A multi-modal energy conversion and storage method based on composite wind energy utilization

Abstract

The application discloses a kind of multi-modal energy conversion storage methods based on composite wind energy utilization, it is related to wind energy utilization technical field, the method includes the following steps: VAWT and HAWT are connected in parallel to converter by unified DC bus output;The DC bus output is respectively introduced to direct drive, gear speed-up hybrid power generation module and multi-modal energy storage module;Dynamically distribute power generation power by intelligent collaborative control module;The intelligent collaborative control module adjusts power distribution strategy in real time by feedback monitoring energy storage unit state and power grid operating state, the application realizes multi-direction wind harvesting, efficient power generation in low-speed and turbulent flow environment by composite wind turbine layout and dynamic flow guide yaw, solves the problem that single wind turbine energy harvesting efficiency is low and cannot adapt to variable wind conditions, enhances wind energy capture rate, improves annual power generation.

Description

Technical Field

[0001] This invention relates to the field of wind energy utilization technology, specifically to a multimodal energy conversion and storage method based on composite wind energy utilization. Background Technology

[0002] As the proportion of renewable energy utilization in cities continues to increase, building-integrated wind power systems (BIWE) have attracted attention due to their advantages such as on-site power generation and reduced pressure on the distribution network. However, traditional single wind turbine layouts and isolated energy storage methods are difficult to cope with operating conditions such as wind speed fluctuations, sudden load changes, and grid outages, resulting in low power generation efficiency, low energy storage utilization, and poor power supply reliability.

[0003] Patent CN118757319B discloses a wind energy recovery and utilization system and method based on tunnel piston wind. The above patent improves wind energy conversion efficiency and ensures tunnel operation safety, and is applicable to energy conservation and emission reduction in urban rail transit and other fields.

[0004] The aforementioned patent achieves efficient recovery and utilization of piston wind energy through a highly efficient wind energy conversion and electrical energy storage mechanism. However, the single wind turbine layout and isolated energy storage method are difficult to cope with operating conditions such as wind speed fluctuations, sudden load changes and grid interruptions.

[0005] Therefore, this application proposes a multimodal energy conversion and storage method based on composite wind energy utilization to achieve efficient power generation in multi-directional wind collection, low-speed and turbulent environments. Summary of the Invention

[0006] The purpose of this invention is to provide a multimodal energy conversion and storage method based on composite wind energy utilization, so as to solve the technical problems mentioned in the background art, which are that traditional single wind turbine layout and isolated energy storage methods are difficult to cope with wind speed fluctuations, load changes and grid interruptions, resulting in low power generation efficiency, low energy storage utilization and poor power supply reliability.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a multimodal energy conversion and storage method based on composite wind energy utilization, the method comprising the following steps:

[0008] Multiple vertical axis micro fans (VAWTs) are installed on the building facade, and adjustable yaw horizontal axis fans (HAWTs) are installed on the building roof. The VAWTs and HAWTs are connected in parallel through a unified DC bus and output to a converter.

[0009] The DC bus output is respectively led to the direct drive, gear speed hybrid power generation module and the multi-mode energy storage module;

[0010] Through the intelligent collaborative control module, based on short-term wind speed forecast and building load forecast, the power generation is dynamically allocated to the electrochemical energy storage unit, compressed air energy storage unit and building direct supply.

[0011] The intelligent collaborative control module monitors the status of the energy storage unit and the grid operation status through feedback, and adjusts the power distribution strategy in real time.

[0012] Preferably, the intelligent collaborative control module includes:

[0013] The prediction unit receives data from local weather sensors and a cloud-based historical operation database, and uses a model combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) to generate 1-hour and 24-hour wind speed predictions, respectively.

[0014] The optimization unit, based on short-term wind speed and building load forecasts, and the SOC and SOH of the electrochemical energy storage unit and compressed air energy storage unit, optimizes the objective function using an improved particle swarm optimization (IPSO) algorithm.

[0015] max(αη gen -βE loss +γR supply )

[0016] η gen Power generation efficiency refers to the wind energy to electricity conversion efficiency of the system under the current wind speed and unit mode, and is defined as the ratio of output electrical power to available wind energy.

[0017] E loss Energy loss refers to the total loss in a system during energy conversion and storage, including:

[0018] Mechanical losses during the wind turbine-generator drive and gear speed increase process;

[0019] DC-AC converter conversion losses;

[0020] Charge-discharge cycle losses of electrochemical energy storage and compressed air energy storage;

[0021] Energy consumption of thermal management and auxiliary equipment within the system;

[0022] R supply Power supply reliability indicators;

[0023] α, β, and γ represent weighting coefficients.

