Storage battery energy storage system configuration method for stabilizing fan output fluctuation

Abstract

The invention belongs to the technical field of power systems and energy storage, and particularly relates to a storage battery energy storage system configuration method for stabilizing fan output fluctuation. In order to solve the problem of low data accuracy after day-ahead prediction and intra-day optimization of the wind turbine generator, an energy storage system is used for performing power compensation on a power grid of the wind turbine generator, and when the actual power generation power of the wind turbine generator is higher than a predicted value, the energy storage system is charged, and redundant electric energy is stored in a storage battery; and when the actual generated power of the wind turbine generator is lower than the predicted value, the energy storage system starts to discharge, and power compensation is carried out on the wind turbine generator. The compensation effect of the method not only depends on the configuration capacity of the energy storage system, but also is related to an error compensation coefficient, when the coefficient is 1, full power compensation is carried out, and when the coefficient is 0, compensation is not carried out. According to the method, the accuracy and the stability of wind turbine generator power prediction are remarkably improved, and the negative influence on a power grid caused by wind power volatility is effectively relieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system and energy storage, and in particular relates to a method for configuring a battery energy storage system for smoothing wind turbine output fluctuations. Background Art

[0002] As the global response to climate change continues to advance, the low-carbon transformation of the energy system has become an important way for countries to achieve sustainable development. Under the guidance of the strategic goals of "carbon peak" and "carbon neutrality", my country's power system is rapidly promoting the large-scale development and utilization of renewable energy represented by wind and solar energy. As an important part of clean energy, wind power has attracted widespread attention due to its low-carbon and environmentally friendly characteristics. However, in the process of large-scale grid connection, wind power has brought severe challenges to the stability of the power grid and the quality of power due to its randomness, intermittency and volatility. In order to ensure the safe and stable access of wind power to the power grid, effective measures are urgently needed to smooth the fluctuations in wind power.

[0003] The main reason for the fluctuation of wind power output is the change of wind speed. Wind power is a cubic function of wind speed, which means that a small fluctuation of wind speed will lead to a sharp fluctuation of wind power. This volatility seriously affects the frequency and voltage stability of the power grid after large-scale wind power is connected to the grid, which may cause instability or even failure of the power system. In order to avoid this situation, the power system needs to have good regulation capabilities to cope with the drastic changes in wind power. Therefore, the introduction of energy storage systems provides a feasible solution to this problem. By storing excess electricity during peak wind power periods and releasing electricity during trough periods, the energy storage system can effectively smooth out wind power fluctuations and improve the stability of wind power connection to the grid.

[0004] As a representative of electrochemical energy storage technology, the battery energy storage system (BESS) has become one of the important means to stabilize wind power fluctuations due to its high energy density, fast response time and flexible adjustment capabilities. BESS can quickly respond to wind power fluctuations within milliseconds according to the needs of the power grid, and maintain the power balance of the power grid by adjusting the charging and discharging status in real time. This makes the battery energy storage system have significant advantages in improving the stability of wind power grid connection and reducing the impact of wind power fluctuations on the power grid.

[0005] The frequency of wind power fluctuations on a short time scale is fast but the amplitude is small, which requires energy storage to respond quickly and charge and discharge frequently. Power-type energy storage with high power density and high cycle times is often used for smoothing. Cui Lin et al. simulated the impact of wind power fluctuations on the power grid as a single machine to an infinite system power oscillation in the literature "Research on Superconducting Magnetic Energy Storage System to Suppress Wind Power Fluctuations". The effect of using superconducting magnetic energy storage devices to compensate for wind power is equivalent to the ability to suppress infinite system power oscillations. Zhang Buhan et al. used a series-parallel supercapacitor energy storage system to smooth the active power fluctuations of wind turbines in the literature "Application of Series-Parallel Supercapacitor Energy Storage System in Wind Power Generation" and designed a control signal of an active power controller to smooth the active power deviation of the energy storage device access point. Chen Yulong et al. connected multiple flywheel energy storage units in parallel on the AC bus to form a flywheel energy storage array in the literature "Power Coordination Control Strategy of Flywheel Energy Storage Array for Wind Power Smoothing". The energy storage array was used to compensate for the high-frequency components in wind power in real time to suppress the fluctuation of wind farm output power.

