Energy storage system and method suitable for offshore wind power

Abstract

The present invention provides an energy storage system and method suitable for offshore wind power, and relates to the technical field of offshore wind power storage. The present invention firstly collects historical time series data of power generation and load demand of offshore wind farms for at least one year in the past through a data acquisition module, identifies the maximum instantaneous load in the historical demand time series data, and calibrates it as the first load time series data according to the data within a certain time range before and after the timestamp, determines the minimum instantaneous power generation in the historical power generation time series data, and calibrates the power generation data of the corresponding time period as the first power generation time series data. Through these data analysis, the capacitance calibration module can accurately set the capacity of the supercapacitor, the supply and demand analysis module determines whether the state of the energy storage device is charging or discharging, when in the charging state, the energy storage adjustment module determines the charging voltage threshold of the supercapacitor, and when in the discharging state, the energy storage adjustment module determines the discharge strategy of the supercapacitor and the battery according to the load fluctuation index.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power energy storage, and in particular to an energy storage system and method suitable for offshore wind power. Background Art

[0002] In offshore wind farms, due to the particularity of the offshore environment, the wind speed fluctuates greatly, which directly leads to the strong intermittent and unstable power generation of the wind farm. In order to solve the problem of power generation fluctuation, energy storage stations are usually set up in wind farms to store energy when there is excess power generation and release energy when there is insufficient power generation to balance the supply and demand relationship. Existing energy storage stations usually use a combination of supercapacitors and batteries, which have their own advantages and disadvantages and complement each other.

[0003] Supercapacitors are mainly used in power systems to cope with instantaneous high power demands or short-term power fluctuations due to their high power density and ability to charge and discharge quickly. Since supercapacitors can respond quickly to rapid changes in load, they are often used to smooth power fluctuations and maintain system voltage stability. However, supercapacitors have a low energy density, which means that their energy storage capacity is limited and they cannot provide continuous power for long periods of time.

[0004] Batteries have a higher energy density and can store a large amount of energy and release it slowly over a long period of time, making them suitable for dealing with continuous load demands or long periods of power shortages. In contrast, batteries have a slower charge and discharge rate and are not as responsive as supercapacitors, so they are not good at dealing with rapid load fluctuations.

[0005] In the prior art, supercapacitors and batteries are usually connected in parallel in the energy storage device of the wind farm to supply power to the load end. When the wind farm generates excess power, the energy storage device stores energy in the supercapacitor and battery; when the power generation is insufficient, the energy storage device releases energy to supplement the load demand. However, in this structure, the capacity setting and charging and discharging strategy of the supercapacitor are often not reasonable, resulting in some deficiencies in practical applications.

[0006] First, the capacity setting of supercapacitors is often based on estimates or empirical values, without fully considering the actual data of historical load demand and power generation fluctuations. This setting method can easily lead to over- or under-capacity. Over-capacity increases the cost and volume of the system, while under-capacity may not provide sufficient power support at critical moments, affecting system stability.

[0007] Secondly, the charge and discharge management strategies of supercapacitors and batteries in the prior art are relatively simple, and usually only determine the charge and discharge operations based on the current load demand and power generation. This approach may cause supercapacitors to be frequently charged and discharged when unnecessary, or not have enough energy released when needed. At the same time, the discharge strategy of the battery also fails to fully consider its synergy with the supercapacitor, resulting in excessive discharge of the battery in some cases, while the supercapacitor fails to fully utilize its fast response advantage.

[0008] These shortcomings make it impossible for existing energy storage stations to fully utilize the advantages of supercapacitors and batteries when dealing with power generation fluctuations and load demand changes in offshore wind farms, resulting in low system efficiency and even unstable power supply. In order to solve these problems, there is an urgent need for an optimization strategy based on actual load and power generation data to reasonably set the capacity of supercapacitors and design a reasonable charge and discharge management strategy to improve the overall performance and reliability of the energy storage system.

[0009] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0010] The object of the present invention is to provide an energy storage system and method suitable for offshore wind power, so as to solve the problems raised in the above background technology.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] An energy storage system suitable for offshore wind power, comprising:

[0013] A data collection module is used to collect historical power generation time series data of offshore wind farm power generation and historical demand time series data of the load end for at least the past year, and ensure that the timestamps of the collected historical power generation time series data and the historical demand time series data of the load end correspond one to one;

[0014] The load analysis module is used to analyze the historical demand time series data, determine the maximum instantaneous load in the historical demand time series data, determine the timestamp corresponding to the maximum instantaneous load, and trace back according to the timestamp to determine the load demand time series data within a time period k before the timestamp corresponding to the maximum instantaneous load, and mark it as the first load time series data;

[0015] The power generation analysis module is used to analyze the historical power generation time series data, determine the minimum instantaneous power generation in the historical power generation time series data, determine the timestamp corresponding to the minimum instantaneous power generation, and trace back according to the timestamp to determine the power generation time series data of the k-length time period before the timestamp corresponding to the minimum power generation, and mark it as the first power generation time series data;

[0016] A capacitance calibration module is used to analyze the first load time series data and the first power generation time series data, collect the rated voltage value of the load end, determine the capacitance and rated voltage value of the supercapacitor, set the supercapacitor and connect the supercapacitor in parallel with the battery in the wind farm energy storage device;

[0017] The supply and demand analysis module is used to collect the current power generation data of the offshore wind farm and the current load demand data of the load end in real time, determine whether the current state of the wind farm energy storage device is charging or discharging, mark the current time as the reference time, and collect continuous power generation data and load demand data for a period of T time in the future based on the reference time to form real-time power generation time series data and real-time demand time series data;

[0018] The energy storage adjustment module is used to generate a power generation fluctuation index and a load fluctuation index according to real-time power generation timing data and real-time demand timing data, and determine the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state, and use the remaining electric energy to charge the battery after reaching the charging voltage threshold. In the discharge state, the discharge strategy of the supercapacitor and the battery is determined according to the power generation data and load demand data at the reference time selected by the load fluctuation index.

[0019] Furthermore, the historical power generation time series data of the power generation is the power generation data of the offshore wind farm at different times, and is recorded continuously in chronological order, with the power generation recorded once every minute. The historical demand time series data of the load end is the total power demand of the load connected to the wind farm at different times in the past at least one year, and is also recorded continuously in chronological order, with the total power demand recorded once every minute. The data points in the historical power generation time series data of the power generation and the historical demand time series data of the load end are numbered in chronological order, and the numbers are mapped one to one.