[0024] Preferably, the direct-drive, gear-speed hybrid power generation module includes:

[0025] The direct-drive submodule is equipped with a hardened tooth surface synchronous motor, which is used to achieve high torque power generation when the wind speed is lower than the preset threshold v2.

[0026] The gear speed increase submodule uses a two-stage planetary gear speed increase device and an external permanent magnet synchronous generator to achieve high-speed and high-efficiency power generation when the wind speed is higher than the preset threshold v1.

[0027] The mode switching unit collects data from the wind speed sensor and compares it with v1 and v2, then automatically switches the output connection status between the direct drive submodule and the gear speed increase submodule.

[0028] The mode switching unit and the optimization unit communicate in real time via the CAN bus to ensure that the switching command is synchronized with the overall power allocation command.

[0029] Preferably, the multimodal energy storage module includes:

[0030] The electrochemical energy storage submodule consists of several modular lithium-ion battery packs. The battery packs collect voltage, current, and temperature data through the BMS and communicate with the intelligent collaborative control module through the MODBUS protocol.

[0031] The compressed air energy storage submodule includes a screw air compressor directly driven by the wake, a high-pressure air tank, and an expander power generation device;

[0032] The intelligent collaborative control module determines the charging and discharging switching of CAES based on the gas storage tank pressure and the grid feed capacity.

[0033] Preferably, the intelligent collaborative control module further includes an emergency switching and self-healing submodule, which automatically performs the following operations when the grid voltage or frequency is detected to exceed ±5% range:

[0034] Disconnect the grid-connected inverter from the power grid and switch to the built-in voltage source inverter VSI;

[0035] According to the optimization unit instructions, the electrochemical energy storage module is released first to meet the building's electricity demand;

[0036] Synchronously adjust the output of the CAES submodule to smooth voltage fluctuations.

[0037] Preferably, after completing the initial prediction, the prediction unit further combines the historical building load curve and the weather forecast time to perform differential correction on the load prediction results, thereby improving the prediction accuracy.

[0038] Preferably, within the fuzzy range of wind speed between v2 and v1, the mode switching unit dynamically adjusts the switching threshold between direct drive and gear modes based on the current SOC of the electrochemical energy storage unit and the current pressure of the compressed air energy storage unit, according to the "lowest loss priority" criterion, to achieve a balance between torque and efficiency.

[0039] Preferably, the electrochemical energy storage submodule and the CAES submodule achieve coordinated temperature regulation through a shared thermal management system, which includes liquid-cooled pipes and heat exchangers to reduce heat loss between different energy storage units and extend the overall lifespan.

[0040] Preferably, the intelligent collaborative control module implements a message publishing and subscription mechanism between sub-modules through the ROS2 bus, and each sub-module must complete local execution and report its status within ≤50ms after receiving a new power command.

[0041] Preferably, in grid-connected mode, the intelligent collaborative control module dynamically adjusts the inverter PWM frequency based on the harmonic content measured in real time by the grid-side LCL filter, so as to control the total harmonic distortion (THD) to ≤3%;

[0042] In offline mode, the intelligent collaborative control module further drives the virtual inertia control algorithm to improve the system's transient response speed.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1. This invention achieves multi-directional wind collection and efficient power generation in low-speed and turbulent environments through a composite wind turbine layout and dynamic flow guidance and yaw, solving the problem of low energy collection efficiency of single wind turbines and their inability to adapt to changing wind conditions, thereby enhancing wind energy capture rate and increasing annual power generation.

[0045] 2. This invention achieves automatic switching of power generation modules according to wind speed by switching between direct drive and gear speed increase hybrid power generation, taking into account both high torque and high efficiency, solving the problem of low efficiency of single drive mode under low or high wind speed conditions, and improving power generation efficiency within a fixed wind speed range.