[0006] The long-term wind power fluctuation amplitude is large but the change is relatively gentle, which requires large-capacity and high-energy-density energy storage to smooth it. Various types of battery energy storage are often used. Sebastian.R et al. used a nickel-chromium battery energy storage system to smooth the active power output of the wind-diesel hybrid system in the paper "Effective Active Power Control of a High Penetration Wind Diesel System with a Ni Cd Battery Energy Storage". Li Guojie et al. chose a vanadium redox flow battery energy storage with low cost and 100% deep discharge in the paper "Vanadium Liquid Flow Energy Storage Battery Modeling and Research on Smoothing Wind Power Fluctuations", and established a mathematical model for the charging and discharging of vanadium liquid flow batteries. Zhang Xinsong et al. designed three battery energy storage operation strategies for the study of battery energy storage system smoothing wind power output in the paper "Comparative Analysis of Energy Storage Operation Strategies for Wind Power Fluctuation Smoothing", comprehensively considering the smoothing effect and the cost of smoothing, and determined the optimal operation strategy.

[0007] In summary, the application of battery energy storage system in wind power grid connection provides an effective technical means to solve the problem of wind power volatility. By smoothing the instantaneous fluctuation of wind power, BESS not only improves the power quality of wind farms, but also enhances the stability of the power grid. In practical applications, reasonable energy storage capacity configuration and advanced control strategies are the key to ensuring the efficient operation of energy storage systems. With the continuous development of energy storage technology and the reduction of application costs, battery energy storage systems will play a more important role in future new energy power systems.

[0008] In the future, with the development of smart grid and energy Internet technologies, battery energy storage systems will be deeply integrated with grid dispatching, wind power forecasting and other technologies to achieve more accurate control of wind power fluctuations. At the same time, further research in reducing the cost of energy storage systems, improving battery life and optimizing energy management will provide a solid foundation for the widespread application of battery energy storage technology in new energy power systems. Summary of the invention

[0009] In view of the above problems, the present invention provides a battery energy storage system configuration method for smoothing the fluctuation of wind turbine output. The method based on the combination of wind power prediction technology and dynamic scheduling of energy storage system can achieve real-time response and effective smoothing of wind power output fluctuation. First, based on historical wind power data and meteorological information, a short-term power prediction model is established to estimate future wind power output, and the prediction results are corrected in combination with the intraday optimization algorithm to minimize the prediction error. Based on the corrected data, the charging and discharging plan of the battery energy storage system is optimized so that it can store excess electric energy during the peak period of wind power, release electric energy when wind power is insufficient, and smooth the total output power of the wind farm. In this process, the battery energy storage system not only responds quickly to wind power fluctuations, provides balanced power, and ensures the stability of grid frequency, but also can regulate loads through energy storage and release, reduce the peak load pressure of the grid, and help the grid efficiently absorb wind power and reduce wind abandonment. This method is also applicable to other fluctuating renewable energy power generation scenarios such as photovoltaics. By optimizing the configuration of the energy storage system, the renewable energy absorption capacity is improved, the risk and cost of grid operation are reduced, and the low-carbon and intelligent development of the energy system is promoted, helping to achieve global energy transformation and carbon emission reduction goals. Therefore, the present invention has important application value in improving power grid stability and promoting sustainable development.

[0010] To achieve the above object, the present invention adopts the following technical solutions:

[0011] The application of battery energy storage technology in microgrids provides an innovative solution for the stability of power systems and the efficient integration of renewable energy. Based on the prediction of wind power output, microgrids can adjust the operating parameters of battery energy storage systems in advance to cope with the dynamic changes in future power supply and demand. This method not only improves the energy utilization efficiency of the power system, but also effectively alleviates the problem of power waste caused by the intermittent and volatile nature of renewable energy. The present invention provides a method for configuring a battery energy storage system to smooth out wind turbine output fluctuations, including:

[0012] When the actual output power of wind power generation exceeds the predicted value, the excess power will be stored in the battery energy storage system. When the actual output power of wind power generation is lower than the predicted value, the battery energy storage system uses the battery to release the stored power to make up for the power shortage. This configuration can not only smooth the fluctuation of wind power output, further optimize the stability of the power grid, but also improve the operating efficiency of the overall energy management system. For details of the simulation circuit topology, see Figure 1 shown.

