Method and related device for assessing the degree of aridity of wind resources

Abstract

The application discloses a kind of wind power resource drought degree evaluation method and related equipment.The method comprises: obtaining the hourly wind speed data of target area in history and evaluation period, and determining hourly wind power output sequence in combination with wind turbine power curve;Based on historical wind power output sequence sample, determine reliable output reference value and recharge threshold value for each duration t based on the statistical distribution of historical sample of each duration t;Calculate the equivalent energy buffer capacity of system in the evaluation window for resisting wind power underproduction;Based on reliable output reference value and recharge threshold value, calculate power gap and power surplus hour by hour;According to the reliable reference gap power that is not satisfied by energy storage and energy storage energy state, calculate operating deficit;Based on equivalent energy buffer capacity and operating deficit, calculate wind power resource drought index.Can solve the problem of the shortcomings of the existing method for quantifying and analyzing wind power resource drought, including fixed threshold method, relative anomaly method and standardized index method.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a method and related equipment for assessing the drought level of wind power resources. Background Technology

[0002] Wind energy, as an important component of clean and renewable energy, is driven by meteorological conditions, exhibiting significant randomness, volatility, and intermittency. When widespread and prolonged periods of low wind speeds occur, wind farm output can plummet, even approaching zero; this phenomenon is known in academia and industry as "wind resource drought." As the penetration rate of wind power in the power system increases annually, wind resource drought events can lead to system power shortages, a surge in peak-shaving and reserve capacity demands, and increased pressure on grid power balance and frequency control, thus posing a severe challenge to the safe and stable operation of the power system. Existing methods for quantifying and analyzing wind resource drought include the fixed threshold method, the relative anomaly method, and the standardized index method. The fixed threshold method, which judges drought based on a single wind resource threshold, fails to reflect the varying degrees of wind resource insufficiency and is highly sensitive to short-term fluctuations in wind resources. It is prone to repeatedly triggering a "drought-relief" state near the critical wind resource threshold and lacks accumulated memory of drought processes, leading to unstable judgment results. The relative anomaly method only linearly reflects the proportion of current wind resources deviating from the average level of previous years, without considering the variability and skewness of wind speed distribution, ignoring the distribution structure and long-term trend changes of wind resources. Furthermore, the same deviation proportion may represent different levels of risk in different regions or seasons, resulting in poor comparability between regions and seasons. Because it relies on real-time output comparison, it often only detects drought after it has lasted for a period of time, leading to a problem of early warning lag. The standardized index method requires a large amount of historical data to fit the probability distribution of wind resources, making it highly dependent on data. It is prone to outliers when the sample size is small or when wind resources fluctuate greatly, and it responds too strongly to short-term disturbances. It also lacks state lag and inertia, limiting its practicality and scheduling adaptability. Summary of the Invention

[0003] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0004] To address the shortcomings of existing methods for quantifying and analyzing wind resource drought, including the fixed threshold method, relative anomaly method, and standardized index method, this invention proposes, in a first aspect, a method for assessing the degree of wind resource drought, comprising:

[0005] Obtain hourly wind speed data for the target area during the historical and assessment periods. The hourly wind speed data is then converted into an hourly wind power output sequence based on the wind turbine power curve. ;

[0006] Based on historical wind power output sequence Based on the historical sample statistical distribution for each duration t, a reliable output benchmark value is determined for each duration t. With recharge threshold ,in, , The conditional empirical quantile function is calculated on samples with non-zero output. The conditional empirical quantile function is calculated on samples that do not reach the upper limit of the rated power. For reliability parameters, These are the parameters for triggering charging.

[0007] The system's equivalent energy buffering capacity to withstand wind power underproduction during the assessment period. ,in, , For the rated capacity of energy storage, For virtual energy storage capacity;

[0008] Based on the reliable output benchmark value With the recharge threshold Calculate the power shortage hourly With electricity surplus ,in, , ;

[0009] Given the energy storage charge / discharge efficiency and power constraints, the energy storage state is... By performing time-by-time recursive simulation, the actual charging power of the energy storage is obtained. With actual energy storage discharge power and to Perform hourly recursive updates;

[0010] Calculate the reliable reference power gap that is not satisfied by energy storage and according to Recursive update unrecoverable red child Based on the energy storage state Calculate the recoverable red child And obtain the running red child Among them, when the energy storage is fully charged ;

[0011] Based on the aforementioned equivalent energy buffering capacity With the aforementioned running red child Calculating the drought index of wind resources ,in The wind power drought index Used to characterize the degree of drought of wind power resources during the assessment period.

[0012] Optionally, the hourly wind power output sequence Calculated according to the following segmented power curve;

[0013] when hour, ;

[0014] when hour, ;

[0015] when hour,

[0016] in, These are the fan cut-in velocity, rated velocity, and cut-out velocity, respectively. This refers to the rated power of the wind turbine.

[0017] Optionally, the The .

[0018] Optionally, the virtual energy storage capacity according to Calculated, where, To evaluate the reliable output benchmark value within the window The representative value is the median. This is the timescale at which the system can withstand or repair itself.

[0019] Optionally, the energy storage state To perform time-by-time recursive simulation, the following must be satisfied:

[0020]

[0021] and,

[0022]

[0023]

[0024] in, The time step is 1 hour. For charging efficiency, For discharge efficiency, and These represent the actual charging power and actual discharging power of the energy storage, respectively. For the rated capacity of energy storage, For minimum available energy storage capacity, Maximum charging power, This represents the maximum discharge power.

[0025] Optionally, the It is updated according to the following recursive relationship:

[0026]

[0027] in, To run the red oocyte attenuation coefficient, and .