[0046] 3. This invention achieves parallel energy storage of electrochemical and CAES and full-process temperature coordinated regulation through a dual-mode energy storage coordination and thermal management system, solving the problems of energy storage islanding and large temperature rise loss, improving energy storage utilization and extending equipment cycle life;

[0047] 4. This invention achieves short-term wind speed load forecasting and IPSO multi-objective scheduling functions through deep prediction and multi-objective optimization control, solving the problem of dynamic coordination between efficiency, loss and reliability that is difficult to balance in traditional methods, thereby improving the overall energy efficiency of the system and enhancing power supply reliability. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the multimodal energy conversion and storage process of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 The present invention provides an embodiment of a multimodal energy conversion and storage method based on composite wind energy utilization, the method comprising the following steps:

[0051] Multiple vertical axis micro fans (VAWTs) are installed on the building facade, and adjustable yaw horizontal axis fans (HAWTs) are installed on the building roof. The VAWTs and HAWTs are connected in parallel through a unified DC bus and output to a converter.

[0052] The DC bus output is respectively led to the direct drive, gear speed hybrid power generation module and the multi-mode energy storage module;

[0053] Through the intelligent collaborative control module, based on short-term wind speed forecast and building load forecast, the power generation is dynamically allocated to the electrochemical energy storage unit, compressed air energy storage unit and building direct supply.

[0054] The intelligent collaborative control module monitors the status of the energy storage unit and the grid operation status through feedback, and adjusts the power allocation strategy in real time.

[0055] The direct-drive, gear-speed hybrid power generation module includes:

[0056] The direct-drive submodule is equipped with a hardened tooth surface synchronous motor, which is used to achieve high torque power generation when the wind speed is lower than the preset threshold v2.

[0057] The gear speed increase submodule uses a two-stage planetary gear speed increase device and an external permanent magnet synchronous generator to achieve high-speed and high-efficiency power generation when the wind speed is higher than the preset threshold v1.

[0058] The mode switching unit collects data from the wind speed sensor and compares it with v1 and v2, then automatically switches the output connection status between the direct drive submodule and the gear speed increase submodule.

[0059] The mode switching unit and the optimization unit communicate in real time via the CAN bus to ensure that the switching command is synchronized with the overall power allocation command.

[0060] Implementation of rooftop + facade composite wind turbine layout and power generation switching:

[0061] The HAWT uses a SWD-1200X-2.5 adjustable yaw miniature fan with a blade diameter of 1.2m and a rated power of 2.5kW. The yaw driver uses a stepper motor and grating ruler feedback (resolution 0.1°), and communicates with the central PLC via RS-485 bus. The VAWT (vertical axis miniature fan) uses a "VAWT-800-1.0" double helix blade type with a blade height of 0.8m and a rated power of 1.0kW, and has a built-in wind speed / speed self-calibration zero sensor. The mounting brackets for the roof and facade fans are hot-dip galvanized steel three-point support bases, with a maximum load capacity of 200kg and a wind speed resistance of 50m / s. When installing the facade VAWT, the brackets are bolted to the curtain wall frame and grounded for protection.

[0062] Each fan has a built-in rectifier module that rectifies the three-phase output to 750V DC; all DC buses are connected to a centralized bus trunking in the machine room, using a 750V / 200A bus trunking; the bus ends are equipped with overvoltage / overcurrent circuit breakers (DC MCCB) with a rated operating voltage of 800V and 160A, and a C-type protection curve;

[0063] The wind speed sensor (ultrasonic, range 0-30m / s, accuracy ±0.1m / s) signal is sampled every 50ms and sent to the PLC; when the wind speed < v2 (3m / s), the PLC sends a relay drive signal to close the direct drive motor bus output and isolate it from the gear module; when the wind speed > v1 (10m / s), it switches to gear power generation; the "lowest loss" strategy is called in the intermediate interval.

[0064] During commissioning, the drive motor was first manually coupled in a windless state to check the integrity of the direct drive / gear switching mechanism. Then, the automatic switching response time (required to be <200ms) was verified in a small wind tunnel (wind speed 5m / s). Finally, on-site trial operation was conducted to verify that the deviation between the power curve and the nominal curve was <5% in the wind speed range of 5m / s-10m / s.

[0065] Please see Figure 1 The present invention provides an embodiment of a multimodal energy conversion and storage method based on composite wind energy utilization, wherein the multimodal energy storage module includes:

[0066] The electrochemical energy storage submodule consists of several modular lithium-ion battery packs. The battery packs collect voltage, current, and temperature data through the BMS and communicate with the intelligent collaborative control module through the MODBUS protocol.

[0067] The compressed air energy storage submodule includes a screw air compressor directly driven by the wake, a high-pressure air tank, and an expander power generation device;

[0068] The intelligent collaborative control module determines the charging and discharging switching of CAES based on the gas storage tank pressure and the grid feed capacity.

