Wind-solar-hydrogen multi-energy complementary intelligent scheduling platform

Abstract

The invention relates to the technical field of comprehensive application of renewable energy sources and hydrogen energy, in particular to a wind-solar-hydrogen multi-energy complementary intelligent scheduling platform. According to the technical scheme, the wind-light-hydrogen multi-energy complementary intelligent scheduling platform comprises a wind-light power generation unit which monitors wind power and photovoltaic power fluctuation in real time; the hybrid energy storage unit comprises a super capacitor and a flywheel for energy storage and respectively responds to millisecond-level and second-level power fluctuations; the hydrogen energy subsystem comprises an electrolytic hydrogen production device, a hydrogen storage tank and a fuel cell and responds to minute-level power scheduling; and the multi-time scale controller is configured to execute millisecond-level, second-level and minute-level cooperative control and dynamically match the wind-light fluctuation and the hydrogen energy response speed. According to the method, through multi-time scale dynamic cooperative control, the matching problem between the wind-solar power generation volatility and the hydrogen energy response lag is effectively solved, and seamless connection between second-level high-frequency fluctuation suppression and minute-level energy scheduling is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated application of renewable energy and hydrogen energy, and in particular to a wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform. Background Art

[0002] With the large-scale application of renewable energy, the proportion of fluctuating power sources such as wind power and photovoltaic power continues to increase. Their inherent intermittent and random nature poses severe challenges to the stable operation of the power grid. The power fluctuations of wind and solar power generation exhibit multi-time scale characteristics: high-frequency components in the millisecond to second range are caused by rapidly changing factors such as turbulence and cloud cover, while low-frequency fluctuations in the minute to hour range are closely related to the migration of weather systems. In existing technologies, a single energy storage or a single scheduling strategy is difficult to cover fluctuations across the entire frequency band, resulting in the following key issues that need to be urgently addressed:

[0003] Limitations of energy storage technology: Although traditional energy storage (such as lithium batteries) can smooth out fluctuations in seconds, they have a short cycle life (about 5,000 times), are expensive, and cannot cope with the rapid charging and discharging needs of high-frequency components (>0.1Hz); hydrogen energy systems (electrolysis, hydrogen storage, fuel cells) as long-term energy storage solutions have minute-level response delays (electrolyzer cold start takes 8-15 minutes, and fuel cell power regulation rate is <2% / second), making it difficult to match the real-time needs of wind and solar fluctuations.

[0004] Multi-energy collaborative control flaws: Existing scheduling strategies often use a single time scale (e.g., optimizing only hourly energy balance). This results in ineffective suppression of high-frequency fluctuations, leading to safety issues such as voltage flicker and frequency overshoot.

[0005] The synergy between hydrogen energy and traditional energy storage lacks a dynamic adaptation mechanism. For example, frequent start and stop of electrolyzers (>5 times a day) will accelerate equipment aging, while over-reliance on lithium batteries will lead to redundant energy storage capacity.

[0006] Transmission delays (>50ms) and bandwidth-hogging low-priority data on industrial field networks (such as Modbus and CAN buses) cause control commands to become out of sync with device status feedback. This can lead to command conflict rates as high as 10%-15%, especially in multi-device collaboration scenarios.

[0007] The lack of an active compensation mechanism for communication delays leads to deviations between the control algorithm and actual execution. For example, the fuel cell power instruction is delayed by more than 5 seconds due to network congestion, exacerbating the risk of wind and solar power curtailment.

[0008] System lifespan and efficiency conflict: Existing technologies fail to incorporate equipment lifespan loss into real-time optimization targets. For example, multiple cold starts per day for an electrolyzer can shorten its lifespan from 60,000 hours to 40,000 hours.

[0009] The efficiency of the hydrogen energy system is subject to operating conditions (for example, the electrolysis efficiency drops by 30% under low load), but the existing scheduling strategy fails to achieve balanced optimization of efficiency and life.