[0013] In addition, before describing the typical wind farm application scenarios, the energy industry standard manual: NB / T10205-2019 is given, which stipulates that the comprehensive evaluation index formula for wind power prediction is as follows:

[0014]

[0016] In the formula: S represents the comprehensive evaluation result of wind power forecast; r1 represents the monthly average accuracy rate, r1 represents the weight of the indicator; r2 represents the monthly average qualified rate, r2 is the weight of the indicator; R represents the monthly average correlation coefficient, R represents the weight of the indicator; E Peak Indicates the monthly average load peak positive deviation rate; E Valley It indicates the negative deviation rate of the monthly average load valley; r HW Monthly average high-custom segment prediction accuracy; LW It indicates the prediction accuracy of the monthly average low wind speed segment; r3 indicates the comprehensive monthly reporting rate of various types of reported data.

[0017] The weight coefficient table in the comprehensive evaluation formula for wind power forecast is given. The power dispatching agency can adjust the index weight appropriately according to actual needs, as shown in Table 1:

[0018] Table 1 Weight table of comprehensive evaluation indexes for wind power prediction

[0019]

[0020] Accuracy: The monthly (annual) average accuracy is the arithmetic mean of the daily accuracy. The formula is as follows:

[0021]

[0022] The requirements for prediction accuracy in the guidelines are as follows: the monthly average accuracy of the short-term wind power forecast of the wind farm should be no less than 80%, and the average rate should be greater than 80%. The accuracy of the method proposed in this application reaches more than 85%, and the rated power of the battery energy storage system is not less than 30% of the total installed power of the wind power generation, and the continuous discharge time at rated power should not be less than 1 hour, so as to determine the specific capacity and power selection of the energy storage system.

[0023] The configuration method of the present invention specifically comprises the following steps:

[0024] Step 1: forecast the output power of the wind turbine generator set by using the day-ahead forecasting technology to generate a forecast curve of the output power of the wind turbine generator set in the next 24 hours;

[0025] Step 2: Based on the day-ahead forecast results, during the actual operation of the wind turbine, the battery energy storage system optimizes the output power forecast value of the wind turbine according to the continuously updated meteorological data and the operating status of the wind farm;

[0026] Step 3: During real-time operation, the battery energy storage system compares the predicted output power of the wind turbine after optimization for the day with the actual output power. When the actual output power is greater than the predicted output power, the battery energy storage system stores the excess electric energy in the battery; and when the actual output power is lower than the predicted output power, the battery energy storage system starts to discharge to make up for the power gap and ensure the balance of supply and demand in the power grid.

[0027] Furthermore, it also includes the configuration and optimization of the battery energy storage system, specifically:

[0028] The configuration of the battery energy storage system includes the capacity design of the battery, the access method of the system on the DC side and its control strategy. According to the deviation between the actual output power of the wind turbine and the predicted value and the operating status of the battery energy storage system, the corresponding power reference value is configured, and the operating parameters of the battery energy storage system are dynamically adjusted to ensure the response speed and compensation accuracy of the battery energy storage system.

[0029] Furthermore, the control strategy adopts a power feedforward control strategy based on the prediction error coefficient. By setting the prediction error compensation coefficient, the compensation strength of the battery energy storage system is adjusted. When the prediction error compensation coefficient is 1, full power compensation is achieved; when the prediction error compensation coefficient is 0, no compensation is performed, thereby flexibly responding to power compensation needs in different situations and optimizing the operating efficiency of the battery energy storage system and the prediction accuracy of the wind turbine.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] 1. This application dynamically compensates for the power fluctuation of the wind turbine through the charging and discharging function of the battery energy storage system, so that the actual power generation of the wind turbine is closer to the predicted value, thereby significantly improving the accuracy and reliability of wind power prediction.

[0032] 2. The error compensation coefficient adjustment mechanism provided in this application can flexibly adjust the compensation strength of the energy storage system and perform power compensation according to actual needs, effectively alleviating the impact of wind power fluctuations on the stability of the power grid and ensuring the continuity and stability of power supply.