[0020] The historical power generation time series data and the historical demand time series data have the same starting timestamp and ending timestamp, and at the same time point, that is, the same timestamp, there are corresponding power generation data and actual total power demand.

[0021] Furthermore, the logic for determining the load demand time series data of the k-length time period before the maximum instantaneous load is as follows:

[0022] The maximum instantaneous load is determined based on historical demand time series data, based on the following formula:

[0023] S j =max(Lf1, Lf2, ..., Lf i ,…,Lf n )

[0024] Among them, Sf j represents the maximum instantaneous load data, j represents the number of the data point corresponding to the maximum instantaneous load data, Sf j =Lf j , Lf i represents the total electricity demand data of the i-th data point in the historical demand time series data, and n represents the total number of data points in the historical demand time series data;

[0025] In the historical demand time series data, except for the first data point, the volatility of each data point is calculated, and the corresponding timestamp of the jth data point is used as the benchmark. The data is traced back in chronological order until the volatility is less than the volatility threshold or there is no data point in the front. The number of data points traced is marked as k, and the tracing time is k minutes. The formula for calculating the volatility is:

[0026]

[0027] Among them, Bd i Indicates the volatility of the total electricity demand data of the ith data point, Lf i-1 Represents the total electricity demand data of the i-1th data point in the historical demand time series data;

[0028] The first load timing data is constructed as follows:

[0029] Ff1=(Lf j-k , Lf j-k+1 ,…,Lf j )

[0030] Wherein, Ff1 represents the first load timing data.

[0031] Furthermore, the logic for determining the power generation time series data of the k-length time period before the minimum instantaneous power generation is:

[0032] The minimum instantaneous power generation is determined based on the historical power generation time series data, and the formula is:

[0033] Sd q =min(Ld1, Ld2, ..., Ld p ,…,Ld n )

[0034] Among them, Sd qrepresents the minimum instantaneous power generation data, q represents the serial number of the minimum instantaneous power generation data, Sd q = Ld q , Ld p represents the power generation data of the p-th data point in the historical power generation time series data;

[0035] In the historical power generation time series data, taking the time stamp corresponding to the q-th data point as a reference, trace backward in chronological order until k data points are traced, and the tracing is completed;

[0036] The formed first power generation time series data is:

[0037] Fd1 = (Ld q-k , Ld q-k+1 , …, Ld q )

[0038] wherein, Fd1 represents the first load time series data.

[0039] Furthermore, the formula for determining the capacitance of the super capacitor is:

[0040]

[0041] wherein, C sc represents the capacitance of the super capacitor, V ra represents the rated voltage value at the load end, and the rated voltage value V of the super capacitor rc = δ * V ra 2 , δ is the voltage ratio coefficient, and 1.5 ≤ δ ≤ 2.

[0042] Furthermore, the method for determining whether the current wind farm energy storage device is in a charging or discharging state is:

[0043] Compare the power generation data fd(α) and the load demand data fz(α) at the reference moment. If fd(α) > fz(α), it is determined that the wind farm energy storage device is in a charging state. If fd(α) < fz(α), it is determined that the wind farm energy storage device is in a discharging state.

[0044] The formed real-time power generation time series data is fd(t), t ∈ [α, α + T], and the formed real-time demand time series data is fz(t), t ∈ [α, α + T], where α is the time stamp of the reference moment and t is the time variable.

[0045] Furthermore, the method for generating the power generation fluctuation index and the load fluctuation index is:

[0046] Divide the time length T into m time periods, the length of each time period is ω0, and m*ω0=T, and calculate the average value and standard deviation of the change rate of the real-time power generation time series data and the real-time demand time series data respectively;

[0047] The formula for calculating the average rate of change and standard deviation of the rate of change of power generation time series data is:

[0048]

[0049] Among them, Δfd(t) represents the rate of change of power generation time series data, μ fd and σ fd They represent the mean value and standard deviation of the rate of change of the power generation time series data, and β represents the sequence number of the time period;

[0050] The formula for calculating the average rate of change and the standard deviation of the rate of change of real-time demand time series data is as follows:

[0051]

[0052] Among them, Δfz(t) represents the rate of change of real-time demand time series data, μ fz and σ fz They represent the average value and standard deviation of the change rate of real-time demand time series data respectively;

[0053] The power generation fluctuation index and load fluctuation index are generated based on the average value and standard deviation of the change rate of real-time power generation time series data and real-time demand time series data. The formula is as follows:

[0054]

[0055] Among them, fd b represents the power generation fluctuation index, fz b Indicates the load fluctuation index.

[0056] Furthermore, the formula for determining the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state is:

[0057]

[0058] Among them, V cy Represents the voltage threshold, V rc Indicates the rated voltage value of the supercapacitor, fd b Represents the power generation fluctuation index.

[0059] Furthermore, the method for determining the discharge strategy of the supercapacitor and the battery in the discharge state is based on:

[0060] Calculate the power gap ΔW at the reference time, and ΔW = fz(α)-fd(α), and use the load fluctuation index and load fluctuation threshold fz by Compare and calculate the maximum discharge capacity Wf of the supercapacitor max ,and C sc Indicates the capacitance of the supercapacitor;

[0061] If fz b ≥fz by , and ΔW≤Wf max , the supercapacitor is used for discharge first, and the battery is used for discharge after the supercapacitor is discharged;

[0062] If fz b ≥fz by , and ΔW>Wf max , using supercapacitors and batteries for discharge at the same time;

[0063] If fz b <fz by , the battery is used for discharge first, and the supercapacitor is used for discharge after the battery is discharged.

