A capacity optimization configuration method and system for a wind-solar-hydrogen hybrid energy storage system

Abstract

The present invention discloses a method and system for optimizing the configuration of the capacity of a wind-solar-hydrogen hybrid energy storage system, which relates to the field of electric energy storage technology. The invention collects historical operating data of wind power generation, photovoltaic power generation and hydrogen energy storage systems, and dynamically calculates the actual available capacity of each subsystem in combination with equipment performance attenuation and load changes, thereby achieving refinement and dynamic correction of capacity configuration and improving the scientific nature and accuracy of the configuration. By constructing the equipment power and capacity attenuation rate, the operating status can be accurately reflected to avoid economic losses and safety risks caused by excess or insufficient capacity. A prediction error model is constructed using historical meteorological and load data to dynamically adjust the capacity boundary, enhance the adaptability to meteorological and load fluctuations, and reduce regulation risks. For single failures of wind energy and solar energy, the compensation capacity demand is calculated and adjustment instructions are generated to improve fault tolerance, increase the utilization rate of renewable energy, and reduce investment and operation and maintenance costs.

Description

Technical Field

[0001] The present invention relates to the field of electric energy storage technology, and specifically to a capacity optimization configuration method and system for a wind-solar-hydrogen hybrid energy storage system. Background Art

[0002] As the global energy structure shifts toward clean, low-carbon energy, renewable energy sources such as wind and photovoltaics are being connected to the power system on a large scale, driving the development and application of green energy. However, as typical intermittent and fluctuating energy sources, the output power of wind and solar energy is significantly affected by meteorological conditions, leading to increased uncertainty and volatility in system operation, posing severe challenges to grid stability and reliability. Power storage technology, especially long-term energy storage technology, has become an important support for mitigating renewable energy fluctuations and ensuring grid stability due to its energy regulation and time-balance capabilities. Hydrogen energy storage, as an emerging long-term energy storage method, has attracted widespread attention due to its advantages such as high energy density and long storage time, and is gradually playing a key role in wind-solar hybrid systems.

[0003] Current wind-solar-hydrogen hybrid energy storage systems face numerous challenges in capacity configuration. Traditional capacity configuration often relies on static initial designs or empirical rules, lacking dynamic response and refined adjustments to equipment performance degradation, meteorological and environmental changes, and load demand fluctuations, making it difficult to achieve optimal capacity configuration. Furthermore, existing solutions often ignore the actual attenuation of equipment and historical operating data, resulting in insufficient or redundant capacity design, which not only affects the system's economic viability but also reduces its safety regulation capabilities. Especially under extreme climatic conditions, the risk of failure of single energy sources, such as wind or solar energy, is not effectively assessed and compensated, further limiting the stability and reliability of the system. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method and system for optimizing the capacity configuration of a wind-solar-hydrogen hybrid energy storage system to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for optimizing the capacity configuration of a wind-solar-hydrogen hybrid energy storage system, comprising the following steps:

[0006] S1. Collect historical operating data of wind power generation, photovoltaic power generation, and hydrogen energy storage systems in the target sub-area, mainly including the power output of each system and overall power load data. Combined with the power supply contribution ratio of each energy system in different time periods, preliminarily set the capacity ratio of wind energy, photovoltaic energy, and hydrogen energy storage systems to form a basic capacity configuration data set.

[0007] S2. Combine historical operation data to construct the wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f, and calculate the available capacity of the i-th subsystem after equipment operation attenuation. , forming a "list of available capacity after equipment operation attenuation";

[0008] S3. Combine historical operation data, read the load curve within the same time scale, and construct the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity , and the available capacity after the equipment of the i-th subsystem runs attenuated Make corrections and calculate the first adjustment capacity value of the i-th subsystem ;

[0009] S4. Based on historical meteorological and load data, wind speed forecast sequence, sunlight forecast sequence and hydrogen energy load forecast curve, a forecast error model is constructed to construct the error coefficient of the i-th subsystem. , calculate and predict the minimum safe capacity boundary of the i-th subsystem of each energy storage subsystem at the future time tg , used to determine the first adjustment capacity value Whether the medium capacity meets the basic regulation requirements under the forecast, and generating a first revised capacity configuration data set;

[0010] S5. Set two extreme scenarios: wind energy failure and solar energy failure. Set the duration ΔTf and maximum load Lmax of a single energy failure. Calculate the compensation capacity requirements of the other two subsystems. :If the corresponding first adjustment capacity value or If one of the conditions is met, a first adjustment instruction is generated and corrected to obtain a second corrected capacity configuration data set.

[0011] Preferably, S1 includes:

[0012] S11. Divide the target area into several sub-areas based on the geographical distribution, terrain characteristics and resource conditions of the project area, with each sub-area serving as an independent operation data analysis unit;

[0013] S12. Within each sub-region, under the premise that the initial capacity ratios of wind power, photovoltaic systems, and hydrogen energy storage systems have been set, collect historical operating data of the corresponding systems by region, including daily power generation data of the wind power system, daily power generation data of the photovoltaic system, and charge and discharge data of the hydrogen energy storage system;

[0014] The initial capacity ratio includes: the capacity ratio of wind energy, solar energy and hydrogen energy storage system is 5:3:2;

[0015] The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 4:4:2;

[0016] The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 3:5:2;

[0017] The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 6:2:2;

[0018] S13. Synchronously collect user load data for each sub-area, including daily total load, load change trend, and peak load value;

[0019] S14. Summarize the historical operating data and user load data of the corresponding system in each sub-region in a unified format, divide the time series by day or hour, and construct a multi-source basic data set including wind power, photovoltaic, hydrogen energy storage and load data;

[0020] S15. Based on the set initial capacity ratio, the multi-source basic data set is associated with the ratio structure of the initial capacity ratio to form a basic capacity configuration data set for subsequent capacity optimization analysis.

