Offshore wind power energy storage configuration method

Abstract

The application discloses a kind of offshore wind power energy storage configuration method, belong to offshore wind power energy storage technical field, method is: first construction wind farm and energy storage station and share hydrogen resource, calculate its energy storage capacity according to hydrogen production power and other elements, determine energy storage equipment capacity according to power ratio and get energy storage configuration proportion according to predicted power generation power, carry out wind farm power generation power prediction, finally when prediction and actual power do not match, by energy storage station peak shaving and frequency modulation and share hydrogen resource management energy.The application comprehensively considers many factors, accurately calculates energy storage demand, realizes the optimization of energy storage and wind farm through accurate prediction and effective control.Combining hydrogen resource sharing improves energy utilization efficiency, various energy storage and power generation methods complement each other, enhance the ability of system to deal with complex working conditions, significantly improve the stability, reliability and economy of offshore wind power system.

Description

Technical Field

[0001] This invention belongs to the field of offshore wind power energy storage technology, and specifically relates to an offshore wind power energy storage configuration method. Background Technology

[0002] Under the global trend of energy transition, offshore wind power has become a key development direction in the renewable energy field due to its abundant resource reserves and relatively stable wind energy environment. However, offshore wind power faces many technical challenges in practical applications, which seriously restrict its further large-scale and efficient integration into the power system and its stable operation.

[0003] Currently, several energy storage technologies have been applied or researched to address the issue of unstable offshore wind power. Traditional battery energy storage systems are one of the more common solutions. Battery energy storage has certain advantages in regulating power fluctuations in a short period of time. For example, lithium-ion batteries can quickly respond to changes in wind power, switching between charging and discharging within seconds to minutes to balance power imbalances during localized periods. However, battery energy storage also has significant limitations. First, its energy density is relatively limited. To meet the needs of long-term, large-scale energy storage, large battery packs are often required, which not only increases the construction cost of the system but also occupies a significant amount of offshore space. Second, the lifespan of batteries is greatly affected by the number of charge-discharge cycles and environmental factors. Frequent deep charge-discharge operations accelerate battery performance degradation, leading to high maintenance costs and the need for regular battery replacement, further increasing operating costs and environmental pressure.

[0004] Besides battery energy storage, other energy storage technologies such as pumped hydro storage have some applications in onshore wind power energy storage, but due to the unique geographical environment and construction conditions at sea, large-scale implementation in offshore wind farms is difficult. While pure hydrogen energy storage systems have potential in terms of energy storage capacity and energy density, their energy conversion efficiency still needs improvement, especially given the significant energy loss during hydrogen production. Furthermore, the storage, transportation, and utilization of hydrogen require complex infrastructure and safety measures, making it difficult for current technologies to achieve efficient, safe, and economical large-scale application.

[0005] While existing methods for wind power forecasting have made some progress, their accuracy still needs improvement. Traditional physics-based forecasting methods, such as those built on meteorological data and wind turbine aerodynamics, struggle to accurately simulate rapid changes in wind speed and direction, as well as their nonlinear relationships with wind turbine power generation, under complex marine meteorological conditions. Data-driven forecasting models, such as neural networks, can handle complex nonlinear data, but they are overly reliant on data, requiring extensive training with historical data. Furthermore, their generalization ability is insufficient when faced with missing, abnormal, or new meteorological conditions, resulting in significant prediction errors.

[0006] Furthermore, existing offshore wind power energy storage systems have shortcomings in overall energy management and coordinated operation. In most cases, the coordination between the energy storage system and the wind farm is not close or intelligent enough, lacking comprehensive optimization and scheduling strategies for various energy storage forms (such as battery energy storage and hydrogen energy storage) and wind farm power generation. For example, under different wind power output scenarios and electricity demand conditions, the charging and discharging processes of battery energy storage and hydrogen energy storage cannot be effectively coordinated, leading to waste of energy storage resources or failure to fully utilize the energy storage system's role in mitigating wind power fluctuations, thereby affecting the stability, reliability, and economics of the entire offshore wind power system.

[0007] In summary, the main problem addressed by this invention is how to optimize the integrated energy management of energy storage and wind farms, and to solve the problems of insufficient coordination and lack of scheduling strategies between existing energy storage and wind farms. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method for configuring offshore wind power energy storage, which utilizes hydrogen resource sharing to improve energy utilization efficiency, complements multiple energy storage and power generation methods, enhances the system's ability to cope with complex operating conditions, and significantly improves the stability, reliability and economy of offshore wind power systems.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0010] A method for configuring offshore wind power energy storage, comprising the following steps:

[0011] Step 1: Construct wind farms and energy storage stations, enabling the energy storage stations to share hydrogen resources with the wind farms. At the same time, connect the energy storage stations, wind farms, and fuel cell stations through transportation hubs, and ensure that the configuration of energy storage stations is proportional to that of wind farms.

[0012] Step 2: Calculate the required energy storage capacity for hydrogen production based on the power, energy, and efficiency of the hydrogen production process;

[0013] Step 3: Determine the energy storage capacity based on the power ratio; the power ratio includes the ratio of wind power to energy storage power, the ratio of hydrogen fuel cell power to energy storage power, and the ratio of hydrogen production power to energy storage power.

[0014] Step 4: Determine the energy storage configuration ratio based on the predicted power generation;

[0015] Step 5: Predict the power generation capacity of the wind farm;

[0016] Step 6: When the predicted power of offshore wind power does not match the actual power, the accuracy of the prediction is increased by using the energy storage station for peak shaving or frequency regulation. The prediction deviation is made up by the energy storage station absorbing or outputting power. The energy storage station and the wind farm share hydrogen resources to achieve comprehensive energy management.

[0017] Preferably, in step 1, the configuration of the energy storage station is proportional to that of the wind farm, which means that the configuration parameters of the energy storage station are set in the same proportion as the corresponding parameters of the wind farm; the configuration parameters include capacity and power.

[0018] Preferably, the calculation process in step 2 is as follows:

[0019] According to power Definition, power It refers to the energy per unit time, that is: ,

[0020] Regarding the power of electro-hydrogen production: ,in Electrogenation time is the time required to complete the electrogenation process. Electro-hydrogen production power indicates the amount of electrical power used to produce hydrogen per unit time. The energy required to produce hydrogen by electricity is the total energy required to complete the hydrogen production process.

[0021] So ;

[0022] Due to efficiency issues in the electro-hydrogen production process, the actual energy required from energy storage devices is greater than the theoretical energy needed for hydrogen production; according to the efficiency calculation formula: ,

[0023] For the electro-hydrogen production process ,in, Electrochemical hydrogen production efficiency is a dimensionless coefficient that represents the efficiency of converting electrical energy into the chemical energy of hydrogen. This indicates the energy output from the electrogenerated hydrogen;

[0024] so ;

[0025] The energy storage capacity required for hydrogen production by electricity In essence, it refers to the capacity of energy storage devices that provide energy for the electro-hydrogen production process. ;

[0026] The formula for calculating the energy storage capacity required for hydrogen production by electrolysis is as follows: ;

[0027] Energy storage capacity required for hydrogen production via electro-hydrogen is used to measure the size of the energy storage equipment required to provide energy for the hydrogen production process via electro-hydrogen.

[0028] Preferably, in step 3, the ratio of wind power to energy storage power is as follows:

[0029] Assume that the wind power reaches its maximum value at a certain moment. The charging power of energy storage devices is limited to The power ratio is set as follows: ,in It is a coefficient less than or equal to 1, determined based on the performance of the energy storage device and system requirements;

[0030] During the discharge process, when the wind power output is lower than the load demand, the energy storage device needs to discharge to supplement the power; let the load demand power be... Wind power is The discharge power of the energy storage device is The power ratio relationship is expressed as follows: ;

[0031] Based on the power ratio and the wind power prediction curve, the charging and discharging power required by the energy storage device in different time periods is calculated, and thus the capacity of the energy storage device is determined. By integrating the wind power prediction curve, the total amount of energy that needs to be stored within a certain period is obtained. Combined with the charging and discharging efficiency of energy storage devices Calculate the capacity of the energy storage device. ;

[0032] in, The maximum value of wind power reached at a certain moment;

[0033] Charging power limitations of energy storage devices;

[0034] A coefficient less than or equal to 1, determined based on the performance of the energy storage device and system requirements, used to represent the ratio between peak wind power and energy storage charging power; : Load power requirement; Wind power output; : Discharge power of energy storage devices; The total amount of energy that needs to be stored within a certain period of time; The charging and discharging efficiency of energy storage devices; Energy storage capacity.