[0010] The multi-time-scale fluctuations of wind and solar power generation and the delayed response of hydrogen energy, the inability of existing energy storage technology to cover full-band fluctuations, communication delays leading to asynchronous control instructions, and the shortening of equipment life due to frequent operations, together lead to low system operation efficiency and high costs. Summary of the Invention

[0011] The present invention proposes a wind, solar and hydrogen multi-energy complementary intelligent scheduling platform, which solves the problems in the existing technology such as the multi-time scale fluctuations of wind and solar power generation and the lag in hydrogen energy response, the inability of existing energy storage technology to cover full-band fluctuations, communication delays leading to control command asynchrony, and shortened equipment life due to frequent operations.

[0012] The technical solutions of the present invention are as follows:

[0013] The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform includes:

[0014] Wind and solar power generation units, which monitor wind and photovoltaic power fluctuations in real time;

[0015] Hybrid energy storage units, including supercapacitors and flywheel energy storage, respond to millisecond and second-level power fluctuations, respectively;

[0016] The hydrogen energy subsystem, which includes an electrolytic hydrogen production device, a hydrogen storage tank, and a fuel cell, responds to minute-level power scheduling;

[0017] A multi-timescale controller, configured to perform millisecond-, second-, and minute-level coordinated control, dynamically matching wind and solar fluctuations with hydrogen energy response speed;

[0018] Time-sensitive networking prioritizes transmission control instructions, and the transmission delay of real-time control instructions is less than 1ms.

[0019] Furthermore, the control logic of the multi-time-scale controller includes:

[0020] The millisecond-level control layer uses a sliding mode control algorithm to compensate for fluctuation components with a frequency higher than 0.1 Hz, and the control gain coefficient K ranges from 0.8 to 1.2;

[0021] The second-level coordination layer uses a model predictive control algorithm to continuously optimize the fuel cell power output and electrolyzer preheating instructions within the next 15 seconds;

[0022] The minute-level scheduling layer generates electrolytic cell start and stop strategies based on the LSTM neural network, limiting the number of starts and stops to no more than three times a day.

[0023] Furthermore, the objective function of the model predictive control is:

[0024] Among them, α+β+γ=1, ΔP grid is the grid power deviation, C life is the electrolytic cell life loss factor, and the weight coefficients α, β, and γ are dynamically adjusted through reinforcement learning.

[0025] Furthermore, the coordinated triggering conditions of the hybrid energy storage unit are:

[0026] When the state of charge of the supercapacitor is lower than 30% and the power fluctuation lasts for more than 10 seconds, the flywheel energy storage is activated;

[0027] When the fluctuation lasts for more than 5 minutes and the pressure of the hydrogen storage tank is lower than 30MPa, the electrolysis hydrogen production device is started.

[0028] Furthermore, in the hydrogen energy subsystem:

[0029] The electrolytic hydrogen production device is an alkaline electrolyzer with a cold start time of ≤8 minutes and an electrolysis efficiency of ≥70%;

[0030] The hydrogen storage tank is equipped with multi-parameter safety warnings, which trigger a shutdown when the hydrogen concentration is greater than 4% or the temperature is greater than 85°C;

[0031] The fuel cell is of proton exchange membrane type, and the power regulation rate is ≥5% / s.

[0032] Furthermore, the time-sensitive network includes:

[0033] Real-time control command channel, using EtherCAT protocol, synchronization period ≤ 100μs;

[0034] The data acquisition channel uses the OPC UA protocol, and the data sampling interval is 1 second;

[0035] Dual-channel hot standby mechanism: when the main channel fails, the delay of switching to the CAN FD backup channel is less than 10ms.

[0036] Furthermore, a reinforcement learning compensator is included, which is configured as follows:

[0037] Input states include supercapacitor state of charge, hydrogen tank pressure, and fuel cell temperature;

[0038] The output action includes the sliding mode control gain K, model prediction weight coefficients α, β, and γ;

[0039] The reward function is: Among them, η H2 is the comprehensive efficiency of the hydrogen energy subsystem.

[0040] Furthermore, the multi-time-scale controller is deployed on an edge computing device, and its hardware configuration includes:

[0041] Dual-core processor, main frequency ≥1.8GHz, capable of running minute-level scheduling tasks;

[0042] FPGA chip, processing millisecond-level control instructions with a logic delay of ≤5ms;

[0043] Storage medium, pre-installed with wind and solar power output prediction model and hydrogen energy equipment life database.

