Offshore wind power stabilization control method assisted by hybrid energy storage system

Abstract

The invention relates to an offshore wind power stabilization control method assisted by a hybrid energy storage system, and is suitable for the technical field of offshore wind power. The method comprises the following steps: determining a t-moment wind power plant grid-connected power meeting a power volatility limit; determining the energy storage power of the energy storage system at the t moment; determining the running state of the energy storage system at the t moment as absorbing or releasing power; when the energy storage system is in a power absorption state at the t moment, determining energy storage battery distribution power and super capacitor distribution power, and enabling the energy storage battery to be in a charging state; when the energy storage system is in a power release state at the t moment, determining the distribution power of the energy storage battery and the distribution power of the super capacitor, and enabling the energy storage battery to be in a discharge state; and the SOC of the energy storage batteries is updated, and when the difference between the updated SOC and the upper and lower limits of the charge coefficient of the energy storage batteries is smaller than a preset value, the energy storage battery in the charging state is switched to the discharging state, and the other energy storage battery in the discharging state is switched to the charging state.

Description

Hybrid Energy Storage System-Assisted Offshore Wind Power Smoothing Control Method Technical Field

[0001] This invention relates to a method for suppressing and controlling offshore wind power with the assistance of a hybrid energy storage system. It is applicable to the field of offshore wind power technology. Background Technology

[0002] Wind energy, as a clean energy source, is receiving significant attention due to its pollution-free nature, driven by policies promoting energy conservation, emission reduction, and air pollution control. Within this broader context of wind power development, offshore wind power is particularly crucial. Globally, the planning and construction of offshore wind power is trending towards expansion, centralization, and deeper-water deployment. Deep-sea areas, with their distances exceeding 100 kilometers from shore and depths exceeding 50 meters, possess abundant wind energy resources. Compared to conventional wind power generation, offshore wind power faces more complex and variable challenges in grid connection and energy transmission.

[0003] Wind power energy storage can reduce the impact of wind power generation fluctuations on the stable operation of the power grid, making wind power energy storage technology a key technology for wind power development. Offshore wind power, due to its inherent volatility and unpredictability, cannot reliably supply wind power to energy storage systems, nor can it effectively utilize energy storage systems, thus affecting the response efficiency of the energy storage systems.

[0004] Energy storage systems typically include energy storage batteries and supercapacitors. Currently, some methods can distribute the low-frequency and high-frequency power fluctuations of wind farms to energy storage batteries and supercapacitors respectively, but this is insufficient to maintain the batteries operating at their optimal state of charge, nor is it conducive to maximizing the battery's lifespan.

[0005] In the field of energy storage systems, numerous factors affect battery life. Among them, frequent charge-discharge switching accelerates internal chemical reactions, thus accelerating battery aging, which is a major factor affecting battery life. However, in offshore wind power energy storage distribution systems, due to the volatility of wind power and the uncertainty of the power to be stored, the energy absorption of the energy storage system inevitably involves frequent switching. This leads to rapid aging and high maintenance costs for battery-based energy storage devices. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for offshore wind power smoothing control assisted by a hybrid energy storage system, in view of the above-mentioned problems.

[0007] The technical solution adopted in this invention is: a hybrid energy storage system-assisted offshore wind power smoothing control method, comprising: determining the grid-connected power of the offshore wind farm at time t that satisfies the power fluctuation rate limit based on the output power of the offshore wind farm at time t and the grid-connected power of the wind farm at time t-1; determining the energy storage power of the energy storage system at time t based on the grid-connected power of the wind farm at time t and the output power of the offshore wind farm at time t; determining whether the operating state of the energy storage system at time t is power absorption or power release based on the energy storage power of the energy storage system at time t; when the energy storage system is in the power absorption state at time t, and based on the energy storage power of the energy storage system and an energy storage battery in the energy storage system... The SOC of the energy storage battery and the supercapacitor are determined, and the energy storage battery is in a charging state. When the energy storage system is in a power release state at time t, the power allocation of the energy storage battery and the supercapacitor are determined based on the energy storage power of the energy storage system and the SOC of another energy storage battery in the energy storage system, and the energy storage battery is in a discharging state. Based on the power allocation of the energy storage battery, the SOC of the energy storage battery is updated, and when the difference between the updated SOC and the upper and lower limits of the energy storage battery's charge coefficient is less than a preset value, the energy storage battery in the charging state is switched to the discharging state, and the other energy storage battery in the discharging state is switched to the charging state.

[0008] The determination of the grid-connected power of the wind farm at time t that satisfies the power fluctuation rate limit, based on the output power of the offshore wind farm at time t and the grid-connected power of the wind farm at time t-1, includes: ;in, Let t be the grid-connected power of the wind farm; Let t be the output power of the offshore wind farm. These are the filter coefficients.

[0009] The determination of the grid-connected power of the wind farm at time t that meets the power fluctuation rate limit includes: the filter coefficient a is initially set to 1, and the filter coefficient a is gradually reduced until the power fluctuation rate meets the power fluctuation rate limit within a preset time scale ending at time t.

[0010] The power volatility limits include power volatility limits on a 1-minute timescale and power volatility limits on a 30-minute timescale.