[0021] Preferably, S2 includes:

[0022] S21, call the basic capacity configuration data set established in S1, and separate the nominal capacity and historical operation data of the three subsystems: wind turbines, photovoltaic modules, and hydrogen energy storage;

[0023] From the historical operation data, the output power attenuation rate of the wind turbine De_f, the efficiency degradation rate of the photovoltaic module Pe_f and the effective capacity attenuation rate of the hydrogen storage tank He_f are constructed to calculate the available capacity C of each energy storage subsystem after the equipment operation attenuation i (ty), the specific method is:

[0024] S211. Assume that the nominal capacity of subsystem i is , year-level time index ty, where ty ≥ 1; ty ∈ {1, 2, …, Nyears}; Nyears represents the total number of years;

[0025] S212: The available capacity of the equipment of the i-th subsystem after operation attenuation is obtained by recursively calculating the following formula year by year. :

[0026]

[0027] in, Take the corresponding wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f respectively;

[0028] The calculation methods for wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f are as follows:

[0029]

[0030]

[0031]

[0032] Where Pref represents the reference year, which is the first year of operation of the wind turbine. The annual average unit wind speed output power of the wind turbine is in kW / m / s. Pn represents the annual average unit wind speed output power in the current statistical year in kW / m / s. The Pn value is calculated under a uniform annual average wind speed of 7 m / s. ηref represents the nominal conversion efficiency of the photovoltaic module in the initial stage of operation. ηn represents the average conversion efficiency in the current year. Indicates the total power generation of the components in the current year, It represents the total horizontal radiation in the current year, and A represents the total area of ​​PV panels;

[0033] Vref represents the maximum effective hydrogen storage capacity of the hydrogen storage tank at the initial stage of operation, in Nm³; Vn represents the maximum effective hydrogen storage capacity in the current statistical period, calculated based on the stable volume at the maximum inflation pressure;

[0034] S22, if =0, default , indicating no measurable degradation; if The value of The values ​​are mapped to the same table to form the “available capacity list after equipment operation degradation”.

[0035] Preferably, S3 includes:

[0036] S31. Based on historical operating data, read the load curve within the same time scale. The capacity redundancy analysis part uses the day-level td index to calculate the minimum daily load support capacity Cmin required by the i-th subsystem during the peak period. i (td), the minimum required capacity allocated to the subsystem in the load demand is allocated according to the demand, and the daily scale redundant capacity factor Dref_i(td) of the i-th subsystem is calculated according to the following formula:

[0037]

[0038] if >0, indicating that the current capacity exceeds the demand and there is redundancy;

[0039] if <0, indicating that the current capacity is less than the demand and there is a gap;

[0040] S32. Because some extreme weather or unexpected fluctuations can cause the redundancy value to be particularly large or small, including continuous rain and extreme peak load conditions, which can cause the i-th system to overreact, the sliding average method is used to calculate the mean value using a 7-day sliding window to update the daily scale redundant capacity factor Dref_i(td) of the i-th subsystem, and obtain the updated daily scale redundant capacity factor of the i-th subsystem :

[0041] ;

[0042] Among them, k represents the arrive The daily index within the time window represents the day number of "previous 6 days + current day". represents the redundant capacity factor on day q;

[0043] S33, based on the updated daily redundant capacity factor of the i-th subsystem Calculate the annual average redundancy factor of the i-th subsystem :

[0044] ;

[0045] Among them, Ny represents the current valid statistical days, which is 366 in leap years and 365 in normal years;

[0046] S34, combined with the minimum daily load support capacity Cmin required by the i-th subsystem during peak hours i (td), calculate the annual average minimum support capacity :

[0047] ;

[0048] S35, combined with the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity Available capacity after equipment operation attenuation for the i-th subsystem Make corrections and calculate the first adjustment capacity value of the i-th subsystem :

[0049] ;

[0050] in, represents the nominal capacity of the ith subsystem.

[0051] Preferably, S4 includes:

[0052] S41. Collect and obtain wind speed forecast data through regional meteorological stations or the weather forecast model NWP, obtain wind speed forecast sequences and sunlight forecast sequences, and obtain a hydrogen energy load forecast curve from the power dispatching center;

[0053] S42. Based on historical meteorological and load data, wind speed forecast sequence, sunlight forecast sequence and hydrogen energy load forecast curve, a prediction error model is constructed. Error samples are extracted for the environmental and load indicators associated with each subsystem, and the error sample value of the i-th subsystem is calculated. :

[0054]

[0055] in, Represents the predicted value of the i-th subsystem resource at the past time point ty, including: wind speed, light intensity or load prediction data; represents the actual value of the resource of the i-th subsystem measured at the past time point ty;

[0056] S43, error sample value of the i-th subsystem Perform normalization to obtain the error coefficient of the i-th subsystem :

[0057]

[0058] Here, ε is a very small positive number that prevents division by zero.

[0059] Preferably, S4 further includes:

[0060] S44: First adjustment capacity value based on the i-th subsystem , error coefficient of the i-th subsystem and the annual average redundancy factor of the i-th subsystem , calculate the minimum safe capacity boundary of the i-th subsystem at the future time tg :

[0061]

[0062] Among them, the minimum safety capacity boundary of the i-th subsystem is Safety capacity thresholds to meet future dispatch needs;

[0063] S45, if , indicating that the current capacity can meet the safety regulation requirements under the forecast load and meteorological conditions;

[0064] if , indicating insufficient capacity, calculation and The difference between the first adjustment capacity value of the i-th subsystem Add and update the first adjustment capacity value of the i-th subsystem , obtain the first revised capacity configuration data set through statistics.

[0065] Preferably, S5 includes:

[0066] S51, read the minimum safe capacity boundary of the i-th subsystem at the future time tg After first revising the capacity configuration dataset, define extreme failure scenarios, including:

[0067] Wind energy failure scenario and solar energy failure scenario, and statistics of the maximum peak load Lmax in the historical scheduling cycle;

[0068] S52, set the fault gap During this period, wind power is set to fail, that is, wind power output All are 0, Indicates the starting time of extreme failure, ΔTf indicates the duration of single energy failure, which is set by experts or based on historical statistics; wind energy function gap coefficient is calculated :

[0069] ;

[0070] Photovoltaic failure, that is, photovoltaic output All are 0, and the photovoltaic function gap coefficient is calculated:

[0071] ;

[0072] S53, setting {h, j, k} = {wind, solar, hydrogen storage} to be different;

[0073] If h is the failure source, calculate the compensation capacity requirements of the other two subsystems :

[0074]

[0075] in, ;

[0076] ;

[0077] in, represents the compensation capacity requirement of the jth non-failure subsystem, represents the compensation capacity requirement of the kth non-failure subsystem, represents the first adjustment capacity value of the jth non-failure subsystem, represents the first adjustment capacity value of the kth non-failure subsystem, Lmax is the maximum peak load of the historical scheduling period; =1, and is the weight coefficient of the non-failed subsystem, which is allocated according to the scheduling strategy, using 50%–50% or according to the proportion of remaining adjustable capacity;

[0078] When wind power fails, i.e. h = wind, the compensation is borne by photovoltaic (j) and hydrogen storage (k);

[0079] When photovoltaic power fails, i.e. h = light, the compensation is borne by wind power (j) and hydrogen storage (k);

[0080] For each type of failure scenario and each future time tg, just follow the above steps to first calculate the gap coefficient and then allocate the compensation capacity.

