A wind-solar-storage energy-based offshore wind power storage optimization method and system

Abstract

The present application belongs to the technical field of wind power, and particularly relates to a method and system for optimizing offshore wind power energy storage based on wind-solar energy storage. The method comprises determining a predicted charging demand of a to-be-tested energy storage device; and determining an energy supply state of the to-be-tested energy storage device based on the predicted charging demand and a current energy supply power of the to-be-tested energy storage device. The present application compares the predicted charging demand with the current energy supply power to determine the energy supply state, and performs a scheduling operation corresponding to the energy supply state, thereby achieving dynamic management of the energy storage device load, ensuring the continuity of offshore wind power system energy supply, and effectively slowing down the performance degradation of the energy storage device.

Description

Technical Field

[0001] This invention belongs to the field of wind power technology, specifically relating to an optimization method and system for offshore wind power energy storage based on wind, solar and energy storage supply. Background Technology

[0002] With the transformation and upgrading of the global energy structure, offshore wind power, as an abundant wind energy resource in the vast sea area, avoids the limitations of land resources and meets large-scale electricity demand and sustainable development. However, the complex and variable nature of the offshore wind environment leads to the intermittency and volatility of wind power generation. Changes in its output power can affect the safe and stable operation of the power grid. Therefore, how to smooth out power fluctuations and ensure power quality has become a technical problem that needs to be solved in this field.

[0003] Existing offshore wind power technologies generally employ energy storage systems to form integrated wind, solar, and energy storage power generation systems. However, their control and management strategies for energy storage devices have shortcomings, which limit the overall efficiency of the system. For example, existing methods use passive response strategies based on fixed thresholds or simple logic rules. When a power deficit or surplus is detected, the energy storage device is instructed to charge and discharge. This ignores the fine-grained judgment of operating conditions, leading to frequent deep charging and discharging of the energy storage device or operation at high rates, thereby accelerating the degradation of its electrochemical performance and shortening its effective lifespan. In addition, there is a lack of foresight and adaptability. Existing control strategies are usually reactive and cannot combine short-term forecasts of future wind power with the real-time health status of the energy storage device for dynamic decision-making. This makes it impossible for energy dispatch to over-discharge before an upcoming long period of no wind, or to perform high-intensity charging and discharging tasks when the device's health is poor, resulting in reduced energy utilization efficiency and causing energy storage system failures.

[0004] In view of this, the present invention proposes an optimization method and system for offshore wind power energy storage based on wind, solar and energy storage. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide an optimization method and system for offshore wind power energy storage based on wind, solar, and energy storage, thereby solving the problem in the prior art that the energy supply cannot be adjusted according to the current operating power and temperature of the energy storage device, and that the energy storage device is prone to increased losses due to abnormal operating conditions.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows: an optimization method for offshore wind power energy storage based on wind, solar and energy storage, comprising: determining the predicted charging demand of the energy storage device under test; and determining the energy supply status of the energy storage device under test based on the predicted charging demand and the current energy supply power of the energy storage device under test. When the power supply status of the energy storage device under test is determined, the scheduling operation corresponding to the power supply status is executed; wherein, determining the predicted charging demand of the energy storage device under test includes: obtaining the current charging demand of the energy storage device under test; The baseline deviation is obtained, which is used to characterize the difference in charging demand between the other energy storage devices in the energy storage device set and the baseline group. Based on the current charging demand and the baseline deviation, the predicted charging demand is generated through combined calculations. The energy supply status includes excess status, charging status and normal status.

[0007] Preferably, obtaining the benchmark deviation includes: selecting one or more energy storage devices from the set of energy storage devices to form a benchmark group; defining energy storage devices in the set of energy storage devices that do not belong to the benchmark group as the remaining energy storage devices; calculating the difference between the charging demand of each energy storage device in the remaining energy storage devices and the charging demand of the benchmark group, and outputting the difference as the benchmark deviation.

[0008] Preferably, selecting one or more energy storage devices from the energy storage device set to form a benchmark group includes: obtaining the rated power and charging demand of each energy storage device in the energy storage device set; calculating the power deviation value between the rated power of each energy storage device and its charging demand; and selecting the energy storage device with the minimum power deviation value to form a benchmark group, wherein when there are multiple energy storage devices with the minimum power deviation value, the energy storage device with the largest rated power is selected from the multiple energy storage devices to form a benchmark group.