[0035] Preferably, in step 3, the ratio of hydrogen fuel cell power to energy storage power is as follows:

[0036] The hydrogen fuel cell plays a role in supplementing power and regulating energy in the system; when the wind power prediction is significantly off or the energy storage device has insufficient power, the hydrogen fuel cell needs to output power; let the rated power of the hydrogen fuel cell be... The power of the energy storage device is To ensure system stability, the power distribution ratio is set as follows:

[0037] ,

[0038] in It is a coefficient determined based on system reliability requirements and the performance of hydrogen fuel cells;

[0039] When the energy storage device is at a low charge level, the hydrogen fuel cell needs to output sufficient power to meet the load demand, and may also need to recharge the energy storage device; at this time, the power ratio needs to take into account the power generation efficiency of the hydrogen fuel cell. Charging efficiency of energy storage devices and load power requirements ;

[0040] Obtain the relation Using this relationship and known system parameters, we can further determine the appropriate ratio of hydrogen fuel cell power to energy storage device power, and thus calculate the energy storage device capacity.

[0041] in, Rated power of hydrogen fuel cells; Power of energy storage devices; A coefficient determined based on system reliability requirements and the performance of the hydrogen fuel cell, used to represent the ratio of hydrogen fuel cell power to energy storage device power; The power generation efficiency of hydrogen fuel cells; : Charging efficiency of energy storage devices.

[0042] Preferably, in step 3, the ratio of hydrogen production power to energy storage power is as follows:

[0043] The electro-hydrogen production process consumes electrical energy, which comes from wind power; let the electro-hydrogen production power be... The output power of the energy storage device is The power ratio relationship is expressed as follows:

[0044] ,

[0045] in It is a coefficient determined based on the efficiency of hydrogen production by electricity and the factors of hydrogen demand;

[0046] The energy efficiency of electro-hydrogen production is known to be The energy required to produce hydrogen within a certain time period is So, what energy is needed to produce hydrogen by electricity? According to the power of hydrogen production by electricity and required energy Based on the output power limitations of the energy storage device, the energy and time required for the energy storage device to produce hydrogen via electrolysis are calculated, thus determining the capacity of the energy storage device.

[0047] ,

[0048] in It is the time cycle for electro-hydrogen production; : Hydrogen production power; Output power of energy storage devices; A coefficient determined based on the efficiency of hydrogen production by electricity and the factors of hydrogen demand, used to represent the proportional relationship between the power of hydrogen production by electricity and the output power of energy storage equipment; Energy efficiency of electro-hydrogen production; The amount of hydrogen energy required to produce within a certain time period; Time period for electro-hydrogen production; Energy storage capacity.

[0049] Preferably, the sub-steps of step 4 are as follows:

[0050] (1) Perform short-term power forecasting, including hourly power forecasting and minute-level power forecasting;

[0051] The prediction and calculation process of hourly power forecasting: Based on historical data and real-time meteorological information, time series analysis methods or machine learning algorithms are used to predict the power generation of offshore wind farms in the next 1-6 hours;

[0052] Based on the predicted hourly power curve, analyze the power fluctuation situation; if it is predicted that the wind power will rise or fall significantly in the next hour, in order to stabilize the output, the energy storage configuration ratio should be able to store excess energy when the power rises and release sufficient energy when the power falls.

[0053] The calculation method is as follows: Let the predicted power change be... The energy storage charge and discharge efficiencies are respectively and Then the energy storage configuration capacity or The energy storage configuration ratio is based on the rated power of the wind farm. Determine the proportionality coefficient. ;

[0054] in, : Predicted hourly power variation : Charging efficiency of energy storage devices; The discharge efficiency of energy storage devices; Energy storage configuration capacity derived from hourly power forecasts; Rated power of wind farm; Hourly energy storage configuration ratio coefficient;

[0055] The prediction and calculation process for minute-level power forecasting: Utilizing more refined meteorological monitoring data and advanced forecasting models, the power generation changes within the next 10-60 minutes are predicted; the energy storage configuration ratio needs to consider the rapid response capability of the energy storage equipment, assuming a rapid response power of... Minute-level energy storage configuration ratio coefficient At the same time, it is also necessary to combine energy storage capacity to ensure that power output can be maintained for a certain period of time, and calculate energy storage capacity based on energy demand. To further determine the proportional relationship with the rated power of the wind farm;

[0056] in, : The fast response power of energy storage devices; : Minute-level energy storage configuration ratio coefficient; Energy storage configuration capacity derived from minute-level power prediction; The number of minutes that power output needs to be maintained;

[0057] (2) Conduct medium- and long-term power forecasting, including daily power forecasting and weekly power forecasting;

[0058] The prediction and calculation process for daily power generation forecasting is as follows: Taking into account historical daily power generation data, weather forecasts, and seasonal factors, a multiple regression analysis or hybrid prediction model is used to predict the power generation variation curve of the wind farm within the next day; daily power generation data under similar weather conditions in the same season over the past few years is analyzed, and combined with current weather forecast information, the power generation range for the next day is predicted; based on the daily power generation forecast results, the energy storage configuration ratio is determined to balance the power supply and demand throughout the day; if the predicted wind power is higher during the day and lower at night, and the daytime electricity demand is relatively stable, the energy storage configuration should be able to store excess electricity during the day for nighttime use; let the average excess power during the day be... The average power deficit at night is The charge / discharge cycle efficiency of energy storage is Then the energy storage configuration capacity , The larger capacity value is taken as the basis for energy storage configuration, and the daily energy storage configuration ratio coefficient is determined. ;

[0059] in, Average excess power during the day; Nighttime average power deficit; Charge-discharge cycle efficiency of energy storage devices; Daytime energy storage configuration capacity based on daily power forecast; Nighttime energy storage configuration capacity based on daily power forecast; Daytime charging time; Nighttime discharge time; Daily energy storage configuration ratio coefficient;

[0060] The prediction and calculation process for weekly power generation forecasting: Predictions are made based on longer-term historical data, long-term weather forecasts, and energy demand trends in the areas surrounding offshore wind farms. The weekly power generation variation patterns in different seasons are analyzed, and combined with the weather forecast for the coming week and the weekly trends of industrial and residential electricity consumption in the region, the approximate range and fluctuations of the wind farm's power generation for the coming week are predicted. For weekly power generation forecasting, the energy storage configuration ratio primarily considers coping with the intermittency and volatility of wind power generation within a week, as well as coordination with other energy supplies. If it is predicted that wind power will be low on certain days while electricity demand in the region is high, the energy storage should be able to store sufficient energy to compensate for the shortfall when wind power is abundant. The maximum power gap within a week is assumed to be... Energy storage configuration capacity Weekly energy storage configuration ratio coefficient ;

[0061] in, The largest power deficit during the week; Energy storage configuration capacity based on weekly power forecast; The total time during which a power deficit may occur; Weekly energy storage configuration ratio coefficient;

[0062] Based on the above predictions and analyses at different time scales, a more reasonable energy storage configuration ratio is derived to optimize the configuration of offshore wind power energy storage systems, thereby improving the stability, reliability, and economy of offshore wind power.

[0063] Preferably, the sub-steps of step 5 are as follows:

[0064] (1) Determine wind power: Real-time data on wind speed, wind direction, turbine blade speed and nacelle temperature are collected by sensors installed on each turbine in the offshore wind farm; at the same time, meteorological data of the sea area where the wind farm is located is collected, including large-scale meteorological information provided by surrounding meteorological stations and local meteorological changes monitored by offshore buoys; based on the collected data, the current wind power is determined by using the established wind power calculation model.

[0065] (2) Predicting energy storage output: Real-time monitoring of the energy storage system to obtain the current power, charging and discharging current and voltage parameters of the energy storage device; at the same time, monitoring the health status of the energy storage system, including changes in the internal resistance of the battery and temperature distribution; the health status will affect the charging and discharging efficiency and power output capability of the energy storage system.

[0066] (3) Establish a prediction model: Based on the historical operating data and current state parameters of the energy storage system, establish an energy storage output prediction model; use time series analysis to analyze the historical variation of the energy storage system output, and combine the current state data to predict the future energy storage output; if it is found that the energy storage system's discharge power exhibits a certain periodic change when the power is within a specific range and the ambient temperature is stable during certain periods in the past, then use the ARIMA model to capture this periodicity and predict the future discharge power change trend;

[0067] (4) Predicting wind power output

[0068] To obtain longer-term weather forecast data, including wind speed, wind direction, temperature, and air pressure for the next few hours or even days;

[0069] Combine meteorological forecast data with geographical information of the sea area where the wind farm is located; geographical information will affect the wind flow, thereby changing the actual wind speed and wind direction at the wind turbine.