[0044] Furthermore, a dynamic verification module is included for:

[0045] Simulate a scenario where wind and solar power drops by 50% from the rated value;

[0046] Verify the supercapacitor's ability to release power within 200ms and the voltage fluctuation rate ≤±2%;

[0047] Generate quantified reports on electrolyzer startup delays and hydrogen conversion efficiency.

[0048] Furthermore, the multi-parameter safety warning method for the hydrogen storage tank includes:

[0049] The sampling frequency of the hydrogen concentration sensor is ≥10Hz;

[0050] Temperature-pressure coupling early warning model: When the temperature is greater than 80°C and the pressure fluctuation rate is greater than 5% / min, the load reduction command is triggered in advance.

[0051] The beneficial effects of the present invention are:

[0052] The beneficial effect of this invention is that it significantly improves the operating efficiency and reliability of the wind, solar, and hydrogen multi-energy system. Through multi-timescale dynamic coordinated control, it effectively solves the difficult problem of matching the volatility of wind and solar power generation with the response lag of hydrogen energy, achieving a seamless connection between second-level high-frequency fluctuation suppression and minute-level energy scheduling. At the same time, combining a lifespan-aware optimization algorithm with a highly reliable communication architecture, it significantly extends the lifespan of hydrogen energy equipment, reduces operation and maintenance costs, and ensures safe and stable system operation in complex scenarios. DETAILED DESCRIPTION

[0053] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0054] This embodiment proposes a wind, solar, and hydrogen multi-energy complementary intelligent scheduling platform, including:

[0055] Wind and solar power generation units, which monitor wind and photovoltaic power fluctuations in real time;

[0056] Hybrid energy storage units, including supercapacitors and flywheel energy storage, respond to millisecond and second-level power fluctuations, respectively;

[0057] The hydrogen energy subsystem, which includes an electrolytic hydrogen production device, a hydrogen storage tank, and a fuel cell, responds to minute-level power scheduling;

[0058] A multi-timescale controller, configured to perform millisecond-, second-, and minute-level coordinated control, dynamically matching wind and solar fluctuations with hydrogen energy response speed;

[0059] Time-sensitive networking prioritizes transmission control instructions, and the transmission delay of real-time control instructions is less than 1ms.

[0060] The control logic of the multi-time-scale controller includes:

[0061] The millisecond-level control layer uses a sliding mode control algorithm to compensate for fluctuation components with a frequency higher than 0.1 Hz, and the control gain coefficient K ranges from 0.8 to 1.2;

[0062] The second-level coordination layer uses a model predictive control algorithm to continuously optimize the fuel cell power output and electrolyzer preheating instructions within the next 15 seconds;

[0063] The minute-level scheduling layer generates electrolytic cell start and stop strategies based on the LSTM neural network, limiting the number of starts and stops to no more than three times a day.

[0064] The objective function of model predictive control is:

[0065] Among them, α+β+γ=1, ΔP grid is the grid power deviation, C life is the electrolytic cell life loss factor, and the weight coefficients α, β, and γ are dynamically adjusted through reinforcement learning.

[0066] The coordinated triggering conditions of the hybrid energy storage unit are:

[0067] When the state of charge of the supercapacitor is lower than 30% and the power fluctuation lasts for more than 10 seconds, the flywheel energy storage is activated;

[0068] When the fluctuation lasts for more than 5 minutes and the pressure of the hydrogen storage tank is lower than 30MPa, the electrolysis hydrogen production device is started.

[0069] In the hydrogen energy subsystem:

[0070] The electrolytic hydrogen production device is an alkaline electrolyzer with a cold start time of ≤8 minutes and an electrolysis efficiency of ≥70%;

[0071] The hydrogen storage tank is equipped with multi-parameter safety warnings, which trigger a shutdown when the hydrogen concentration is greater than 4% or the temperature is greater than 85°C;

[0072] The fuel cell is of proton exchange membrane type, and the power regulation rate is ≥5% / s.

[0073] Time-Sensitive Networking includes:

[0074] Real-time control command channel, using EtherCAT protocol, synchronization period ≤ 100μs;

[0075] The data acquisition channel uses the OPC UA protocol, and the data sampling interval is 1 second;

[0076] Dual-channel hot standby mechanism: when the main channel fails, the delay of switching to the CAN FD backup channel is less than 10ms.