[0028] Optional, also includes:

[0029] Based on wind resource drought index Output drought severity classification results, in Under these circumstances, it is a safe operating zone. In the case of a warning buffer zone, In the case of an emergency supply zone, the emergency supply zone is determined to be in a state of wind power resource drought.

[0030] Secondly, the present invention also proposes a device for assessing the drought level of wind power resources, comprising:

[0031] The acquisition unit is used to acquire hourly wind speed data for the target area during historical and assessment periods. The hourly wind speed data is then converted into an hourly wind power output sequence based on the wind turbine power curve. ;

[0032] Computational unit, used for:

[0033] Based on historical wind power output sequence Based on the historical sample statistical distribution for each duration t, a reliable output benchmark value is determined for each duration t. With recharge threshold ,in, , The conditional empirical quantile function is calculated on samples with non-zero output. The conditional empirical quantile function is calculated on samples that do not reach the upper limit of the rated power. For reliability parameters, These are the parameters for triggering charging.

[0034] The system's equivalent energy buffering capacity to withstand wind power underproduction during the assessment period. ,in, , For the rated capacity of energy storage, For virtual energy storage capacity;

[0035] Based on the reliable output benchmark value With the recharge threshold Calculate the power shortage hourly With electricity surplus ,in, , ;

[0036] Given the energy storage charge / discharge efficiency and power constraints, the energy storage state is... By performing time-by-time recursive simulation, the actual charging power of the energy storage is obtained. With the actual discharge power of energy storage and to Perform hourly recursive updates;

[0037] Calculate the reliable reference power gap that is not satisfied by energy storage and according to Recursive update unrecoverable red child Based on the energy storage state Calculate the recoverable red child And obtain the running red child Among them, when the energy storage is fully charged ;

[0038] Prediction unit, used for prediction based on the equivalent energy buffering capacity With the aforementioned running red child Calculating the drought index of wind resources ,in The wind power drought index Used to characterize the degree of drought of wind power resources during the assessment period.

[0039] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the wind resource drought assessment method as described in any of the first aspects above.

[0040] Fourthly, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the wind resource drought assessment method of any of the above claims in the first aspect.

[0041] In summary, the wind power resource drought assessment method proposed in this application converts wind power underproduction into an accumulative operational deficit, which is then quantified relative to the equivalent buffer capacity. This operational deficit accumulates underproduction in the form of energy, preventing frequent drought-relief cycles caused by short-term rebounds. Energy storage power, capacity, and efficiency constraints are explicitly incorporated. and The recursive nature of WDI allows changes to be traced back to whether the deficit is too deep, the recharge window is insufficient, or the power limit is restricted. The irrecoverable deficit accumulates the gaps that energy storage cannot cover and can serve as a quantitative basis for external power support at the dispatch level, rather than just a binary warning of drought. Because recharge is limited by power and acceptance, WDI will not immediately recover to a high value due to short-term high winds, which is more in line with engineering recovery mechanisms.

[0042] Correspondingly, the wind resource drought assessment device, electronic device, and computer-readable storage medium of the present invention also have the same effect. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0044] Figure 1 This application provides a schematic flowchart of a method for assessing the drought level of wind resources.

[0045] Figure 2 A graph showing the time variation characteristics of wind power output from wind turbines in three different regions between 1984 and 2023, provided for embodiments of this application;

[0046] Figure 3 A schematic diagram illustrating the annual frequency, duration, and intensity of wind farm drought events in three different regions between 1984 and 2023, provided for embodiments of this application;

[0047] Figure 4 Examples of wind resource drought characteristics and mechanisms in a wind farm from 1984 to 2023 under two scenarios: no energy storage and 4-hour energy storage, provided for embodiments of this application;

[0048] Figure 5 A schematic diagram of a wind power resource drought assessment device provided in this application embodiment;

[0049] Figure 6 This is a schematic diagram of an electronic device for assessing the drought level of wind resources, provided as an embodiment of this application. Detailed Implementation

[0050] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0051] To address the shortcomings of existing methods for quantifying and analyzing wind-induced drought, including the fixed threshold method, relative anomaly method, and standardized index method, please refer to [link to relevant documentation]. Figure 1 This is a schematic diagram of a method for assessing the drought level of wind resources provided in an embodiment of this application, which may specifically include steps S110 to S170.

[0052] S110, Obtain hourly wind speed data for the target area within the historical and assessment period. The hourly wind speed data is then converted into an hourly wind power output sequence based on the wind turbine power curve. ;

[0053] S120, based on historical wind power output sequence Based on the historical sample statistical distribution for each duration t, a reliable output benchmark value is determined for each duration t. With recharge threshold ,in, , The conditional empirical quantile function is calculated on samples with non-zero output. The conditional empirical quantile function is calculated on samples that do not reach the upper limit of the rated power. For reliability parameters, These are the parameters for triggering charging.

[0054] S130, Calculate the equivalent energy buffer capacity of the system to withstand wind power underproduction during the assessment period. ,in, , For the rated capacity of energy storage, Virtual energy storage capacity; where equivalent energy buffer capacity refers to the equivalent combined capacity of the energy storage system capacity and other flexible resources of the system, such as backup power or peak-shaving capacity, in the energy dimension.

[0055] S140, based on the reliable output reference value With the recharge threshold Calculate the power shortage hourly With electricity surplus ,in, , ;

[0056] S150, under given energy storage charge / discharge efficiency and power constraints, the energy storage state... By performing time-by-time recursive simulation, the actual charging power of the energy storage is obtained. With the actual discharge power of energy storage and to Perform hourly recursive updates;

[0057] S160, Calculate the reliable reference power gap that is not met by energy storage. and according to Recursive update unrecoverable red child Based on the energy storage state Calculate the recoverable infant And obtain the running red child Among them, when the energy storage is fully charged .