[0064] The present invention further provides an energy storage method applicable to offshore wind power, the energy storage method is obtained by executing the above energy storage system, and the specific steps include:

[0065] S1: Collect the historical power generation time series data of the offshore wind farm and the historical demand time series data of the load end for at least the past year, and ensure that the timestamps of the collected historical power generation time series data and the historical demand time series data of the load end correspond one to one;

[0066] S2: Analyze the historical demand time series data, determine the maximum instantaneous load in the historical demand time series data, determine the timestamp corresponding to the maximum instantaneous load, and trace back according to the timestamp to determine the load demand time series data within a time period k length before the timestamp corresponding to the maximum instantaneous load, and mark it as the first load time series data;

[0067] S3: Analyze the historical power generation time series data, determine the minimum instantaneous power generation in the historical power generation time series data, determine the timestamp corresponding to the minimum instantaneous power generation, and trace back according to the timestamp to determine the power generation time series data of the k-length time period before the timestamp corresponding to the minimum power generation, and mark it as the first power generation time series data;

[0068] S4: Analyze the first load time series data and the first power generation time series data, collect the rated voltage value of the load end, determine the capacitance and rated voltage value of the super capacitor, set the super capacitor and connect the super capacitor in parallel with the battery in the wind farm energy storage device;

[0069] S5: collect the current power generation data of the offshore wind farm and the current load demand data of the load end in real time, determine whether the current state of the wind farm energy storage device is charging or discharging, mark the current time as the reference time, and collect continuous power generation data and load demand data for a time length of T in the future based on the reference time to form real-time power generation time series data and real-time demand time series data;

[0070] S6: Generate a power generation fluctuation index and a load fluctuation index according to the real-time power generation time series data and the real-time demand time series data, and determine the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state, and use the remaining electric energy to charge the battery after reaching the charging voltage threshold. In the discharging state, determine the discharge strategy of the supercapacitor and the battery according to the power generation data and load demand data at the reference time according to the load fluctuation index selection.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention firstly collects historical time series data of power generation and load demand of offshore wind farms for at least one year in the past through a data acquisition module, and ensures that the timestamps of these data correspond one to one, identifies the maximum instantaneous load in the historical demand time series data through a load analysis module, and calibrates the data within a certain time range before and after the timestamp as the first load time series data. Similarly, the minimum instantaneous power generation in the historical power generation time series data is determined through the power generation analysis module, and the power generation data of the corresponding time period is calibrated as the first power generation time series data. Through these data analysis, the capacitance calibration module accurately sets the capacity of the supercapacitor. This capacity setting is based on real historical data rather than empirical estimation, so it can more accurately reflect the actual supply and demand fluctuations of the system and avoid the problem of over- or under-setting the capacity.

[0073] The supply and demand analysis module of the present invention collects the current power generation data and load demand data of the wind farm in real time, and determines whether the state of the energy storage device is charging or discharging. When the system is in the charging state, the energy storage adjustment module determines the charging voltage threshold of the supercapacitor according to the real-time power generation fluctuation index, and dynamically adjusts the charging voltage threshold of the supercapacitor according to the power generation fluctuation situation. When the system is in the discharging state, the energy storage adjustment module determines the discharge strategy of the supercapacitor and the battery according to the load fluctuation index combined with the power generation data and load demand data at the reference time. Through the setting of the capacitor calibration module, the capacity and charging and discharging strategy of the supercapacitor and the battery can be coordinated and optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic diagram of the overall system structure of the present invention;

[0075] Figure 2 It is a schematic diagram of the historical power generation time series data and the historical demand time series data in the present invention;

[0076] Figure 3 It is a schematic diagram of the first load time series data and the first power generation time series data in the present invention;

[0077] Figure 4 It is a schematic diagram of real-time power generation time series data and real-time demand time series data in the present invention;

[0078] Figure 5 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0079] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0080] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0081] Example:

[0082] See also Figure 1-Figure 4 , the present invention provides a technical solution:

[0083] An energy storage system suitable for offshore wind power includes a data acquisition module, a load analysis module, a power generation analysis module, a capacitance calibration module, a supply and demand analysis module and an energy storage adjustment module, wherein:

[0084] The data acquisition module collects the historical power generation timing data of the offshore wind farm power generation and the historical demand timing data of the load end for at least the past year, and ensures that the timestamps of the collected historical power generation timing data and the historical demand timing data of the load end correspond one to one. The data acquisition module sends the collected data to the load analysis module and the power generation analysis module.

[0085] The historical power generation time series data of power generation is the power generation data of the offshore wind farm at different times, and is recorded continuously in chronological order, with the power generation recorded once every minute. The historical demand time series data of the load end is the total power demand of the load connected to the wind farm at different times in the past at least one year, and is also recorded continuously in chronological order, with the total power demand recorded once every minute. The total power demand reflects the power consumption of the power user supplied by the offshore wind farm, and the data points within the historical power generation time series data of power generation and the historical demand time series data of the load end are numbered in chronological order, and the numbers are mapped one by one. The units of power generation data and total power demand are both kilowatts, and the starting timestamp and ending timestamp of the historical power generation time series data and the historical demand time series data of the load end are the same, and at the same time point, i.e., the same timestamp, there are corresponding power generation data and actual total power demand.

[0086] The historical power generation time series data contains the records of the power generation of the wind farm at different times, which are collected at intervals of 1 minute to form a time series. For example, the time of starting collection is marked as zero minutes, and then the data is collected at intervals of one minute. Similarly, the historical demand time series data on the load side is a sequence that records the power demand of the load connected to the wind farm at different times. The format is similar to the power generation. Both sets of data are recorded continuously in chronological order, which means that each data point has a specific timestamp, and these timestamps are the same. For example, if the power generation is 1000kW at a certain point in time, and the power demand is 800kW at the same point in time, the data points of the two can be associated, and each data point will be assigned a serial number when numbering, starting from 1 and increasing in chronological order. For example, the first data point of power generation data is numbered 1, the second is 2, and so on. The one-to-one mapping of numbers means that each corresponding data point between power generation data and load demand data can be accurately associated with this number. When checking the power generation numbered 10, you can directly find the load demand numbered 10, and you can clearly compare the power generation and electricity demand at the same time point. You can easily compare the power generation and load demand, analyze the operating efficiency of the wind farm, load adaptation, etc., and establish a correlation between power generation and demand, which can help identify abnormal situations, such as when power generation is lower than demand in certain time periods.

[0087] The load analysis module analyzes the historical demand time series data, determines the maximum instantaneous load in the historical demand time series data, and determines the timestamp corresponding to the maximum instantaneous load. It then traces back based on the timestamp to determine the load demand time series data within a time period of k length before the timestamp corresponding to the maximum instantaneous load, and marks it as the first load time series data.