[0011] When the energy storage system is in the power absorption state at time t, the power allocation for the energy storage battery and the power allocation for the supercapacitor are determined based on the energy storage power of the energy storage system and the SOC of the energy storage battery in the energy storage system. This includes: determining the ideal power allocation for the energy storage battery and the power allocation for the supercapacitor based on the energy storage power of the energy storage system; determining the edge proximity parameter k based on the SOC of the energy storage battery and the upper and lower limits of the energy storage battery's charge coefficient; and determining the power allocation for the energy storage battery based on the following formula, including... In the formula, Distribute power to the energy storage battery; Distribute power to the ideal energy storage battery; This is a warning value for the battery edge proximity parameter; based on the energy storage power of the energy storage system and the power allocated to the energy storage battery. Determine the power distribution of the supercapacitor. .

[0012] The determination of ideal energy storage battery allocation power and supercapacitor allocation power based on the energy storage power of the energy storage system includes: ;in, Allocate power to the energy storage battery at time t; Let be the energy storage power of the energy storage system at time t; These are the preset filter coefficients.

[0013] The step of updating the SOC of the energy storage battery based on the allocated power of the energy storage battery includes: battery charging status and energy storage battery allocated power. : Battery discharge status and power distribution of energy storage batteries. : ;in, The rated capacity of the energy storage battery. and These represent the charge and discharge efficiencies of the energy storage battery.

[0014] A hybrid energy storage system-assisted offshore wind power smoothing control device includes: a grid-connected power determination module, used to determine the grid-connected power of the offshore wind farm at time t that satisfies the power fluctuation rate limit based on the output power of the offshore wind farm at time t and the grid-connected power of the wind farm at time t-1; an energy storage power determination module, used to determine the energy storage power of the energy storage system at time t based on the grid-connected power of the wind farm at time t and the output power of the offshore wind farm at time t; an operating status judgment module, used to determine whether the operating state of the energy storage system at time t is absorbing or releasing power based on the energy storage power of the energy storage system at time t; and a power allocation determination module I, used to determine the energy storage power of the energy storage system when the energy storage system is in the power absorption state at time t, based on the energy storage power of the energy storage system and the energy storage system... The first energy storage battery's SOC determines the allocated power of the energy storage battery and the supercapacitor, indicating that the energy storage battery is in a charging state. The second allocation power determination module is used to determine the allocated power of the energy storage battery and the supercapacitor based on the energy storage power of the energy storage system and the SOC of another energy storage battery in the energy storage system when the energy storage system is in a power release state at time t, indicating that the energy storage battery is in a discharging state. The third state switching module is used to update the SOC of the energy storage battery based on the allocated power of the energy storage battery, and when the difference between the updated SOC and the upper and lower limits of the energy storage battery's charge coefficient is less than a preset value, it switches the energy storage battery in the charging state to the discharging state and switches the other energy storage battery in the discharging state to the charging state.

[0015] A storage medium storing a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the offshore wind power suppression control method.

[0016] A power stabilization control device for offshore wind power includes a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the offshore wind power stabilization control method.

[0017] The beneficial effects of this invention are as follows: This invention uses a variable filtering coefficient algorithm to determine the grid-connected power of the offshore wind farm at time t, based on the output power of the wind farm at time t-1 and the grid-connected power of the wind farm at time t-1. By dynamically reducing the filtering coefficient, it effectively smooths out the wind power output, thereby significantly reducing the impact of power fluctuations caused by wind speed fluctuations on the power grid. Furthermore, by precisely adjusting the filtering coefficient, this algorithm can also optimize the operating efficiency of the wind farm, reduce energy waste, and simultaneously improve the grid's capacity to accommodate renewable energy.

[0018] This invention determines the power allocation to the energy storage batteries based on the energy storage power of the energy storage system, and adjusts the power allocation based on the state of charge (SOC) of the energy storage batteries in the system to prevent the SOC from exceeding the safe range. This invention employs a dual-battery cooperative approach, with each battery responsible for power absorption and release, avoiding frequent charge-discharge switching, preventing accelerated internal chemical reactions, and extending battery life. Attached Figure Description

[0019] Figure 1 is a schematic diagram of the offshore wind farm system structure with a hybrid energy storage system in the embodiment; Figure 2 is a flowchart of the real-time smoothing algorithm for offshore wind power based on variable coefficient dynamic filtering in the embodiment; Figure 3 is a flowchart of the hybrid energy storage system in the embodiment; Figure 4 is a schematic diagram of the edge control of the energy storage coefficient in the embodiment; Figure 5 is a schematic diagram of the battery / supercapacitor energy storage allocation algorithm considering SOC edge control in the embodiment; Figure 6 is a schematic diagram of the dual-battery coordinated charge and discharge control process in the embodiment; Figure 7 is a schematic diagram of the original / smoothed power of the offshore wind farm in the embodiment; Figure 8 is a schematic diagram of the smoothing filter coefficient of the offshore wind farm in the embodiment; Figure 9 is a schematic diagram of the 1-minute power fluctuation value of the offshore wind farm in the embodiment; Figure 10 is a schematic diagram of the 30-minute power fluctuation value of the offshore wind farm in the embodiment. Figure 11 is a schematic diagram of the energy storage power value of the offshore wind farm in the embodiment; Figure 12 is a schematic diagram of the 1-minute fluctuation value with coefficient filter a=0.5 in the embodiment; Figure 13 is a schematic diagram of the 30-minute fluctuation value with coefficient filter a=0.5 in the embodiment; Figure 14 is a schematic diagram of the energy storage power with coefficient filter a=0.9 in the embodiment; Figure 15 is a schematic diagram of the energy storage power in the embodiment; Figure 16 is a schematic diagram of the battery energy storage power in the embodiment; Figure 17 is a schematic diagram of the supercapacitor energy storage power in the embodiment; Figure 18 is a schematic diagram of the total state of charge of the battery in the embodiment; Figure 19 is a schematic diagram of the state of charge of battery A in the embodiment; Figure 20 is a schematic diagram of the state of charge of battery B in the embodiment; Figure 21 is a schematic diagram of the state of charge of the supercapacitor in the embodiment. Detailed Implementation