[0069] The electrochemical energy storage submodule and the CAES submodule achieve coordinated temperature regulation through a shared thermal management system, which includes liquid-cooled pipes and heat exchangers to reduce heat loss between different energy storage units and extend the overall lifespan.

[0070] Specific implementation of dual-mode energy storage synergy and thermal management:

[0071] Electrochemical energy storage uses 50 parallel-series 48V / 200Ah lithium battery cells (total capacity ≈240kWh), with a voltage balancing module connected after every 16 series cells; compressed air energy storage uses a SC-1000 screw compressor with direct-drive tailflow and a designed displacement of 1m³. 3 / min, with 5m 3 / 8bar gas storage tank;

[0072] Coolant selection: Antifreeze and corrosion-resistant closed-loop coolant (EGW 50%, antifreeze to -40℃), with a designed circulation flow rate of 10L / min; Piping and heat exchanger: Independent branches of the battery pack and gas storage tank converge to a central heat exchanger (plate type, heat exchange area 2m²). 2 Pump model CP-25 / 8, pressure 0.8MPa; temperature control logic:

[0073] If the battery temperature is >40℃, start the circulation pump and air-cooled exhaust fan.

[0074] If the compressor runs continuously for more than 30 minutes, the bypass valve will open, and the cooling airflow will flow back to the cooler from the storage tank loop.

[0075] The BMS and PLC are connected via MODBUS-TCP, and the SOC, SOH, and individual unit temperature are uploaded every 500ms. The pressure of the gas storage tank is collected by a 4-20mA differential pressure transmitter, and the PLC performs 1s filtering before intelligent switching.

[0076] Calibrate thermocouples and differential pressure transmitters monthly, replace coolant filters quarterly, and check for leaks in the gas tank safety valve every six months.

[0077] Please see Figure 1 The present invention provides an embodiment of a multimodal energy conversion and storage method based on composite wind energy utilization, wherein the intelligent collaborative control module includes:

[0078] The prediction unit receives data from local weather sensors and a cloud-based historical operation database, and uses a model combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) to generate 1-hour and 24-hour wind speed predictions, respectively.

[0079] The optimization unit, based on short-term wind speed and building load forecasts, and the SOC and SOH of the electrochemical energy storage unit and compressed air energy storage unit, optimizes the objective function using an improved particle swarm optimization (IPSO) algorithm.

[0080] max(αη gen -βE loss +γR supply )

[0081] η gen Power generation efficiency refers to the wind energy to electricity conversion efficiency of the system under the current wind speed and unit mode, and is defined as the ratio of output electrical power to available wind energy.

[0082] E loss Energy loss refers to the total loss in a system during energy conversion and storage, including:

[0083] Mechanical losses during the wind turbine-generator drive and gear speed increase process;

[0084] DC-AC converter conversion losses;

[0085] Charge-discharge cycle losses of electrochemical energy storage and compressed air energy storage;

[0086] Energy consumption of thermal management and auxiliary equipment within the system;

[0087] R supply Power supply reliability indicators;

[0088] α, β, and γ represent weighting coefficients;

[0089] Implementation of short-term forecasting and multi-objective optimization:

[0090] Local ultrasonic anemometers, 3D wind vanes, and temperature and humidity sensors are connected to the edge gateway via RS-485 bus; sampling is performed every 2 minutes, and data is reported to the cloud database (InfluxDB); the cloud database contains 2 years of historical data, including wind speed, temperature and humidity, light intensity, and total building power, with 80% of the samples retained after cleaning.

[0091] CNN-LSTM hybrid model: CNN part has 3 layers of convolutional kernels (32 / 64 / 128), kernelsize=(3,3); LSTM has 2 layers with hiddensize=128; learning rate 0.001, batch size 64, training epochs 50; deployed on an edge server, the model calls TensorRT for acceleration, and the prediction and inference time for a single 1-hour test is <50ms;

[0092] Optimized algorithm configuration: IPSO: 30 particles, learning factor c1=c2=1.5, inertia weight linearly decreasing from 0.9 to 0.4; objective function weights α:β:γ=6:3:1; optimized time window of 1 minute to ensure that the latest prediction is obtained and scheduling instructions are issued every 1 minute;

[0093] Dynamic threshold adjustment: In the wind speed range of 5-10m / s, if SOC < 20% and gas storage pressure is greater than 6 bar, the threshold v2 will be temporarily increased from 3m / s to 4m / s to reduce the number of inefficient gear power generation.