[0088] In this embodiment, the logic for determining the load demand time series data of the k-length time period before the maximum instantaneous load is:

[0089] The maximum instantaneous load is determined based on historical demand time series data, based on the following formula:

[0090] S j =max(Lf1, Lf2, ..., Lf i ,…,Lf n )

[0091] Among them, Sf j represents the maximum instantaneous load data, j represents the number of the data point corresponding to the maximum instantaneous load data, Sf j =Lf j , Lf i It represents the total electricity demand data of the ith data point in the historical demand time series data, n represents the total number of data points in the historical demand time series data, and the maximum instantaneous load is the maximum value of the load demand time series data. In this way, the electricity demand situation when the load peak occurs can be understood.

[0092] In the historical demand time series data, except for the first data point, the volatility of each data point is calculated, and the corresponding timestamp of the jth data point is used as the benchmark. The data is traced back in chronological order until the volatility is less than the volatility threshold or there is no data point in the front. The number of data points traced is marked as k, and the tracing time is k minutes. The formula for calculating the volatility is:

[0093]

[0094] Among them, Bd i Indicates the volatility of the total electricity demand data of the ith data point, Lf i-1 Represents the total electricity demand data of the i-1th data point in the historical demand time series data.

[0095] The volatility reflects the fluctuation of the total load demanded by the load end. If the volatility is less than the fluctuation threshold, it means that the load is relatively stable. Otherwise, it means that the load has been increasing rapidly. This embodiment calculates the maximum instantaneous load and the load for a period of time when the load increases rapidly before reaching the instantaneous load, so as to obtain the situation when the load end is large in a short time.

[0096] By analyzing historical demand time series data and determining the maximum instantaneous load, the goal is to find the peak power consumption in a given time period, which helps to understand the specific power consumption when the load peak occurs. After finding the maximum instantaneous load, by calculating the volatility of each data point, the data is systematically traced back to identify the fluctuation characteristics of load demand before reaching the peak. This tracing process can understand the changes in load when it transitions from a relatively stable state to a peak, and by setting the fluctuation threshold, it can accurately locate the data point where the load begins to grow rapidly.

[0097] The first load timing data is constructed as follows:

[0098] Ff1=(Lf j-k , Lf j-k+1 ,…,Lf j )

[0099] Among them, Ff1 indicates that the first load time series data is a set, including a series of load data from data point jk to data point j, Lf j-k Indicates the load data of data point jk, which is the starting point data for tracing back. Lf j Represents the load data at data point j, which is the data point where the maximum instantaneous load is located.

[0100] In specific operation, the data set Ff1 reflects the maximum instantaneous load Sf j By analyzing this data series, you can get detailed information about how the load gradually increases from a relatively stable state to the peak value.

[0101] The power generation analysis module analyzes the historical power generation time series data, determines the minimum instantaneous power generation in the historical power generation time series data, and determines the timestamp corresponding to the minimum instantaneous power generation. It traces back based on the timestamp to determine the power generation time series data of the k-length time period before the timestamp corresponding to the minimum power generation, and marks it as the first power generation time series data.

[0102] In this example, the logic for determining the power generation time series data of the k-length time period before the minimum instantaneous power generation is:

[0103] The minimum instantaneous power generation is determined based on the historical power generation time series data, and the formula is:

[0104] Sd q =min(Ld1, Ld2, ..., Ld p ,…,Ld n )

[0105] Among them, Sd q represents the minimum instantaneous power generation data, q represents the number of the data point corresponding to the minimum instantaneous power generation data, Sdq =Ld q , Ld p It represents the power generation data of the pth data point in the historical power generation time series data. Since the data points of the historical power generation time series data and the historical demand time series data are strictly one-to-one corresponding, the total number of data points in the historical power generation time series data is also n.

[0106] In the historical power generation time series data, the corresponding timestamp of the qth data point is used as the benchmark, and the data is traced back in chronological order until k data points are traced back and the tracing is completed. If there are less than k data points when tracing back to the first data point, the first data point will be copied and supplemented until k data points are reached.

[0107] There is usually a close relationship between the power generation and load demand of power systems such as offshore wind power and energy storage. In order to analyze the interaction and changing trends between power generation and load demand in a specific time period, it is necessary to ensure that the power generation and load data are compared within the same time span. If the time window lengths of the two data sets are inconsistent, it is difficult to accurately evaluate whether the power generation can meet the load demand in the corresponding time period.

[0108] By ensuring that both time series data trace back to the same k data points, the analysis results can be made more comparable. When analyzing the power system, maintaining the same k data points can ensure the use of a unified analysis framework and reduce analysis errors caused by different time spans.

[0109] The minimum instantaneous power generation is the weakest moment of the offshore wind power generation device, which is convenient for understanding the minimum power generation capacity of the offshore wind power generation device under the most unfavorable conditions.

[0110] The first power generation timing data is:

[0111] Fd1=(Ld q-k , Ld q-k+1 ,…,Ld q )

[0112] Wherein, Fd1 represents the first load time series data, and the first power generation time series data constitutes a power generation time series data set tracing back to the minimum instantaneous power generation, and this set includes a series of power generation data from data point qk to data point q, wherein Ld q-k Indicates the power generation data from the starting point of tracing back, Ld q Indicates the data point corresponding to the minimum instantaneous power generation. By analyzing this section of power generation time series data, we can understand the power generation changes in the period before the lowest power generation point.

[0113] The capacitor calibration module analyzes the first load timing data and the first power generation timing data, collects the rated voltage value of the load end, determines the capacitance and rated voltage value of the supercapacitor, sets the supercapacitor and connects the supercapacitor in parallel with the battery in the wind farm energy storage device.

[0114] In this embodiment, the formula for determining the capacitance of the supercapacitor is:

[0115]

[0116] Among them, C sc Represents the capacitance of the supercapacitor, V ra Indicates the rated voltage value of the load end, the rated voltage value of the supercapacitor V rc =δ*V ra 2 , δ is the voltage proportional coefficient, and 1.5≤δ≤2.

[0117] |(Lf j-k +Lf j-k+1 +…+Lf j )-(Ld q-k +Ld q-k+1 +…+Ld q )| indicates the cumulative difference between the minimum instantaneous power generation and the maximum instantaneous load, and the power generation and load demand in the previous period. By accumulating the power generation and load demand at a certain point in time, the energy imbalance of the system during this period can be captured. The absolute value of this difference ensures that no matter whether the power generation is greater than or less than the load demand, the supercapacitor can compensate for the difference for the system.

[0118] k is the number of time points traced back, and each data point is separated by 1 minute, because the time length of the traced time point is k minutes, and one hour is 60 minutes. Converting the accumulated power difference into energy provides a more accurate assessment of the total energy that the supercapacitor needs to store or release during this period.