[0058] S170, based on the aforementioned equivalent energy buffering capacity With the aforementioned running red child Calculating the drought index of wind resources ,in The wind power drought index Used to characterize the degree of drought of wind power resources during the assessment period.

[0059] Understandably, wind resource drought is defined as an extreme weather event where wind speed or wind power output remains below a certain reliability threshold for an extended period. The aforementioned method uses the cumulative operating deficit to describe the energy gap integral caused by the system's output falling below the reliability benchmark during drought. It emphasizes that this index simultaneously includes both idle energy storage capacity and grid supply gap to characterize the continuous state of the system from internal buffer depletion to external collapse, and considers the support and peak-recharge capabilities of the energy storage system to assess the severity of wind resource drought. The aforementioned index for quantifying the degree of wind resource drought can be called the operating deficit and asymmetric recovery. The Wind Drought Index (WDI) accumulates the portion of wind power output that is consistently below reliable levels into a deficit, taking into account the support of energy storage systems and peak recharge capacity, to assess the severity of wind resource drought. Wind power output refers to the electricity generated by wind turbines, primarily affected by wind speed and turbine parameters. It is a key indicator for measuring wind farm efficiency and contribution to the grid, typically measured in kilowatts or megawatts. Rated power of a wind turbine refers to the maximum electrical power that a wind turbine can continuously output under rated wind speed conditions, also typically measured in kilowatts or megawatts. Asymmetric recovery refers to the physical characteristic that, during system recovery from a drought state, the charging recovery rate is slower than the discharging consumption rate due to limitations imposed by physical equipment such as converter power limits, battery charging rates, and grid capacity.

[0060] Understandably, the Wind Resource Drought Index (WDI) addresses the phenomenon of a sharp drop in wind power output caused by widespread and prolonged low wind speeds. It accumulates the continuous underproduction of wind power at a relatively reliable supply level as an operational deficit, and converts energy storage buffers and the system's non-energy storage support capacity into an equivalent energy buffer capacity, establishing a relative quantitative relationship between the deficit and the buffer, thus forming the Wind Resource Drought Index. The scheme emphasizes that the recovery process after drought is not symmetrical and reversible. Even with a high output window, recharging is still limited by charging power and system acceptance capacity. Therefore, an asymmetric recovery mechanism is introduced to make the index more stable in reflecting the recovery process after drought, avoiding drastic state jumps caused by short-term rebounds. Compared to methods relying solely on a single threshold, this scheme accumulates the portion of output below the reliable level as an operational deficit, avoiding frequent state switching due to short-term fluctuations near the threshold, thereby improving the stability and interpretability of wind and drought identification. This is achieved through recursion under constraints of energy storage charging power limits, discharging power limits, capacity boundaries, and efficiency. Furthermore, by separating recoverable and unrecoverable deficits, the system can stably reflect the asymmetric recovery process with limited replenishment, avoiding mistaking short-term high winds for complete recovery. Based on the value range and physical meaning of WDI, the system state can be divided into a safe operating zone, a warning buffer zone, and an emergency replenishment zone. In the emergency replenishment zone, gap filling instructions are generated by combining the growth rate of unrecoverable deficits, enabling precise activation of backup resources or purchase of power according to the gap magnitude, avoiding blind activation of backups.

[0061] For example, The calculation formula is as follows:

[0062] (1)

[0063] in, This indicates the total energy buffer capacity of the system to resist / make up for wind power underproduction within the assessment window, which consists of energy storage buffer and other flexible resource buffers of the system. Deadlinet Cumulative operating deficit (unit: MWh); This refers to the equivalent energy buffer capacity (unit: MWh). According to this definition, When the red child is not running , When the red ball equals the energy storage capacity , If the red calamity exceeds the energy storage capacity ,but A negative value indicates a drought state in wind power resources, and the larger the negative value (the greater the excess capacity). Approaching -1.

[0064] For example, for a selected time range (such as a month or a year), based on hourly wind speed data and given energy storage parameters, calculate... First, a wind power output time series is constructed, obtaining hourly wind speed data at a height of 100 meters from measured data or reanalysis datasets (such as ERA5) for the target area during the assessment period. Based on the wind turbine power curve, the wind speed... Converted into corresponding wind power output Sequence. The formula for calculating wind power output is as follows:

[0065] (2)

[0066] in, These are the wind turbine cut-in wind speed (the minimum wind speed at which power generation begins), rated wind speed (the wind speed at which rated power is reached), and cut-out wind speed (the wind speed at which power generation is stopped for safety reasons). This refers to the rated power of the fan. For example, the parameters of a certain model of fan, H146-3.4, are as follows: , , , .

[0067] For example, the next step can be to determine a reliable output reference value. With recharge threshold To describe the wind power output in each duration t Reliable power supply levels and high-output periods available for energy storage recharge, based on years of wind power output The statistical distribution is used to determine the reliable output benchmark value. With recharge threshold To avoid the wind power output samples showing numerous duplicate values ​​at 0 and upper limits due to the cut-off wind speed and rated power upper limits, which would cause the conventional quantile threshold to degenerate to 0 or the upper limit and become ineffective, this invention uses conditional climate quantile functions to construct... and The calculation formula is as follows:

[0068] (3)

[0069] (4)

[0070] in, For a moment t Reliable output benchmark, used for underproduction judgment: when This indicates that the wind power output is below the reliability benchmark and an operating deficit has been generated; For a moment t The recharge threshold is used to trigger energy storage recharge: when This indicates that the wind power is in a high-output range and can be used for energy storage recharging. Wind power output sequence Duration t The empirical quantile function over many years (considering seasonal differences, for each duration) t (Corresponding to the historical distribution of that day and time throughout the year). Non-zero output sample The conditional quantile function calculated above, such as after sorting the output samples in ascending order, takes the smallest sample value that satisfies the empirical cumulative distribution not less than the target quantile level as the quantile point; The sample size has not reached the upper limit. The conditional distribution function calculated above can be calculated in the same way as the empirical quantile function mentioned above. These parameters determine the quantile level of the reliability benchmark. The charging trigger parameters determine the quantile level of the high output threshold.