[0094] Please see Figure 1 One embodiment of the present invention provides a multimodal energy conversion and storage method based on composite wind energy utilization. The intelligent collaborative control module further includes an emergency switching and self-healing submodule, which automatically performs the following operations when the grid voltage or frequency exceeds ±5% range:

[0095] Disconnect the grid-connected inverter from the power grid and switch to the built-in voltage source inverter VSI;

[0096] According to the optimization unit instructions, the electrochemical energy storage module is released first to meet the building's electricity demand;

[0097] Synchronously adjust the output of the CAES submodule to smooth voltage fluctuations;

[0098] The intelligent collaborative control module implements a message publishing and subscription mechanism between sub-modules through the ROS2 bus, and each sub-module must complete local execution and report its status within ≤50ms after receiving a new power command.

[0099] After completing the initial forecast, the forecasting unit further performs differential correction on the load forecast results by combining the building's historical load curve and the weather forecast time, thereby improving the forecast accuracy.

[0100] Implementation of emergency switchover and microgrid self-healing:

[0101] The grid-side voltage / frequency is acquired by a high-precision PT / CT sensor; the PLC detects the voltage every 10ms; if any sample exceeds ±5%, an alarm will be triggered and an emergency procedure will be initiated.

[0102] The grid-connected inverter and VSI are each equipped with a high-speed mechanical disconnect switch, with a drive signal response time of <50ms; the bidirectional inverter is selected from the INV-200 series, with an overload capacity of 150%, and automatically switches to constant voltage and constant frequency mode after switching.

[0103] Grid recovery determination: Voltage / frequency returns within ±1% for 20 consecutive sampling cycles (within 200ms); PLC sends synchronization pulse, grid connection is completed within 2s, and the second harmonic of the inverter output phase is <2%;

[0104] All switching events and module statuses are stored in a circular log format on the SD card and sent to the cloud every 1 minute, supporting incident playback and analysis.

[0105] Please see Figure 1 One embodiment of the present invention is a multi-mode energy conversion and storage method based on composite wind energy utilization. In grid-connected mode, the intelligent collaborative control module dynamically adjusts the inverter PWM frequency based on the harmonic content measured in real time by the grid-side LCL filter, so as to control the total harmonic distortion (THD) to ≤3%.

[0106] In offline mode, the intelligent collaborative control module further drives the virtual inertia control algorithm to improve the system's transient response speed;

[0107] Implementation of harmonic and inertia control in grid-connected mode:

[0108] LCL filter design: Design parameters: L1=2mH, C=20μF (Y connection), L2=1mH, grid current resonant frequency is adjusted to 2kHz; an adjustable reactance-resistance parallel damping network (R=1Ω / C=2μF) is used to suppress the resonance peak;

[0109] Based on real-time FFT analysis (sampling frequency), when the fifth harmonic content is >1%, the PWM frequency is automatically adjusted within the 4-6kHz range to keep THD ≤3%.

[0110] The VSI controller has a built-in second-order virtual inertia model: M=0.1s, D=0.05; when the load suddenly increases by 10%, it automatically outputs equivalent inertia power ≥ 10% of the rated power within 100ms, so that the frequency deviation is ≤ 0.5Hz.

[0111] In the laboratory, a ±10% load step test was used to verify that harmonic control was achieved in the virtual inertia response, with THD < 3% and frequency deviation < 0.5Hz, and the waveform was recorded by a waveform sampler.

[0112] Working principle: The vertical axis micro wind turbine VAWT on the building facade and the adjustable yaw horizontal axis wind turbine HAWT on the roof work together to collect wind energy from multiple directions. The gear speed increase or direct drive power generation module automatically switches according to the instantaneous wind speed to ensure efficient power generation under different wind conditions.

[0113] After the power generation output is combined through the DC bus, it is dynamically allocated between electrochemical cells and compressed air energy storage (CAES) according to the system load forecast and energy storage status (SOC / SOH), or directly fed into the building load to achieve optimal energy storage and utilization.

[0114] The local weather and short-term load forecasting model provides wind speed and load trends. The improved particle swarm optimization (IPSO) algorithm optimizes power generation, energy storage, and power supply schemes for multiple objectives. It detects the grid status and automatically switches to microgrid mode when the grid power is abnormal. It ensures uninterrupted power supply through bidirectional inverters and redundant energy storage.