[0077] A reinforcement learning compensator is also included, which is configured as:

[0078] Input states include supercapacitor state of charge, hydrogen tank pressure, and fuel cell temperature;

[0079] The output action includes the sliding mode control gain K, model prediction weight coefficients α, β, and γ;

[0080] The reward function is: Among them, η H2 is the comprehensive efficiency of the hydrogen energy subsystem.

[0081] The multi-time-scale controller is deployed on edge computing devices. Its hardware configuration includes:

[0082] Dual-core processor, main frequency ≥1.8GHz, capable of running minute-level scheduling tasks;

[0083] FPGA chip, processing millisecond-level control instructions with a logic delay of ≤5ms;

[0084] Storage medium, pre-installed with wind and solar power output prediction model and hydrogen energy equipment life database.

[0085] Also includes dynamic verification modules for:

[0086] Simulate a scenario where wind and solar power drops by 50% from the rated value;

[0087] Verify the supercapacitor's ability to release power within 200ms and the voltage fluctuation rate ≤±2%;

[0088] Generate quantified reports on electrolyzer startup delays and hydrogen conversion efficiency.

[0089] The multi-parameter safety warning method for hydrogen storage tanks includes:

[0090] The sampling frequency of the hydrogen concentration sensor is ≥10Hz;

[0091] Temperature-pressure coupling early warning model: When the temperature is greater than 80°C and the pressure fluctuation rate is greater than 5% / min, the load reduction command is triggered in advance.

[0092] This paper proposes a wind, solar, and hydrogen multi-energy complementary intelligent scheduling platform, which mainly builds a multi-time-scale dynamic collaborative architecture and solves the response lag and efficiency bottleneck of existing technologies through three-level control, algorithm fusion, and communication optimization. It specifically includes the following components:

[0093] Wind and solar power generation units: real-time collection of power data and detection of fluctuation spectrum;

[0094] Hybrid energy storage unit: supercapacitor (response time <10ms) suppresses millisecond-level fluctuations, and flywheel energy storage (response time <50ms) smoothes out second-level fluctuations;

[0095] Hydrogen energy subsystem: alkaline electrolyzer (cold start ≤ 8 minutes), hydrogen storage tank (35MPa) and fuel cell (regulation rate ≥ 5% / second) handle minute-level energy scheduling;

[0096] Multi-timescale controller: hierarchical execution of millisecond-level sliding mode control, second-level MPC optimization, and minute-level LSTM prediction;

[0097] Time-Sensitive Networking (TSN): Divides communication priorities into three levels to ensure real-time command transmission latency of <1ms.

[0098] How it works

[0099] (1) Three-level dynamic collaborative control mechanism

[0100] Millisecond-level control layer:

[0101] Use sliding mode control algorithm to suppress high frequency fluctuations (>0.1Hz). Calculate power fluctuation derivatives in real time Output supercapacitor charge and discharge instructions: (K∈[0.8,1.2])

[0102] When the fluctuation frequency is detected to be >0.1Hz, the supercapacitor responds first and suppresses more than 90% of the high-frequency components.

[0103] Second-level coordination layer:

[0104] The model predictive control (MPC) algorithm is used to optimize the fuel cell power output, and the objective function is:

[0105]

[0106] Among them, C life is the electrolytic cell life loss factor, and the weight coefficients α, β, and γ are dynamically adjusted through reinforcement learning.

[0107] Minute-level scheduling layer:

[0108] An LSTM neural network predicts wind and solar power output over the next two hours and generates an electrolyzer start and stop schedule. The LSTM input includes historical 72 hours of power data, weather forecasts (temperature, irradiance, wind speed), and electricity price information. It outputs an electrolyzer start time window and limits the number of starts and stops to ≤3 per day.