[0071] For example, calculating equivalent energy buffering capacity Equivalent energy buffering capacity (Unit: MWh), representing the sum of energy storage capacity and the system's non-energy storage support capacity (unit: MWh), calculated using the following formula:

[0072] (5)

[0073] (6)

[0074] in, Rated capacity of the energy storage system (unit: MWh); Virtual energy storage capacity, also known as the equivalent energy buffer that the system can withstand (unit: MWh), represents the "equivalent energy buffer" that the system can rely on other resources (thermal power, hydropower, external transmission, demand response, etc.) to withstand the power shortage when there is no energy storage or insufficient energy storage. To assess a reliable reference power representative value (MW) within the window (or in a long-term climate sense), it is recommended to take... The median (or the mean or other robust statistics may be used as needed). The system's tolerable / repairable timescale (h) represents the typical duration during which the system can withstand and repair the shortfall by relying on non-energy storage resources (reserve and regulation capabilities, etc.) after a wind power shortage occurs. Specifically, it refers to the equivalent dispatchable energy space obtained through inter-regional grid interconnection scheduling or demand-side response, which, together with the physical battery capacity, constitutes the system's total buffer pool.

[0075] For example, calculating the power shortage and electricity surplus Based on the two dynamic thresholds mentioned above, the power shortage can be calculated hourly. and electricity surplus :

[0076] (7)

[0077] (8)

[0078] in, For a moment The power shortage (unit: MW); when At that time, the system experienced a power shortage. ;when Then it is . For a moment Electricity surplus (unit: MW), when At that time, the system has surplus electricity that can be used for energy storage charging, that is... Otherwise, the electricity surplus is zero. At any given moment, it is impossible for both to occur simultaneously. and (Wind power cannot be both below the benchmark and above the threshold).

[0079] For example, during the state simulation of an energy storage system, the following methods can be used: Indicates the time of energy storage t Energy State of Cell (SoC, unit: MWh), assuming the initial energy storage state Given (e.g., full charge or a specified value), the energy storage state update at each time step follows energy conservation and efficiency loss:

[0080] (9)

[0081] in, The energy state of the stored energy at the previous moment (unit: MWh); The time step is 1 hour. For charging efficiency, For discharge efficiency; and These represent the actual charging power and actual discharging power of the energy storage (unit: MW), respectively. Their values ​​are constrained by both the wind power surplus / deficit and the capacity of the energy storage device. The specific calculation formula is as follows:

[0082] (10)

[0083] (11)

[0084] in, Rated energy storage capacity (unit: MWh); This is the minimum available energy storage capacity (unit: MWh), typically set to 0; Maximum charging power (unit: MW), The maximum discharge power is given in MW. For example, in formula (10)... This indicates the current incomplete energy storage capacity, which can be expressed in MWh. Divide by This is converted into the maximum power that can be charged within 1 hour; the minimum value of the three terms in formula (10) is taken to ensure that the energy storage charging power does not exceed the available surplus, the rated power of the equipment, and the remaining allowable charging amount of the capacity. In formula (11) This represents the available energy in the current energy storage (MWh), multiplied by and divide by The maximum power that can be continuously output within 1 hour is obtained. Formula (11) ensures that the actual discharge power does not exceed the production shortage demand, the rated discharge power of the equipment, and the power supply capacity of the energy storage.

[0085] It is understandable that the charging and discharging behavior of energy storage under wind power fluctuations can be simulated hourly using equations (7)-(11): when there is a surplus When there is a shortage, the energy storage should be charged as much as possible but not exceed the limit; when there is a shortage... When the energy storage is fully charged and there is still a surplus, the surplus is discarded and will not affect subsequent deficits; if the energy storage is depleted and there is still a deficit, the unmet portion will be included in the uncompensated deficit.

[0086] For example, the next step is to run the red child. The calculation, running the red child (Unit: MWh) represents the cumulative unmet energy demand of wind power output relative to a reliable baseline, consisting of two parts: recoverable deficit. and an irrecoverable child The initial moment of innocence For any given timet , The calculation formula is as follows:

[0087] (12)

[0088] Can restore infantile health Defined as the energy shortfall that is not yet fully charged, representing the deficit that can be compensated for by subsequent charging (unit: MWh). The calculation formula is as follows:

[0089] (13)

[0090] When the energy storage is fully charged ,but (Irrecoverable red child); when the energy storage section is emptied, this value is the energy difference between the energy storage section and the point where it is fully charged.

[0091] Irrecoverable infant This is defined as the accumulated energy gap that is not filled by energy storage due to limitations in storage power or capacity; that is, the portion of the deficit that cannot be compensated by subsequent wind power surplus. The calculation formula is as follows:

[0092] (14)

[0093] in, The irrecoverable red child at the previous time step (unit: MWh); The operating deficit attenuation coefficient is used to simulate the process of the power grid gradually recovering through backup resources after wind power shortages. The recommended value is 0.98, which means that the operating deficit naturally decreases by 2% per hour in the early stage. This refers to the reliable benchmark energy gap (unit: MW) that is not met by energy storage. In other words, when a power shortage occurs... Furthermore, energy storage and discharge cannot fully meet the requirements. The part. The calculation formula is as follows:

[0094] (15)

[0095] In conclusion, It is the accumulation of all power gaps that fail to meet reliable benchmarks over time, and This reflects the current degree to which energy storage is not fully charged, and it changes dynamically with the charging and discharging of energy storage. When wind power is in a prolonged slump and energy storage is gradually depleted, It will increase until it reaches (Energy storage depletion); if the production shortage continues to exceed energy storage capacity, The increase indicates an accumulation of deficits exceeding storage capacity. Conversely, when a sufficient surplus of high output occurs... At that time, energy storage charging improves It can restore the infancy of a newborn child. Reduced or even reduced to zero, but the original state cannot be restored. Once generated, it will not decrease.