[0119] The definition of capacitance is a parameter related to energy and voltage. The square of the rated voltage at the load end is used as the denominator so that the resulting dimension is consistent with the capacitance (i.e., farad). The energy difference can be reasonably converted into the required capacitance. Calculating the capacitance value of the supercapacitor is a key step to ensure its effective function in the offshore system. By accurately calculating the capacitance value, it can be ensured that the supercapacitor has sufficient energy storage capacity during the charging and discharging process, thereby effectively responding to various fluctuations and demands in the system.

[0120] Rated voltage V ra$V_{ref}$ is the standard voltage at the load end, which is used to convert the energy demand into capacitance. The voltage proportionality coefficient $\delta$ is used to adjust the relationship between the actual voltage and the standard voltage, ensuring that the supercapacitor can work effectively under actual operating conditions. $1.5\leq\delta\leq2$, which allows the supercapacitor to work at a voltage higher than the rated voltage at the load end, thereby increasing the energy storage capacity.

[0121] The power supply and demand analysis module collects the power generation data of the offshore wind farm at the current moment and the load demand data at the load end at the current moment in real time, determines whether the state of the energy storage device of the current wind farm is charging or discharging, marks the current moment as the reference moment, and collects the continuous power generation data and load demand data for the subsequent time length of $T$ with the reference moment as the benchmark to form real-time power generation time series data and real-time demand time series data.

[0122] In this embodiment, the method for determining whether the state of the energy storage device of the current wind farm is charging or discharging is as follows:

[0123] Compare the power generation data $fd(\alpha)$ and the load demand data $fz(\alpha)$ at the reference moment. If $fd(\alpha)>fz(\alpha)$, it is determined that the energy storage device of the wind farm is in the charging state. If $fd(\alpha)<fz(\alpha)$, it is determined that the energy storage device of the wind farm is in the discharging state.

[0124] The formed real-time power generation time series data is $fd(t)$, $t\in[\alpha,\alpha + T]$, and the formed real-time demand time series data is $fz(t)$, $t\in[\alpha,\alpha + T]$, where $\alpha$ is the timestamp of the reference moment and $t$ is the time variable.

[0125] In the power system, the real-time balance of power generation and load demand is very important. As a renewable energy power generation facility, the power generation of an offshore wind farm usually has uncertainty and volatility. The main role of the energy storage device is to maintain the stability of the system by storing (charging) or releasing energy (discharging) when there is a difference between power generation and load demand.

[0126] If $fd(\alpha)>fz(\alpha)$, that is, the power generation is greater than the load demand, then there is excess energy in the system. At this time, the energy storage device needs to store the excess energy for future use when the power generation is insufficient. Therefore, it is determined that the energy storage device is in the charging state. If $fd(\alpha)<fz(\alpha)$, that is, the power generation is less than the load demand, then the energy supply of the system is insufficient. At this time, the energy storage device needs to release the previously stored energy to meet the current load demand. Therefore, it is determined that the energy storage device is in the discharging state.

[0127] fd(α) and fz(α) are used to ensure that the judgment is based on the latest system status, so that the energy storage device can respond promptly to changes in supply and demand in the power system. By monitoring the power generation and demand data in the time interval from the reference time α to α+T, the charging and discharging status of the energy storage device can be dynamically adjusted.

[0128] The difference between the current power generation and load demand is used to timely judge the charging and discharging status of the energy storage device. Since the power generation of the wind farm has a large volatility, the continuous power generation data and load demand data for a period of T time in the future are collected based on the reference time, which is convenient for the subsequent dynamic adjustment of the charging and discharging status of the energy storage device.

[0129] The energy storage adjustment module generates a power generation fluctuation index and a load fluctuation index according to the real-time power generation timing data and the real-time demand timing data, and determines the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state, and charges the battery with the remaining electric energy after reaching the charging voltage threshold. In the discharging state, the discharge strategy of the supercapacitor and the battery is determined according to the power generation data and load demand data at the reference time selected by the load fluctuation index.

[0130] Furthermore, the method for generating the power generation fluctuation index and the load fluctuation index is as follows:

[0131] Divide the time length T into m time periods, the length of each time period is ω0, and m*ω0=T, and calculate the average value and standard deviation of the change rate of the real-time power generation time series data and the real-time demand time series data respectively;

[0132] The formula for calculating the average rate of change and standard deviation of the rate of change of power generation time series data is:

[0133]

[0134] Among them, Δfd(t) represents the rate of change of power generation time series data, μ fd and σ fd They represent the mean value and standard deviation of the rate of change of power generation time series data respectively, and β represents the sequence number of the time period.

[0135] A fixed time length T is divided into several time periods of equal length, and the data of power generation and load demand are discretized by time periods to generate a fluctuation index. The fluctuation index can reflect the overall trend and discrete degree of the rate of change of power generation and load demand. The rate of change of power generation time series data is the rate of change of power generation data points after each interval of length ω0 time period, that is, the difference between the power generation at the current time point and the power generation at the end of the previous time period. The rate of change can reflect the fluctuation amplitude of power generation within a given time period.

[0136] The average value of the rate of change of the power generation time series data reflects the average trend of the power generation change in the entire time period. If the average value of the rate of change of the power generation time series data is large, it means that the power generation has an obvious trend of change in the entire time period. The standard deviation of the rate of change of the power generation time series data reflects the volatility and discreteness of the power generation change. If the standard deviation of the rate of change of the power generation time series data is large, it means that the rate of change of the power generation between different time periods has a large fluctuation, that is, the power generation is unstable.

[0137] This method is not only applicable to analyzing the fluctuation of power generation, but also can be applied to the fluctuation of load demand by generating power generation fluctuation index and load fluctuation index.

[0138] The formula for calculating the average rate of change and the standard deviation of the rate of change of real-time demand time series data is as follows:

[0139]

[0140] Among them, Δfz(t) represents the rate of change of real-time demand time series data, μ fz and σ fz They respectively represent the average value and standard deviation of the change rate of real-time demand time series data.

[0141] The method for calculating the average rate of change and standard deviation of the real-time demand time series data is similar to the calculation method of the rate of change of power generation data mentioned above. The purpose is to analyze the fluctuation characteristics of load demand in order to better understand the dynamic changes in demand in the power system. The rate of change of load demand data can be compared with the demand at the end of the previous time period to obtain the magnitude of the change in load demand during this time period. By averaging the rate of change in each time period, the overall change trend of load demand during the observation period can be obtained.