[0020] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0021] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0022] Example 1: As shown in Figure 1, the offshore wind farm system structure includes a wind farm and a hybrid energy storage system. The wind farm includes all the wind turbines on the offshore platform, and its total output power is the original power of the wind farm. A hybrid energy storage system consists of a battery of a given capacity and a supercapacitor, responsible for storing raw power. The power is absorbed and smoothed out so that it can be integrated into the offshore power grid. The energy storage system also includes a DC / AC power converter to realize the real-time power of the energy storage system. The input and output are shown in the following formula: ;in, Let t be the energy storage power of the energy storage system at time t; Let be the output power of the wind farm at time t; Let t be the grid-connected power of the wind farm.

[0023] The offshore wind power smoothing control method assisted by the hybrid energy storage system in this embodiment specifically includes the following steps: S100, based on the output power of the offshore wind farm at time t and the grid-connected power of the wind farm at time t-1, determine the grid-connected power of the wind farm at time t that meets the power fluctuation rate limit.

[0024] Currently, there are many methods applied to wind power level suppression scenarios, such as the averaging method, the windowing method, and power prediction. In this embodiment, the offshore wind power sampling period is 1 second, and the power fluctuations are on two scales: 1 minute and 30 minutes. Due to the short sampling period, the accuracy of the time-series power prediction algorithm does not yet meet the requirements for real-time adjustment; at the same time, the uncertainty and fluctuation problems of offshore wind power are more prominent than those of onshore wind power, and the averaging method and the windowing method are difficult to accurately limit its fluctuations. Therefore, this embodiment proposes to adopt a filtering algorithm with flexible, simple, and easy-to-control characteristics, and can adjust the wind power level suppression effect by controlling the filtering coefficient in real time.

[0025] In this embodiment, the input signal is the raw power of the offshore wind farm. The output signal is a power signal that conforms to grid connection standards. Since wind power signals need to be sampled, the filtered power of the wind farm is expressed as: In the formula, It is a time constant; The sampling period for the wind power system is 1 second in this example. Let t be the initial output power of the offshore wind farm. Let t be the filtered power of the wind farm. Let the filtered power of the wind farm at time t-1 be defined, and define the filtering coefficients. The above formula can be written as: From the above formula, we can see that the output power at time t is... Received Output power at time Input power at time t The combined effect of these factors results in a weighted sum. When the filter coefficient a is close to 1, Weight is large, output power When the filter is closer to 0, its effect on wind power is smaller; when the filter coefficient 'a' is close to 0, Output power at time Weight of output power The filter is closer to the wind power source, and its effect on wind power is greater, resulting in a smoother output power.

[0026] When offshore wind power is connected to the main power grid, the primary grid connection requirement is volatility limitation. The definitions of power fluctuations at different time scales for offshore wind power are as follows. Since the sampling time is 1 second, the active power fluctuation rate at second t is defined as the ratio of the difference between the maximum and minimum power in two time windows (1 minute and 30 minutes) ending at time t to the rated power of the wind farm, as shown in the following formula: ;in, This represents the wind power sampling value within a time window ending in t seconds. The rated power of the wind farm is given. According to the formula definition, the 30-minute fluctuation rate at time t is accumulated from the 1-minute fluctuation rates of all time points in the past 30 minutes. Therefore, when considering wind power smoothing, the smoothing of the 1-minute fluctuation value should be considered first. After the 1-minute fluctuation value meets the following constraints, then consider whether the 30-minute fluctuation value can meet them.

[0027] ;in, and These represent the maximum fluctuations of wind power limited by grid connection on two time scales: 1 minute and 30 minutes.

[0028] When applying the filtering algorithm, the wind power at time t is altered by changing the filtering coefficients, thus affecting the volatility index. Wind power fluctuations over 1 minute and 30 minutes show a strong correlation. The 30-minute volatility is calculated over a longer time window, covering multiple 1-minute volatility calculation windows. This means the 30-minute volatility is determined by the maximum and minimum values ​​among the first 30 1-minute volatility values. Therefore, the 30-minute volatility is considered an aggregation of 1-minute volatility over a longer time scale, as shown in the following equation: ;in, This indicates the maximum output power per second within that minute. This represents the minimum output power per second within that minute. The core of this formula is to assess volatility over a longer timeframe by analyzing the fluctuations of each minute, thereby establishing a relationship between 1-minute and 30-minute fluctuations. The 1-minute volatility of the first 30 minutes determines the 30-minute volatility at time t, thus providing a reference for determining the filter coefficients.

