next round of iteration; In each other round of iteration, use the fitness function of the squirrel population adjusted by the real-time acquisition interval Calculate the fitness values of the positions of each squirrel in the squirrel population adjusted by the real-time acquisition interval updated in the previous round of iteration to obtain the fourth fitness value set; take the maximum fitness value in the fourth fitness value set and the corresponding position of the squirrel as the fourth global best fitness and the fourth global best position respectively; update the positions of each squirrel in the squirrel population adjusted by the real-time acquisition interval updated in the previous round of iteration according to the fourth global best fitness and the fourth global best position; after the update is completed, add 1 to the current iteration number of the interval adjustment and enter the next round of iteration; S425. When is satisfied, stop iteration to obtain the second final global best fitness and the second final global best position; otherwise, continue iteration until until that time; use the second final global best fitness as the current optimized proportion data of the power shortage time period in the cycle; When the current optimized proportion data of the power shortage time period in the cycle is less than the proportion threshold of the power shortage time period in the cycle, use the second final global best position as the current final real-time acquisition interval; otherwise, return to S424 to continue the iteration until the current optimized proportion data of the power shortage time period in the cycle is less than the proportion threshold of the power shortage time period in the cycle.

[0022] Embodiment 2

[0023] Please refer to Figure 4 , this embodiment discloses an energy storage capacity regulation system for a power generation system based on the complementarity of optical, wind, and energy storage. The system can implement the method of the above embodiment, including an energy storage capacity impact acquisition parameter setting module, an energy storage capacity regulation strategy type setting module, an energy storage capacity regulation strategy mapping equation construction module, a current power generation system energy storage capacity impact parameter acquisition module, a current energy storage capacity regulation strategy mapping module, and an acquisition time interval adjustment module; The energy storage capacity impact acquisition parameter setting module sets several types of parameters that affect the energy storage capacity of the power generation system and the acquisition cycle types of optical, wind, and energy storage data, and obtains an energy storage capacity impact parameter type set and an optical, wind, and energy storage data acquisition cycle type set; The energy storage capacity regulation strategy type setting module sets several types of energy storage capacity regulation strategies for the power generation system according to the energy storage capacity impact parameter type set and the optical, wind, and energy storage data acquisition cycle type set, and obtains an energy storage capacity regulation strategy type set; The energy storage capacity regulation strategy mapping equation construction module, in cooperation with the energy storage capacity impact parameter type set, the optical, wind, and energy storage data acquisition cycle type set, and the energy storage capacity regulation strategy type set, collects various types of energy storage capacity impact parameter data and corresponding energy storage capacity regulation strategy data at multiple time points during various optical, wind, and energy storage data acquisition cycles before the energy storage capacity regulation strategy is adjusted in history, and constructs a final energy storage capacity regulation strategy mapping equation; The current power generation system energy storage capacity impact parameter acquisition module collects the energy storage capacity impact parameters in each type of acquisition cycle corresponding to the current power generation system by setting the current real-time acquisition interval and performs data fitting to obtain a current energy storage capacity impact parameter change equation matrix set; The current energy storage capacity regulation strategy mapping module inputs each change equation matrix in the current energy storage capacity impact parameter change equation matrix set into the final energy storage capacity regulation strategy mapping equation for mapping to obtain a current energy storage capacity regulation strategy data set; The acquisition time interval adjustment module adjusts the current real-time acquisition interval according to the percentage data of the time period in which the generated power of the current power generation system cannot meet the power required by the electrical load after adopting the energy storage capacity regulation strategy data in the current energy storage capacity regulation strategy dataset, so as to obtain the current final real-time acquisition interval.

[0024] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0025] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not elaborate all the details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. A method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage, characterized in that: The following steps are involved: S1. Setting several parameter types that have an impact on the energy storage capacity of the power generation system and the energy storage capacity control strategy types of the power generation system to obtain a set of energy storage capacity influencing parameter types and a set of energy storage capacity control strategy types; S2. Collect multiple sets of various types of energy storage capacity influencing parameter data and corresponding energy storage capacity control strategy data at multiple time points in various historical collection cycles in conjunction with the energy storage capacity influencing parameter type set and the energy storage capacity control strategy type set, and construct a final energy storage capacity control strategy mapping equation; S3, by setting the current real-time collection interval to collect the energy storage capacity influencing parameters of the current power generation system and inputting them into the final energy storage capacity control strategy mapping equation for mapping, to obtain the current energy storage capacity control strategy data set; S4. adjusting the energy storage capacity control strategy of the current power generation system according to the current energy storage capacity control strategy data set; The current real-time collection interval is adjusted according to the percentage data of the time period during which the power generation of the current power generation system after adjustment cannot meet the power required by the power load, which accounts for the current energy storage capacity control strategy adjustment evaluation period.

2. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Setting several types of parameters that have an impact on the energy storage capacity of the power generation system to obtain a set of parameter types that affect the energy storage capacity; and then setting several types of collection cycle types of solar-wind-power-storage data to obtain a set of solar-wind-power-storage data collection cycle types; S12. According to the energy storage capacity influencing parameter type set and the solar-wind-power storage data collection cycle type set, several types of energy storage capacity control strategies for power generation systems are set to obtain an energy storage capacity control strategy type set.

3. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 2 is characterized in that: The S2 comprises the following steps: S21, in conjunction with the energy storage capacity control strategy type set, the energy storage capacity influencing parameter type set and the solar-wind power storage data collection cycle type set, collect multiple groups of various types of energy storage capacity influencing parameter data and corresponding energy storage capacity control strategy data at multiple time points in various solar-wind power storage data collection cycles before the energy storage capacity control strategy is adjusted in history, and perform data equation fitting on the collected energy storage capacity influencing parameter data to obtain a historical energy storage capacity influencing parameter change equation matrix set and a historical energy storage capacity control strategy data set; S22. Constructing a final energy storage capacity control strategy mapping equation using the historical energy storage capacity influencing parameter change equation matrix set and the historical energy storage capacity control strategy data set.

4. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 3 is characterized in that: The S22 comprises the following steps: S221, constructing an initial energy storage capacity control strategy mapping equation in conjunction with the historical energy storage capacity influencing parameter change equation matrix set and the historical energy storage capacity control strategy data set; S222, inputting the equation data in each historical energy storage capacity influencing parameter change equation matrix in the historical energy storage capacity influencing parameter change equation matrix set into the initial energy storage capacity control strategy mapping equation for mapping, and obtaining the historical energy storage capacity control strategy initial mapping data set; S223, adjusting the constant coefficient of the initial energy storage capacity regulation strategy mapping equation according to the historical energy storage capacity regulation strategy initial mapping data set and the historical energy storage capacity regulation strategy data set, and after the adjustment is completed, obtaining the final energy storage capacity regulation strategy mapping equation.

5. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 4 is characterized in that: The S223 comprises the following steps: S2231. When there is initial mapping data in the initial mapping data set of the historical energy storage capacity control strategy that is different from the historical energy storage capacity control strategy data corresponding to the historical energy storage capacity control strategy data set, the constant coefficient of the initial energy storage capacity control strategy mapping equation is adjusted until there is no initial mapping data in the initial mapping data set of the historical energy storage capacity control strategy that is different from the historical energy storage capacity control strategy data corresponding to the historical energy storage capacity control strategy data set, and a final energy storage capacity control strategy mapping equation is obtained; otherwise, no adjustment is required.

6. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 5 is characterized in that: In S2231, the constant coefficient of the Squirrel initial energy storage capacity control strategy mapping equation is adjusted using the Squirrel optimization algorithm.

7. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 6 is characterized in that: The S3 comprises the following steps: S31, setting the current energy storage capacity control strategy adjustment evaluation cycle and the current real-time collection interval; in conjunction with the energy storage capacity influencing parameter type set and the solar wind power storage data collection cycle type set, in the current energy storage capacity control strategy adjustment evaluation cycle, real-time collection of energy storage capacity influencing parameters in each type of collection cycle corresponding to the current power generation system is performed according to the current real-time collection interval and data fitting is performed to obtain the current energy storage capacity influencing parameter change equation matrix set; S32, inputting each current energy storage capacity influencing parameter change equation matrix in the current energy storage capacity influencing parameter change equation matrix set into the final energy storage capacity control strategy mapping equation for mapping, to obtain a current energy storage capacity control strategy data set.

8. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 7 is characterized in that: The S4 comprises the following steps: S41, regulating the energy storage capacity of the current power generation system within the current energy storage capacity regulation strategy adjustment evaluation cycle according to the current energy storage capacity regulation strategy data set; when the current energy storage capacity regulation strategy adjustment evaluation cycle ends, counting the percentage data of the time period in which the power generation of the current power generation system cannot meet the power required by the power load in the current energy storage capacity regulation strategy adjustment evaluation cycle, and recording it as the current cycle power shortage time period percentage data; S42, setting a threshold value for the proportion of periodic power shortage time periods; when the current periodic power shortage time period proportion data is greater than or equal to the periodic power shortage time period proportion threshold value, adjusting the current real-time collection interval until the current periodic power shortage time period proportion data is less than the periodic power shortage time period proportion threshold value, and obtaining the current final real-time collection interval; otherwise, there is no need to adjust the current real-time collection interval.

9. The method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage according to claim 8 is characterized in that: Adjusting the current real-time collection interval in S42 includes the following steps: S421, setting the value interval of the current real-time collection interval to obtain the value interval of the current real-time collection interval; constructing a real-time collection interval to adjust the squirrel population; setting the maximum number of iterations of the real-time collection interval to adjust the squirrel population is And the current number of iterations is , respectively recorded as the maximum number of iterations for interval adjustment and the current number of iterations for interval adjustment; S422, adjusting the initial position of each squirrel in the squirrel population by setting the real-time collection interval according to the current real-time collection interval value interval, to obtain a second initial position set; S423, constructing the fitness function of adjusting the squirrel population by the real-time collection interval; S424, start iteration, and set the current iteration number of the interval adjustment to 1 before iteration; in each round of iteration, use the fitness function of the real-time acquisition interval adjustment squirrel population to calculate the fitness value of the position of each squirrel in the real-time acquisition interval adjustment squirrel population updated in the previous round of iteration, and update the position of each squirrel in the real-time acquisition interval adjustment squirrel population updated in the previous round of iteration; S425, when When , stop the iteration and get the second final global best fitness and the second final global best position; otherwise, continue to iterate until The second final global optimal fitness is used as the proportion data of insufficient power time period in the current optimized cycle; When the data of the proportion of the periodic power shortage time period after the current optimization is less than the threshold value of the proportion of the periodic power shortage time period, the second final global optimal position is used as the current final real-time collection interval; otherwise, return to S424 and continue to iterate until the data of the proportion of the periodic power shortage time period after the current optimization is less than the threshold value of the proportion of the periodic power shortage time period.

10. A system for implementing the method for regulating the energy storage capacity of a power generation system based on the complementarity of photovoltaic, wind power and storage as described in any one of claims 1 to 9.

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