[0109] (2) Communication-control deep coupling

[0110] TSN Prioritization:

[0111] Priority Data Type protocol Maximum delay Function VLAN 0 Real-time control instructions EtherCAT 1ms Sliding mode control command transmission VLAN 1 Equipment status monitoring OPC UA 1s Fuel cell temperature, hydrogen storage tank pressure VLAN 2 Predictive Scheduling Instructions MQTT 1min Start and stop schedule generated by LSTM

[0112] Dual-channel hot standby mechanism:

[0113] The switching delay between the primary channel (EtherCAT) and the backup channel (CAN FD) is ≤10ms. When the packet loss rate of the primary channel exceeds 0.1% or the network delay exceeds 2ms, it automatically switches to the backup channel and triggers load reduction protection for the hydrogen energy subsystem.

[0114] (3) Equipment life and efficiency optimization

[0115] Lifespan-aware scheduling:

[0116] Introducing the life loss factor C into the MPC objective function life , and its calculation formula is:

[0117]

[0118] Among them, T start,i , i is the cold start time of the electrolytic cell when it is started for the i-th time, T life The rated lifespan is 60,000 hours. By limiting the number of starts and stops per day, the electrolyzer lifespan is increased to over 55,000 hours.

[0119] Reinforcement Learning Compensator:

[0120] The DDPG (Deep Deterministic Policy Gradient) network is used to dynamically optimize the control parameters. The input states include the supercapacitor SOC, hydrogen tank pressure, and fuel cell temperature. The output action is the sliding mode control gain K and the MPC weight coefficients α, β, and γ. The reward function is designed as:

[0121]

[0122] Among them, η H2 The comprehensive efficiency of the hydrogen energy subsystem (electrolysis efficiency × fuel cell efficiency) is calculated, and the system is trained to achieve a balance between efficiency and life.

[0123] The response speed has been improved, and the overall response time of the hydrogen energy subsystem has been shortened from >5 minutes in the traditional solution to ≤15 seconds; the wind and solar power fluctuation suppression rate has been increased to 95% (80% in the traditional solution).

[0124] The equipment life is extended, the number of electrolyzer starts and stops is reduced from 5-8 times a day to ≤3 times, and the life is extended from 40,000 hours to 55,000 hours; the supercapacitor cycle life is increased to 1 million times (the traditional lithium battery solution is 5,000 times).

[0125] Communication reliability is enhanced, with real-time instruction transmission delay less than 1ms (traditional solutions are >50ms); network packet loss rate is reduced from 0.1% to 0.001%.

[0126] Economic benefits are optimized, the cost of the energy storage system is reduced by 25% (supercapacitors + flywheels replace part of the lithium batteries), and the hydrogen energy utilization rate (electrolyzer + fuel cell) is increased from 55% to 68%.

[0127] 1. Hardware configuration and deployment

[0128] (1) Wind and solar power generation unit

[0129] Data acquisition: A HIOKIPW3390 power analyzer with a sampling rate of 1kHz was used to detect the power fluctuation spectrum in real time.

[0130] Fluctuation detection: Decompose the fluctuation components through FFT (Fast Fourier Transform) and identify high-frequency components >0.1Hz.

[0131] (2) Hybrid energy storage unit

[0132] Supercapacitor bank: Maxwell 3000F module is used, with a single unit capacity of 18kW, a response time of 5ms, and a cycle life of 1 million times;

[0133] Flywheel energy storage: Using the Beacon Power 20kW system, it has an energy storage capacity of 200kWh, a response time of 30ms, and a charge and discharge efficiency of 95%.

[0134] (3) Hydrogen energy subsystem

[0135] Electrolyzer: NEL M4000 alkaline electrolyzer, rated power 4MW, cold start time 8 minutes, electrolysis efficiency 72%;

[0136] Hydrogen storage tank: 35MPa carbon fiber wrapped storage tank, volume 50m 3 , equipped with a hydrogen concentration sensor (accuracy ±0.1%) and a temperature-pressure coupling early warning model;

[0137] Fuel cell: Ballard FCgen-H2PM, rated power 2MW, regulation rate 5% / s, efficiency curve is:

[0138] η FC =0.58-0.015(P FC -0.5P rated ) 2

[0139] (4) Multi-time scale controller

[0140] Hardware platform: NI cRIO-9045 edge controller with a dual-core 1.91GHz processor and a Xilinx Kintex-7 FPGA;

[0141] Software Architecture:

[0142] Millisecond-level thread: runs on the FPGA, processes the sliding mode control algorithm, and has a cycle of 5ms;

[0143] Second-level thread: runs on CPU core 1, performs MPC optimization, and has a 15-second rolling window.