[0033] 3. In view of the problem that the data accuracy of wind turbines after day-ahead prediction and intraday optimization is less than 85%, a method of using an energy storage system to compensate the wind turbine power grid for power is proposed to improve the accuracy of the forecast data. When the actual power generation of the wind turbine is higher than the forecast value, the energy storage system is charged and the excess electric energy is stored in the battery; when the actual power generation of the wind turbine is lower than the forecast value, the energy storage system begins to discharge and compensate the wind turbine for power. The compensation effect of this method depends not only on the configuration capacity of the energy storage system, but also on the error compensation coefficient. When the coefficient is 1, full power compensation is performed, and when the coefficient is 0, no compensation is performed. This method significantly improves the accuracy and stability of wind turbine power forecasting and effectively alleviates the negative impact of wind power volatility on the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the simulation circuit topology;

[0035] Figure 2 This is a waveform diagram of Belgian wind farm data;

[0036] Figure 3 It is the wind abandonment / load shedding power diagram;

[0037] Figure 4 The power prediction error of the DC side storage (available capacity is 0.15 pu) given the voltage deviation;

[0038] Figure 5 The power prediction error of the DC side storage (available capacity is 0.38 pu) given the voltage deviation;

[0039] Figure 6 The capacity and prediction accuracy curve of DC side storage for a given voltage deviation;

[0040] Figure 7 The power prediction error of the DC side storage (with an available capacity of 0.15 pu) given by the prediction error deviation;

[0041] Figure 8 The power prediction error of the DC side storage (available capacity is 0.38 pu) given by the prediction error deviation;

[0042] Fig. 9 The comparison chart of storage capacity and prediction accuracy under two control strategies;

[0043] Fig.10 It is a three-dimensional diagram of storage capacity-prediction error coefficient-accuracy. DETAILED DESCRIPTION

[0044] In order to further illustrate the technical solution of the present invention, the present invention is further described below through embodiments.

[0045] The wind power generation data used in this paper comes from the operating data of the Belgian power grid operator ELIA from September 18, 2023 to September 20, 2023, with a sampling interval of 15 minutes. The specific prediction scheme includes day-ahead prediction and real-time update. The day-ahead prediction is updated at 17:40 every day, while the real-time prediction is updated every hour. The total installed capacity of the wind farm studied is 5315.3MW. Figure 2 The wind power waveform during this period is shown. After normalizing the wind power data, it can be observed that the wind power fluctuates greatly. Although the day-ahead forecast can track the changes in the actual value well, with a forecast accuracy of 75.63%, it fails to fully meet the accuracy requirements. In comparison, the real-time forecast is more effective, with an accuracy of 79.41%, but it still does not meet the accuracy requirements specified by the national standard. Therefore, the dispatch method that relies solely on day-ahead forecasting and intraday optimization cannot fully meet the power system's requirements for wind power forecasting.

[0046] When analyzing the impact of forecast errors on grid operation, by comparing actual wind power data with the day-ahead forecast value, the change curves of wind power abandonment and load shedding are obtained, such as Figure 3 As shown. At time point t = 85, the actual prediction error of wind power is 0.085pu, indicating that wind power is underestimated. At this time, the wind power abandonment reaches 0.085×5315.3MW=451MW, and the corresponding wind abandonment rate is 12.8%. Another typical time point is t = 147, when the wind power prediction error is -0.227pu, indicating that the prediction is overestimated. At this time, the power that needs to be cut off reaches 0.227×5315.3MW=1205MW, and the load shedding rate is 46%. It can be seen from this that due to the existence of prediction errors, the operation of wind farms faces greater wind abandonment and load shedding problems, which not only affects the power generation efficiency of wind farms, but also increases the operation risk and cost of power systems.

[0047] In summary, the existence of wind curtailment and load shedding leads to the occurrence of prediction errors, and the predicted error value will lead to a large operating risk cost for the wind farm. Therefore, the predicted power compensation is performed by configuring an energy storage system, and the wind curtailment and load shedding rates are reduced to reduce the operating risk cost. The present invention proposes to compensate for the predicted wind power by configuring a battery energy storage system to reduce the operating risks and costs caused by prediction errors. The present invention takes a permanent magnet direct-drive wind power generation system as the research object, and proposes a scheme for configuring an energy storage system on the DC side, so as to dynamically compensate for the deviation of wind power through the energy storage system, thereby improving the accuracy of wind power prediction and reducing the occurrence of wind curtailment and load shedding. The energy storage configuration method includes the following aspects:

[0048] Day-ahead forecast: The output of wind turbines is forecasted through day-ahead forecasting technology to generate a wind power forecast curve for the next 24 hours. Day-ahead forecasts are an important reference for power grid dispatching plans, and their accuracy directly affects the effects of subsequent intraday optimization and real-time dispatching.