[0081] Preferably, S5 further includes:

[0082] S54. For each future time point tg, extract the first adjusted capacity value of the i-th subsystem , and determine whether the current compensation needs are met, including:

[0083] If the corresponding first adjustment capacity value ,and , it means that no correction is required; if any one of the items is not satisfied, it means that the current capacity cannot meet the regulation requirements under the failure of a single energy source, and a first adjustment instruction is generated for adjusting the corresponding first adjustment capacity value. Add the difference with the corresponding compensation capacity requirement to obtain the second adjustment capacity value , and updates the capacity configuration to form a second revised capacity configuration data set.

[0084] A capacity optimization configuration system for a wind-solar-hydrogen hybrid energy storage system, comprising:

[0085] The data acquisition module is used to collect historical operating data of wind power generation, photovoltaic power generation, and hydrogen energy storage systems in the target sub-area, mainly including the power output of each system and the overall power load data. Based on the power supply contribution ratio of each energy system in different time periods, the capacity ratio of wind energy, photovoltaic energy, and hydrogen energy storage systems is preliminarily determined to form a basic capacity configuration data set.

[0086] The equipment degradation analysis module combines historical operating data to construct the wind turbine output power degradation rate De_f, the photovoltaic module efficiency degradation rate Pe_f, and the hydrogen storage tank effective capacity degradation rate He_f. It calculates the available capacity Ci(t) of each energy storage subsystem after equipment operation degradation and forms a "list of available capacity after equipment operation degradation";

[0087] The first capacity correction module combines historical operation data, reads the load curve within the same time scale, and constructs the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity , and the available capacity after the equipment of the i-th subsystem runs attenuated Make corrections and calculate the first adjustment capacity value of the i-th subsystem ;

[0088] The prediction error analysis module builds a prediction error model based on historical meteorological and load data, wind speed prediction sequence, sunlight prediction sequence and hydrogen energy load prediction curve, and constructs the error coefficient of the i-th subsystem , calculate and predict the minimum safe capacity boundary of the i-th subsystem of each energy storage subsystem at the future time tg , used to determine the first adjustment capacity value Whether the medium capacity meets the basic regulation requirements under the forecast, and generating a first revised capacity configuration data set;

[0089] The single energy failure simulation module sets two extreme scenarios: wind energy failure and solar energy failure. It also sets the duration ΔTf and maximum load Lmax of the single energy failure and calculates the compensation capacity requirements of the other two subsystems. :If the corresponding first adjustment capacity value or If one of the conditions is met, a first adjustment instruction is generated and corrected to obtain a second corrected capacity configuration data set.

[0090] The present invention provides a method and system for optimizing the capacity configuration of a wind-solar-hydrogen hybrid energy storage system. This method has the following beneficial effects:

[0091] This invention collects historical operating data from wind power generation, photovoltaic power generation, and hydrogen energy storage systems within a target subregion. Combined with equipment performance degradation and load variations, it dynamically calculates the actual available capacity of each subsystem, enabling refined and dynamic capacity allocation. This significantly improves the scientific nature and accuracy of capacity allocation and avoids the shortcomings of traditional static design. By constructing the wind turbine power degradation rate, photovoltaic module efficiency degradation rate, and hydrogen tank capacity degradation rate, it accurately reflects the equipment's operating status, ensuring that capacity design matches actual equipment performance, reducing economic losses and safety risks caused by over- or undercapacity, and improving system stability. A prediction error model constructed using historical meteorological and load data quantifies prediction uncertainty, dynamically adjusts capacity boundaries, and enhances the adaptability of capacity allocation to future weather and load fluctuations, effectively reducing the adjustment risk caused by prediction errors. For single energy source failure scenarios, wind and solar energy are configured with a set failure duration and maximum load, scientifically calculating the compensation capacity requirements of other subsystems and promptly generating capacity adjustment instructions. This ensures stable system operation in extreme weather or equipment failure scenarios, improving the overall system's fault tolerance and reliability. By rationally allocating capacity, reducing unnecessary energy storage redundancy, optimizing resource allocation, and improving the utilization rate of renewable energy, investment and operation and maintenance costs can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 A schematic diagram of the steps of a method for optimizing capacity configuration of a wind-solar-hydrogen hybrid energy storage system according to the present invention;

[0093] Figure 2 This is a schematic diagram of the capacity optimization configuration system flow of a wind-solar-hydrogen hybrid energy storage system of the present invention. DETAILED DESCRIPTION

[0094] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0095] Example 1

[0096] See also Figure 1 The present invention provides a method for optimizing the capacity configuration of a wind-solar-hydrogen hybrid energy storage system, comprising the following steps:

[0097] S1. Collect historical operating data of wind power generation, photovoltaic power generation, and hydrogen energy storage systems in the target sub-area, mainly including the power output of each system and overall power load data. Combined with the power supply contribution ratio of each energy system in different time periods, preliminarily set the capacity ratio of wind energy, photovoltaic energy, and hydrogen energy storage systems to form a basic capacity configuration data set.

[0098] S2. Combine historical operation data to construct the wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f, and calculate the available capacity of the i-th subsystem after equipment operation attenuation. , forming a "list of available capacity after equipment operation attenuation";

[0099] S3. Combine historical operation data, read the load curve within the same time scale, and construct the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity , and the available capacity after the equipment of the i-th subsystem runs attenuated Make corrections and calculate the first adjustment capacity value of the i-th subsystem ;

[0100] S4. Based on historical meteorological and load data, wind speed forecast sequence, sunlight forecast sequence and hydrogen energy load forecast curve, a forecast error model is constructed to construct the error coefficient of the i-th subsystem. , calculate and predict the minimum safe capacity boundary of the i-th subsystem of each energy storage subsystem at the future time tg , used to determine the first adjustment capacity value Whether the medium capacity meets the basic regulation requirements under the forecast, and generating a first revised capacity configuration data set;

[0101] S5. Set two extreme scenarios: wind energy failure and solar energy failure. Set the duration ΔTf and maximum load Lmax of a single energy failure. Calculate the compensation capacity requirements of the other two subsystems. :If the corresponding first adjustment capacity value or If one of the conditions is met, a first adjustment instruction is generated and corrected to obtain a second corrected capacity configuration data set.