[0029] Based on the fluctuation relationship between wind power data over 1 minute and 30 minutes during wind power level suppression, this embodiment employs a filtering coefficient approximation method to iteratively traverse the wind power data sequence and dynamically control wind power output, ensuring that power fluctuations at both time scales do not exceed preset limits. The algorithm flow is shown in Figure 2.

[0030] First, the algorithm initializes the necessary parameters, including the total duration T, the time step Ts, the rated power Pr of the wind farm, and the limits on power fluctuation rate within one minute and thirty minutes (respectively). and The power volatility over the two time scales satisfies the following equation: ;

[0031] At the core of the algorithm, dynamic approximation of the filter coefficients is used to calculate and update the wind power output power time-by-time. Specifically, the output power at each time t... It is obtained by weighted averaging of the output at the previous moment and the current wind power input, where the weights are determined by the filter coefficient 'a'. The filter coefficient 'a' may be adjusted at each time point to ensure that the calculated power fluctuation values ​​at the two time scales satisfy the constraints of and , respectively.

[0032] If power fluctuations exceed the set limits at any time, the algorithm will dynamically reduce the filter coefficient 'a' until the fluctuation value falls within an acceptable range. This process continuously monitors and adjusts the value of 'a', reflecting this in real-time adjustments to the power output, thereby effectively suppressing power fluctuations. The following scenarios will be discussed in detail:

[0033] a=1, that is = hour, and At this point, there is no need for wind power suppression, and the power output can meet the grid connection requirements.

[0034] a=1, that is = hour, At this point, a wind level suppression algorithm is performed, with a step size of [missing information]. The filter coefficient 'a' is gradually reduced until the power fluctuation within one minute falls within an acceptable range. The filter coefficient 'a' is initially set to 1 and gradually reduced until the power fluctuation rate within a preset timescale (ending at time t) meets the power fluctuation rate limit. The specific formula is as follows: ;in, , Specifically, it is as follows: ;in, It is an adjustment coefficient used to control the size of the decrement step. This demonstrates the sensitivity of decreasing filter coefficients to power fluctuations. The change in power output at the current time t, that is, the difference in power output from the previous moment to the current moment. This is the rated power of the wind farm.

[0035] a<1, , At this point, a wind level suppression algorithm is performed, with a step size of [missing information]. Gradually reduce the filter coefficient 'a' until the power fluctuation drops to an acceptable range after 30 minutes.

[0036] ; ;

[0037] Through the aforementioned variable-coefficient dynamic filtering algorithm, this embodiment effectively smooths out wind power output, thereby significantly reducing the impact of power fluctuations caused by wind speed fluctuations on the power grid. Furthermore, by precisely adjusting the filtering coefficients, this algorithm can also optimize the operating efficiency of wind farms, reduce energy waste, and simultaneously improve the grid's capacity to accommodate renewable energy.

[0038] The key to real-time wind power fluctuation mitigation strategies lies in calculating the target power to ensure that the processed active wind power meets grid-connected power fluctuation rate constraints, covering two different time scales. The effectiveness of wind power mitigation is primarily influenced by the filter coefficients in the filtering algorithm, and is also constrained by the wind power adjustment time scale (1 second), which poses a challenge to improving algorithm performance. To optimize this process, a variable-coefficient filtering algorithm that adaptively adjusts the filter coefficients across both time scales is proposed.

[0039] S200. Based on the grid-connected power of the wind farm at time t and the output power of the offshore wind farm at time t, determine the energy storage power of the energy storage system at time t.

[0040] This embodiment calculates the smoothed target wind power using a filtering algorithm that adaptively adjusts the filtering coefficients. The difference between the target power and the original output power of the wind farm represents the power that the energy storage system needs to compensate. Since no single energy storage device can currently fully integrate characteristics such as charge-discharge cycle count, energy density, and power density, hybrid energy storage systems composed of energy storage batteries and supercapacitors, with their complementary energy and power characteristics, have become an effective solution for smoothing wind power fluctuations.

[0041] As shown in Figure 3, due to the real-time power consumption requirements of offshore wind farms, the hybrid energy storage system dynamically allocates the required power to ensure that the wind power output meets the standards for grid connection operation, and dynamically distributes the power to be stored to the two energy storage units: the large-capacity battery and the supercapacitor. The power to be balanced is as follows: ;in, Let t be the energy storage power of the energy storage system. Let t be the output power of the offshore wind farm. Let t be the grid-connected power of the wind farm.

[0042] From the perspective of energy storage systems, the power to be stored After power allocation algorithm, the power is stored separately by battery and supercapacitor, as follows: ;in, Allocate power to the energy storage battery at time t; Distribute power to the supercapacitor at time t.

[0043] S300. Based on the energy storage power of the energy storage system at time t, determine whether the operating state of the energy storage system at time t is absorbing or releasing power.