[0049] Intraday optimization: Based on the day-ahead forecast results, during the actual operation of wind power, the system optimizes the wind turbine output forecast data intraday according to the continuously updated meteorological data and the operating status of the wind farm. Intraday optimization further corrects the day-ahead forecast value to provide a more accurate wind power forecast and a reliable basis for real-time dispatch.

[0050] Real-time comparison and dynamic compensation: During real-time operation, the system compares the optimized wind power forecast value with the actual output. When the actual output of the wind turbine is greater than the forecast value, the energy storage system stores the excess electricity in the battery; when the actual output of the wind turbine is lower than the forecast value, the energy storage system will begin to discharge to make up for the power gap and ensure the balance of supply and demand in the power grid. In this process, the energy storage system not only effectively reduces the wind abandonment rate, but also reduces the occurrence of load shedding through timely compensation of electricity, thereby reducing the dispatch pressure on the power system.

[0051] Energy storage system configuration and optimization: The configuration of the energy storage system not only includes the capacity design of the battery, but also involves the access method of the energy storage system on the DC side and its control strategy. According to the wind power forecast deviation and the operating status of the energy storage system, the corresponding power reference value is configured, and the operating parameters of the energy storage system are dynamically adjusted through advanced data processing methods to ensure the response speed and compensation accuracy of the energy storage system. In the process of optimizing the configuration of the energy storage system, the volatility of wind power and the dispatching requirements of the power system are taken into account to ensure that the energy storage system can achieve efficient power regulation under different working conditions.

[0052] Through the dynamic configuration and optimization control of the above energy storage system, the present invention significantly improves the accuracy of wind power prediction, enhances the operational stability of the power grid, and effectively reduces the operational risks and economic costs of wind farms. The configuration of the energy storage system not only alleviates the impact of wind power volatility on the power system, but also reduces the dependence of wind power fluctuations on conventional power scheduling to a certain extent, and improves the penetration rate of renewable energy in the power system. In addition, the introduction of the energy storage system also provides effective technical support for the grid connection of large-scale renewable energy generation in the future. From a technical point of view, the configuration scheme of the energy storage system has strong flexibility in the implementation of the DC side, and can achieve efficient linkage with the wind power generation system and the power grid. In future research, the capacity configuration and power control algorithm of the energy storage system can be further optimized to adapt to more complex power system conditions and larger-scale wind power grid connection requirements. At the same time, by introducing more advanced prediction algorithms and data analysis tools, the accuracy of wind power prediction is further improved, providing a more accurate basis for power grid scheduling.

[0053] In summary, the energy storage system configuration method proposed in the present invention effectively improves the accuracy of wind power prediction, significantly reduces wind abandonment and load shedding caused by prediction errors, reduces the risk of power grid operation, and provides a solid technical guarantee for the large-scale grid connection of renewable energy in the future.

[0054] Example 1

[0055] In this embodiment, the wind turbine generator set adopts the maximum power tracking control strategy for the machine side control, and the grid side adopts the vector control strategy based on grid voltage orientation. The given value of active power is the predicted value of wind power, and the energy storage system adopts power feedforward control considering the battery state of charge (SOC). The power reference value of the energy storage system is set according to the DC side voltage deviation to achieve compensation for power imbalance.

[0056] pass Figure 4 It can be seen that the DC side battery energy storage system can eliminate some of the wind power prediction errors, but overall it cannot completely eliminate all errors. This is mainly due to the configuration of the energy storage system on the DC side, and the control target is to maintain the stability of the DC side voltage. When a prediction error occurs, unbalanced power will be generated on the wind turbine side and the grid side, and this unbalanced power will be compensated by the energy storage system. Specifically, when power imbalance occurs, the battery begins to discharge until the SOC reaches the set threshold, and the energy storage system stops the discharge operation. The initial SOC is set to 50%, the per-unit capacity of the battery is 0.25, the per-unit value of its effective available capacity ranges from 0.04 to 0.2, and the actual available capacity is 0.15pu. After compensating for the prediction error, it is calculated that the power prediction accuracy after the DC side is configured with an energy storage system is increased to 77.13%, which is 1.5 percentage points higher than when the energy storage system is not added.