[0070] (5) Optimize the prediction model: Based on historical wind power data and integrated meteorological forecast data, construct a wind power prediction model; use machine learning algorithms to continuously optimize the prediction model;

[0071] (6) Based on the predicted wind power and energy storage output, formulate the energy storage charging and discharging control strategy; when the predicted wind power is greater than the current electricity demand and the energy storage system is not fully charged, start the energy storage charging process: set a charging threshold, when the difference between the wind power and the electricity demand exceeds the threshold and the energy storage SOC is lower than the set upper limit, control the energy storage system to charge with an appropriate charging power; when the predicted wind power is less than the electricity demand, if the energy storage system has sufficient power, start the energy storage discharging process; determine the discharging threshold, when the difference between the electricity demand and the wind power exceeds the threshold and the energy storage SOC is higher than the set lower limit, control the energy storage system to discharge; the discharge power also needs to be adjusted according to the capacity and current state of the energy storage system to meet the electricity demand and maintain the stable operation of the system.

[0072] Preferably, the sub-step of step 6 is as follows:

[0073] (1) The strategy to deal with the mismatch between predicted power and actual power is to establish a real-time power monitoring system to accurately measure and record the predicted power and actual power of offshore wind power respectively; to use high-precision power sensors installed on the transmission lines of the wind farm to collect power data at high frequency; to calculate the deviation between the predicted power curve and the actual power curve by comparing them; when the deviation exceeds the preset threshold, it is determined that a power mismatch has occurred.

[0074] When the actual power of offshore wind power is higher than the predicted power and the grid absorption capacity is limited, the energy storage station enters the charging peak shaving mode. The energy storage station receives the excess wind power energy with an appropriate charging power based on the difference between the actual power and the predicted power and its own charging capacity.

[0075] When the actual power output of offshore wind power is lower than the predicted power output and the electricity demand is large, the energy storage station discharges to regulate peak demand. The energy storage station outputs electrical energy to supplement the power grid or electrical equipment according to the size of the power gap and its own discharge capacity.

[0076] Energy storage stations participate in grid frequency regulation through rapid response control; when fluctuations in offshore wind power cause changes in grid frequency, energy storage stations quickly adjust their charging and discharging power.

[0077] (2) The strategy for energy storage stations and wind farms to share hydrogen resources to achieve comprehensive energy management is as follows: the excess electricity generated by the wind farm can be used to electrolyze water to produce hydrogen, and the produced hydrogen is stored in a hydrogen storage facility connected to the energy storage station; the hydrogen fuel cells in the energy storage station use the stored hydrogen to generate electricity; during periods of excess wind power, the water electrolysis hydrogen production equipment operates at its rated power and stores the produced hydrogen; when wind power is insufficient or electricity demand is high, the hydrogen fuel cells use the stored hydrogen to generate electricity and output electricity to supplement the power grid or the internal power system of the wind farm; the capacity and pressure parameters of the hydrogen storage facility need to be reasonably designed according to the power generation scale of the wind farm, the energy demand of the energy storage station, and the production and consumption rate of hydrogen.

[0078] The energy management system enables coordinated management of hydrogen resources between wind farms and energy storage stations. Based on wind power forecasts, actual power monitoring, energy storage station status, and electricity demand information, the energy management system optimizes hydrogen production, storage, and usage strategies.

[0079] Sharing hydrogen resources enables the effective use of surplus electricity from wind farms, reducing wind curtailment. Meanwhile, energy storage stations can flexibly supplement electricity through hydrogen fuel cell power generation in different energy supply and demand scenarios, improving the overall utilization efficiency of the entire energy system.

[0080] The coordinated operation of energy storage stations and wind farms based on shared hydrogen resources enhances the stability and reliability of the entire energy system. In the face of large fluctuations in wind power, grid failures, or extreme weather conditions, the system can ensure a stable power supply by switching and complementing various energy storage and power generation methods.

[0081] An offshore wind power energy storage configuration optimization system employs the aforementioned offshore wind power energy storage configuration method.

[0082] The present invention can achieve the following beneficial effects:

[0083] 1. This invention, through detailed power matching relationships, such as precise matching of wind power and energy storage power, hydrogen fuel cell power and energy storage power, and electro-hydrogen production power and energy storage power, as well as reasonable energy storage configuration ratios derived from predicting power generation at different time scales (hourly, minute, daily, and weekly), can ensure that the capacity of energy storage equipment is highly compatible with the needs of wind farms, effectively cope with various changes in wind power, and improve the efficiency of energy storage and release.

[0084] 2. Comprehensively utilize various advanced technologies for wind farm power generation prediction, including integrating multi-source data (meteorological information, wind turbine parameters, geographic information, etc.) and employing multiple prediction models (such as time series analysis, machine learning algorithms, etc.). When the prediction is inaccurate, utilize energy storage stations for rapid peak shaving and frequency regulation, enabling the system to respond promptly to power fluctuations, ensuring stable power output, reducing the impact on the power grid, and enhancing the reliability of system operation.

[0085] 3. The energy storage station shares hydrogen resources with the wind farm, enabling coordinated management of hydrogen production, storage, and utilization. Hydrogen is produced and stored when wind power is surplus, and supplemented by hydrogen fuel cell power generation when power is insufficient or during peak electricity demand. This not only makes full use of surplus wind power and reduces wind curtailment, but also improves the flexibility and adaptability of the entire energy system under different supply and demand scenarios through hydrogen energy storage for long-term energy replenishment, thereby enhancing the overall energy utilization efficiency.

[0086] 4. The combination and intelligent switching of multiple energy storage and power generation methods (battery energy storage, hydrogen energy storage, and wind farm power generation) enable the system to ensure a stable power supply even under complex operating conditions such as large fluctuations in wind power, grid failures, or extreme weather. For example, in the event of a grid failure, the hydrogen fuel cells in the energy storage station can independently power critical equipment within the wind farm, preventing wind farm shutdowns and improving survivability and overall system stability under extreme conditions. Attached Figure Description

[0087] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0088] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0089] Preferred solutions include Figure 1 As shown, a method for configuring offshore wind power energy storage includes the following steps:

[0090] Step 1: Construct wind farms and energy storage stations, enabling the energy storage stations to share hydrogen resources with the wind farms. At the same time, connect the energy storage stations, wind farms, and fuel cell stations through transportation hubs, and ensure that the configuration of energy storage stations is proportional to that of wind farms.

[0091] Step 2: Calculate the required energy storage capacity for hydrogen production based on the power, energy, and efficiency of the hydrogen production process.

[0092] Step 3: Determine the energy storage capacity based on the power ratio. The power ratio includes the ratio of wind power to energy storage power, the ratio of hydrogen fuel cell power to energy storage power, and the ratio of hydrogen production power to energy storage power.

[0093] Step 4: Determine the energy storage configuration ratio based on the predicted power generation.

[0094] Step 5: Predict the power generation capacity of the wind farm.

[0095] Step 6: When the predicted power of offshore wind power does not match the actual power, the accuracy of the prediction is increased by using the energy storage station for peak shaving or frequency regulation. The prediction deviation is made up by the energy storage station absorbing or outputting power. The energy storage station and the wind farm share hydrogen resources to achieve comprehensive energy management.

[0096] Furthermore, in step 1, "the energy storage station configuration is proportional to the wind farm" means that the configuration parameters of the energy storage station and the corresponding parameters of the wind farm are set in the same proportional relationship. Configuration parameters include capacity and power.

[0097] Furthermore, the calculation process in step 2 is as follows:

[0098] According to the definition of power, power is the energy consumed per unit of time, that is... Regarding the power of electric hydrogen production, ,So .

[0099] Due to efficiency issues in the electro-hydrogen production process, the actual energy required from energy storage devices is greater than the theoretical energy needed for hydrogen production (because some energy is lost during the conversion process). According to the efficiency calculation formula: ,

[0100] For the electro-hydrogen production process ,

[0101] so .

[0102] The energy storage capacity required for hydrogen production by electricity In essence, it refers to the capacity of energy storage devices that provide energy for the electro-hydrogen production process. .

[0103] The formula for calculating the energy storage capacity required for hydrogen production by electrolysis is as follows: .