[0009] Preferably, determining the power supply status of the energy storage device under test includes: when the current power supply is greater than the rated output limit of the energy storage device under test, the power supply status is determined to be an over-limit status; when the current power supply is not greater than the rated output limit and the current power supply is less than the predicted charging demand, the power supply status is determined to be a charging status; and when the current power supply is not greater than the rated output limit and the current power supply is not less than the predicted charging demand, the power supply status is determined to be a normal status.

[0010] Preferably, when the energy supply status of the energy storage device under test is determined, the scheduling operation corresponding to the energy supply status includes: when the energy supply status is in an over-limit state, issuing a control command to reduce the current energy supply power; when the energy supply status is in a normal state, issuing a control command to maintain the current energy supply power. And when the energy supply status is charging status, the system selects and controls the replacement energy storage device to perform the energy supply task according to the preset cycle energy storage sequence.

[0011] Preferably, selecting a replacement energy storage device includes: selecting an energy storage device from the energy storage devices included in the cyclic energy storage sequence that has an upper limit parameter greater than the predicted charging demand and a current operating temperature lower than a preset upper limit operating temperature, as the replacement energy storage device.

[0012] An offshore wind power energy storage optimization system based on wind, solar and energy storage supply includes: a demand forecasting module for determining the predicted charging demand of the energy storage device under test; The status determination module is used to determine the energy supply status of the energy storage device under test based on the predicted charging demand determined by the response demand prediction module and the current energy supply power of the energy storage device under test. And a scheduling control module, used to execute scheduling operations corresponding to the power supply status determined by the response status determination module.

[0013] Preferably, it further includes: a deviation calculation module, used to select a benchmark group from the set of energy storage devices, and calculate the difference between the charging demand of the remaining energy storage devices in the set of energy storage devices and the charging demand of the benchmark group, so as to output the benchmark deviation; wherein, the demand prediction module is used to generate a predicted charging demand based on the current charging demand of the energy storage device under test and the benchmark deviation output by the deviation calculation module.

[0014] Preferably, the state determination module is configured to: determine the energy supply state as an over-limit state when the current energy supply power is greater than the rated output limit of the energy storage device under test; determine the energy supply state as a charging state when the current energy supply power is not greater than the rated output limit and the current energy supply power is less than the predicted charging demand; and determine the energy supply state as a normal state when the current energy supply power is not greater than the rated output limit and the current energy supply power is not less than the predicted charging demand.

[0015] Preferably, the scheduling control module is configured to: issue a control command to reduce the current energy supply power when the energy supply status is in an over-capacity state; issue a control command to maintain the current energy supply power when the energy supply status is in a normal state; and select and control a replacement energy storage device to perform the energy supply task according to a preset cyclic energy storage sequence when the energy supply status is in a charging state.

[0016] Beneficial effects: 1. This invention extracts the historical temperature and historical output power of the energy storage device, determines the temperature threshold based on the temperature change trend, and corrects the historical output power data based on the threshold; it takes into account the performance impact of the energy storage device caused by temperature, eliminates the data deviation caused by temperature fluctuations, and enables the predicted charging demand determined based on the corrected power data to accurately reflect the actual operating status of the energy storage device, thereby improving the accuracy of energy storage optimization scheduling.

[0017] 2. This invention selects a benchmark group from the set of energy storage devices, calculates the difference in charging demand between the remaining energy storage devices and the benchmark group to generate a benchmark deviation, and then combines the current charging demand of the energy storage device under test with the benchmark deviation to generate a predicted charging demand. By introducing the benchmark group and the benchmark deviation to calibrate the charging demand prediction, the data fluctuations of individual energy storage devices are smoothed, and the system operation characteristics are incorporated to obtain accurate and stable charging demand prediction results.