[0096] For example, finally, the wind resource drought index is calculated. The calculation first involves selecting the required assessment time period and hourly wind speed data based on the drought assessment needs of wind resources. And calculate wind power output. Based on historical data over many years, calculate the reliable power output benchmark for each moment. With recharge threshold Then, determine the equivalent energy buffer capacity. And calculate the power shortage With electricity surplus And at a given energy storage capacity Calculate the actual charging power under the constraints of charging and discharging power / efficiency. With discharge power Recursively update stored energy Subsequently, according to Calculate the recoverable red child and an irrecoverable child Thus, the total operating red child is obtained. Finally, Substituting into formula (1), we obtain the wind resource drought index. Repeat the above process to obtain the WDI sequence or the statistical indicators of interest for each time step within the target period.

[0097] For example, it can also be based on the wind resource drought index. WDI The adaptive hierarchical control generates scheduling control commands based on real-time calculations by the controller. WDI The following hierarchical control strategy will be implemented for the value:

[0098] Safe operating area ( The system is in surplus. The controller issues a command allowing the energy storage system to participate in grid frequency regulation ancillary services, and to operate at maximum power. Absorbing wind power surplus.

[0099] Early warning buffer zone ( The accumulated deficit begins to consume energy storage. The controller locks the energy storage release authority, using it only to smooth out wind power fluctuations and stop participating in external market transactions in order to preserve survival capacity.

[0100] Emergency supply area ( At this point, the physical energy storage has been exhausted or is nearly exhausted, and the system enters the virtual energy accumulation phase. When the price falls below a preset threshold (e.g., -0.2), the system automatically generates a long-term gap-filling command, activating a backup diesel generator or purchasing inter-regional spot power. The size of the gap-filling power is related to the irrecoverable deficit. The growth rate is proportional to the amount of funds available, thus achieving precise gap compensation rather than blindly activating full reserves.

[0101] For example, wind resource drought events can also be identified and warned, and a wind resource drought index can be generated. ;when hour, This indicates that the energy storage is fully charged and there is no accumulated short-term power shortage, meaning the system is in a safe state; when hour, This indicates that the energy storage is critically depleted, and the system is at a critical point; when hour, This indicates a capacity deficit (usually caused by extremely persistent low wind speeds), signifying that the system has entered a state of wind resource drought; with Increase A value approaching -1 indicates a more severe wind resource drought. Through this indicator system, this invention can dynamically track and quantify the degree of insufficient wind power output relative to a reliable benchmark, and the buffering role played by energy storage. The process of the index decreasing from 1 to 0 and then to a negative value depicts the development of a drought event: from normal (sufficient energy storage) to strain (exhausted energy storage) and then to depletion (irrecoverable power shortage). This index provides a direct basis for power system planning and operation.

[0102] Understandably, the above method quantifies the occurrence and severity of wind resource drought by calculating the cumulative energy gap relative to the energy storage replenishment capacity over a certain period. A method for quantifying wind resource drought using operational deficits and asymmetric recovery (…) This method is a novel approach to characterizing wind resource drought. Compared to fixed threshold, relative anomaly, and standardized index methods, it places greater emphasis on the dynamic processes and engineering risks of wind resource drought. Primarily used to characterize the cumulative and recovery features of time series data in low-value ranges, it has broad application prospects in the wind energy field, including wind resource assessment, grid dispatching decisions, energy storage allocation, reserve capacity arrangement, and new energy consumption strategies. It can also be used for financial market analysis, meteorological data analysis, and economic indicator monitoring. In describing wind resource drought, it boasts advantages such as strong physical interpretability, wide applicability, and stable output signals. Compared to traditional statistical methods such as fixed threshold, relative anomaly, and standardized index (SREPI), this method eliminates false drought warnings caused by low wind speeds but fully charged batteries by introducing energy storage status feedback. Its unique unrecoverable deficit mechanism fills the gap in traditional battery management systems that cannot quantify the deficit after battery depletion, providing grid dispatchers with accurate external power support requirements, rather than simply providing wind resource drought warnings. Furthermore, this invention also considers the asymmetric recovery caused by converter power limitations, making the prediction of the end time of wind power droughts more consistent with the actual physical characteristics of power equipment than traditional meteorological statistical methods. This method not only has significant application value in areas such as wind power shortage risk assessment, energy storage and reserve capacity configuration, and grid dispatch early warning, but also possesses versatility, allowing for smooth transfer to drought analysis of other types of renewable energy, such as the analysis of persistent events of insufficient photovoltaic power generation and the assessment of supply shortages during the dry season for hydropower, thus providing new tools and ideas for broader energy reliability research.

[0103] It should be noted that when using the above methods to assess wind resource drought, wind power output is used to represent wind energy. However, wind speed, capacity factor, or wind energy density can also be used to characterize wind energy, depending on the research needs. In addition, the wind resource drought quantification methods and systems based on depletion and asymmetric recovery can also be used to assess and monitor other forms of energy drought, such as solar energy and hydropower.