[0104] in, Hydrogen production power, measured in watts, represents the amount of electrical power used to produce hydrogen per unit time.

[0105] The energy required to produce hydrogen by electricity, measured in joules, is the total energy required to complete the hydrogen production process.

[0106] Electrogenization efficiency is a dimensionless coefficient that represents the efficiency of converting electrical energy into the chemical energy of hydrogen.

[0107] Energy storage capacity required for hydrogen electroproduction, measured in watt-hours or other energy units, is used to measure the size of the energy storage equipment required to provide energy for the hydrogen electroproduction process.

[0108] Electrogenization time, measured in hours, is the time required to complete the electrogenization process.

[0109] Furthermore, in step 3, the ratio of wind power to energy storage power is as follows:

[0110] In offshore wind power systems, wind power output is dynamic and influenced by natural factors such as wind speed and direction. To ensure that energy storage devices can effectively store excess energy generated by wind power, the peak and valley values ​​of wind power output need to be considered.

[0111] For example, suppose the wind power reaches its maximum value at a certain moment. The charging power of energy storage devices is limited to The power ratio is set as follows: ,in It is less than or equal to The coefficient is determined based on the performance of the energy storage device and system requirements. This coefficient takes into account factors such as the efficiency and safety limitations of the energy storage device during charging. Similarly, during discharging, when the wind power is lower than the load demand, the energy storage device needs to discharge to supplement the power. Let the load demand power be... Wind power is The discharge power of the energy storage device is The power ratio relationship is expressed as follows: (when Based on these power ratios and wind power prediction curves, the charging and discharging power required by the energy storage device in different time periods is calculated, thus determining the capacity of the energy storage device. For example, by integrating the wind power prediction curve, the total amount of energy that needs to be stored within a certain time period can be obtained. Combined with the charging and discharging efficiency of energy storage devices Calculate the capacity of the energy storage device. .

[0112] The maximum value of wind power at a given moment, measured in watts.

[0113] : Charging power limit of energy storage devices, in watts.

[0114] : A less than or equal to The coefficient is determined based on the performance of the energy storage equipment and system requirements, and is used to represent the ratio between the peak wind power and the energy storage charging power.

[0115] : Load power requirement, in watts.

[0116] Wind power output, measured in watts.

[0117] Discharge power of energy storage devices, measured in watts.

[0118] The total amount of energy that needs to be stored within a certain period of time, measured in joules.

[0119] The charging and discharging efficiency of energy storage devices (here, charging and discharging efficiency are referred to uniformly; if there is a distinction in practical applications, they can be defined separately). and ).

[0120] Energy storage capacity, measured in watt-hours or other energy units.

[0121] Furthermore, in step 3, the ratio of hydrogen fuel cell power to energy storage power is as follows:

[0122] Hydrogen fuel cells play a role in supplementing power and regulating energy within the system. When wind power forecasts are significantly inaccurate or the energy storage device's capacity is insufficient, the hydrogen fuel cell needs to output power. Let the rated power of the hydrogen fuel cell be... The power of the energy storage device is To ensure system stability, the power distribution ratio can be set as follows: ,in This coefficient is determined based on system reliability requirements and the performance of the hydrogen fuel cell. For example, when the energy storage device is in a low-charge state, the hydrogen fuel cell needs to output sufficient power to meet the load demand, and may also need to recharge the energy storage device. In this case, the power matching relationship needs to take into account the power generation efficiency of the hydrogen fuel cell. Charging efficiency of energy storage devices and load power requirements The relation can be obtained. Using this relationship and known system parameters, we can further determine the appropriate ratio of hydrogen fuel cell power to energy storage device power, thereby calculating the energy storage device capacity.

[0123] Rated power of a hydrogen fuel cell, measured in watts.

[0124] Power of energy storage devices, measured in watts.

[0125] A coefficient determined based on system reliability requirements and the performance of the hydrogen fuel cell, used to represent the ratio of hydrogen fuel cell power to energy storage device power.

[0126] The power generation efficiency of hydrogen fuel cells. : Charging efficiency of energy storage devices.

[0127] Furthermore, in step 3, the ratio of hydrogen production power to energy storage power is as follows:

[0128] The electro-hydrogen production process consumes electrical energy, which can be obtained from wind power. Let the electro-hydrogen production power be... The output power of the energy storage device is (Used to provide the electrical energy required for electro-hydrogen production), the power ratio relationship can be expressed as follows: ,in This coefficient is determined based on factors such as the efficiency of hydrogen production by electricity and hydrogen demand. The power output of hydrogen production by electricity is also related to the capacity of the energy storage equipment. For example, given that the energy efficiency of hydrogen production by electricity is... The energy required to produce hydrogen within a certain time period is So, what energy is needed to produce hydrogen by electricity? Based on the power of hydrogen production by electricity and required energy By considering the output power limitations of energy storage devices, the energy and time required for the energy storage device to generate hydrogen through electro-hydrogen can be calculated, thus determining the capacity of the energy storage device. For example, ,in It refers to the time cycle for electro-hydrogen production.

[0129] Hydrogen production power, measured in watts.

[0130] Output power of energy storage devices, measured in watts.

[0131] A coefficient determined based on factors such as hydrogen production efficiency and hydrogen demand, used to represent the proportional relationship between hydrogen production power and the output power of energy storage equipment.

[0132] Energy efficiency of hydrogen production by electricity.

[0133] The amount of hydrogen energy required to produce within a certain time period, measured in joules.

[0134] : The time period for electro-hydrogen production, in hours.

[0135] Energy storage capacity, measured in watt-hours or other energy units.

[0136] Furthermore, the sub-steps of step 4 are as follows:

[0137] I. Short-term power forecast

[0138] 1. Hourly Power Forecasting: Forecasting and Calculation Process: Based on historical data and real-time meteorological information (such as wind speed, wind direction, air pressure, etc.), time series analysis methods (such as the ARIMA model) or machine learning algorithms (such as neural networks) are used to predict the power generation of offshore wind farms in the next 16 hours. For example, wind speed and power generation data from the same time period (such as 9:00-10:00 AM) over the past few weeks or even months are collected to build a model to predict the power generation trend in the next hour from the current moment. Based on the predicted hourly power curve, the power fluctuation is analyzed. If a significant increase or decrease in wind power is predicted in the next hour, for example, from 50MW to 80MW or from 30MW, in order to stabilize output, the energy storage configuration should be able to store excess energy when power increases (e.g., the required energy storage capacity can store an energy increment of about 30MW) and release sufficient energy when power decreases (e.g., provide a power supplement of 20MW). The calculation method can be: Let the predicted power change be... The energy storage charge and discharge efficiencies are respectively and Then the energy storage configuration capacity (While charging) or (During discharge), the energy storage configuration ratio can be determined based on the rated power of the wind farm. Determine, such as the proportionality coefficient .

[0139] : The predicted hourly power change, in watts.

[0140] : Charging efficiency of energy storage devices.

[0141] Discharge efficiency of energy storage devices.

[0142] Energy storage configuration capacity based on hourly power forecasts, expressed in watt-hours or other energy units.

[0143] Rated power of wind farm, in watts.

[0144] : Hourly energy storage configuration ratio coefficient.

[0145] 2. Minute-level power prediction:

[0146] Prediction and Calculation Process: Utilizing more refined meteorological monitoring data and advanced prediction models (such as the LSTM model in deep learning, which has good processing capabilities for time series data), the project predicts power generation changes within the next 10-60 minutes. For example, by installing high-precision meteorological sensors near offshore wind farms, wind speed and direction data are collected every minute, and combined with historical data, the data is input into an LSTM model to predict power changes within the next 30 minutes. For minute-level predictions, the focus is on rapid power fluctuations to address instantaneous power generation changes caused by gusts. If it is predicted that wind power may rapidly decrease from 60MW to 40MW within 2 minutes in the next 30 minutes, energy storage should be able to provide 20MW of power support in a short time. In this case, the energy storage configuration ratio needs to consider the rapid response capability of the energy storage equipment, assuming a rapid response power of [missing value]. Minute-level energy storage configuration ratio coefficient:

[0147] ,

[0148] At the same time, it is also necessary to combine energy storage capacity to ensure that power output can be maintained for a certain period of time, such as calculating energy storage capacity based on energy demand. (To determine the number of minutes required to maintain power output), further determine the proportional relationship with the wind farm's rated power.

[0149] The fast response power of energy storage devices, measured in watts.

[0150] : Minute-level energy storage configuration ratio coefficient.