[0102] In this embodiment, the present invention collects historical operating data from wind power generation, photovoltaic power generation, and hydrogen energy storage systems within a target subregion. Combined with equipment performance degradation and load variations, the actual available capacity of each subsystem is dynamically calculated, enabling refined and dynamic capacity allocation. This significantly improves the scientific nature and accuracy of capacity allocation and avoids the shortcomings of traditional static design. By constructing the wind turbine power degradation rate, photovoltaic module efficiency degradation rate, and hydrogen tank capacity degradation rate, the system accurately reflects the equipment's operating status, ensuring that capacity design matches actual equipment performance, reducing economic losses and safety risks caused by over- or undercapacity, and improving system stability. A prediction error model constructed using historical meteorological and load data quantifies prediction uncertainty, dynamically adjusts capacity boundaries, and enhances the adaptability of capacity allocation to future weather and load fluctuations, effectively reducing the adjustment risk caused by prediction errors. For single energy source failure scenarios, the system sets the failure duration and maximum load, scientifically calculates the compensation capacity requirements of other subsystems, and promptly generates capacity adjustment instructions. This ensures stable system operation in extreme weather or equipment failure scenarios, improving the overall system's fault tolerance and reliability. By rationally allocating capacity, reducing unnecessary energy storage redundancy, optimizing resource allocation, and improving the utilization rate of renewable energy, investment and operation and maintenance costs can be reduced.

[0103] Example 2

[0104] This embodiment is explained in Example 1. Specifically, S1 includes:

[0105] S11. Divide the target area into several sub-areas based on the geographical distribution, terrain characteristics and resource conditions of the project area, with each sub-area serving as an independent operation data analysis unit;

[0106] S12. Within each sub-region, under the premise that the initial capacity ratios of wind power, photovoltaic systems, and hydrogen energy storage systems have been set, collect historical operating data of the corresponding systems by region, including daily power generation data of the wind power system, daily power generation data of the photovoltaic system, and charge and discharge data of the hydrogen energy storage system;

[0107] Initial capacity ratio includes: Initial capacity ratio includes but is not limited to:

[0108] The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 5:3:2, which is suitable for areas with abundant wind resources, medium sunlight and certain energy storage and regulation capabilities.

[0109] The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 4:4:2, which is suitable for areas with balanced wind and solar resources and moderate load fluctuations;

[0110] The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 3:5:2, which is suitable for scenarios with good sunlight resources, high load during the day, and energy storage support required at night;

[0111] The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 6:2:2, which is suitable for scenarios where wind energy is dominant, sunshine conditions are poor and energy storage demand is moderate.

[0112] S13. Synchronously collect user load data for each sub-area, including daily total load, load change trend, and peak load value;

[0113] S14. Summarize the historical operating data and user load data of the corresponding system in each sub-region in a unified format, divide the time series by day or hour, and construct a multi-source basic data set including wind power, photovoltaic, hydrogen energy storage and load data;

[0114] S15. Based on the set initial capacity ratio, the multi-source basic data set is associated with the ratio structure of the initial capacity ratio to form a basic capacity configuration data set for subsequent capacity optimization analysis.

[0115] In this embodiment, by subdividing the project area into multiple sub-areas and combining the geographical distribution, resource conditions and electricity load characteristics of each area, the present invention realizes the refined management of capacity configuration. A variety of initial capacity ratio schemes are targeted at different wind and solar resources and load demand characteristics, which improves the pertinence and applicability of the configuration scheme, avoids a one-size-fits-all extensive design, and improves the overall efficiency of the system. By collecting detailed historical operating data of wind power generation, photovoltaic power generation and hydrogen energy storage systems, and combining them with the temporal changes of user loads, a basic data set of multi-source fusion is constructed, providing solid data support for subsequent dynamic capacity optimization. The high spatiotemporal resolution and multi-dimensional coverage of the data help to fully reflect the system operation characteristics and load requirements, and enhance the accuracy and reliability of model analysis.

[0116] Example 3

[0117] This embodiment is explained in Example 1. Specifically, S2 includes:

[0118] S21, call the basic capacity configuration data set established in S1, and separate the nominal capacity and historical operation data of the three subsystems: wind turbines, photovoltaic modules, and hydrogen energy storage;

[0119] From the historical operation data, the output power attenuation rate of the wind turbine De_f, the efficiency degradation rate of the photovoltaic module Pe_f and the effective capacity attenuation rate of the hydrogen storage tank He_f are constructed to calculate the available capacity C of each energy storage subsystem after the equipment operation attenuation i (ty), the specific method is:

[0120] S211. Assume that the nominal capacity of subsystem i is , year-level time index ty, where ty ≥ 1; ty ∈ {1, 2, …, Nyears}; Nyears represents the total number of years;

[0121] S212: The available capacity of the equipment of the i-th subsystem after operation attenuation is obtained by recursively calculating the following formula year by year. :

[0122]

[0123] in, Take the corresponding wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f respectively;

[0124] The calculation methods for wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f are as follows:

[0125]

[0126]

[0127]

[0128] Where Pref represents the reference year, which is the first year of operation of the wind turbine. The annual average unit wind speed output power of the wind turbine is in kW / m / s. Pn represents the annual average unit wind speed output power in the current statistical year in kW / m / s. The Pn value is calculated under a uniform annual average wind speed of 7 m / s. ηref represents the nominal conversion efficiency of the photovoltaic module in the initial stage of operation. ηn represents the average conversion efficiency in the current year. Indicates the total power generation of the components in the current year, It represents the total horizontal radiation in the current year, and A represents the total area of ​​PV panels;

[0129] Vref represents the maximum effective hydrogen storage capacity of the hydrogen storage tank at the initial stage of operation, in Nm³; Vn represents the maximum effective hydrogen storage capacity in the current statistical period, calculated based on the stable volume at the maximum inflation pressure;

[0130] S22, if =0, default , indicating no measurable degradation; if The value of The values ​​are mapped to the same table to form a “list of available capacity after equipment operation degradation”.

[0131] In this embodiment, this step systematically analyzes the historical operating data of wind turbines, photovoltaic modules and hydrogen storage tanks to effectively quantify the operating attenuation of each subsystem equipment and accurately reflect its actual available capacity. By recursively calculating the attenuation rate of equipment performance year by year, it is possible to dynamically reveal the performance degradation trend of different energy equipment over time, avoiding the deviation caused by relying solely on the designed nominal capacity. Combined with the changes in wind speed output power of wind turbines, the degradation of conversion efficiency of photovoltaic modules and the actual changes in the capacity of hydrogen storage tanks, a list of available capacity after equipment operation attenuation is formed, achieving accurate assessment of equipment status and rational use of capacity resources. This method helps to avoid over- or under-configuration of capacity and improve the economy and safety of system configuration.