[0104] According to some embodiments, see Figures 2 to 4 The above method is applied to three wind farms in different regions, as shown in Table 1. The three wind farms are the first wind farm, the second wind farm, and the third wind farm. Figure 2 This paper presents the temporal variation series of wind power output of three wind farms over nearly 40 years (1984-2023). Based on the wind power output data of the past 40 years, the hourly Wind Resource Drought Index (WDI) was calculated from 1984 to 2024 to reflect the dynamic changes in wind resource drought in different years of the wind farms. Figure 3This study presents the annual frequency, duration, and intensity changes of wind resource drought events at three wind farms from 1984 to 2023. Comparing the three wind farms, the second wind farm experienced the most frequent (25 cumulative events), longest duration (2273 cumulative hours), and highest intensity (143.18 cumulative hours) wind resource drought events over the past 40 years. This indicates that wind resource droughts at this wind farm are more likely to develop into long-duration, deep-deficient systemic production shortages (making energy storage more difficult to replenish), with the risk primarily driven by the combined effects of duration and intensity. In contrast, the first and third wind farms mostly experienced low-frequency, short-duration, and low-intensity droughts, with only a few years, such as 2019-2020, showing moderately high duration or intensity, making them more closely resemble occasional and recoverable wind droughts. Regarding decadal differences, the second wind farm has a higher proportion of red years in its high-value area, indicating a more prominent wind resource drought risk over the past 20 years (2004-2023). Figure 4 The study demonstrates the mitigating effect of energy storage on the drought risk of wind resources from both long-term statistical distribution and single-event mechanism perspectives. Firstly, in the diurnal distribution map from 1984 to 2023, the scenario without energy storage ( Figure 4 a) The presence of a large number of orange pixels indicates that, over a significant number of years and periods within the year, the system has been operating at a reliable power output baseline. Under constraints, it will enter The wind resource drought state means that the wind power output below the reliable level has a "continuous accumulation" characteristic over time, and is more likely to exceed the system's equivalent buffer capacity without energy storage buffer, thus manifesting as more frequent and dispersed wind resource drought events. In contrast, after configuring 4h energy storage ( Figure 4 (c) The significant reduction and increased fragmentation of orange pixels indicate that energy storage significantly reduced the frequency and duration of drought events through inter-hour power transfer. This is mainly because when wind power is short-term low, energy storage provides compensation through discharge; when wind power is short-term high, energy storage absorbs excess power through charging, thus weakening the continuous accumulation of under-generation, making the system more stable, and reducing... This situation occurred. Secondly, Figure 4 It also amplifies the wind resource drought event that lasted for about a week from November 1st to November 6th, 2022. Figure 4 (b, d) reveals process evidence for this energy storage replenishment mechanism. In the absence of energy storage ( Figure 4 b) The net grid-connected power output (solid black line) was significantly lower than the reliable benchmark (dashed black line) for multiple periods, resulting in an orange gap. Frequent occurrences and large peak values ​​indicate that the underproduction cannot be absorbed by the internal buffer and can only be exposed as an unmet demand gap; while in the 4-hour energy storage scenario ( Figure 4 d) The green discharge band appears during low wind periods, making the net grid-connected power output closer to the reliable power output benchmark. This significantly reduced the area and intensity of the orange gap, directly demonstrating the ability of energy storage to convert short-term power shortages into offsets that can be mitigated by pre-stored energy. It's important to emphasize that a small amount of residual orange gap is still visible in the figure, indicating that while 4-hour energy storage can significantly alleviate, it does not completely eliminate wind resource drought lasting for several days. When the duration of low wind speeds exceeds the support time of energy storage, or when the high-output recharge window is insufficient, the system may still experience unmet needs and enter a drought state. Therefore, energy storage significantly reduces the impact of grid-connected net output and its alignment with reliable output benchmarks. The probability of an event occurring and the intensity of exposure are considered, but for long-lasting, highly cumulative drought processes, longer-duration energy storage, increased power constraints, or the addition of other flexible resources (equivalent buffers) are still needed. Only then can risks be further reduced. The Wind Resource Drought (WDI) method of this invention can effectively quantify the characteristics of wind resource drought and monitor short-, medium-, and long-term wind resource drought conditions according to different time scales. This method can provide a scientific basis for wind resource drought assessment, support the site selection and planning of wind energy projects, optimize wind farm energy storage configuration and grid dispatch strategies, and improve power system stability and wind energy resource utilization efficiency. Table 1 shows the basic information of three wind farms. Figure 3 The annual frequency (times / year, defined as 4 consecutive hours of WDI less than 0 as one wind resource drought event), duration (hours / year, cumulative hours of events per year), and intensity (cumulative WDI value within the event, absolute value) of wind resource drought events in different regions are shown. Years from 1984 to 2003 are marked in black, and years from 2004 to 2023 are marked in red. Figure 4 (a, c) shows the daily WDI discrimination results: orange indicates WDI < 0 (wind resource drought occurs, i.e., the operating deficit exceeds the equivalent buffer capacity), and blue indicates WDI ≥ 0. (a) is the scenario without energy storage, and (c) is the scenario with 4-hour energy storage (energy capacity is rated power × 4h). The black box marks a typical wind drought process lasting about one week (2022-11-01—2022-11-06), and the arrows correspond to the detailed diagram on the right. (b, d) shows the hourly wind power output decomposition of this typical process: blue represents the original wind power output. Green represents the energy storage discharge power. Yellow indicates energy storage charging power. Orange indicates a gap that does not meet the reliable output benchmark. The solid black line represents the net grid-connected power output, and the dashed black line represents the reliable power output benchmark. (b) represents the scenario without energy storage, and (d) represents the scenario with 4 hours of energy storage. Among them, net grid-connected power output refers to the effective amount of electricity actually delivered to the grid by renewable energy sources such as photovoltaic or wind power after they are connected to the grid, after deducting their own consumption such as inverter losses or reactive power demand.