[0057] Then, after increasing the available capacity to 0.38pu, Figure 5 The power prediction error compensation effect in this case is demonstrated. Compared with the battery with a capacity of 0.15pu, the error curve performs better after compensation, and the prediction accuracy is improved to 80.04%, an increase of 2.91 percentage points from before. This shows that with the increase of energy storage capacity, the prediction error compensation effect is significantly enhanced.

[0058] However, although the battery energy storage system configured on the DC side performs well in balancing power fluctuations, its control goal is still mainly to maintain the stability of the DC voltage. Therefore, in the case of power imbalance, the energy storage system will store excess energy or release it to the grid. However, in this operating mode, the energy storage system cannot completely eliminate all power prediction errors, especially the error peak cannot be effectively compensated. As Figure 4 As shown in , some of the prediction errors are fully compensated, but the maximum and minimum error values ​​have not changed significantly. For example, at time t = 85, the wind curtailment rate is still as high as 12.8%, while at time t = 147, the load shedding rate reaches 46%.

[0059] As the capacity of the energy storage system increases (from 0.15pu to 0.38pu), although the wind curtailment rate drops to 9.76% at 23.28 hours, a decrease of 3.2 percentage points compared with the previous time, the load shedding rate does not change significantly at 36.5 hours. This shows that although increasing the energy storage capacity can partially improve the wind curtailment phenomenon, it has little effect on the load shedding amount.

[0060] To further analyze the impact of energy storage capacity on system performance, this embodiment gradually increases the capacity of the energy storage system from 0.1pu to 0.5pu with an interval of 0.05pu, and samples the power prediction accuracy under different capacities. The results are as follows: Figure 6 As shown. By fitting the curve of the data, the relationship curve between the energy storage system capacity and the power prediction accuracy under the DC side voltage stabilization control strategy is obtained. When the prediction accuracy is required to reach more than 85%, the required energy storage capacity is 0.465pu.

[0061] It can be seen that under low capacity conditions, the energy storage system does not significantly improve the risk cost of wind farm operation. This phenomenon is not due to insufficient capacity of the energy storage system, but because the energy storage system can only absorb or release a limited amount of electricity in a short period of time, that is, it is limited by the battery SOC threshold. Therefore, excessive energy storage capacity will not significantly improve system performance, but will increase the construction cost of the energy storage system. Therefore, in addition to reasonably configuring the capacity of the energy storage system, the control strategy needs to be optimized and adjusted to achieve the best power compensation effect.

[0062] Example 2

[0063] In this embodiment, the energy storage system is used to compensate for the wind power prediction error. Its control strategy still uses vector control based on grid voltage orientation, and the power set value is the product of the day-ahead prediction error value and the compensation coefficient. The energy storage system is configured on the DC side, and the control strategy is power feedforward control considering SOC, with the goal of maintaining the stability of the DC side voltage. The initial SOC is set to 50%, and the available capacity of the energy storage system is 0.15pu.

[0064] Figure 7The compensation effect of the energy storage system on the overall power forecast error after modifying the control strategy is demonstrated. At this time, the system can compensate for the overall error more effectively, although it is still limited by the battery capacity. For example, at the time of 21.28 hours, the day-ahead forecast error value decreased by 29.9%, and the wind curtailment rate decreased from 12.8% to 8.97%; and at the time of 36.5 hours, the load shedding rate decreased from 46% to 24.03%. It can be seen that by adopting a power feedforward control strategy based on the error coefficient, the energy storage system can respond to the power forecast error more promptly and effectively, reducing the wind curtailment and load shedding phenomena, and the prediction accuracy is improved to 80.87%.