[0018] 3. This invention determines the energy supply status by comparing the predicted charging demand with the current energy supply power, and executes scheduling operations corresponding to the energy supply status: in the over-capacity state, a control command is issued to reduce the energy supply power; in the charging state, the energy storage device is selected and replaced according to the cyclic energy storage sequence to perform the energy supply task, thereby realizing dynamic management of the load of the energy storage device; by intervening in the over-capacity state and orderly rotating the energy storage device, the continuous high load or deep discharge of the energy storage device is avoided, which effectively slows down the performance degradation of the energy storage device while ensuring the continuity of the offshore wind power system's energy supply. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Example 1

[0022] like Figure 1 As shown, this embodiment provides an optimization method for offshore wind power energy storage based on wind, solar, and energy storage. Through accurate prediction and intelligent scheduling, it improves the energy supply stability and equipment operation safety of integrated offshore wind, solar, and energy storage power stations. Specifically, it includes the following steps: Information collection and preliminary processing: The energy storage device set is screened, and all energy storage devices with a cumulative operating time of not less than a preset time threshold are collected; then newly put into operation or recently maintained devices are excluded to ensure that the analysis data comes from devices that have entered a stable operating state; for each selected energy storage device, its historical output power data recorded in time series form is obtained.

[0023] Based on historical output power data, continuous periods of power output greater than zero are identified as discharge events. The power value in each discharge event is integrated over time to calculate the electrical energy released during that discharge, thereby determining multiple sets of charging demands. Each set of charging demands is numerically equal to the electrical energy released in a complete discharge event.

[0024] Finally, the charging demands of multiple groups were categorized and statistically analyzed, and the charging demand values ​​were divided into several preset numerical ranges, such as 0-100 Wh, 100-200 Wh, etc. The frequency of charging demand falling within each numerical range during the historical monitoring period was also counted, forming a frequency distribution data table or histogram. The statistical results were defined as benchmark statistical parameters to provide a global behavioral reference baseline for subsequent individual deviation analysis.

[0025] Furthermore, power correction based on temperature impact: Considering the variable temperature of the marine environment and the heat generated by the energy storage device itself, this step corrects the historical output power data to reflect the impact of temperature on performance. For any energy storage device, multiple historical temperature data points and their precisely time-corresponding historical output power data are extracted. Determining the critical point of performance change requires establishing a temperature threshold. Multiple historical temperature data points of the energy storage device within a preset period are collected, and the temperature difference between adjacent data points in these historical temperature data points is calculated to obtain a series of temperature change rates. Then, the change of these temperature change rates over time is calculated to identify the moment with the greatest temperature rise acceleration, and the temperature data point corresponding to this moment is determined as the critical temperature node, and its temperature value is the temperature threshold. This threshold is dynamically updated: when the temperature value of newly acquired historical temperature data is higher than the temperature value of the current critical temperature node, the newly acquired historical temperature data point is determined as a new critical temperature node, and the temperature threshold is updated to ensure that it always reflects the most severe thermal conditions experienced by the device. After determining the temperature threshold, the historical output power data is corrected based on this threshold. When the operating temperature of the device is greater than or equal to the temperature threshold, its stable output power will decrease. The correction process is to multiply the original output power data by a correction factor less than 1. The value of the correction factor is determined by a preset temperature-related lookup table or segmented correspondence. Its value decreases as the operating temperature exceeds the temperature threshold, thereby obtaining a corrected output power that better reflects the true load-bearing capacity.

[0026] Furthermore, the benchmark group selection and benchmark deviation calculation are as follows: the most representative benchmark group is selected from the energy storage device set to establish a reliable comparison reference; the rated power of each energy storage device in the energy storage device set is obtained, and the charging demand of each device in the same historical period is obtained; then the power deviation value between the rated power of each energy storage device and its average charging demand is calculated to reflect the gap between the actual usage intensity of the device and its design capacity.

[0027] Based on the calculated power deviation values, devices with power deviation values ​​within the same preset range are grouped into the same energy storage device group. Then, a benchmark group is determined according to the order of the average charging times of each energy storage device group. The device group with the earliest average charging time typically represents the main force responding to grid demand, and its behavior is more general. During the selection process, if multiple energy storage device groups have the same power deviation value, the group with the largest rated power is selected as the benchmark group, as high-power equipment is generally designed to be more stable, and its operating parameters are more reliable as a benchmark. After selecting the benchmark group, energy storage devices that do not belong to the benchmark group are defined as the remaining energy storage devices. Finally, the arithmetic mean of the charging demands of all energy storage devices within the benchmark group is calculated to obtain the average charging demand of the benchmark group. Then, the difference between the charging demand of each energy storage device in the remaining energy storage devices and the average charging demand of the benchmark group is calculated, and this difference is output as the benchmark deviation. This benchmark deviation quantifies the difference in the typical behavior of each individual device relative to the group.