[0151] Energy storage configuration capacity based on minute-level power prediction, in watt-hours or other energy units.

[0152] : The number of minutes that power output needs to be maintained.

[0153] II. Medium- and Long-Term Power Forecast

[0154] 1. Daily Power Forecast: Forecasting and Calculation Process: Taking into account historical daily power generation data, weather forecasts (including forecasts of wind speed, direction, temperature, and other meteorological elements for the next 24 hours), and seasonal factors, a multivariate regression analysis or a hybrid forecasting model (such as combining physical models and data-driven models) is used to predict the power generation variation curve of the wind farm within the next day. For example, analyzing daily power generation data under similar weather conditions in the same season over the past few years, combined with current weather forecast information, the power generation range for the next day is predicted. Based on the daily power forecast results, the energy storage configuration ratio is determined to balance the power supply and demand throughout the day. If the predicted wind power is higher during the day and lower at night, and the daytime electricity demand is relatively stable, the energy storage configuration should be able to store excess electricity during the day for nighttime use. Let the average excess power during the day be... The average power deficit at night is The charge / discharge cycle efficiency of energy storage is Then the energy storage configuration capacity (Daytime charging time). (For nighttime discharge time), take the larger capacity value as the basis for energy storage configuration, and use the daily energy storage configuration ratio coefficient. .

[0155] Average excess power during the day, in watts. ).

[0156] Average nighttime power deficit, in watts ).

[0157] The charge-discharge cycle efficiency of energy storage devices.

[0158] Daytime energy storage configuration capacity based on daily power forecast, in watt-hours. (or other energy units).

[0159] Nighttime energy storage configuration capacity based on daily power forecasts, in watt-hours. (or other energy units).

[0160] Daytime charging time, in hours ).

[0161] Nighttime discharge time, in hours ).

[0162] Daily energy storage configuration ratio coefficient.

[0163] 2. Weekly power prediction:

[0164] Forecasting and Calculation Process: Forecasts are made based on longer-term historical data (such as weekly power generation data over the past few months or even years), long-term weather forecasts, and energy demand trends in the areas surrounding offshore wind farms. For example, by analyzing the weekly power generation variation patterns in different seasons and combining the weather forecast for the coming week with the weekly trends of industrial and residential electricity consumption in the region, the approximate range and fluctuations of the wind farm's power generation for the coming week are predicted. For weekly power forecasting, the energy storage configuration ratio primarily considers coping with the intermittency and volatility of wind power generation within a week, as well as coordination with other energy supplies (such as grid power and backup power). If it is predicted that wind power will be low on certain days while electricity demand in the region is high, energy storage should be able to store sufficient energy to compensate for the shortfall when wind power is abundant. Let the maximum power gap within the week be... Energy storage configuration capacity (Total time when a power shortage may occur), weekly energy storage configuration ratio coefficient .

[0165] Maximum power deficit during the week, measured in watts.

[0166] Energy storage configuration capacity based on weekly power forecasts, expressed in watt-hours or other energy units.

[0167] : The total time during which a power shortage may occur, in hours.

[0168] Weekly energy storage configuration ratio coefficient.

[0169] Based on the above predictions and analyses at different time scales, a more reasonable energy storage configuration ratio is derived to optimize the configuration of offshore wind power energy storage systems and improve the stability, reliability, and economy of offshore wind power.

[0170] Furthermore, the sub-steps of step 5 are as follows:

[0171] (1) Determine wind power: Real-time data collection of wind speed, wind direction, turbine blade speed and nacelle temperature parameters is obtained by sensors installed on each turbine in the offshore wind farm. These data are the basis for determining wind power. For example, wind speed data is particularly important because there is a specific functional relationship between wind power and wind speed (generally, between the cut-in wind speed and the rated wind speed, the power increases with the cube of the wind speed, and the power remains constant after exceeding the rated wind speed).

[0172] At the same time, meteorological data of the sea area where the wind farm is located is collected. The meteorological data includes large-scale meteorological information provided by surrounding meteorological stations and local meteorological changes monitored by offshore buoys. This meteorological data helps to understand more comprehensively the environmental factors that affect wind power, such as changes in air pressure, which may affect air density and thus affect the power generation efficiency of wind turbines.

[0173] Based on the collected data, the current wind power is determined using the established wind power calculation model;

[0174] Common calculation models include those based on physical principles, which calculate power according to aerodynamic theory and the characteristic curves of the wind turbine. For example, according to Betz's law, the power captured by a wind turbine is related to wind speed, air density, turbine blade radius, and power factor. By measuring these parameters and knowing the characteristics of the wind turbine, the theoretical wind power can be calculated.

[0175] Data-driven models, such as neural network models, can also be used. These models train the neural network using a large amount of historical data (including parameters such as wind speed, wind direction, and temperature, and their corresponding wind power data), enabling the model to learn the complex mapping relationships between the data. When real-time collected parameters are input, the neural network can quickly predict the current wind power. The advantage of this model is its ability to adapt to complex nonlinear relationships and its ability to continuously optimize its predictive performance as new data accumulates.

[0176] (2) Predicting energy storage output: Real-time monitoring of the energy storage system to obtain the current power, charging and discharging current and voltage parameters of the energy storage device; at the same time, monitoring the health status of the energy storage system, including changes in battery internal resistance and temperature distribution; the health status will affect the charging and discharging efficiency and power output capability of the energy storage system; an increase in battery internal resistance may lead to an increase in energy loss during the charging and discharging process, thereby affecting the accuracy of energy storage output prediction.

[0177] For example, in battery energy storage systems, monitoring the battery's state of charge (SOC) can reveal the percentage of remaining battery capacity, thereby estimating its discharge and recharge potential.

[0178] (3) Establish a predictive model: Based on the historical operating data of the energy storage system (including charging and discharging power data under different power states and environmental conditions) and current state parameters, establish an energy storage output prediction model. Time series analysis methods, such as the Autoregressive Moving Average (ARIMA) model, can be used to analyze the historical variation of the energy storage system's output and combine it with current state data to predict future energy storage output. For example, if it is found that the energy storage system's discharge power exhibits a certain periodic variation during certain periods in the past, when the power is within a specific range and the ambient temperature is stable, then the ARIMA model can be used to capture this periodicity and predict the future trend of discharge power variation.

[0179] For complex energy storage systems, such as hydrogen energy storage systems involving multiple stages including hydrogen production, storage, and fuel cell power generation, more sophisticated models may be required, such as hybrid models combining physicochemical process models and data-driven models. For example, these models could consider physicochemical processes such as energy conversion efficiency during hydrogen production and the impact of pressure and temperature changes during hydrogen storage on fuel cell power generation, while simultaneously using historical data to optimize some empirical parameters in the model to improve the accuracy of energy storage output prediction.

[0180] (4) Predicting wind power output

[0181] Obtain longer-term weather forecast data, including detailed meteorological information such as wind speed, wind direction, temperature, and air pressure for the next few hours or even days. This weather forecast data can come from professional meteorological agencies or numerical weather prediction models. For example, the global weather forecast data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) has high accuracy and resolution, which can provide strong support for offshore wind power forecasting.

[0182] This involves combining meteorological forecast data with geographic information about the sea area where the wind farm is located (such as topography and ocean roughness). Geographic information influences wind flow, thereby altering the actual wind speed and direction at the wind turbine location. For example, islands or shoals in the sea may cause localized wind speed increases or changes in wind direction. By incorporating these geographic factors into the data using Geographic Information System (GIS) technology, the actual wind energy resources at the wind turbine location can be predicted more accurately.

[0183] (5) Optimize the prediction model: Based on historical wind power data and integrated meteorological forecast data, construct a wind power prediction model. Machine learning algorithms, such as Support Vector Machine (SVM), Random Forest (RF), or Convolutional Neural Network (CNN) in deep learning algorithms, can be used. These models can learn the complex relationship between meteorological data and wind power. For example, the Random Forest algorithm can improve the robustness of prediction by constructing multiple decision trees to classify and predict wind power changes under different combinations of meteorological factors.

[0184] Continuously optimize the prediction model, use techniques such as cross-validation to evaluate its performance, and adjust the model's parameters or structure based on the evaluation results. For example, if the model is found to have large prediction errors under certain specific meteorological conditions, such as strong wind shear, the accuracy of the model's predictions under these special conditions can be improved by adding relevant data features (such as the vertical gradient of wind speed) or adjusting the model's decision rules.