[0132] Example 4

[0133] This embodiment is explained in Example 1. Specifically, S3 includes:

[0134] S31. Based on historical operating data, read the load curve within the same time scale. The capacity redundancy analysis part uses the day-level td index to calculate the minimum daily load support capacity Cmin required by the i-th subsystem during the peak period. i (td), the minimum required capacity allocated to the subsystem in the load demand is allocated according to the demand, and the daily scale redundant capacity factor Dref_i(td) of the i-th subsystem is calculated according to the following formula:

[0135]

[0136] if >0, indicating that the current capacity exceeds the demand and there is redundancy;

[0137] if <0, indicating that the current capacity is less than the demand and there is a gap;

[0138] S32. Because some extreme weather or unexpected fluctuations can cause the redundancy value to be particularly large or small, including continuous rain and extreme peak load conditions, which can cause the i-th system to overreact, the sliding average method is used to calculate the mean value using a 7-day sliding window to update the daily scale redundant capacity factor Dref_i(td) of the i-th subsystem, and obtain the updated daily scale redundant capacity factor of the i-th subsystem :

[0139] ;

[0140] Among them, k represents the arrive The daily index within the time window represents the day number of "previous 6 days + current day". represents the redundant capacity factor on day q;

[0141] S33, based on the updated daily redundant capacity factor of the i-th subsystem Calculate the annual average redundancy factor of the i-th subsystem :

[0142] ;

[0143] Among them, Ny represents the current valid statistical days, which is 366 in leap years and 365 in normal years;

[0144] S34, combined with the minimum daily load support capacity Cmin required by the i-th subsystem during peak hours i (td), calculate the annual average minimum support capacity :

[0145] ;

[0146] S35, combined with the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity Available capacity after equipment operation attenuation for the i-th subsystem Make corrections and calculate the first adjustment capacity value of the i-th subsystem :

[0147]

[0148] in, Representing the nominal capacity of the i-th subsystem, "redundancy level" × "minimum requirement" can be used to calculate how much to add or subtract.

[0149] In this embodiment, this step constructs a redundant capacity factor of the subsystem by deeply analyzing the historical load curve and the actual capacity status of the equipment, effectively reflecting the dynamic balance state of capacity supply and demand. By combining the minimum support capacity of the daily load and the capacity redundancy factor, the time nodes of excess or insufficient capacity are accurately identified, thereby improving the timeliness and pertinence of system capacity management. The 7-day sliding average method is introduced to smooth the abnormal data caused by extreme weather and load fluctuations, avoiding the system from over-responding due to short-term drastic changes, and enhancing the stability and robustness of capacity adjustment. The calculation of the annual average redundancy factor further summarizes the long-term trend of capacity supply and demand, and provides a macro reference for the scientific planning of system capacity. By combining the minimum support capacity and the redundancy factor to dynamically correct the equipment operation attenuation capacity, a reasonable increase or decrease in capacity is achieved, avoiding over-configuration or insufficient capacity, and improving the economy and reliability of the system. This method effectively supports the safe and stable operation of the wind-solar-hydrogen hybrid energy storage system, and ensures the efficient regulation and load matching of the power grid for renewable energy.

[0150] Example 5

[0151] This embodiment is explained in Example 1. Specifically, S4 includes:

[0152] S41. Collect and obtain wind speed forecast data through regional meteorological stations or the weather forecast model NWP, obtain wind speed forecast sequences and sunlight forecast sequences, and obtain a hydrogen energy load forecast curve from the power dispatching center;

[0153] S42. Based on historical meteorological and load data, wind speed forecast sequence, sunlight forecast sequence and hydrogen energy load forecast curve, a prediction error model is constructed. Error samples are extracted for the environmental and load indicators associated with each subsystem, and the error sample value of the i-th subsystem is calculated. :

[0154]

[0155] in, Represents the predicted value of the i-th subsystem resource at the past time point ty, including: wind speed, light intensity or load prediction data; represents the actual value of the resource of the i-th subsystem measured at the past time point ty;

[0156] S43, error sample value of the i-th subsystem Perform normalization to obtain the error coefficient of the i-th subsystem :

[0157]

[0158] Here, ε is a very small positive number that prevents division by zero.

[0159] S44: First adjustment capacity value based on the i-th subsystem , error coefficient of the i-th subsystem and the annual average redundancy factor of the ith subsystem , calculate the minimum safe capacity boundary of the i-th subsystem at the future time tg :

[0160]

[0161] Among them, the minimum safety capacity boundary of the i-th subsystem is Safety capacity thresholds to meet future dispatch needs;

[0162] S45, if , indicating that the current capacity can meet the safety regulation requirements under the forecast load and meteorological conditions;

[0163] if , indicating insufficient capacity, calculation and The difference between the first adjustment capacity value of the i-th subsystem Add and update the first adjustment capacity value of the i-th subsystem , obtain the first revised capacity configuration data set through statistics.

[0164] In this embodiment, by introducing high-precision wind speed, sunlight and hydrogen energy load forecast data, combined with historical meteorological and load data, a dynamic capacity safety assessment mechanism based on the error model is established, which effectively improves the scientific nature and foresight of capacity configuration. By normalizing the error coefficient, the uncertainty of the forecast data is quantified, further providing a basis for the setting of capacity boundaries, ensuring that capacity adjustments take into account possible future fluctuation risks. Based on the combination of the error coefficient and the annual redundancy factor, the calculated minimum safe capacity boundary accurately reflects the actual needs of the system under variable environmental and load conditions, avoiding the blindness and irrationality of traditional static configuration. By dynamically judging whether the capacity meets future scheduling needs, the first adjustment capacity value is adjusted in time to achieve adaptive correction of capacity, ensuring the stable operation of the system under load peaks or extreme meteorological conditions. This method effectively reduces the waste of energy storage resources, improves system safety and economic benefits, and provides solid data support and theoretical guarantee for the optimal configuration of wind-solar-hydrogen hybrid energy storage systems.