[0028] Furthermore, predicting charging demand generation: predicting the charging demand of the energy storage device under test in the next time period; the specific process for generating the predicted charging demand is as follows: obtaining the current charging demand of the energy storage device under test, and obtaining the reference deviation corresponding to the energy storage device under test from the previous step. Then, the initial predicted charging demand is obtained by adding the current charging demand of the energy storage device under test to the benchmark deviation. Subsequently, a protective comparison judgment is performed: if the calculated initial predicted charging demand is lower than the current charging demand of the energy storage device under test due to the negative benchmark deviation, the current charging demand is determined as the predicted charging demand to prevent the risk of underestimating the actual demand; otherwise, if the initial predicted charging demand is not lower than the current charging demand, the initial predicted charging demand is determined as the predicted charging demand.

[0029] Furthermore, the energy supply status determination is as follows: After obtaining the predicted charging demand, it is necessary to determine whether the current energy supply capacity of the energy storage device under test is sufficient to meet future demand. The predicted charging demand of the energy storage device under test is compared with its current energy supply power, and the energy supply status of the device is output based on the comparison result. Among them, the energy supply status is specifically divided into three types: excess status, charging status and normal status, and the judgment logic has a clear priority. Specifically: Obtain the rated output limit of the energy storage device under test and perform a safety assessment. When the current power supply exceeds the rated output limit, the device is at risk of overload, and the power supply status is determined to be in an overload state. If the current power supply is not greater than the rated output limit, a supply-demand balance assessment is then performed. When the current power supply is less than the predicted charging demand, it means that the existing power cannot meet future demand, and the device needs to replenish energy. In this case, the power supply status is determined to be in a charging state. If neither of the above two conditions is met, that is, the current power supply is not greater than the rated output limit and not less than the predicted charging demand, it indicates that the supply and demand are balanced, and the power supply status is determined to be in a normal state.

[0030] Furthermore, scheduling control and state switching: Based on the determined energy supply status, corresponding control commands are issued to execute scheduling operations. When the energy supply status is in an over-limit state, a control command is issued to reduce the current energy supply power. By controlling its power conversion system, the output power is forcibly reduced to within the rated output limit. When the energy supply status is in a normal state, a control command is issued to maintain the current energy supply power. When the energy supply status is in a charging state, the replacement scheduling logic is initiated, and the energy storage device under test becomes the device to be replaced. Based on a preset cycle energy storage sequence, balanced use of energy storage devices is achieved. For example, a list of devices arranged in descending order of historical cycle count and state of charge (SOH) is used to select replacement and standby energy storage devices. The selection process follows safety and effectiveness judgment: each energy storage device is checked sequentially from the cycle energy storage sequence. Only when the current available electrical energy of a device is greater than the predicted charging demand of the device to be replaced, and its current operating temperature is lower than the preset upper limit of operating temperature, is it considered qualified. The specific calculation is based on its current state of charge (SOC). The first device that meets the criteria is selected as the replacement energy storage device and is controlled by the control system to take over the power supply task, while the device to be replaced switches to charging mode. The next device in the sequence that meets the criteria is designated as the backup energy storage device to deal with emergencies.

[0031] Example 2

[0032] like Figure 2 The present embodiment provides an offshore wind power energy storage optimization system based on wind, solar and energy storage. By dynamically predicting the charging demand of the energy storage device and combining it with its current power supply, the system can determine its power supply status and perform targeted scheduling operations to improve the operating efficiency of the entire offshore wind power energy storage system.

[0033] In its implementation, this system can be deployed in one or more central control servers, edge computing devices, or embedded controllers, and communicate with multiple energy storage devices, wind turbine generators, and other related monitoring and data acquisition systems in offshore wind farms. Specifically, it includes the following modules: The deviation calculation module is used to calculate the benchmark deviation, providing a key reference for subsequent charging demand forecasting. In the specific execution process, a benchmark group is selected from the set of energy storage devices containing multiple energy storage devices; the rated power of each energy storage device in the set and the average charging demand reported by the battery management system (BMS) or other monitoring units are obtained; then the power deviation value between the rated power of each energy storage device and its average charging demand is calculated, and the energy storage device with the smallest power deviation value is selected to form the benchmark group. The energy storage device whose current operating state is closest to its design conditions is selected as the reference standard.