[0185] (6) Formulate a charging and discharging control strategy for energy storage based on the predicted wind power and energy storage output. When the predicted wind power is greater than the current electricity demand and the energy storage system is not fully charged, start the energy storage charging process. For example, set a charging threshold. When the difference between the wind power and the electricity demand exceeds the threshold and the energy storage SOC is lower than the set upper limit (e.g., 90%), control the energy storage system to charge at an appropriate charging power. The charging power can be determined according to the characteristics and current state of the energy storage system, ensuring efficient charging while avoiding excessive pressure on the energy storage system.

[0186] When the predicted wind power output is less than the electricity demand, the energy storage system will initiate a discharge process if it has sufficient charge. A discharge threshold is determined; when the difference between the electricity demand and the wind power output exceeds this threshold and the energy storage SOC is higher than a set lower limit (e.g., 20%), the energy storage system is controlled to discharge. The discharge power also needs to be adjusted according to the energy storage system's capacity and current state to meet the electricity demand and maintain stable system operation.

[0187] In this embodiment, an Energy Management System (EMS) is used to execute the charging and discharging operations of the energy storage system. Based on a defined control strategy, the EMS sends charging and discharging commands to the energy storage system, controlling the power conversion devices (such as inverters) of the energy storage equipment to convert and transmit electrical energy. For example, during charging, the EMS controls the inverter to convert the AC power generated by wind power into DC power suitable for charging the energy storage system, and adjusts the charging current and voltage; during discharging, it controls the inverter to convert the DC power output from the energy storage system into AC power for grid connection or direct supply to electrical equipment. Simultaneously, the EMS monitors the operating status of the energy storage system in real time to ensure the safety and stability of the charging and discharging process, such as monitoring whether the battery temperature and voltage are within the normal range. If abnormalities occur, the charging and discharging strategy is adjusted promptly or protective measures are taken.

[0188] In this embodiment, the total power generation is calculated by combining the wind power and the charging / discharging power of the energy storage system. When the energy storage system is charging, the total power generation equals the wind power minus the energy storage charging power; when the energy storage system is discharging, the total power generation equals the wind power plus the energy storage discharging power. For example, if the current wind power is 50MW and the energy storage charging power is 10MW, the total power generation is 40MW; if the energy storage discharging power is 8MW, the total power generation is 58MW. The total output power generation is monitored in real time to ensure it meets grid connection requirements and the needs of the electrical equipment. The monitored total power generation data is fed back to the wind farm's control system and energy management system for further optimization and adjustment of wind power prediction, energy storage charging / discharging control, and other aspects. For example, if the total power generation fluctuates significantly beyond the grid's allowable range, the system can adjust the energy storage charging / discharging strategy, such as increasing the energy storage discharging power to smooth fluctuations, or adjusting the parameters of the wind power prediction model to improve prediction accuracy, thereby optimizing subsequent power output control.

[0189] Furthermore, the sub-steps of step 6 are as follows:

[0190] I. Strategies for addressing the mismatch between predicted and actual power:

[0191] 1. Power monitoring and deviation judgment:

[0192] A real-time power monitoring system is established to accurately measure and record both the predicted and actual power of offshore wind power. For example, high-precision power sensors are installed on the transmission lines of the wind farm to collect power data at a high frequency (e.g., several times per second). By comparing the predicted and actual power curves, the deviation between the two is calculated (including the magnitude and duration of the deviation). When the deviation exceeds a pre-set threshold, a power mismatch is identified. The threshold can be determined comprehensively based on factors such as the scale of the wind farm, grid connection requirements, and the performance of the energy storage system. For example, for a large offshore wind farm, the power deviation threshold might be set at approximately 10% of the rated power.

[0193] 2. Peak shaving operation:

[0194] Peak-shaving charging: When the actual offshore wind power output exceeds the predicted output and the grid's absorption capacity is limited, the energy storage station enters peak-shaving charging mode. Based on the difference between the actual and predicted output and its own charging capacity, the energy storage station receives excess wind power energy at an appropriate charging power. For example, if the actual wind power output is 20MW higher than the predicted output, and the energy storage station's maximum charging power is 15MW, then the station will charge at 15MW, processing the excess 5MW of energy through other methods (such as wind curtailment control or coordination with other adjustable loads). During charging, the energy storage station needs to consider factors such as the battery's state of charge (SOC), charging efficiency, and battery health. Generally, when the battery's SOC is low, a larger charging current can be used, but as the SOC increases, the charging current needs to be gradually reduced to avoid overcharging and extend battery life.

[0195] Peak shaving discharge: When the actual power output of offshore wind power is lower than the predicted power output and the electricity demand is high, the energy storage station performs peak shaving discharge. Based on the size of the power shortfall and its own discharge capacity, the energy storage station outputs electricity to supplement the grid or electrical equipment. For example, if the electricity demand is 15MW higher than the actual wind power output, and the energy storage station's discharge capacity can reach 10MW, then the energy storage station will discharge at 10MW, and the remaining 5MW power shortfall can be made up by other backup power sources or grid regulation. During the discharge process, the status of the energy storage system must also be monitored, such as ensuring that the battery SOC does not fall below a low safety threshold (e.g., 20%) to guarantee the continuous operation and stability of the energy storage system.

[0196] 3. Frequency modulation operation:

[0197] Energy storage stations participate in grid frequency regulation through rapid response control. When fluctuations in offshore wind power cause changes in grid frequency, the energy storage station quickly adjusts its charging and discharging power. For example, when the grid frequency rises (indicating excess power generation), the energy storage station increases its charging power to absorb excess energy, causing the grid frequency to drop; when the grid frequency falls (insufficient power generation), the energy storage station increases its discharging power to replenish energy, causing the grid frequency to rise again. The frequency regulation response speed of energy storage stations is typically required to be completed within seconds or even less, which necessitates advanced power electronic control technology and the high power density characteristics of energy storage equipment. For example, using an advanced inverter control system, the charging and discharging power of the energy storage station can be quickly adjusted according to small changes in grid frequency (e.g., ±0.1Hz), with an adjustment range of approximately 10% to 20% of the energy storage station's rated power.

[0198] II. Energy storage stations and wind farms share hydrogen resources to achieve integrated energy management.

[0199] 1. Hydrogen resource sharing model:

[0200] Hydrogen production and storage sharing: Excess electricity generated by wind farms can be used to produce hydrogen through water electrolysis. The produced hydrogen is stored in hydrogen storage facilities connected to an energy storage station. Hydrogen fuel cells in the energy storage station can then use this stored hydrogen to generate electricity. For example, during periods of wind power surplus, the water electrolysis hydrogen production equipment operates at its rated power, storing the produced hydrogen. When wind power is insufficient or electricity demand is high, the hydrogen fuel cells use the stored hydrogen to generate electricity, supplementing the power grid or the wind farm's internal power system. The capacity and pressure parameters of the hydrogen storage facilities need to be rationally designed based on factors such as the wind farm's power generation scale, the energy requirements of the energy storage station, and the hydrogen production and consumption rates. For example, for a 100MW offshore wind farm, the associated hydrogen storage facility capacity might be designed to store several hours to several days' worth of hydrogen production to cope with different energy supply and demand situations.

[0201] Coordinated Energy Flow Management: Hydrogen resources are managed collaboratively between wind farms and energy storage stations through an Energy Management System (EMS). Based on wind power forecasts, actual power monitoring, energy storage station status (including battery charge and hydrogen fuel cell status), and electricity demand, the EMS optimizes hydrogen production, storage, and usage strategies. For example, if wind power is predicted to remain low and energy storage station batteries are insufficient in the near future, the EMS will proactively increase hydrogen production and storage to meet energy demands through hydrogen fuel cell power generation when needed. Conversely, if wind power is high and energy storage station batteries are nearly full, hydrogen production will be reduced, prioritizing the storage of excess energy in the batteries to improve energy efficiency.

[0202] 2. Overall management benefits:

[0203] Improving energy efficiency: Sharing hydrogen resources allows for the effective utilization of surplus electricity from wind farms, reducing wind curtailment. Simultaneously, energy storage stations can flexibly supplement electricity through hydrogen fuel cell power generation under different energy supply and demand scenarios, improving the overall efficiency of the energy system. For example, compared to simple battery storage, the introduction of hydrogen storage enables continuous power supply during prolonged periods of low wind speeds or high electricity demand, increasing the annual power generation utilization rate of wind farms by approximately 5%-10%.