[0044] When the energy storage system is in the power absorption state at time t, the power allocated to the energy storage battery and the power allocated to the supercapacitor are determined based on the energy storage power of the energy storage system and the SOC of one of the energy storage batteries in the energy storage system. The energy storage battery is in the charging state.

[0045] When the energy storage system is in the power release state at time t, the power allocated to the energy storage battery and the power allocated to the supercapacitor are determined based on the energy storage power of the energy storage system and the SOC of another energy storage battery in the energy storage system. The energy storage battery is in the discharge state.

[0046] In this embodiment, considering battery life, a dual-battery system is used for charging and discharging. The energy storage battery, which was originally considered as a single unit, is divided into two parts, energy storage battery A and energy storage battery B, based on their power and capacity parameters. Initially, one energy storage battery is designated as the power absorption element (the battery is in a charging state), and the other energy storage battery is designated as the power release element (the battery is in a discharging state).

[0047] The initial state of charge (SOC) of energy storage batteries A and B is set to 50%, an intermediate value, ensuring maximum flexibility for the system at the outset. Furthermore, through historical data analysis and battery performance models, the SOC operating range for each energy storage unit is determined to avoid overcharging and over-discharging.

[0048] During charging or discharging operations, the power output of an energy storage system is subject to certain constraints to ensure normal system operation and prevent damage from overcharging or over-discharging. In this process, the State of Charge (SOC) is a key indicator. The charge coefficient is defined as follows: ;

[0049] In this embodiment, since power at the second level is used as the algorithm input, the charge coefficient is also written in the following form: ;in, This refers to the energy storage unit capacity prior to this charge / discharge cycle. The rated capacity of the energy storage unit. and These refer to the charging and discharging efficiency of the equipment. and The charging and discharging power of the equipment, This represents the duration of the charging and discharging process. Combining the above two equations and considering the charge coefficients of the battery and supercapacitor respectively, we obtain the following four SOC change formulas: Battery charging status ( ): Battery discharge status ( ): Supercapacitor discharge status ( ): Supercapacitor discharge status ( ): In energy storage allocation algorithms, ensuring that the state of charge of batteries and supercapacitors is maintained within the specified safe range is the most important prerequisite. Therefore, in subsequent algorithms, the constraint of the charge coefficient of energy storage units accounts for a large part of the consideration.

[0050] The coordinated control strategy of hybrid energy storage systems aims to ensure that the characteristics of each component are maximized within the normal operating range to effectively cope with wind power fluctuations. Energy storage batteries, due to their large charge-discharge timescale, are suitable for handling long-term power variations; conversely, supercapacitors, due to their rapid charge-discharge capabilities, are suitable for absorbing short-term high-frequency fluctuations. The technical performance parameters of various energy storage devices are shown in Table 1.

[0051] Table 1 Technical performance parameters of batteries and supercapacitors

[0052] By using filtering algorithms, low-frequency and high-frequency power fluctuations can be effectively distinguished, allowing them to be allocated selectively to energy storage batteries and supercapacitors. This ensures that each energy storage device can handle energy changes at the appropriate frequency based on its performance advantages. However, while filtering algorithms can effectively allocate low-frequency and high-frequency power fluctuations from wind farms to energy storage batteries and supercapacitors respectively, this method is insufficient to maintain batteries operating at their optimal state of charge and is not conducive to maximizing battery lifespan.

[0053] To address these issues, a real-time control strategy for an offshore wind power hybrid energy storage system is proposed. This algorithm aims to optimize the capacity allocation of batteries and capacitors, primarily satisfying two core requirements: First, batteries mainly handle long-term fluctuation regulation, while capacitors regulate short-term fluctuations, ensuring that the state of charge (SOC) of both batteries and capacitors remains within a set range. Second, extending battery lifespan also needs to be considered. Therefore, the algorithm consists of two steps to address these two aspects respectively. The two parts will be described in detail below.

[0054] The first objective of the real-time control strategy for offshore wind power hybrid energy storage system is to allocate the energy to be stored to both batteries and supercapacitors, while ensuring that the allocation process meets the SOC constraint of the energy storage system, that is, to ensure that the state of charge of the batteries and supercapacitors is maintained within a predetermined safe range.

[0055] During the algorithm's control process, the SOC state of the energy storage system needs to be considered when making decisions. Definition This is the edge proximity parameter for the energy storage system, whose value is represented by the device's state of charge (SOC). It is used to assess how close the battery's SOC is to its maximum and minimum allowable values, as shown in the following formula: ;in, This represents the upper limit of the charge coefficient of the energy storage battery. This represents the lower limit of the charge coefficient of energy storage batteries. This represents the current charge factor of the energy storage battery.

[0056] when At that time, the energy storage device is in a charging state. The closer the state of charge is to the maximum charge constraint at the end of charging, the better. This indicates the distance from the energy storage device to its maximum state of charge.

[0057] when At that time, the energy storage device is in a discharging state. The closer the state of charge is to the minimum charge constraint at the end of charging, the better. This indicates the distance from the energy storage device to its minimum state of charge.