[0142] The power generation fluctuation index and load fluctuation index are generated based on the average value and standard deviation of the change rate of real-time power generation time series data and real-time demand time series data. The formula is as follows:

[0143]

[0144] Among them, fd b represents the power generation fluctuation index, fz b Indicates the load fluctuation index.

[0145] The power generation fluctuation index measures the relative volatility of power generation by calculating the ratio of the standard deviation of the power generation change rate to the average value, reflecting the relative volatility and instability of power generation in the power system. If the index value is high, it means that the power generation volatility is large, and the power generation in the system is not stable enough relative to its average level. The load fluctuation index measures the volatility on the power demand side, reflecting the relative volatility and instability of load demand in the power system. If the load fluctuation index is high, it means that the load demand changes frequently and with a large amplitude. High load volatility indicates that the power load is changing dramatically.

[0146] Dividing the standard deviation of the rate of change by the mean value, a relative volatility measure is constructed, which is used to measure the standardized index of the relative discreteness of power generation time series data and real-time demand time series data, and can well reflect the data volatility. The standard deviation of the rate of change reflects the discreteness of the data, that is, the fluctuation range of power generation or load demand. If the standard deviation is large, it means that the uncertainty is high and the volatility is large. The average value of the rate of change represents the central trend of the data, that is, the overall trend of power generation or load demand.

[0147] By taking a ratio, the volatility index can eliminate the impact of different system sizes, making the index more universal and comparable. For example, under the same volatility, if the average value is larger, the index will be smaller, indicating that the volatility is smaller relative to its average level and the system is more stable; conversely, if the average value is smaller, the index will be larger, indicating that the volatility is larger relative to its average level and the system is more unstable.

[0148] In this embodiment, the formula for determining the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state is:

[0149]

[0150] Among them, V cy Represents the voltage threshold, V rc Indicates the rated voltage value of the supercapacitor, fd b Represents the power generation fluctuation index.

[0151] is an exponential function, where It shows exponential decay, when fd b When it is small, the exponential decay term is close to 1, resulting in is approximately 0; and when fd b When increasing, Rapidly decays to close to 0, at this time Gradually approaches 1, when the power generation fluctuation index fd b When it is smaller, the power generation is more stable. Approximately 0 Approximately 0, at this time the charging voltage threshold V cy It will be lower, close to zero, because when the power generation is stable, maintaining a low charging voltage of the supercapacitor can reduce the impact of charging the supercapacitor on the balance of the offshore wind power system.

[0152] With the power generation fluctuation index fd b Increase means that the fluctuation of power generation increases. Rapidly rising, making V cy Close to V rc ,When the volatility of power generation increases, the charging voltage threshold of the supercapacitor increases and is closer to its rated voltage, and the supercapacitor stores more energy to cope with possible ,system fluctuations.

[0153] The charging voltage threshold of the supercapacitor is dynamically adjusted according to the fluctuation of power generation. When the offshore wind power generation fluctuates greatly, the energy reserve is increased by raising the charging voltage threshold to enhance the buffering capacity of the power system. When the offshore wind power generation is stable, the charging voltage threshold is lowered to reduce unnecessary energy reserves and optimize the overall efficiency of the offshore wind power device.

[0154] Supercapacitors are used as transient power regulation devices. Their charging state directly affects the stability of offshore wind power generation systems. By dynamically adjusting the charging voltage threshold based on the power generation fluctuation index, they can automatically increase reserve energy when volatility increases, thereby providing additional stability support.

[0155] The use of the exponential function makes the adjustment of the charging voltage threshold present a nonlinear characteristic. The nonlinear adjustment makes the voltage threshold change slowly when the fluctuation index is small, and the voltage threshold increases rapidly when the fluctuation index is large. Under stable conditions, excessive voltage adjustment will cause unnecessary power loss, while under unstable conditions, rapidly increasing the voltage threshold can effectively cope with sudden fluctuations in power demand.

[0156] Furthermore, the method for determining the discharge strategy of the supercapacitor and the battery in the discharge state is based on:

[0157] Calculate the power gap ΔW at the reference time, and ΔW = fz(α)-fd(α), and use the load fluctuation index and load fluctuation threshold fz by Compare and calculate the maximum discharge capacity Wf of the supercapacitor max ,and C sc Wf represents the capacitance of the supercapacitor. ΔW reflects the imbalance between load demand and power generation supply at a specific moment. A larger ΔW means that the current load demand far exceeds the power generation, and the energy storage system needs to quickly fill this gap. A smaller ΔW means that the supply and demand are relatively balanced. maxIt indicates the maximum energy that a supercapacitor can provide when fully charged.

[0158] If fz b ≥fz by , and ΔW≤Wf max , the supercapacitor is used for discharge first, and the battery is used for discharge after the supercapacitor is discharged. b ≥fz by When it is determined that the load fluctuates violently, a supercapacitor with a fast response speed is needed to discharge first, because the supercapacitor can quickly provide high-power discharge in a short time and is suitable for dealing with violent fluctuations.

[0159] If fz b ≥fz by , and ΔW>Wf max , use supercapacitor and battery to discharge at the same time, ΔW>Wf max This means that the power gap exceeds the maximum energy reserve that the supercapacitor can provide alone, indicating that the current load demand is very high and that the supercapacitor alone cannot meet this demand. In order to prevent the power system from being underpowered, sufficient energy must be added. At this time, the supercapacitor and the battery need to discharge at the same time to fill this large power gap together to ensure the normal operation of the system. The supercapacitor can quickly make up for part of the power gap when discharging, helping the system to cope with instantaneous power fluctuations. Although the battery has a slow response speed, it has a high energy density and can provide continuous and stable power output. At this time, if the supercapacitor alone cannot meet the demand, it is necessary to call on the battery at the same time to provide additional energy support. This combination can immediately meet high load demands and maintain power supply stability for a long time.

[0160] If fz b <fz by Regardless of the power gap ΔW and the maximum discharge capacity Wf of the supercapacitor max The relationship between the two is that the battery is used for discharge first. After the battery is discharged, the supercapacitor is used for discharge. At this time, the load fluctuation is small. In this case, the battery is used as the priority discharge device because of its strong discharge capacity and long duration, which is more suitable for smooth demand.