[0204] Enhancing System Stability and Reliability: The collaborative operation of energy storage stations and wind farms based on shared hydrogen resources enhances the stability and reliability of the entire energy system. In the face of significant fluctuations in wind power output, grid failures, or extreme weather, the system can ensure a stable power supply through the switching and complementarity of various energy storage and power generation methods. For example, in the event of a short-term grid failure, the energy storage station can use hydrogen fuel cells to independently power critical equipment within the wind farm, maintaining basic operation and preventing a complete shutdown of the wind farm due to grid failure. This improves the survivability and reliability of the wind farm under complex operating conditions.

[0205] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for configuring offshore wind power energy storage, characterized in that... Includes the following steps: Step 1: Construct wind farms and energy storage stations, enabling the energy storage stations to share hydrogen resources with the wind farms. At the same time, connect the energy storage stations, wind farms, and fuel cell stations through transportation hubs, and ensure that the configuration of energy storage stations is proportional to that of wind farms. Step 2: Calculate the required energy storage capacity for hydrogen production based on the power, energy, and efficiency of the hydrogen production process; Step 3: Determine the energy storage capacity based on the power ratio; the power ratio includes the ratio of wind power to energy storage power, the ratio of hydrogen fuel cell power to energy storage power, and the ratio of hydrogen production power to energy storage power. Step 4: Determine the energy storage configuration ratio based on the predicted power generation; Step 5: Predict the power generation capacity of the wind farm; Step 6: When the predicted power of offshore wind power does not match the actual power, the accuracy of the prediction is increased by using the energy storage station for peak shaving or frequency regulation. The prediction deviation is made up by the energy storage station absorbing or outputting power. The energy storage station and the wind farm share hydrogen resources to achieve comprehensive energy management.

2. The method for configuring offshore wind power energy storage according to claim 1, characterized in that: In step 1, "the configuration of the energy storage station is proportional to that of the wind farm" means that the configuration parameters of the energy storage station are set in the same proportional relationship as the corresponding parameters of the wind farm; the configuration parameters include capacity and power.

3. The method for configuring offshore wind power energy storage according to claim 1, characterized in that: The calculation process for step 2 is as follows: According to power Definition, power It refers to the energy per unit time, that is: , Regarding the power of electro-hydrogen production: ,in Electrogenation time is the time required to complete the electrogenation process. Electro-hydrogen production power indicates the amount of electrical power used to produce hydrogen per unit time. The energy required to produce hydrogen by electricity is the total energy required to complete the hydrogen production process. So ; Due to efficiency issues in the electro-hydrogen production process, the actual energy required from energy storage devices is greater than the theoretical energy needed for hydrogen production; according to the efficiency calculation formula: , For the electro-hydrogen production process ,in, Electrochemical hydrogen production efficiency is a dimensionless coefficient that represents the efficiency of converting electrical energy into the chemical energy of hydrogen. This indicates the energy output from the electrogenerated hydrogen; so ; The energy storage capacity required for hydrogen production by electricity In essence, it refers to the capacity of energy storage devices that provide energy for the electro-hydrogen production process. ; The formula for calculating the energy storage capacity required for hydrogen production by electrolysis is as follows: ; Energy storage capacity required for hydrogen production via electro-hydrogen is used to measure the size of the energy storage equipment required to provide energy for the hydrogen production process via electro-hydrogen.

4. The method for configuring offshore wind power energy storage according to claim 1, characterized in that: In step 3, the ratio of wind power to energy storage power is as follows: Assume that the wind power reaches its maximum value at a certain moment. The charging power of energy storage devices is limited to The power ratio is set as follows: ,in It is a coefficient less than or equal to 1, determined based on the performance of the energy storage device and system requirements; During the discharge process, when the wind power output is lower than the load demand, the energy storage device needs to discharge to supplement the power; let the load demand power be... Wind power is The discharge power of the energy storage device is The power ratio relationship is expressed as follows: ; Based on the power ratio and the wind power prediction curve, the charging and discharging power required by the energy storage device in different time periods is calculated, and thus the capacity of the energy storage device is determined. By integrating the wind power prediction curve, the total amount of energy that needs to be stored within a certain period is obtained. Combined with the charging and discharging efficiency of energy storage devices Calculate the capacity of the energy storage device. ; in, The maximum value of wind power reached at a certain moment; Charging power limitations of energy storage devices; A coefficient less than or equal to 1, determined based on the performance of the energy storage device and system requirements, used to represent the ratio between peak wind power and energy storage charging power; : Load power requirement; Wind power output; : Discharge power of energy storage devices; The total amount of energy that needs to be stored within a certain period of time; The charging and discharging efficiency of energy storage devices; Energy storage capacity.

5. The method for configuring offshore wind power energy storage according to claim 1, characterized in that: In step 3, the ratio of hydrogen fuel cell power to energy storage power is as follows: The hydrogen fuel cell plays a role in supplementing power and regulating energy in the system; when the wind power prediction is significantly off or the energy storage device has insufficient power, the hydrogen fuel cell needs to output power; let the rated power of the hydrogen fuel cell be... The power of the energy storage device is To ensure system stability, the power distribution ratio is set as follows: ， in It is a coefficient determined based on system reliability requirements and the performance of hydrogen fuel cells; When the energy storage device is at a low charge level, the hydrogen fuel cell needs to output sufficient power to meet the load demand, and may also need to recharge the energy storage device; at this time, the power ratio needs to take into account the power generation efficiency of the hydrogen fuel cell. Charging efficiency of energy storage devices and load power requirements ; Obtain the relation Using this relationship and known system parameters, we can further determine the appropriate ratio of hydrogen fuel cell power to energy storage device power, and thus calculate the energy storage device capacity. in, Rated power of hydrogen fuel cells; Power of energy storage devices; A coefficient determined based on system reliability requirements and the performance of the hydrogen fuel cell, used to represent the ratio of hydrogen fuel cell power to energy storage device power; The power generation efficiency of hydrogen fuel cells; : Charging efficiency of energy storage devices.

6. The method for configuring offshore wind power energy storage according to claim 1, characterized in that: In step 3, the ratio of hydrogen production power to energy storage power is as follows: The electro-hydrogen production process consumes electrical energy, which comes from wind power; let the electro-hydrogen production power be... The output power of the energy storage device is The power ratio relationship is expressed as follows: ， in It is a coefficient determined based on the efficiency of hydrogen production by electricity and the factors of hydrogen demand; The energy efficiency of electro-hydrogen production is known to be The energy required to produce hydrogen within a certain time period is So, what energy is needed to produce hydrogen by electricity? According to the power of hydrogen production by electricity and required energy Based on the output power limitations of the energy storage device, the energy and time required for the energy storage device to produce hydrogen via electrolysis are calculated, thus determining the capacity of the energy storage device. ， in It is the time cycle for electro-hydrogen production; : Hydrogen production power; Output power of energy storage devices; A coefficient determined based on the efficiency of hydrogen production by electricity and the factors of hydrogen demand, used to represent the proportional relationship between the power of hydrogen production by electricity and the output power of energy storage equipment; Energy efficiency of electro-hydrogen production; The amount of hydrogen energy required to produce within a certain time period; Time period for electro-hydrogen production; Energy storage capacity.