[0058] therefore, The State of Charge (SOC) of a battery is considered a "dangerous value," meaning a parameter indicating that the SOC is nearing its limit. When this warning limit is reached, a limiting function is selected to restrict the power output of the energy storage device in the corresponding direction. Commonly used limiting functions consider higher-order coefficients or power-law coefficients. This embodiment uses quadratic coefficients to ensure that the power curve is within a certain range. It curls downwards. That is: In the formula, To allocate power for ideal battery energy storage, The warning value is the battery edge proximity parameter. The edge control effect of the energy storage coefficient is shown in Figure 4.

[0059] The specific battery / supercapacitor energy storage allocation algorithm considering SOC edge control is shown in Figure 5: First, the energy storage power to be stored in the energy storage system is obtained according to steps S100 and S200, and the energy storage system is allocated according to this power.

[0060] Initialize system parameters, including the basic configuration of the energy storage system, namely the capacity of the battery and supercapacitor, as well as relevant parameters during the process, such as the SOC limit of energy storage elements and the SOC warning limit.

[0061] Based on the energy storage power of the energy storage system, the ideal power allocation between the energy storage battery (when the energy storage system is in power absorption mode, this battery is the one of the two batteries in the charging state; when releasing power, this battery is the one of the two batteries in the power generation state) and the supercapacitor is calculated. First, the ideal allocated capacity of the battery is calculated using a filtering algorithm with set coefficients: In the formula, The preset filter coefficients are determined based on empirical values ​​of offshore wind farm fluctuations. Correspondingly, the power of the supercapacitor is the total energy storage capacity minus the battery power, i.e.: = ;

[0062] Then, calculate the edge proximity parameter. Based on the ideal allocated capacity of the battery, the power distribution of the energy storage battery is adjusted to prevent the battery's State of Charge (SOC) from exceeding the set limit. Specifically, if the battery's SOC is close to the boundary, the power absorption of the battery and supercapacitor is adjusted to prevent the battery's SOC from exceeding the safe range. The supercapacitor power is adjusted simultaneously with the battery power adjustment.

[0063] Finally, at each time step, the current power allocation and the SOC status of the battery and supercapacitor are recorded. If at any point in time the capacity of the energy storage device is found to be insufficient to meet demand, the system will trigger a capacity shortage warning and suggest device configuration or capacity adjustment. Once the power allocation for the entire set time range is completed, the process ends, marking the completion of a full energy storage management cycle.

[0064] S400: Based on the energy storage battery allocation power obtained in step S300, and combined with the SOC change formula, update the SOC of the corresponding energy storage battery. When the difference between the updated SOC and the upper and lower limits of the energy storage battery's charge coefficient is less than a preset value, switch the charging energy storage battery to the discharging state and switch the discharging energy storage battery to the charging state.

[0065] Assume energy storage battery A absorbs energy while energy storage battery B releases it. When battery A reaches its upper limit of charge or energy storage battery B reaches its lower limit of charge, a switch between charge and discharge states is triggered. Battery A switches from charging to discharging, while battery B switches from discharging to charging, thus switching between charging and discharging components, and this cycle continues.

[0066] When the received power exceeds the maximum power capacity of a single energy storage unit, or when it cannot fully respond due to SOC limitations, the remaining power demand will be compensated by another set of energy storage units to ensure the power balance of the overall system.

[0067] The following analysis uses a specific example: The rated power of the offshore wind farm is set at 6MW. Under grid connection requirements, the maximum fluctuation rate at two time scales is: power fluctuation not exceeding 2% in 1 minute and power fluctuation not exceeding 10% in 30 minutes. To evaluate the effectiveness of the filtering algorithm based on adaptive adjustment of the filtering coefficient in ensuring that the wind power leveling target meets grid connection standards and reduces energy storage usage, this embodiment also introduces a filtering algorithm with a fixed filtering coefficient for comparative analysis. The simulation results are shown in Figures 7-14. In Figure 7, the gray curve represents the original power of the offshore wind farm, and the blue curve represents the leveled power of the offshore wind farm. Comparing the two curves, it can be seen that the leveled curve is much smoother than the original. Figure 8 shows the change of the filtering coefficient during the leveling process of the offshore wind farm. Figures 9 and 10 show the leveled fluctuation values ​​of the offshore wind farm at 1 minute and 30 minutes, respectively. It can be seen that they are all within the grid connection standards of the wind farm, indicating that the wind power leveling algorithm constrains the fluctuation value of the original power of the offshore wind farm at both time scales. Figure 8 shows the energy storage capacity of offshore wind farms, which is the difference between the blue and gray curves in Figure 7. This part will be analyzed as an input value in the algorithm in the next part.

[0068] Comparing Figures 11 to 14, it can be seen that when the filter coefficient a = 0.5, the 1-minute and 30-minute fluctuation values ​​do not meet the grid-connected power fluctuation limits of 2% and 10%, respectively; when the filter coefficient a = 0.9, the energy storage system is overburdened. Therefore, traditional fixed-coefficient filtering algorithms struggle to strike a balance between meeting grid connection requirements and reducing the burden on energy storage.

[0069] These analyses show that the filtering algorithm based on adaptive adjustment of the filtering coefficient proposed in this application can not only effectively smooth out wind power fluctuations and ensure that grid connection standards are met, but also reduce energy storage requirements and improve the economic operating efficiency of wind power systems in practical applications.