[0161] The order of using supercapacitors and batteries is based on their respective characteristics. Supercapacitors have a fast response speed but a relatively low energy density, making them suitable for short-term high-power needs; while batteries have a high energy density but a slow response speed, making them suitable for long-term, stable power supply.

[0162] By calculating the power gap and comparing the load fluctuation value with the load fluctuation threshold, the discharge order of supercapacitors and batteries can be intelligently determined. This strategy not only reasonably utilizes the characteristics of both to enhance the flexibility and reliability of the power system under different load fluctuation conditions, but also ensures stable power supply while improving overall operating efficiency.

[0163] See also Figure 5 The present invention further provides an energy storage method applicable to offshore wind power, the energy storage method is obtained by executing the above energy storage system, and the specific steps include:

[0164] S1: Collect the historical power generation time series data of the offshore wind farm and the historical demand time series data of the load end for at least the past year, and ensure that the timestamps of the collected historical power generation time series data and the historical demand time series data of the load end correspond one to one;

[0165] S2: Analyze the historical demand time series data, determine the maximum instantaneous load in the historical demand time series data, determine the timestamp corresponding to the maximum instantaneous load, and trace back according to the timestamp to determine the load demand time series data within a time period k length before the timestamp corresponding to the maximum instantaneous load, and mark it as the first load time series data;

[0166] S3: Analyze the historical power generation time series data, determine the minimum instantaneous power generation in the historical power generation time series data, determine the timestamp corresponding to the minimum instantaneous power generation, and trace back according to the timestamp to determine the power generation time series data of the k-length time period before the timestamp corresponding to the minimum power generation, and mark it as the first power generation time series data;

[0167] S4: Analyze the first load time series data and the first power generation time series data, collect the rated voltage value of the load end, determine the capacitance and rated voltage value of the super capacitor, set the super capacitor and connect the super capacitor in parallel with the battery in the wind farm energy storage device;

[0168] S5: collect the current power generation data of the offshore wind farm and the current load demand data of the load end in real time, determine whether the current state of the wind farm energy storage device is charging or discharging, mark the current time as the reference time, and collect continuous power generation data and load demand data for a time length of T in the future based on the reference time to form real-time power generation time series data and real-time demand time series data;

[0169] S6: Generate a power generation fluctuation index and a load fluctuation index according to the real-time power generation time series data and the real-time demand time series data, and determine the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state, and use the remaining electric energy to charge the battery after reaching the charging voltage threshold. In the discharging state, determine the discharge strategy of the supercapacitor and the battery according to the power generation data and load demand data at the reference time according to the load fluctuation index selection.

[0170] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0171] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0172] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0173] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An energy storage system suitable for offshore wind power, characterized in that: include: A data collection module is used to collect historical power generation time series data of offshore wind farm power generation and historical demand time series data of the load end for at least the past year, and ensure that the timestamps of the collected historical power generation time series data and the historical demand time series data of the load end correspond one to one; The load analysis module is used to analyze the historical demand time series data, determine the maximum instantaneous load in the historical demand time series data, determine the timestamp corresponding to the maximum instantaneous load, and trace back according to the timestamp to determine the load demand time series data within a time period k before the timestamp corresponding to the maximum instantaneous load, and mark it as the first load time series data; The power generation analysis module is used to analyze the historical power generation time series data, determine the minimum instantaneous power generation in the historical power generation time series data, determine the timestamp corresponding to the minimum instantaneous power generation, and trace back according to the timestamp to determine the power generation time series data of the k-length time period before the timestamp corresponding to the minimum instantaneous power generation, and mark it as the first power generation time series data; A capacitance calibration module is used to analyze the first load time series data and the first power generation time series data, collect the rated voltage value of the load end, determine the capacitance and rated voltage value of the supercapacitor, set the supercapacitor and connect the supercapacitor in parallel with the battery in the wind farm energy storage device; The supply and demand analysis module is used to collect the current power generation data of the offshore wind farm and the current load demand data of the load end in real time, determine whether the current state of the wind farm energy storage device is charging or discharging, mark the current time as the reference time, and collect the continuous power generation data and load demand data of the next t time length based on the reference time to form real-time power generation time series data and real-time demand time series data; The energy storage adjustment module is used to generate a power generation fluctuation index and a load fluctuation index according to real-time power generation timing data and real-time demand timing data, and determine the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state, and use the remaining electric energy to charge the battery after reaching the charging voltage threshold. In the discharge state, the discharge strategy of the supercapacitor and the battery is determined according to the power generation data and load demand data at the reference time selected by the load fluctuation index.

2. An energy storage system suitable for offshore wind power according to claim 1, characterized in that: The historical power generation time series data of the power generation is the power generation data of the offshore wind farm at different times, and is recorded continuously in chronological order, with the power generation recorded once every minute. The historical demand time series data of the load end is the total power demand of the load connected to the wind farm at different times in the past at least one year, and is also recorded continuously in chronological order, with the total power demand recorded once every minute. The data points in the historical power generation time series data of the power generation and the historical demand time series data of the load end are numbered in chronological order, and the numbers are mapped one to one; The historical power generation time series data and the historical demand time series data have the same starting timestamp and ending timestamp, and at the same time point, that is, the same timestamp, there are corresponding power generation data and actual total power demand.

3. An energy storage system suitable for offshore wind power according to claim 2, characterized in that: The logic for determining the load demand time series data for the k-length time period before the maximum instantaneous load is: Determine the maximum instantaneous load based on historical demand time series data, and the formula is as follows: Sf j =max(Lf1、Lf2、…、Lf i 、…、Lf n ) Among them, Sf j represents the maximum instantaneous load data, j represents the number of the data point corresponding to the maximum instantaneous load data, Sf j =Lf j , Lf i represents the total electricity demand data of the i-th data point in the historical demand time series data, and n represents the total number of data points in the historical demand time series data; In the historical demand time series data, except for the first data point, calculate the volatility of each data point. Taking the time stamp corresponding to the j-th data point as the benchmark, trace back in chronological order until the volatility is less than the volatility threshold or there are no more data points in the front, and mark the number of traced data points as k. Then the traced time is k minutes. The formula for calculating the volatility is as follows: Among them, Bd i Indicates the volatility of the total electricity demand data of the ith data point, Lf i-1 Represents the total electricity demand data of the i-1th data point in the historical demand time series data; The formed first load time series data is: Ff1=(Lf j-k 、Lf j-k+1 、…、Lf j ) Among them, Ff1 represents the first load time series data.