[0144] Minute-level thread: runs on CPU core 2, calls the pre-trained TinyLSTM model (with 10^5 parameters), and has a prediction period of 120 minutes.

[0145] 2. Control algorithm implementation details

[0146] (1) Millisecond-level sliding mode control

[0147] Fluctuation detection: Calculate power derivative every 5ms When the absolute value exceeds a threshold (e.g., 10kW / s), the supercapacitor response is triggered;

[0148] Gain adjustment: Dynamically adjust the K value according to the supercapacitor SOC:

[0149]

[0150] (2) Second-level MPC optimization

[0151] Optimization variable: fuel cell power P FC , flywheel energy storage power P flywheel ;

[0152] Constraints:

[0153] Solver: Using IPOPT nonlinear optimization library, single solution time is <50ms.

[0154] (3) Minute-level LSTM prediction

[0155] Input features: historical power data (72 hours, 1-minute intervals), weather forecast (temperature, wind speed, irradiance), real-time electricity price;

[0156] Output result: electrolytic cell start and stop schedule for the next 2 hours. The format is as follows:

[0157] Time window action Estimated excess power 08:00-10:00 start up 1.2MW 14:00-16:00 downtime -0.8MW

[0158] 3. Communication network configuration

[0159] (1)TSN protocol stack

[0160] Clock synchronization: Using IEEE 1588PTP protocol, synchronization error ≤±1μs;

[0161] Traffic shaping: Based on IEEE 802.1Qbv Time-Aware Shaper (TAS), VLAN 0 is assigned a fixed time slot (1ms per cycle).

[0162] (2) Dual-channel hot standby

[0163] Main channel: EtherCAT, transmits real-time control instructions, cycle 1ms;

[0164] Backup channel: CAN FD, bandwidth 2Mbps, used to transmit status monitoring data;

[0165] Switching logic: When the main channel does not receive a confirmation signal for three consecutive cycles, it switches to the backup channel within 10ms.

[0166] Example 1

[0167] High-frequency fluctuation suppression verification

[0168] Test scenario: simulates turbulent fluctuations in a 10MW wind farm (fluctuation frequency 0.5Hz, amplitude ±1MW);

[0169] Implementation steps:

[0170] The wind and solar power generation unit detects a high-frequency component (0.5 Hz) and triggers sliding mode control;

[0171] The supercapacitor responds within 5ms and releases 0.9MW of power (K = 1.0);

[0172] Flywheel energy storage supplements 0.1MW of power to maintain grid stability;

[0173] Results: The voltage fluctuation rate dropped from ±5% to ±0.8%; the supercapacitor SOC dropped by 2%, and the hydrogen energy subsystem was not triggered.

[0174] Example 2

[0175] Long-term energy scheduling test

[0176] Test scenario: 24-hour wind and solar power output fluctuations (minimum 3MW, maximum 12MW);

[0177] Implementation steps:

[0178] The LSTM prediction module generates a start-stop schedule for the electrolyzer (starting 2 times and stopping 1 time);

[0179] MPC optimizes fuel cell power with an average regulation rate of 4.8% / second;

[0180] The pressure of the hydrogen storage tank is maintained in the range of 28-32MPa;

[0181] Results: The life loss of the electrolyzer was reduced by 40%; the comprehensive efficiency of hydrogen energy reached 67.5%, and the wind curtailment rate was <3%.

[0182] Example 3

[0183] Communication network stress testing

[0184] Test method: inject 10^6 random data packet collisions to simulate a network attack;

[0185] Implementation steps:

[0186] The packet loss rate of the main channel (EtherCAT) increased to 0.5%;

[0187] Switch to the backup channel (CAN FD) to transmit real-time commands;

[0188] Trigger the hydrogen storage tank load reduction protection (power drops to 80%);

[0189] Results: The switching delay was 9.8ms, and the command transmission was restored; the hydrogen concentration over-limit warning response time was <30ms.