[0070] To further verify the proposed coordinated control strategy for the hybrid energy storage system, a target power determination method based on a filtering algorithm was first applied to obtain the real-time power required for the energy storage system to absorb offshore wind power grid connection, as shown in Figure 15. Then, using the energy storage system power absorption strategy proposed in this application, the charging and discharging of the dual energy storage batteries and supercapacitor were coordinated to absorb the low- and high-frequency components of the stored power. The parameters of the hybrid energy storage system during simulation are shown in Table 2.

[0071] Table 2 Parameters of Hybrid Energy Storage System

[0072] The simulation results are shown in the figures. Figure 16 shows the real-time charge and discharge power of the battery, demonstrating that the battery effectively utilized its energy characteristics and absorbed the low-frequency components of the fluctuations. Figure 17 shows the real-time charge and discharge power of the supercapacitor, which absorbed the high-frequency fluctuation components. Figures 18 and 21 show the total state of charge (SOC) of the battery and supercapacitor, with the red and green curves representing the minimum and maximum SOC values, respectively. It is evident that none of the energy storage units exceeded the SOC limit. Figures 19 and 20 show the energy storage processing of battery A and battery B, demonstrating that they not only maintained the strategy of continuous charging and discharging before reaching the SOC limit but also performed functional conversion and took on responsibilities upon reaching the SOC boundary, meeting the requirements of the strategy. In summary, the strategy proposed in this application not only meets the wind power consumption requirements but also takes into account the SOC of the energy storage system and improves battery life.

[0073] This embodiment delves into the charging and discharging behavior of a hybrid energy storage system and its impact on battery life, and develops a management strategy based on a filtering algorithm. This strategy utilizes the basic framework of a filtering algorithm, combining the battery's current state of charge and historical charging and discharging frequencies to dynamically adjust the filtering coefficients. This adaptive adjustment allows the battery to primarily absorb low-frequency power fluctuations, while the supercapacitor handles the high-frequency components, optimizing energy distribution and improving the system's response efficiency.

[0074] Detailed simulations of the system confirmed the effectiveness of the strategy. Results showed that this method not only ensures the batteries and supercapacitors operate in a safe state of charge, but also significantly reduces the number of charge-discharge cycles, effectively extending battery life. This strategy, which reduces the need for frequent charging and discharging, directly lowers the overall maintenance cost of the hybrid energy storage system, providing a more economical and reliable solution for wind farm energy management.

[0075] Example 2: This example is a hybrid energy storage system-assisted offshore wind power smoothing control device, comprising: a grid-connected power determination module, used to determine the grid-connected power of the offshore wind farm at time t that satisfies the power fluctuation rate limit based on the output power of the offshore wind farm at time t and the grid-connected power of the wind farm at time t-1; an energy storage power determination module, used to determine the energy storage power of the energy storage system at time t based on the grid-connected power of the wind farm at time t and the output power of the offshore wind farm at time t; an operating status judgment module, used to determine whether the operating state of the energy storage system at time t is absorbing or releasing power based on the energy storage power of the energy storage system at time t; and a power allocation determination module I, used to determine the power allocation based on the energy storage power of the energy storage system when the energy storage system is in the power absorption state at time t. The system uses the SOC of one energy storage battery in the energy storage system to determine the allocated power of the energy storage battery and the allocated power of the supercapacitor, where the energy storage battery is in a charging state; the allocated power determination module II is used to determine the allocated power of the energy storage battery and the allocated power of the supercapacitor based on the energy storage power of the energy storage system and the SOC of another energy storage battery in the energy storage system when the energy storage system is in a power release state at time t, where the energy storage battery is in a discharging state; the state switching module is used to update the SOC of the energy storage battery based on the allocated power of the energy storage battery, and when the difference between the updated SOC and the upper and lower limits of the energy storage battery's charge coefficient is less than a preset value, to switch the energy storage battery in the charging state to the discharging state, and to switch the other energy storage battery in the discharging state to the charging state.

[0076] Example 3: This example is a storage medium that stores a computer program that can be executed by a processor. When the computer program is executed, it implements the steps of the offshore wind power smoothing control method described in Example 1.

[0077] Example 4: This example is an offshore wind power suppression control device, which has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the offshore wind power suppression control method described in Example 1.

[0078] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion 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 described in the various embodiments of this invention. 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.

[0079] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0080] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0081] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0082] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0083] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0084] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for offshore wind power smoothing control assisted by a hybrid energy storage system, characterized in that, include: Based on the offshore wind farm output power at time t and the wind farm grid-connected power at time t-1, determine the wind farm grid-connected power at time t that satisfies the power fluctuation rate limit. Based on the grid-connected power of the wind farm at time t and the output power of the offshore wind farm at time t, the energy storage power of the energy storage system at time t is determined. Based on the energy storage power of the energy storage system at time t, the operating state of the energy storage system at time t is determined to be either absorbing or releasing power. When the energy storage system is in the power absorption state at time t, based on the energy storage power of the energy storage system and the SOC of one energy storage battery in the energy storage system, the power allocation of the energy storage battery and the power allocation of the supercapacitor are determined, and the energy storage battery is in the charging state. When the energy storage system is in the power release state at time t, based on the energy storage power of the energy storage system and the SOC of another energy storage battery in the energy storage system, the power allocation of the energy storage battery and the power allocation of the supercapacitor are determined, and the energy storage battery is in the discharging state. Based on the power allocation of the energy storage battery, the SOC of the energy storage battery is updated, and when the difference between the updated SOC and the upper and lower limits of the energy storage battery's charge coefficient is less than a preset value, the energy storage battery in the charging state is switched to the discharging state, and the other energy storage battery in the discharging state is switched to the charging state.