[0165] Example 6

[0166] This embodiment is explained in Example 1. Specifically, S5 includes:

[0167] S51, read the minimum safe capacity boundary of the i-th subsystem at the future time tg After first revising the capacity configuration dataset, define extreme failure scenarios, including:

[0168] Wind energy failure scenario and solar energy failure scenario, and statistics of the maximum peak load Lmax in the historical scheduling cycle;

[0169] S52, set the fault gap During this period, wind power is set to fail, that is, wind power output All are 0, Indicates the starting time of extreme failure, ΔTf indicates the duration of single energy failure, which is set by experts or based on historical statistics; wind energy function gap coefficient is calculated :

[0170] ;

[0171] Photovoltaic failure, that is, photovoltaic output All are 0, and the photovoltaic function gap coefficient is calculated:

[0172] ;

[0173] S53, setting {h, j, k} = {wind, solar, hydrogen storage} to be different;

[0174] If h is the failure source, calculate the compensation capacity requirements of the other two subsystems :

[0175]

[0176] in, ;

[0177] ;

[0178] in, represents the compensation capacity requirement of the jth non-failure subsystem, represents the compensation capacity requirement of the kth non-failure subsystem, represents the first adjustment capacity value of the jth non-failure subsystem, represents the first adjustment capacity value of the kth non-failure subsystem, Lmax is the maximum peak load of the historical scheduling period; =1, and is the weight coefficient of the non-failed subsystem, which is allocated according to the scheduling strategy, using 50%–50% or according to the proportion of remaining adjustable capacity;

[0179] When wind power fails, i.e. h = wind, the compensation is borne by photovoltaic (j) and hydrogen storage (k);

[0180] When photovoltaic power fails, i.e. h = light, the compensation is borne by wind power (j) and hydrogen storage (k);

[0181] For each type of failure scenario and each future time tg, just follow the above steps to first calculate the gap coefficient and then allocate the compensation capacity.

[0182] S54. For each future time point tg, extract the first adjusted capacity value of the i-th subsystem , and determine whether the current compensation needs are met, including:

[0183] If the corresponding first adjustment capacity value ,and , it means that no correction is required; if any one of the items is not satisfied, it means that the current capacity cannot meet the regulation requirements under the failure of a single energy source, and a first adjustment instruction is generated for adjusting the corresponding first adjustment capacity value. Add the difference with the corresponding compensation capacity requirement to obtain the second adjustment capacity value , and updates the capacity configuration to form a second revised capacity configuration data set.

[0184] For example: When the second adjustment capacity value for: ;when When the second adjustment capacity value for: .

[0185] This step systematically evaluates and verifies the fault tolerance of the capacity configuration for the extreme failure scenarios of a single energy source that may occur in the wind-solar-hydrogen hybrid energy storage system, effectively enhancing the safety and stability of the system. By setting two key extreme situations of wind energy and solar energy failure, combined with the historical maximum peak load, the functional gap coefficient is accurately calculated, and the impact of failure on the system capacity is scientifically quantified. Based on the dynamic compensation capacity demand allocation strategy, a reasonable compensation distribution between non-failure subsystems is achieved, avoiding the risk of insufficient capacity caused by a single energy source failure. Further, through the real-time comparison of the first adjustment capacity value with the compensation demand, the adjustment instruction is dynamically generated to achieve timely replenishment and adjustment of the capacity, ensuring that the system can still meet the load regulation demand during the fault.

[0186] Example 7

[0187] A wind-solar-hydrogen hybrid energy storage system capacity optimization configuration system, please refer to Figure 2 ,include:

[0188] The data acquisition module is used to collect historical operating data of wind power generation, photovoltaic power generation, and hydrogen energy storage systems in the target sub-area, mainly including the power output of each system and the overall power load data. Based on the power supply contribution ratio of each energy system in different time periods, the capacity ratio of wind energy, photovoltaic energy, and hydrogen energy storage systems is preliminarily determined to form a basic capacity configuration data set.

[0189] The equipment degradation analysis module combines historical operating data to construct the wind turbine output power degradation rate De_f, the photovoltaic module efficiency degradation rate Pe_f, and the hydrogen storage tank effective capacity degradation rate He_f. It calculates the available capacity Ci(t) of each energy storage subsystem after equipment operation degradation and forms a "list of available capacity after equipment operation degradation";

[0190] The first capacity correction module combines historical operation data, reads the load curve within the same time scale, and constructs the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity , and the available capacity after the equipment of the i-th subsystem runs attenuated Make corrections and calculate the first adjustment capacity value of the i-th subsystem ;

[0191] The prediction error analysis module builds a prediction error model based on historical meteorological and load data, wind speed prediction sequence, sunlight prediction sequence and hydrogen energy load prediction curve, and constructs the error coefficient of the i-th subsystem , calculate and predict the minimum safe capacity boundary of the i-th subsystem of each energy storage subsystem at the future time tg , used to determine the first adjustment capacity value Whether the medium capacity meets the basic regulation requirements under the forecast, and generating a first revised capacity configuration data set;

[0192] The single energy failure simulation module sets two extreme scenarios: wind energy failure and solar energy failure. It also sets the duration ΔTf and maximum load Lmax of the single energy failure and calculates the compensation capacity requirements of the other two subsystems. :If the corresponding first adjustment capacity value or If one of the conditions is met, a first adjustment instruction is generated and corrected to obtain a second corrected capacity configuration data set.

[0193] In this embodiment, the data acquisition module accurately captures historical operating and load data for wind, photovoltaic, and hydrogen energy storage systems within each subregion. Combined with the power supply contribution ratios over different time periods, this module scientifically generates a basic capacity configuration dataset, ensuring the rationality and representativeness of the initial capacity design. The equipment degradation analysis module comprehensively considers the power and capacity degradation factors of equipment during operation, dynamically calculating the available capacity after equipment degradation. This avoids the risk of capacity miscalculation due to equipment aging and improves the authenticity and accuracy of capacity configuration.

[0194] The first capacity correction module, based on historical load curves, utilizes redundancy factors and minimum support capacity indicators to effectively correct equipment capacity attenuation, ensuring that capacity allocation is neither excessive nor insufficient, thereby improving resource utilization. The prediction error analysis module dynamically calculates the minimum safe capacity boundary for future moments by constructing a prediction error model for wind speed, sunlight, and load. This effectively quantifies the capacity fluctuation risk caused by prediction uncertainty and provides a scientific basis for capacity configuration. The single energy failure simulation module introduces extreme failure scenarios to simulate the capacity compensation requirements in the event of a single source failure of wind or solar energy. Through real-time adjustment and capacity compensation mechanisms, it enhances the system's fault tolerance for extreme events and ensures stable system operation.

[0195] Overall, this system achieves dynamic optimization and intelligent adjustment of capacity configuration through a technical path that combines data-driven and model prediction. It not only improves the regulation capability and safety margin of the hybrid energy storage system, but also effectively reduces operation and maintenance costs and economic risks.