[0105] Table 1

[0106]

[0107] In some cases, the construction of conditional climate quantile functions based on multi-year distributions is taken into consideration. and This emphasizes that each duration t corresponds to the historical distribution of that day and time within a year, reflecting seasonal differences. However, in some regions, wind resources are not only controlled by seasons but also influenced by interdecadal climate modes or circulation patterns, manifesting as an overall shift or change in the shape of wind speed distribution in the same season under different climate year types. If all years are still mixed together to estimate quantiles, then... In some years, the system may be systematically too high or too low, leading to a persistent bias in D(t), ultimately causing the growth rate of the operating deficit to deviate from the actual resource drought risk. Based on this, historical years can be first divided into several climate states (e.g., state A, state B, state C), and then conditional quantiles can be calculated only in the sample subset corresponding to the state most likely to be in the current situation. and It has the ability to update according to changes in climate conditions. In some examples, this is used to determine the reliable output reference value. With the recharge threshold Previously, it also included: constructing a climate state identification model based on multi-year wind speed sequences and at least one climate indicator related to circulation or sea surface temperature, to output the climate state or posterior probability of the climate state at each moment within the assessment period; and calculating the conditional empirical quantile function on the historical sample subset corresponding to the climate state, thereby obtaining the climate state corresponding to the climate state. and .

[0108] In some cases, instantaneous gaps are taken into consideration. With instantaneous surplus Driven by the recursive nature of energy storage charging and discharging and operation, wind power output often fluctuates frequently around a threshold in many scenarios. Relying solely on instantaneous judgment can easily lead to frequent switching, such as discharging after an hour of underproduction and then stopping again after a slightly higher output in the next hour. This switching can cause two problems: firstly, over-triggering can lead to E... t The charge-discharge sequence is unstable; secondly, The growth of power shortages may be amplified by scattered gaps or masked by scattered recharges, thus weakening the consistency of identifying event-level droughts. Based on this, a duration constraint can be used to distinguish between short-term fluctuations and persistent anomalies: only when underproduction or high output persists for a certain proportion within a window is the current underproduction or recharge considered a valid trigger. In some examples, the power shortage... With the aforementioned power surplus The calculation also includes a duration constraint: when determining underproduction at the current moment, the duration constraint is based on P(τ) within a preset time window in the past. <P ref The comparison between the hit rate of (τ) and the first hit threshold determines the validity of the underproduction at the current moment, and the production is generated or amplified only when the underproduction is valid. When determining whether recharging is possible at the current moment, based on the fact that P(τ) > P within a preset time window in the past... lim The comparison between the hit rate of (τ) and the second hit threshold determines the validity of the refill at the current moment, and the data is generated only when the refill is valid. .

[0109] In some cases, taking into account and put This can be interpreted as an equivalent energy buffer that allows a system to withstand energy shortages by relying on other resources when there is no energy storage or insufficient energy storage. In many systems where hydropower is a key regulating resource, the hydropower support capacity is strongly affected by the following hidden constraints: changes in water head caused by reservoir capacity and water level cause changes in available power output over time; ecological discharge and irrigation water supply create a lower limit constraint; and water inflow forecasts and seasonal dry periods lead to a significant reduction in available energy. If we still consider... Setting it as a constant will overestimate the system buffer during the dry season, making the WDI overly optimistic; and underestimate the system buffer during the wet season, making the WDI overly pessimistic. Based on this, in some examples, the actual charging power of the energy storage... With the actual discharge power of the energy storage The constraints are also related to the battery's temperature state and state of charge: during the energy storage process... During the recursion, the battery temperature state is simultaneously recursively calculated, and the charging power upper limit function and the discharging power upper limit function are determined based on the battery temperature state and the state of charge, so that the... With the It is limited by the charging power upper limit function and the discharging power upper limit function, respectively.

[0110] In some cases, considering that the actual charging and discharging power of energy storage is limited by both the deficit or surplus and the equipment capacity, and is determined in the form of a minimum, the state of energy is recursively derived based on efficiency loss. In real systems, the equipment capacity is not a fixed constant. The acceptable charging rate of the battery decreases significantly at low temperatures, and the upper limit of charging power becomes smaller; at high temperatures or when the temperature rises too quickly, the charging and discharging power will be derated to protect the cells and inverter; when approaching full charge or approaching the low charge boundary, the battery management system will limit the charging or discharging power. Therefore, if a constant is still used... and Overestimating the recovery rate or gap-resistance capability can lead to an underestimation of the asymmetry where recovery is slower than consumption, thus distorting the WDI for certain extreme conditions. Based on this, in some examples, the output of the wind resource drought index also includes risk aversion quantification: uncertainty sampling is performed on wind speed input, power curve parameters, and energy storage efficiency parameters to generate multiple candidate sequences of wind resource drought indices, and the risk aversion drought index is calculated based on the conditional expectation of the most unfavorable quantile set in the candidate sequences. This risk aversion drought index is then used for drought status determination or early warning triggering.

[0111] Please see Figure 5 This is one embodiment of the wind resource drought assessment device in this application. The wind resource drought assessment device may include:

[0112] Acquisition unit 21 is used to acquire hourly wind speed data of the target area during the historical and assessment periods. The hourly wind speed data is then converted into an hourly wind power output sequence based on the wind turbine power curve. ;

[0113] Calculation unit 22, used for:

[0114] Based on historical wind power output sequence Based on the historical sample statistical distribution for each duration t, a reliable output benchmark value is determined for each duration t. With recharge threshold ,in, , The conditional empirical quantile function is calculated on samples with non-zero output. The conditional empirical quantile function is calculated on samples that do not reach the upper limit of the rated power. For reliability parameters, These are the parameters for triggering charging.