7. The method for configuring offshore wind power energy storage according to claim 1, characterized in that: The sub-steps of step 4 are as follows: (1) Perform short-term power forecasting, including hourly power forecasting and minute-level power forecasting; The prediction and calculation process of hourly power forecasting: Based on historical data and real-time meteorological information, time series analysis methods or machine learning algorithms are used to predict the power generation of offshore wind farms in the next 1-6 hours; Based on the predicted hourly power curve, analyze the power fluctuation situation; if it is predicted that the wind power will rise or fall significantly in the next hour, in order to stabilize the output, the energy storage configuration ratio should be able to store excess energy when the power rises and release sufficient energy when the power falls. The calculation method is as follows: Let the predicted power change be... The energy storage charge and discharge efficiencies are respectively and Then the energy storage configuration capacity or The energy storage configuration ratio is based on the rated power of the wind farm. Determine the proportionality coefficient. ; in, : Predicted hourly power variation : Charging efficiency of energy storage devices; The discharge efficiency of energy storage devices; Energy storage configuration capacity derived from hourly power forecasts; Rated power of wind farm; Hourly energy storage configuration ratio coefficient; The prediction and calculation process for minute-level power forecasting: Utilizing more refined meteorological monitoring data and advanced forecasting models, the power generation changes within the next 10-60 minutes are predicted; the energy storage configuration ratio needs to consider the rapid response capability of the energy storage equipment, assuming a rapid response power of... Minute-level energy storage configuration ratio coefficient At the same time, it is also necessary to combine energy storage capacity to ensure that power output can be maintained for a certain period of time, and calculate energy storage capacity based on energy demand. To further determine the proportional relationship with the rated power of the wind farm; in, : The fast response power of energy storage devices; : Minute-level energy storage configuration ratio coefficient; Energy storage configuration capacity derived from minute-level power prediction; The number of minutes that power output needs to be maintained; (2) Conduct medium- and long-term power forecasting, including daily power forecasting and weekly power forecasting; The prediction and calculation process for daily power generation forecasting is as follows: Taking into account historical daily power generation data, weather forecasts, and seasonal factors, a multiple regression analysis or hybrid prediction model is used to predict the power generation variation curve of the wind farm within the next day; daily power generation data under similar weather conditions in the same season over the past few years is analyzed, and combined with current weather forecast information, the power generation range for the next day is predicted; based on the daily power generation forecast results, the energy storage configuration ratio is determined to balance the power supply and demand throughout the day; if the predicted wind power is higher during the day and lower at night, and the daytime electricity demand is relatively stable, the energy storage configuration should be able to store excess electricity during the day for nighttime use; let the average excess power during the day be... The average power deficit at night is The charge / discharge cycle efficiency of energy storage is Then the energy storage configuration capacity , The larger capacity value is taken as the basis for energy storage configuration, and the daily energy storage configuration ratio coefficient is determined. ; in, Average excess power during the day; Nighttime average power deficit; Charge-discharge cycle efficiency of energy storage devices; Daytime energy storage configuration capacity based on daily power forecast; Nighttime energy storage configuration capacity based on daily power forecast; Daytime charging time; Nighttime discharge time; Daily energy storage configuration ratio coefficient; The prediction and calculation process for weekly power generation forecasting: Predictions are made based on longer-term historical data, long-term weather forecasts, and energy demand trends in the areas surrounding offshore wind farms. The weekly power generation variation patterns in different seasons are analyzed, and combined with the weather forecast for the coming week and the weekly trends of industrial and residential electricity consumption in the region, the approximate range and fluctuations of the wind farm's power generation for the coming week are predicted. For weekly power generation forecasting, the energy storage configuration ratio primarily considers coping with the intermittency and volatility of wind power generation within a week, as well as coordination with other energy supplies. If it is predicted that wind power will be low on certain days while electricity demand in the region is high, the energy storage should be able to store sufficient energy to compensate for the shortfall when wind power is abundant. The maximum power gap within a week is assumed to be... Energy storage configuration capacity Weekly energy storage configuration ratio coefficient ; in, The largest power deficit during the week; Energy storage configuration capacity based on weekly power forecast; The total time during which a power deficit may occur; Weekly energy storage configuration ratio coefficient; Based on the above predictions and analyses at different time scales, a more reasonable energy storage configuration ratio is derived to optimize the configuration of offshore wind power energy storage systems, thereby improving the stability, reliability, and economy of offshore wind power.

8. The method for configuring offshore wind power energy storage according to claim 1, characterized in that: The sub-steps of step 5 are as follows: (1) Determine wind power: Real-time data on wind speed, wind direction, turbine blade speed and nacelle temperature are collected by sensors installed on each turbine in the offshore wind farm; at the same time, meteorological data of the sea area where the wind farm is located is collected, including large-scale meteorological information provided by surrounding meteorological stations and local meteorological changes monitored by offshore buoys; based on the collected data, the current wind power is determined by using the established wind power calculation model. (2) Predicting energy storage output: Real-time monitoring of the energy storage system to obtain the current power, charging and discharging current and voltage parameters of the energy storage device; at the same time, monitoring the health status of the energy storage system, including changes in the internal resistance of the battery and temperature distribution; the health status will affect the charging and discharging efficiency and power output capability of the energy storage system. (3) Establish a prediction model: Based on the historical operating data and current state parameters of the energy storage system, establish an energy storage output prediction model; use time series analysis to analyze the historical variation of the energy storage system output, and combine the current state data to predict the future energy storage output; if it is found that the energy storage system's discharge power exhibits a certain periodic change when the power is within a specific range and the ambient temperature is stable during certain periods in the past, then use the ARIMA model to capture this periodicity and predict the future discharge power change trend; (4) Predicting wind power output To obtain longer-term weather forecast data, including wind speed, wind direction, temperature, and air pressure for the next few hours or even days; Combine meteorological forecast data with geographical information of the sea area where the wind farm is located; geographical information will affect the wind flow, thereby changing the actual wind speed and wind direction at the wind turbine. (5) Optimize the prediction model: Based on historical wind power data and integrated meteorological forecast data, construct a wind power prediction model; use machine learning algorithms to continuously optimize the prediction model; (6) Based on the predicted wind power and energy storage output, formulate the energy storage charging and discharging control strategy; when the predicted wind power is greater than the current electricity demand and the energy storage system is not fully charged, start the energy storage charging process: set a charging threshold, when the difference between the wind power and the electricity demand exceeds the threshold and the energy storage SOC is lower than the set upper limit, control the energy storage system to charge with an appropriate charging power; when the predicted wind power is less than the electricity demand, if the energy storage system has enough power, start the energy storage discharging process. Determine the discharge threshold. When the difference between electricity demand and wind power exceeds the threshold and the energy storage SOC is higher than the set lower limit, control the energy storage system to discharge. The discharge power also needs to be adjusted according to the capacity and current state of the energy storage system in order to meet the power demand and maintain the stable operation of the system.

9. A method for configuring offshore wind power energy storage according to claim 1, characterized in that: The sub-steps of step 6 are: (1) The strategy to deal with the mismatch between predicted power and actual power is to establish a real-time power monitoring system to accurately measure and record the predicted power and actual power of offshore wind power respectively; to use high-precision power sensors installed on the transmission lines of the wind farm to collect power data at high frequency; to calculate the deviation between the predicted power curve and the actual power curve by comparing them; when the deviation exceeds the preset threshold, it is determined that a power mismatch has occurred. When the actual power of offshore wind power is higher than the predicted power and the grid absorption capacity is limited, the energy storage station enters the charging peak shaving mode. The energy storage station receives the excess wind power energy with an appropriate charging power based on the difference between the actual power and the predicted power and its own charging capacity. When the actual power output of offshore wind power is lower than the predicted power output and the electricity demand is large, the energy storage station discharges to regulate peak demand. The energy storage station outputs electrical energy to supplement the power grid or electrical equipment according to the size of the power gap and its own discharge capacity. Energy storage stations participate in grid frequency regulation through rapid response control; when fluctuations in offshore wind power cause changes in grid frequency, energy storage stations quickly adjust their charging and discharging power. (2) The strategy of sharing hydrogen resources between the energy storage station and the wind farm to achieve comprehensive energy management is as follows: the excess electricity generated by the wind farm can be used to electrolyze water to produce hydrogen, and the produced hydrogen is stored in a hydrogen storage facility connected to the energy storage station; the hydrogen fuel cell in the energy storage station uses the stored hydrogen to generate electricity; during periods of excess wind power, the water electrolysis hydrogen production equipment operates at rated power and stores the produced hydrogen. When wind power is insufficient or electricity demand is at its peak, hydrogen fuel cells use stored hydrogen to generate electricity, which is then used to supplement the power grid or the internal power system of the wind farm. The capacity and pressure parameters of the hydrogen storage facilities need to be designed reasonably based on the power generation scale of the wind farm, the energy demand of the energy storage station, and the production and consumption rate of hydrogen. A collaborative management system is used to manage hydrogen resources between wind farms and energy storage stations. The energy management system optimizes hydrogen production, storage, and usage strategies based on wind power forecasts, actual power monitoring, energy storage station status, and electricity demand information. Sharing hydrogen resources enables the effective use of surplus electricity from wind farms, reducing wind curtailment. Meanwhile, energy storage stations can flexibly supplement electricity through hydrogen fuel cell power generation in different energy supply and demand scenarios, improving the overall utilization efficiency of the entire energy system. The coordinated operation of energy storage stations and wind farms based on shared hydrogen resources enhances the stability and reliability of the entire energy system. In the face of large fluctuations in wind power, grid failures, or extreme weather conditions, the system can ensure a stable power supply by switching and complementing various energy storage and power generation methods.

10. An offshore wind power energy storage configuration optimization system, characterized in that: The method for configuring offshore wind power energy storage according to any one of claims 1-9 was adopted.

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