[0115] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A multimodal energy conversion and storage method based on composite wind energy utilization, characterized in that: The method includes the following steps: Multiple vertical axis micro fans (VAWTs) are installed on the building facade, and adjustable yaw horizontal axis fans (HAWTs) are installed on the building roof. The VAWTs and HAWTs are connected in parallel through a unified DC bus and output to a converter. The DC bus output is respectively led to the direct drive, gear speed hybrid power generation module and the multi-mode energy storage module; Through the intelligent collaborative control module, based on short-term wind speed forecast and building load forecast, the power generation is dynamically allocated to the electrochemical energy storage unit, compressed air energy storage unit and building direct supply. The intelligent collaborative control module monitors the status of the energy storage unit and the grid operation status through feedback, and adjusts the power allocation strategy in real time. The intelligent collaborative control module includes: The prediction unit receives data from local weather sensors and a cloud-based historical operation database, and uses a model combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) to generate 1-hour and 24-hour wind speed predictions, respectively. The optimization unit optimizes the objective function using an improved particle swarm optimization algorithm (IPSO) based on short-term wind speed and building load forecasts, as well as the SOC and SOH of the electrochemical energy storage unit and the compressed air energy storage unit. The direct-drive, gear-speed hybrid power generation module includes: The direct-drive submodule is equipped with a hardened tooth surface synchronous motor, which is used to achieve high torque power generation when the wind speed is lower than the preset threshold v2. The gear speed increase submodule uses a two-stage planetary gear speed increase device and an external permanent magnet synchronous generator to achieve high-speed and high-efficiency power generation when the wind speed is higher than the preset threshold v1. The mode switching unit collects data from the wind speed sensor and automatically switches the output connection status between the direct drive submodule and the gear speed increase submodule after comparing v1 and v2. The mode switching unit and the optimization unit communicate in real time via the CAN bus to ensure that the switching command is synchronized with the overall power allocation command. Within the fuzzy range of wind speed between v2 and v1, the mode switching unit dynamically adjusts the switching threshold between direct drive and gear modes based on the current SOC of the electrochemical energy storage unit and the current pressure of the compressed air energy storage unit, according to the "lowest loss priority" criterion, to achieve a balance between torque and efficiency.

2. The multimodal energy conversion and storage method based on composite wind energy utilization according to claim 1, characterized in that: The multimodal energy storage module includes: The electrochemical energy storage submodule consists of several modular lithium-ion battery packs. The battery packs collect voltage, current, and temperature data through the BMS and communicate with the intelligent collaborative control module through the MODBUS protocol. The compressed air energy storage submodule includes a screw air compressor directly driven by the wake, a high-pressure air tank, and an expander power generation device; The intelligent collaborative control module determines the charging and discharging switching of CAES based on the gas storage tank pressure and the grid feed capacity.

3. The multimodal energy conversion and storage method based on composite wind energy utilization according to claim 1, characterized in that: The intelligent collaborative control module also includes an emergency switching and self-healing submodule, which automatically performs the following operations when the grid voltage or frequency exceeds ±5% range: Disconnect the grid-connected inverter from the power grid and switch to the built-in voltage source inverter VSI; According to the optimization unit instructions, the electrochemical energy storage module is released first to meet the building's electricity demand; Synchronously adjust the output of the CAES submodule to smooth voltage fluctuations.

4. The multimodal energy conversion and storage method based on composite wind energy utilization according to claim 1, characterized in that: After completing the initial forecast, the forecasting unit further performs differential correction on the load forecast results by combining the building's historical load curve with the weather forecast time, thereby improving the forecast accuracy.

5. A multimodal energy conversion and storage method based on composite wind energy utilization according to claim 2, characterized in that: The electrochemical energy storage submodule and the CAES submodule achieve coordinated temperature regulation through a shared thermal management system, which includes liquid-cooled pipes and heat exchangers to reduce heat loss between different energy storage units and extend the overall lifespan.

6. The multimodal energy conversion and storage method based on composite wind energy utilization according to claim 1, characterized in that: The intelligent collaborative control module implements a message publishing and subscription mechanism between sub-modules through the ROS2 bus. After receiving a new power command, each sub-module must complete local execution and report its status within ≤50ms.

7. A multimodal energy conversion and storage method based on composite wind energy utilization according to claim 1, characterized in that: In grid-connected mode, the intelligent collaborative control module dynamically adjusts the inverter's PWM frequency based on the harmonic content measured in real time by the grid-side LCL filter, so as to control the total harmonic distortion (THD) to ≤3%. In offline mode, the intelligent collaborative control module further drives the virtual inertia control algorithm to improve the system's transient response speed.

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