2. The method for offshore wind power smoothing control assisted by a hybrid energy storage system according to claim 1, characterized in that, The determination of the grid-connected power of the wind farm at time t that satisfies the power fluctuation rate limit, based on the output power of the offshore wind farm at time t and the grid-connected power of the wind farm at time t-1, includes: ;in, Let t be the grid-connected power of the wind farm; Let t be the output power of the offshore wind farm. These are the filter coefficients.

3. The method for offshore wind power smoothing control assisted by a hybrid energy storage system according to claim 2, characterized in that, The determination of the grid-connected power of the wind farm at time t that meets the power fluctuation rate limit includes: the filter coefficient a is initially set to 1, and the filter coefficient a is gradually reduced until the power fluctuation rate meets the power fluctuation rate limit within a preset time scale ending at time t.

4. The method for offshore wind power smoothing control assisted by a hybrid energy storage system according to claim 1, 2, or 3, characterized in that, The power volatility limits include power volatility limits on a 1-minute timescale and power volatility limits on a 30-minute timescale.

5. The method for offshore wind power smoothing control assisted by a hybrid energy storage system according to claim 1, characterized in that, When the energy storage system is in the power absorption state at time t, the power allocation for the energy storage battery and the power allocation for the supercapacitor are determined based on the energy storage power of the energy storage system and the SOC of the energy storage battery in the energy storage system. This includes: determining the ideal power allocation for the energy storage battery and the power allocation for the supercapacitor based on the energy storage power of the energy storage system; determining the edge proximity parameter k based on the SOC of the energy storage battery and the upper and lower limits of the energy storage battery's charge coefficient; and determining the power allocation for the energy storage battery based on the following formula, including... In the formula, Distribute power to the energy storage battery; Distribute power to the ideal energy storage battery; This is a warning value for the battery edge proximity parameter; based on the energy storage power of the energy storage system and the power allocated to the energy storage battery. Determine the power distribution of the supercapacitor. 。 6. The method for offshore wind power smoothing control assisted by a hybrid energy storage system according to claim 5, characterized in that, The determination of ideal energy storage battery allocation power and supercapacitor allocation power based on the energy storage power of the energy storage system includes: ;in, Allocate power to the energy storage battery at time t; Let be the energy storage power of the energy storage system at time t; These are the preset filter coefficients.

7. The method for offshore wind power smoothing control assisted by a hybrid energy storage system according to claim 1, characterized in that, The step of updating the SOC of the energy storage battery based on the allocated power of the energy storage battery includes: battery charging status and energy storage battery allocated power. ： Battery discharge status and power distribution of energy storage batteries. ： ;in, This refers to the rated capacity of the energy storage battery. and These represent the charge and discharge efficiencies of the energy storage battery.

8. A hybrid energy storage system-assisted offshore wind power smoothing control device, characterized in that, include: The grid-connected power determination module is used to determine the grid-connected power of the wind farm at time t that satisfies the power fluctuation rate limit, based on the output power of the offshore wind farm at time t and the grid-connected power of the wind farm at time t-1. The energy storage power determination module is used to determine the energy storage power of the energy storage system at time t based on the grid-connected power of the wind farm and the output power of the offshore wind farm at time t. The operating status judgment module is used to determine whether the energy storage system is absorbing or releasing power at time t based on its energy storage power. The power allocation determination module I is used to determine the power allocation of the energy storage battery and the supercapacitor when the energy storage system is in the power absorption state at time t, based on the energy storage power of the energy storage system and the SOC of one of the energy storage batteries in the system, where the energy storage battery is in a charging state. The power determination module II is used to determine the power allocation of the energy storage battery and the power allocation of the supercapacitor based on the energy storage power of the energy storage system and the SOC of another energy storage battery in the energy storage system when the energy storage system is in the power release state at time t. The energy storage battery is in the discharge state. The state switching module is used to update the SOC of the energy storage battery based on the power allocation of the energy storage battery, and when the difference between the updated SOC and the upper and lower limits of the energy storage battery's charge coefficient is less than a preset value, the energy storage battery in the charging state is switched to the discharging state, and the other energy storage battery in the discharging state is switched to the charging state.

9. A storage medium having a computer program stored thereon that can be executed by a processor, characterized in that, When the computer program is executed, it implements the steps of the offshore wind power suppression control method according to any one of claims 1 to 7.

10. A power stabilization and control device for offshore wind power, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the computer program is executed, it implements the steps of the offshore wind power suppression control method according to any one of claims 1 to 7.

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