[0196] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0197] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by those skilled in the art according to actual conditions. The above is only a preferred specific implementation method of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A capacity optimization configuration method for a wind-solar-hydrogen hybrid energy storage system, characterized in that: The following steps are involved: S1. Collect historical operating data of wind power generation, photovoltaic power generation, and hydrogen energy storage systems in the target sub-area, mainly including the power output of each system and overall power load data. Combined with the power supply contribution ratio of each energy system in different time periods, preliminarily set the capacity ratio of wind energy, photovoltaic energy, and hydrogen energy storage systems to form a basic capacity configuration data set. S2. Combine historical operation data to construct the wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f, and calculate the available capacity of the i-th subsystem after equipment operation attenuation. , forming a "list of available capacity after equipment operation attenuation"; S3. Combine historical operation data, read the load curve within the same time scale, and construct the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity , and the available capacity after the equipment of the i-th subsystem runs attenuated Make corrections and calculate the first adjustment capacity value of the i-th subsystem ; S4. Based on historical meteorological and load data, wind speed forecast sequence, sunlight forecast sequence and hydrogen energy load forecast curve, a forecast error model is constructed to construct the error coefficient of the i-th subsystem. , calculate and predict the minimum safe capacity boundary of the i-th subsystem of each energy storage subsystem at the future time tg , used to determine the first adjustment capacity value Whether the medium capacity meets the basic regulation requirements under the forecast, and generating a first revised capacity configuration data set; S5. Set two extreme scenarios: wind energy failure and solar energy failure. Set the duration ΔTf and maximum load Lmax of a single energy failure. Calculate the compensation capacity requirements of the other two subsystems. :If the corresponding first adjustment capacity value or If one of the conditions is met, a first adjustment instruction is generated and corrected to obtain a second corrected capacity configuration data set.

2. A capacity optimization configuration method for a wind-solar-hydrogen hybrid energy storage system according to claim 1, characterized in that: S1 includes: S11. Divide the target area into several sub-areas based on the project area's geographical distribution, terrain characteristics, and resource conditions. Each sub-area serves as an independent operational data analysis unit. S12. Within each sub-region, under the premise that the initial capacity ratios of wind power, photovoltaic systems, and hydrogen energy storage systems have been set, collect historical operating data of the corresponding systems by region, including daily power generation data of the wind power system, daily power generation data of the photovoltaic system, and charge and discharge data of the hydrogen energy storage system; The initial capacity ratio includes: the capacity ratio of wind energy, solar energy and hydrogen energy storage system is 5:3:2; The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 4:4:2; The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 3:5:2; The capacity ratio of wind energy, solar energy and hydrogen energy storage system is 6:2:2; S13. Synchronously collect user load data for each sub-area, including daily total load, load change trend, and peak load value; S14. Summarize the historical operating data and user load data of the corresponding system in each sub-region in a unified format, divide the time series by day or hour, and construct a multi-source basic data set including wind power, photovoltaic, hydrogen energy storage and load data; S15. Based on the set initial capacity ratio, the multi-source basic data set is associated with the ratio structure of the initial capacity ratio to form a basic capacity configuration data set for subsequent capacity optimization analysis.

3. A capacity optimization configuration method for a wind-solar-hydrogen hybrid energy storage system according to claim 2, characterized in that S2 include: S21, call the basic capacity configuration data set established in S1, and separate the nominal capacity and historical operation data of the three subsystems: wind turbines, photovoltaic modules, and hydrogen energy storage; From the historical operation data, the output power attenuation rate of the wind turbine De_f, the efficiency degradation rate of the photovoltaic module Pe_f and the effective capacity attenuation rate of the hydrogen storage tank He_f are constructed to calculate the available capacity C of each energy storage subsystem after the equipment operation attenuation i (ty), the specific method is: S211. Assume that the nominal capacity of subsystem i is , year-level time index ty, where ty ≥ 1; ty ∈ {1, 2, …, Nyears}; Nyears represents the total number of years; S212: The available capacity of the equipment of the i-th subsystem after operation attenuation is obtained by recursively calculating the following formula year by year. : in, Take the corresponding wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f respectively; The calculation methods for wind turbine output power attenuation rate De_f, photovoltaic module efficiency degradation rate Pe_f and hydrogen storage tank effective capacity attenuation rate He_f are as follows: Where Pref represents the reference year, which is the first year of operation of the wind turbine. The annual average unit wind speed output power of the wind turbine is in kW / m / s. Pn represents the annual average unit wind speed output power in the current statistical year in kW / m / s. The Pn value is calculated under a uniform annual average wind speed of 7 m / s. ηref represents the nominal conversion efficiency of the photovoltaic module in the initial stage of operation. ηn represents the average conversion efficiency in the current year. Indicates the total power generation of the components in the current year, It represents the total horizontal radiation in the current year, and A represents the total area of ​​PV panels; Vref represents the maximum effective hydrogen storage capacity of the hydrogen storage tank at the initial stage of operation, in Nm³; Vn represents the maximum effective hydrogen storage capacity in the current statistical period, calculated based on the stable volume at the maximum inflation pressure; S22, if =0, default , indicating no measurable degradation; if The value of The values ​​are mapped to the same table to form a "list of available capacity after equipment operation degradation".

4. A capacity optimization configuration method for a wind-solar-hydrogen hybrid energy storage system according to claim 3, characterized in that S3 include: S31. Based on historical operating data, read the load curve within the same time scale. The capacity redundancy analysis part uses the day-level td index to calculate the minimum daily load support capacity Cmin required by the i-th subsystem during the peak period. i (td), the minimum required capacity allocated to the subsystem in the load demand is allocated according to the demand, and the daily scale redundant capacity factor Dref_i(td) of the i-th subsystem is calculated according to the following formula: if >0, indicating that the current capacity exceeds the demand and there is redundancy; if <0, indicating that the current capacity is less than the demand and there is a gap; S32. Because some extreme weather or unexpected fluctuations can cause the redundancy value to be particularly large or small, including continuous rain and extreme peak load conditions, which can cause the i-th system to overreact, the sliding average method is used to calculate the mean value using a 7-day sliding window to update the daily scale redundant capacity factor Dref_i(td) of the i-th subsystem, and obtain the updated daily scale redundant capacity factor of the i-th subsystem : ； Among them, k represents the arrive The daily index within the time window represents the day number of "previous 6 days + current day". represents the redundant capacity factor on day q; S33, based on the updated daily redundant capacity factor of the i-th subsystem Calculate the annual average redundancy factor of the i-th subsystem : ； Among them, Ny represents the current valid statistical days, which is 366 in leap years and 365 in normal years; S34, combined with the minimum daily load support capacity Cmin required by the i-th subsystem during peak hours i (td), calculate the annual average minimum support capacity : ； S35, combined with the annual average redundancy factor of the i-th subsystem and the average annual minimum support capacity Available capacity after equipment operation attenuation for the i-th subsystem Make corrections and calculate the first adjustment capacity value of the i-th subsystem : ； in, represents the nominal capacity of the ith subsystem.

