Wind-solar power generation intelligent hydrogen production system

Abstract

The invention discloses a wind-solar power generation intelligent hydrogen production system, which relates to the technical field of power generation and hydrogen production, and comprises a wind-solar prediction unit, a hydrogen production regulation unit and a scheduling matching unit, the wind and light prediction unit is used for training a constructed wind and light output prediction model based on wind and light output historical data and corresponding meteorological data and geographic information data, and obtaining a wind and light output prediction result in a certain time period in the future according to the obtained optimal wind and light output prediction model; the hydrogen production adjusting unit is used for adjusting the load state and the operation interval of the hydrogen production device according to the wind and light output prediction result; and the scheduling matching unit is used for storing redundant electric energy through the energy storage equipment or maintaining the normal work of the hydrogen production device when the wind and light output prediction result cannot meet the demand. According to the method, the influence of geographic positions, weather and other factors on wind and light output is considered, wind and light power generation can be accurately predicted in time, and economic benefits are improved while stable operation of the system is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation for hydrogen production, and more specifically, to a smart hydrogen production system for wind and solar power generation. Background Art

[0002] In China, the installed capacities of wind power and photovoltaic power both rank first in the world, and the total installed capacity accounts for about 28% of the total installed capacity of global renewable energy. Due to the limitation of power transmission capacity, affected by the randomness, seasonality and reverse peak shaving characteristics of wind power and photovoltaic power generation, there are a large number of phenomena of abandoned wind and abandoned light. To recover abandoned wind and light, and at the same time use renewable energy to replace fossil fuels for hydrogen production to achieve carbon emission reduction, hydrogen production from wind and light is a feasible solution.

[0003] However, the characteristics of volatility and randomness of wind and solar power generation are contradictory to the requirements of "safety, stability, long-term operation, full load and excellent performance" in the traditional hydrogen production process, which has become a technical bottleneck for such projects and affects the safety, economy and stability of the hydrogen production system. The invention patent with the publication number CN117498389A proposes a method and device for wind and solar power generation for hydrogen production and hydrogen mixing control. In a photovoltaic power plant, wind and solar power generation equipment is controlled to supply off-grid wind and solar power to an electrolytic water hydrogen production device. Based on the maximum power point tracking method, the electrolytic water power is controlled in real time to electrolyze hydrogen from off-grid wind and solar power. The electrolytic water power can be adjusted in real time through the hydrogen production power supply in the electrolytic water hydrogen production device with the maximum power point tracking control function, which can reduce the proportion of abandoned wind and light.

[0004] However, the existing wind and solar power generation for hydrogen production technology does not consider the influence of other factors such as geographical location and meteorology on wind and solar power output, cannot accurately predict wind and solar power generation in a timely manner, and fails to improve economic benefits while maintaining the stable operation of the system. These are the technical problems that need to be urgently solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a smart hydrogen production system for wind and solar power generation, which solves the problems existing in the background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A smart hydrogen production system for wind and solar power generation, comprising: a wind and solar prediction unit, a hydrogen production adjustment unit, and a scheduling matching unit;

[0008] The wind and solar prediction unit trains a wind and solar power output prediction model based on historical wind and solar power output data and corresponding meteorological data and geographical information data, and obtains a wind and solar power output prediction result for a certain period in the future according to the obtained optimal wind and solar power output prediction model;

[0009] The hydrogen production adjustment unit is used to adjust the load state and operation range of the hydrogen production device according to the wind and solar power output prediction result;

[0010] A scheduling matching unit is configured to store excess electric energy through an energy storage device or maintain the normal operation of a hydrogen production device when the predicted results of the wind and photovoltaic power generation cannot meet the demand.

[0011] Optionally, the meteorological data includes at least one of ambient temperature, humidity, air pressure, wind speed, wind direction, precipitation, cloud cover, sunshine hours, and light radiation intensity; the geographic information data includes at least one of terrain, topography, longitude and latitude, altitude, and water resource distribution.

[0012] Optionally, the historical wind and photovoltaic power generation data includes: wind farm data, photovoltaic power station data within a preset area, and wind power and photovoltaic power at different times.

[0013] Optionally, the wind and photovoltaic power generation prediction unit includes: an analysis module, a model construction module, a model training module, and a prediction module;

[0014] The analysis module is configured to combine the collected historical wind and photovoltaic power generation data in pairs and perform wind and photovoltaic coupling analysis through the Pearson correlation coefficient to obtain an analysis result;

[0015] The model construction module is configured to preliminarily construct a wind and photovoltaic power generation prediction model through an artificial neural network algorithm;

[0016] The model training module is configured to input the historical wind and photovoltaic power generation data, the corresponding meteorological data, and the geographic information data into the wind and photovoltaic power generation prediction model for model training until the model converges to obtain an optimal wind and photovoltaic power generation prediction model;

[0017] The prediction module is configured to input the collected real-time data into the optimal wind and photovoltaic power generation prediction model to obtain a prediction result.

[0018] Optionally, the wind and photovoltaic power generation prediction model adopts a three-layer BP neural network, including an input layer, an output layer, and a hidden layer. Each layer is fully connected, and there is a weight value for each connection;

[0019] Among them, the input layer nodes correspond to the input variables of the model, that is, the historical wind and photovoltaic power generation data, the corresponding meteorological data, and the geographic information data;

[0020] The number of output layer nodes is 1, and the output vector is the wind and photovoltaic power generation on the predicted day.