[0115] The system's equivalent energy buffering capacity to withstand wind power underproduction during the assessment period. ,in, , For the rated capacity of energy storage, For virtual energy storage capacity;

[0116] Based on the reliable output benchmark value With the recharge threshold Calculate the power shortage hourly With electricity surplus ,in, , ;

[0117] Given the energy storage charge / discharge efficiency and power constraints, the energy storage state is... By performing time-by-time recursive simulation, the actual charging power of the energy storage is obtained. With the actual discharge power of energy storage and to Perform hourly recursive updates;

[0118] Calculate the reliable reference power gap that is not satisfied by energy storage and according to Recursive update unrecoverable red child Based on the energy storage state Calculate the recoverable infant And obtain the running red child Among them, when the energy storage is fully charged ;

[0119] Prediction unit 23, used for prediction based on the equivalent energy buffering capacity With the aforementioned running red child Calculating the drought index of wind resources ,in The wind power drought index Used to characterize the degree of drought of wind power resources during the assessment period.

[0120] like Figure 6 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned methods for assessing the drought level of wind power resources.

[0121] Since the electronic device described in this embodiment is the device used to implement the wind power resource drought assessment device in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any device used by those skilled in the art to implement the method in this application embodiment is within the scope of protection of this application.

[0122] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0123] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0124] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The process for assessing the drought level of wind resources in the corresponding embodiment.

[0129] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for assessing the drought severity of wind power resources, characterized in that, include: Obtain hourly wind speed data for the target area during the historical and assessment periods. The hourly wind speed data is then converted into an hourly wind power output sequence based on the wind turbine power curve. ; Based on historical wind power output sequence Based on the historical sample statistical distribution for each duration t, a reliable output benchmark value is determined for each duration t. With recharge threshold ,in, , The conditional empirical quantile function is calculated on samples with non-zero output. The conditional empirical quantile function is calculated on samples that do not reach the upper limit of the rated power. For reliability parameters, These are the parameters for triggering charging. The system's equivalent energy buffering capacity to withstand wind power underproduction during the assessment period. ; Based on the reliable output benchmark value With the recharge threshold Calculate the power shortage hourly With electricity surplus ; Given the energy storage charge / discharge efficiency and power constraints, the energy storage state is... By performing time-by-time recursive simulation, the actual charging power of the energy storage is obtained. With the actual discharge power of energy storage and to Perform hourly recursive updates; Calculate the reliable reference power gap that is not satisfied by energy storage and according to Recursive update unrecoverable red child Based on the energy storage state Calculate the recoverable red child And obtain the running red child Among them, when the energy storage is fully charged ; Based on the aforementioned equivalent energy buffering capacity With the aforementioned running red child Calculating the drought index of wind resources , among which, based on Calculating the drought index of wind resources The wind power drought index The calculation system is used to characterize the degree of wind resource drought during the assessment period, and is responsible for the equivalent energy buffering capacity to withstand wind power underproduction during the assessment period. ,include: based on The system's equivalent energy buffering capacity to withstand wind power underproduction during the assessment period. ,in, For the rated capacity of energy storage, The virtual energy storage capacity, the virtual energy storage capacity according to Calculated To evaluate the reliable output benchmark value within the window The representative value is the median. The timescale under which the system can withstand or repair itself; The hourly wind power output sequence Calculated according to the following segmented power curve; when hour, ; when hour, ; when , ; in, , , These are the fan cut-in velocity, rated velocity, and cut-out velocity, respectively. This refers to the rated power of the wind turbine.

2. The method as described in claim 1, characterized in that, The energy storage state To perform time-by-time recursive simulation, the following must be satisfied: and, in, The time step is 1 hour. For charging efficiency, For discharge efficiency, and These represent the actual charging power and actual discharging power of the energy storage, respectively. For the rated capacity of energy storage, For minimum available energy storage capacity, Maximum charging power, This represents the maximum discharge power.

3. The method as described in claim 1, characterized in that, The It is updated according to the following recursive relationship: in, To operate the red oocyte attenuation coefficient, and .

4. The method as described in claim 1, characterized in that, Also includes: Based on wind resource drought index Output drought severity classification results: exist Under these circumstances, it is a safe operating zone. In the case of a warning buffer zone, In the case of an emergency supply zone, the emergency supply zone is determined to be in a state of wind power resource drought.

5. A device for assessing the drought severity of wind power resources, characterized in that, The apparatus comprising, using the method as described in any one of claims 1 to 4, includes: The acquisition unit is used to acquire hourly wind speed data for the target area during historical and assessment periods. The hourly wind speed data is then converted into an hourly wind power output sequence based on the wind turbine power curve. ; Computational unit, used for: Based on historical wind power output sequence Based on the historical sample statistical distribution for each duration t, a reliable output benchmark value is determined for each duration t. With recharge threshold ,in, , The conditional empirical quantile function is calculated on samples with non-zero output. The conditional empirical quantile function is calculated on samples that do not reach the upper limit of the rated power. For reliability parameters, These are the parameters for triggering charging. The system's equivalent energy buffering capacity to withstand wind power underproduction during the assessment period. ; Based on the reliable output benchmark value With the recharge threshold Calculate the power shortage hourly With electricity surplus ; Given the energy storage charge / discharge efficiency and power constraints, the energy storage state is... By performing time-by-time recursive simulation, the actual charging power of the energy storage is obtained. With the actual discharge power of energy storage and to Perform hourly recursive updates; Calculate the reliable reference power gap that is not satisfied by energy storage and according to Recursive update unrecoverable red child Based on the energy storage state Calculate the recoverable red child And obtain the running red child Among them, when the energy storage is fully charged ; Prediction unit, used for prediction based on the equivalent energy buffering capacity With the aforementioned running red child Calculating the drought index of wind resources .

6. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program stored in the memory, implements the steps of the wind resource drought assessment method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the wind power resource drought assessment method as described in any one of claims 1-4.

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