5. A method for optimizing capacity configuration of a wind-solar-hydrogen hybrid energy storage system according to claim 4, characterized in that S4 include: S41. Collect and obtain wind speed forecast data through regional meteorological stations or the weather forecast model NWP, obtain wind speed forecast sequences and sunlight forecast sequences, and obtain a hydrogen energy load forecast curve from the power dispatching center; S42. Based on historical meteorological and load data, wind speed forecast sequence, sunlight forecast sequence and hydrogen energy load forecast curve, a prediction error model is constructed. Error samples are extracted for the environmental and load indicators associated with each subsystem, and the error sample value of the i-th subsystem is calculated. : in, Represents the predicted value of the i-th subsystem resource at the past time point ty, including: wind speed, light intensity or load prediction data; represents the actual value of the resource of the i-th subsystem measured at the past time point ty; S43, error sample value of the i-th subsystem Perform normalization to obtain the error coefficient of the i-th subsystem : Here, ε is a very small positive number that prevents division by zero.

6. A capacity optimization configuration method for a wind-solar-hydrogen hybrid energy storage system according to claim 5, characterized in that: The S4 also includes: S44: First adjustment capacity value based on the i-th subsystem , error coefficient of the i-th subsystem and the annual average redundancy factor of the ith subsystem , calculate the minimum safe capacity boundary of the i-th subsystem at the future time tg : Among them, the minimum safety capacity boundary of the i-th subsystem is Safety capacity thresholds to meet future dispatch needs; S45, if , indicating that the current capacity can meet the safety regulation requirements under the forecast load and meteorological conditions; if , indicating insufficient capacity, calculation and The difference between the first adjustment capacity value of the i-th subsystem Add and update the first adjustment capacity value of the i-th subsystem , obtain the first revised capacity configuration data set through statistics.

7. A method for optimizing capacity configuration of a wind-solar-hydrogen hybrid energy storage system according to claim 6, characterized in that S5 include: S51, read the minimum safe capacity boundary of the i-th subsystem at the future time tg After first revising the capacity configuration dataset, define extreme failure scenarios, including: Wind energy failure scenario and solar energy failure scenario, and statistics of the maximum peak load Lmax in the historical scheduling cycle; S52, set the fault gap During this period, wind power is set to fail, that is, wind power output All are 0, Indicates the starting time of extreme failure, ΔTf indicates the duration of single energy failure, which is set by experts or based on historical statistics; wind energy function gap coefficient is calculated : ； Photovoltaic failure, that is, photovoltaic output All are 0, and the photovoltaic function gap coefficient is calculated: ； S53, setting {h, j, k} = {wind, solar, hydrogen storage} to be different; If h is the failure source, calculate the compensation capacity requirements of the other two subsystems : in, ; ； in, represents the compensation capacity requirement of the jth non-failure subsystem, represents the compensation capacity requirement of the kth non-failure subsystem, represents the first adjustment capacity value of the jth non-failure subsystem, represents the first adjustment capacity value of the kth non-failure subsystem, Lmax is the maximum peak load of the historical scheduling period; =1, and is the weight coefficient of the non-failed subsystem, which is allocated according to the scheduling strategy, using 50%–50% or according to the proportion of remaining adjustable capacity; When wind power fails, i.e. h = wind, the compensation is borne by photovoltaic (j) and hydrogen storage (k); When photovoltaic power fails, that is, h = light, the compensation is borne by wind power (j) and hydrogen storage (k).

8. A capacity optimization configuration method for a wind-solar-hydrogen hybrid energy storage system according to claim 7, characterized in that: The S5 also includes: S54. For each future time point tg, extract the first adjusted capacity value of the i-th subsystem , and determine whether the current compensation needs are met, including: If the corresponding first adjustment capacity value ,and , it means that no correction is required; if any one of the items is not satisfied, it means that the current capacity cannot meet the regulation requirements under the failure of a single energy source, and a first adjustment instruction is generated for adjusting the corresponding first adjustment capacity value. Add the difference with the corresponding compensation capacity requirement to obtain the second adjustment capacity value , and updates the capacity configuration to form a second revised capacity configuration data set.

9. A capacity optimization configuration system for a wind-solar-hydrogen hybrid energy storage system, applied to a capacity optimization configuration method for a wind-solar-hydrogen hybrid energy storage system according to any one of claims 1 to 8, characterized in that: include: The data acquisition module is used to collect historical operating data of wind power generation, photovoltaic power generation, and hydrogen energy storage systems in the target sub-area, mainly including the power output of each system and the overall power load data. Based on the power supply contribution ratio of each energy system in different time periods, the capacity ratio of wind energy, photovoltaic energy, and hydrogen energy storage systems is preliminarily determined to form a basic capacity configuration data set. The equipment degradation analysis module combines historical operating data to construct the wind turbine output power degradation rate De_f, the photovoltaic module efficiency degradation rate Pe_f, and the hydrogen storage tank effective capacity degradation rate He_f. It calculates the available capacity Ci(t) of each energy storage subsystem after equipment operation degradation and forms a "list of available capacity after equipment operation degradation"; The first capacity correction module combines historical operation data, reads the load curve within the same time scale, and constructs the annual average redundancy factor of the i-th subsystem. and the average annual minimum support capacity , and the available capacity after the equipment of the i-th subsystem runs attenuated Make corrections and calculate the first adjustment capacity value of the i-th subsystem ; The prediction error analysis module builds a prediction error model based on historical meteorological and load data, wind speed prediction sequence, sunlight prediction sequence and hydrogen energy load prediction curve, and constructs the error coefficient of the i-th subsystem , calculate and predict the minimum safe capacity boundary of the i-th subsystem of each energy storage subsystem at the future time tg , used to determine the first adjustment capacity value Whether the medium capacity meets the basic regulation requirements under the forecast, and generating a first revised capacity configuration data set; The single energy failure simulation module sets two extreme scenarios: wind energy failure and solar energy failure. It also sets the duration ΔTf and maximum load Lmax of the single energy failure and calculates the compensation capacity requirements of the other two subsystems. :If the corresponding first adjustment capacity value or If one of the conditions is met, a first adjustment instruction is generated and corrected to obtain a second corrected capacity configuration data set.

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