[0190] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform, characterized by: include: Wind and solar power generation units, which monitor wind and photovoltaic power fluctuations in real time; Hybrid energy storage units, including supercapacitors and flywheel energy storage, respond to millisecond and second-level power fluctuations, respectively; The hydrogen energy subsystem, which includes an electrolytic hydrogen production device, a hydrogen storage tank, and a fuel cell, responds to minute-level power scheduling; A multi-timescale controller, configured to perform millisecond-, second-, and minute-level coordinated control, dynamically matching wind and solar fluctuations with hydrogen energy response speed; Time-sensitive networking prioritizes transmission control instructions, and the transmission delay of real-time control instructions is less than 1ms.

2. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 1 is characterized in that: The control logic of the multi-time-scale controller includes: The millisecond-level control layer uses a sliding mode control algorithm to compensate for fluctuation components with a frequency higher than 0.1 Hz, and the control gain coefficient K ranges from 0.8 to 1.2; The second-level coordination layer uses a model predictive control algorithm to continuously optimize the fuel cell power output and electrolyzer preheating instructions within the next 15 seconds; The minute-level scheduling layer generates electrolytic cell start and stop strategies based on the LSTM neural network, limiting the number of starts and stops to no more than three times a day.

3. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 2 is characterized in that: The objective function of the model predictive control is: Among them, α+β+γ=1, ΔP grid is the grid power deviation, C life is the electrolytic cell life loss factor, and the weight coefficients α, β, and γ are dynamically adjusted through reinforcement learning.

4. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 1 is characterized in that: The coordinated triggering conditions of the hybrid energy storage unit are: When the state of charge of the supercapacitor is lower than 30% and the power fluctuation lasts for more than 10 seconds, the flywheel energy storage is activated; When the fluctuation lasts for more than 5 minutes and the pressure of the hydrogen storage tank is lower than 30MPa, the electrolysis hydrogen production device is started.

5. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 1 is characterized in that: In the hydrogen energy subsystem: The electrolytic hydrogen production device is an alkaline electrolyzer with a cold start time of ≤8 minutes and an electrolysis efficiency of ≥70%; The hydrogen storage tank is equipped with multi-parameter safety warnings, which trigger a shutdown when the hydrogen concentration is greater than 4% or the temperature is greater than 85°C; The fuel cell is of proton exchange membrane type, and the power regulation rate is ≥5% / s.

6. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 1 is characterized in that: The time-sensitive network includes: Real-time control command channel, using EtherCAT protocol, synchronization period ≤ 100μs; The data acquisition channel uses the OPC UA protocol, and the data sampling interval is 1 second; Dual-channel hot standby mechanism: when the main channel fails, the delay of switching to the CAN FD backup channel is less than 10ms.

7. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 1 is characterized in that: A reinforcement learning compensator is also included, which is configured as: Input states include supercapacitor state of charge, hydrogen tank pressure, and fuel cell temperature; The output action includes the sliding mode control gain K, model prediction weight coefficients α, β, and γ; The reward function is: Among them, η H2 is the comprehensive efficiency of the hydrogen energy subsystem.

8. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 1 is characterized in that: The multi-time-scale controller is deployed on an edge computing device, and its hardware configuration includes: Dual-core processor, main frequency ≥1.8GHz, capable of running minute-level scheduling tasks; FPGA chip, processing millisecond-level control instructions with a logic delay of ≤5ms; Storage medium, pre-installed with wind and solar power output prediction model and hydrogen energy equipment life database.

9. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 1 is characterized in that: Also includes dynamic validation modules for: Simulate a scenario where wind and solar power drops by 50% from the rated value; Verify the supercapacitor's ability to release power within 200ms and the voltage fluctuation rate ≤±2%; Generate quantified reports on electrolyzer startup delays and hydrogen conversion efficiency.

10. The wind, solar, and hydrogen multi-energy complementary intelligent dispatching platform according to claim 5 is characterized in that: The multi-parameter safety early warning method for the hydrogen storage tank includes: The sampling frequency of the hydrogen concentration sensor is ≥10Hz; Temperature-pressure coupling early warning model: When the temperature is greater than 80°C and the pressure fluctuation rate is greater than 5% / min, the load reduction command is triggered in advance.

Images