4. An energy storage system suitable for offshore wind power according to claim 3, characterized in that: The logic for determining the power generation time series data in a time period of length k before the time stamp corresponding to the minimum instantaneous power generation is as follows: Determine the minimum instantaneous power generation based on historical power generation time series data, and the formula is as follows: Sd q =min(Ld1、Ld2、…、Ld p 、…、Ld n ) Among them, Sd q represents the minimum instantaneous power generation data, q represents the number of the minimum instantaneous power generation data, Sd q =Ld q , Ld p Represents the power generation data of the pth data point in the historical power generation time series data; In the historical power generation time series data, taking the time stamp corresponding to the q-th data point as the benchmark, trace back in chronological order until k data points are traced, and the tracing is completed; The formed first power generation time series data is: Fd1=(Ld q-k 、Ld q-k+1 、…、Ld q ) Among them, Fd1 represents the first load time series data.

5. An energy storage system suitable for offshore wind power according to claim 4, characterized in that: The formula for determining the capacitance of the super capacitor is: Among them, C sc Represents the capacitance of the supercapacitor, V ra Indicates the rated voltage value of the load end, the rated voltage value of the supercapacitor V rc =δ*V ra 2 , δ is the voltage proportional coefficient, and 1.5≤δ≤2.

6. The energy storage system suitable for offshore wind power according to claim 1, characterized in that: The method for determining whether the current energy storage device of the wind farm is in the charging or discharging state is: Compare the power generation data fd(α) and the load demand data fz(α) at the reference time. If fd(α)>fz(α), it is determined that the energy storage device of the wind farm is in the charging state. If fd(α)<fz(α), it is determined that the energy storage device of the wind farm is in the discharging state. The formed real-time power generation time series data is fd(t), t∈[α, α+T], and the formed real-time demand time series data is fz(t), t∈[α, α+T], where α is the time stamp of the reference time and t is the time variable.

7. An energy storage system suitable for offshore wind power according to claim 6, characterized in that: The method for generating the power generation fluctuation index and the load fluctuation index is: Divide the time length T into m time periods equally, each time period has a length of ω0, and m*ω0 = T. Calculate the average value of the change rate and the standard deviation of the change rate of the real-time power generation time series data and the real-time demand time series data respectively; The formula for calculating the average value of the change rate and the standard deviation of the change rate of the power generation time series data is: Among them, Δfd(t) represents the rate of change of power generation time series data, μ fd and σ fd They represent the mean value and standard deviation of the rate of change of the power generation time series data, and β represents the sequence number of the time period; The formula for calculating the average value of the change rate and the standard deviation of the change rate of the real-time demand time series data is: Among them, Δfz(t) represents the rate of change of real-time demand time series data, μ fz and σ fz They represent the average value and standard deviation of the change rate of real-time demand time series data respectively; Based on the average value of the change rate and the standard deviation of the change rate of the real-time power generation time series data and the real-time demand time series data, generate the power generation fluctuation index and the load fluctuation index. The formula is as follows: Among them, fd b represents the power generation fluctuation index, fz b Indicates the load fluctuation index.

8. The energy storage system suitable for offshore wind power according to claim 1, characterized in that: The formula for determining the charging voltage threshold of the super capacitor according to the power generation fluctuation index in the charging state is: Among them, V cy Represents the voltage threshold, V rc Indicates the rated voltage value of the supercapacitor, fd b Represents the power generation fluctuation index.

9. An energy storage system suitable for offshore wind power according to claim 8, characterized in that: The method for determining the discharge strategies of the super capacitor and the battery in the discharging state is: Calculate the power gap ΔW at the reference time, and ΔW = fz(α)-fd(α), and use the load fluctuation index and load fluctuation threshold fz by Compare and calculate the maximum discharge capacity Wf of the supercapacitor max ,and C sc Indicates the capacitance of the supercapacitor; If fz b ≥fz by , and ΔW≤Wf max , the supercapacitor is used for discharge first, and the battery is used for discharge after the supercapacitor is discharged; If fz b ≥fz by , and ΔW>Wf max , using supercapacitors and batteries for discharge at the same time; If fz b <fz by , the battery is used for discharge first, and the supercapacitor is used for discharge after the battery is discharged.

10. An energy storage method suitable for offshore wind power, characterized in that: The energy storage method is obtained by being executed by the energy storage system described in any one of claims 1-9. The specific steps include: S1: Collect the historical power generation time series data of the offshore wind farm in the past at least one year and the historical demand time series data at the load end, and ensure that the time stamps of the collected historical power generation time series data of the power generation and the historical demand time series data at the load end correspond one by one; S2: Analyze the historical demand time series data, determine the maximum instantaneous load in the historical demand time series data, determine the timestamp corresponding to the maximum instantaneous load, and trace back according to the timestamp to determine the load demand time series data within a time period k length before the timestamp corresponding to the maximum instantaneous load, and mark it as the first load time series data; S3: Analyze the historical power generation time series data, determine the minimum instantaneous power generation in the historical power generation time series data, and determine the timestamp corresponding to the minimum instantaneous power generation, and trace back according to the timestamp to determine the power generation time series data of the k-length time period before the timestamp corresponding to the minimum instantaneous power generation, and mark it as the first power generation time series data; S4: Analyze the first load time series data and the first power generation time series data, collect the rated voltage value of the load end, determine the capacitance and rated voltage value of the super capacitor, set the super capacitor and connect the super capacitor in parallel with the battery in the wind farm energy storage device; S5: collect the current power generation data of the offshore wind farm and the current load demand data of the load end in real time, determine whether the current state of the wind farm energy storage device is charging or discharging, mark the current time as the reference time, and collect continuous power generation data and load demand data for a time length of T in the future based on the reference time to form real-time power generation time series data and real-time demand time series data; S6: Generate a power generation fluctuation index and a load fluctuation index according to the real-time power generation time series data and the real-time demand time series data, and determine the charging voltage threshold of the supercapacitor according to the power generation fluctuation index in the charging state, and use the remaining electric energy to charge the battery after reaching the charging voltage threshold. In the discharging state, determine the discharge strategy of the supercapacitor and the battery according to the power generation data and load demand data at the reference time according to the load fluctuation index selection.

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