[0065] As the energy storage system capacity further increases to 0.38pu, the compensation effect is significantly enhanced. After the error coefficient is set to 0.6, the prediction accuracy is improved to 88.69%. Figure 8 As shown in the figure, at the time point 21.28 hours, the day-ahead forecast error value decreased by 59.98%, and the wind curtailment rate dropped from 12.8% to 5.13%; and at 36.5 hours, the load shedding rate dropped from 46% to 13.78%, meeting the requirements of the operating criteria.

[0066] Fig. 9 The changes in energy storage capacity and power prediction accuracy under two different control strategies are compared. It can be seen that the control strategy that uses power prediction error as the active power given value of the energy storage system has significantly better overall accuracy than the strategy that uses DC voltage deviation as the power given value. When the power prediction accuracy is required to reach more than 85%, the required available capacity of the energy storage system is 0.265pu.

[0067] In addition, by establishing a three-dimensional relationship diagram of energy storage capacity-prediction error coefficient-power prediction accuracy ( Fig.10 ), and further analyzed the influence of the error compensation coefficient on the compensation effect. Under the same energy storage capacity conditions, the prediction accuracy fluctuates with the change of the error compensation coefficient, and there is an optimal value. With the increase of energy storage capacity, the prediction accuracy gradually improves; however, with the increase of the error compensation coefficient, the accuracy shows a downward trend. By optimizing the compensation coefficient to 0.46, the required energy storage capacity is 0.265pu, which can achieve the best prediction accuracy.

[0068] In summary, the use of battery energy storage systems as a means of compensating wind power forecast errors can significantly improve the accuracy of power forecasts and reduce wind abandonment and load shedding by optimizing energy storage capacity configuration and control strategies. At the same time, reasonable capacity configuration and compensation strategies can not only effectively reduce the operating risks of wind farms, but also reduce the construction costs of energy storage systems. Future research can further explore more efficient control algorithms and optimization strategies to cope with more complex wind farm operating environments and achieve more efficient power regulation and system stability.

Claims

1. A method for configuring a battery energy storage system for smoothing wind turbine output fluctuations, characterized in that: The configuration method predicts power compensation by configuring the energy storage system, specifically: When the actual output power of wind power generation exceeds the predicted value, the excess electricity will be stored in the battery energy storage system. When the actual output power of wind power generation is lower than the predicted value, the battery energy storage system will use the battery to release the stored electricity to make up for the power shortage.

2. A battery energy storage system configuration method for smoothing wind turbine output fluctuations according to claim 1, characterized in that: The configuration method specifically comprises the following steps: Step 1: forecast the output power of the wind turbine generator set by using the day-ahead forecasting technology to generate a forecast curve of the output power of the wind turbine generator set in the next 24 hours; Step 2: Based on the day-ahead forecast results, during the actual operation of the wind turbine, the battery energy storage system optimizes the output power forecast value of the wind turbine according to the continuously updated meteorological data and the operating status of the wind farm; Step 3: During real-time operation, the battery energy storage system compares the predicted output power of the wind turbine after optimization for the day with the actual output power. When the actual output power is greater than the predicted output power, the battery energy storage system stores the excess electric energy in the battery; and when the actual output power is lower than the predicted output power, the battery energy storage system starts to discharge to make up for the power gap and ensure the balance of supply and demand in the power grid.

3. A battery energy storage system configuration method for smoothing wind turbine output fluctuations according to claim 2, characterized in that: It also includes the configuration and optimization of battery energy storage systems, specifically: The configuration of the battery energy storage system includes the capacity design of the battery, the access method of the system on the DC side and its control strategy. According to the deviation between the actual output power of the wind turbine and the predicted value and the operating status of the battery energy storage system, the corresponding power reference value is configured, and the operating parameters of the battery energy storage system are dynamically adjusted to ensure the response speed and compensation accuracy of the battery energy storage system.

4. A battery energy storage system configuration method for smoothing wind turbine output fluctuations according to claim 3, characterized in that: The control strategy adopts a power feedforward control strategy based on the prediction error coefficient. By setting the prediction error compensation coefficient, the compensation strength of the battery energy storage system is adjusted. When the prediction error compensation coefficient is 1, full power compensation is achieved; when the prediction error compensation coefficient is 0, no compensation is performed, thereby flexibly responding to power compensation needs in different situations and optimizing the operating efficiency of the battery energy storage system and the prediction accuracy of the wind turbine.

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