[0034] When there are multiple energy storage devices with the minimum power deviation value, the one with the largest rated power is selected from these multiple energy storage devices to form a benchmark group to enhance the representativeness and stability of the benchmark. After determining the benchmark group, the energy storage devices in the set of energy storage devices that do not belong to the benchmark group are defined as the remaining energy storage devices. Finally, the difference between the charging demand of each energy storage device in the remaining energy storage devices and the average charging demand of the benchmark group is calculated one by one. That is, if the benchmark group contains multiple devices, the average or weighted average of their charging demands can be used, and the set of these differences is output as the benchmark deviation.

[0035] The core task of the demand forecasting module is to determine the predicted charging demand of the energy storage device under test; to obtain the current charging demand of the energy storage device under test, and to receive the benchmark deviation output by the deviation calculation module; then, based on the current charging demand and the benchmark deviation, to generate the predicted charging demand through combined calculation; and to perform a weighted summation or dynamic correction on the current charging demand of the energy storage device under test and the benchmark deviation that reflects the characteristics of its group, so that the obtained predicted charging demand can reflect its own state, as well as its relative position and trend in the entire energy storage device set, making it more forward-looking and accurate.

[0036] The status determination module is used to determine the energy supply status of the energy storage device under test based on the predicted charging demand and the current energy supply power; it receives the predicted charging demand generated by the demand prediction module and obtains the current energy supply power of the energy storage device under test from the monitoring system; the module internally presets the rated output upper limit of the energy storage device under test and determines the status through a series of logical comparisons.

[0037] The system determines whether the current power supply exceeds the rated output limit. If so, it indicates that the device is operating under overload conditions, posing a safety risk, and the power supply status is determined to be in an overload state. If the current power supply is not greater than the rated output limit, the next step is to compare the current power supply with the predicted charging demand. If the current power supply is less than the predicted charging demand, it indicates that the device's power supply capacity is insufficient to meet its predicted charging needs, requiring additional energy or energy rotation. In this case, the power supply status is determined to be in a charging state. If the current power supply is not greater than the rated output limit and is not less than the predicted charging demand, it indicates that the device is currently operating within a healthy and sustainable range, capable of meeting its own charging needs, and the power supply status is determined to be in a normal state.

[0038] The scheduling and control module is the final execution unit of the system. It responds to the power supply status determined by the status determination module and performs scheduling operations corresponding to the power supply status. When the received power supply status is an over-limit status, it immediately generates and issues a control command to reduce the current power supply. This command can be sent to the power control system (PCS) of the energy storage device.

[0039] The device is kept safe by reducing its output power or temporarily removing it from the main power supply queue; when the received power supply status is normal, a control command is issued to maintain the current power supply, allowing the energy storage device to continue to perform the power supply task stably according to the current status; when the received power supply status is charging, the replacement scheduling logic is started.

[0040] Based on a preset cyclic energy storage sequence, which defines the priority or rotation order of energy storage devices participating in energy supply, the system selects and controls replacement energy storage devices to perform energy supply tasks. During the selection process, the system traverses the energy storage devices included in the cyclic energy storage sequence and filters each candidate device.

[0041] The selection criteria include: the energy storage limit parameter of the device must be greater than the predicted charging demand of the device to be replaced to ensure that it is capable of taking over the task; at the same time, its current operating temperature must be lower than the preset operating temperature limit to ensure that it operates under safe conditions; when an energy storage device that meets all the conditions is found, it is identified as the replacement energy storage device, and a control command is issued to make it take over the power supply task of the original device to be replaced, while instructing the original device to be replaced to enter charging or standby mode.

[0042] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand and implement the present invention. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An optimized method for offshore wind power energy storage based on wind, solar, and energy storage supply, characterized in that, include: Determine the predicted charging demand of the energy storage device under test; Based on the predicted charging demand and the current power supply of the energy storage device under test, the power supply status of the energy storage device under test is determined. When the power supply status of the energy storage device under test is determined, the scheduling operation corresponding to the power supply status is executed; wherein, determining the predicted charging demand of the energy storage device under test includes: obtaining the current charging demand of the energy storage device under test; The baseline deviation is obtained, which is used to characterize the difference in charging demand between the other energy storage devices in the energy storage device set and the baseline group. Based on the current charging demand and the baseline deviation, the predicted charging demand is generated through combined calculations. The energy supply status includes excess status, charging status and normal status.

2. The method for optimizing offshore wind power energy storage based on wind, solar, and energy storage according to claim 1, characterized in that, Obtaining the benchmark deviation includes: selecting one or more energy storage devices from the energy storage device set to form a benchmark group; Energy storage devices that do not belong to the benchmark group are defined as the remaining energy storage devices; the difference between the charging demand of each remaining energy storage device and the charging demand of the benchmark group is calculated, and the difference is output as the benchmark deviation.

3. The method for optimizing offshore wind power energy storage based on wind, solar, and energy storage according to claim 2, characterized in that, Selecting one or more energy storage devices from the energy storage device set to form a benchmark group includes: obtaining the rated power and charging requirements of each energy storage device in the energy storage device set; Calculate the power deviation between the rated power of each energy storage device and its charging demand; and select the energy storage device with the smallest power deviation to form a benchmark group. When there are multiple energy storage devices with the smallest power deviation, select the energy storage device with the largest rated power from among the multiple energy storage devices to form the benchmark group.

4. The method for optimizing offshore wind power energy storage based on wind, solar, and energy storage according to claim 1, characterized in that, Determining the power supply status of the energy storage device under test includes: when the current power supply is greater than the rated output limit of the energy storage device under test, the power supply status is determined to be an over-limit status; When the current power supply is not greater than the rated output limit and the current power supply is less than the predicted charging demand, the power supply status is determined to be charging status; and when the current power supply is not greater than the rated output limit and the current power supply is not less than the predicted charging demand, the power supply status is determined to be normal status.

5. The method for optimizing offshore wind power energy storage based on wind, solar, and energy storage according to claim 4, characterized in that, When the energy supply status of the energy storage device under test is determined, the scheduling operation corresponding to the energy supply status includes: when the energy supply status is in an over-limit state, issuing a control command to reduce the current energy supply power; When the power supply status is normal, a control command is issued to maintain the current power supply. And when the energy supply status is charging status, the system selects and controls the replacement energy storage device to perform the energy supply task according to the preset cycle energy storage sequence.

6. The method for optimizing offshore wind power energy storage based on wind, solar, and energy storage according to claim 5, characterized in that, Selecting replacement energy storage devices includes: selecting energy storage devices from the energy storage devices included in the cycle energy storage sequence that have an upper limit parameter greater than the predicted charging demand and a current operating temperature lower than a preset upper limit operating temperature, as replacement energy storage devices.

7. An optimized offshore wind power energy storage system based on wind, solar, and energy storage, characterized in that, include: The demand forecasting module is used to determine the predicted charging demand of the energy storage device under test. The status determination module is used to determine the energy supply status of the energy storage device under test based on the predicted charging demand determined by the response demand prediction module and the current energy supply power of the energy storage device under test. And a scheduling control module, used to execute scheduling operations corresponding to the power supply status determined by the response status determination module.

8. The offshore wind power energy storage optimization system based on wind, solar, and energy storage according to claim 7, characterized in that, Also includes: The deviation calculation module is used to select a benchmark group from the set of energy storage devices and calculate the difference between the charging demand of the remaining energy storage devices in the set and the charging demand of the benchmark group, so as to output the benchmark deviation. The demand forecasting module generates a predicted charging demand based on the current charging demand of the energy storage device under test and the benchmark deviation output by the deviation calculation module.

9. The offshore wind power energy storage optimization system based on wind, solar, and energy storage according to claim 7, characterized in that, The status determination module is configured to determine the energy supply status as an over-limit state when the current energy supply power is greater than the rated output limit of the energy storage device under test. When the current power supply is not greater than the rated output limit and the current power supply is less than the predicted charging demand, the power supply status is determined to be the charging status. And when the current power supply is not greater than the rated output limit and the current power supply is not less than the predicted charging demand, the power supply status is determined to be normal.

10. An offshore wind power energy storage optimization system based on wind, solar, and energy storage according to claim 7, characterized in that, The dispatch control module is configured to issue a control command to reduce the current power supply when the power supply status is in an over-limit state. When the power supply status is normal, a control command is issued to maintain the current power supply. And when the energy supply status is charging status, the system selects and controls the replacement energy storage device to perform the energy supply task according to the preset cycle energy storage sequence.

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