[0021] Optionally, the wind and photovoltaic power generation prediction unit further includes an evaluation and correction module, which evaluates and corrects the wind and photovoltaic power generation prediction result through the coefficient of determination to obtain the final wind and photovoltaic power generation prediction system at a preset moment.

[0022] Optionally, the hydrogen production regulation unit includes one or more electrolytic cell hydrogen production systems; the electrolytic cell hydrogen production system is a device that uses electricity as input energy to produce hydrogen.

[0023] Optionally, the energy storage device includes a plurality of energy storage batteries, and the plurality of energy storage batteries are connected in series or in parallel.

[0024] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a wind-solar power generation intelligent hydrogen production system. Considering the influence of geographical location, meteorological and other factors on the output of wind and solar power, the wind-solar coupling analysis is carried out on historical data and the prediction model is trained, so that the model learns the coupling relationship between wind power and photovoltaic power. Then, the coefficient of determination is used to evaluate and correct the prediction results of real-time data, realizing high-precision prediction of wind-solar output. According to the prediction model, the upper and lower limits of wind-solar output are estimated, and the load status and operation range of the hydrogen production system under different working conditions are obtained, realizing real-time change of hydrogen production with wind-solar output. In addition, an energy storage device is introduced into the hydrogen production system. By storing excess electric energy through the energy storage device or maintaining the normal operation of the hydrogen production system when the wind-solar output cannot meet the demand, further aiming at the optimal economic operation, the power distribution of wind-solar output and the energy storage device is balanced, so that the system operates stably while improving economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0026] Figure 1 It is a structural diagram of the wind-solar power generation intelligent hydrogen production system provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0028] Hydrogen energy is an important part of the future national energy system and also an important carrier for the green and low-carbon transformation of energy-consuming terminals. In recent years, hydrogen energy has attracted the attention of more and more countries and has become an important part of the energy transformation and economic development of countries around the world. Wind and solar power generation itself has no carbon emissions and is the best choice for hydrogen production. Such hydrogen is called "green hydrogen". Now, with the implementation of wind and solar policies, the upstream energy for hydrogen production is more abundant; conversely, wind and solar power generation has always faced the problem of needing to absorb excess electric energy. Either curtail wind and solar power to reduce production capacity, or store it. And the cost of adding transmission lines is much higher. Now the development of hydrogen energy actually also solves the energy storage problem of wind and solar power generation.

[0029] In view of the contradiction between wind and solar power generation and the requirements of traditional hydrogen production, and the problem that existing hydrogen production technologies do not consider the influence of other factors, the embodiments of the present invention disclose a smart hydrogen production system for wind and solar power generation, as Figure 1 shown, including: a wind and solar prediction unit, a hydrogen production regulation unit, and a scheduling matching unit;

[0030] The wind and solar prediction unit trains a wind and solar power output prediction model based on historical wind and solar power output data, corresponding meteorological data, and geographical information data, and obtains the wind and solar power output prediction results for a certain period in the future according to the obtained optimal wind and solar power output prediction model;

[0031] The hydrogen production regulation unit is used to adjust the load state and operating range of the hydrogen production device according to the wind and solar power output prediction results;

[0032] The scheduling matching unit is used to store excess electric energy through energy storage devices or maintain the normal operation of the hydrogen production device when the wind and solar power output prediction results cannot meet the demand.

[0033] In the distributed wind power generation and photovoltaic power generation areas, the number of sub-regions is large. The photovoltaic power and wind power are affected by the geographical location and climate conditions of the local area, and there is a coupling relationship between the wind and solar power output of the two. Therefore, corresponding meteorological data and geographical information data are obtained while collecting historical data, considering the influence of geographical location and climate environment on wind and solar power output. Specifically, the meteorological data includes at least one of ambient temperature, humidity, air pressure, wind speed, wind direction, precipitation, cloud cover, sunshine hours, and light radiation intensity; the geographical information data includes at least one of terrain, topography, longitude and latitude, altitude, and water resource distribution.

[0034] In the actual environment, there is a direct correlation between photovoltaic output and light radiation intensity, and between wind turbine output and wind speed. The wind speed affects the coverage area and moving speed of cloud masses, and at the same time, the light radiation intensity also affects the heat dissipation ability of wind turbines; within the same sub-region, the light radiation intensity is basically the same. By exploring the coupling relationship between historical data and meteorological data and geographical information data, it is beneficial to improve the prediction accuracy.

[0035] Furthermore, the historical data of wind and solar power output includes: the data of wind farms, the data of photovoltaic power stations within a preset area, as well as the wind power and photovoltaic power at different times.

[0036] Before inputting the collected historical data into the prediction model, the integrity, accuracy, and effectiveness of the data can be detected and normalized. Specifically, the data can be integrated into a matrix, the missing values can be identified and marked, the mean and standard deviation of each row in the dataset can be calculated, and the adjacent data can be used for correction, which helps to improve the accuracy of model prediction.

[0037] Furthermore, the wind and solar prediction unit includes: an analysis module, a model construction module, a model training module, and a prediction module;

[0038] The analysis module is used to combine the historical data of wind and solar power output collected pairwise, and perform wind-solar coupling analysis through the Pearson correlation coefficient to obtain the analysis result;

[0039] The model construction module is used to initially construct a wind and solar power output prediction model through the artificial neural network algorithm;

[0040] The model training module is used to input the historical data of wind and solar power output, as well as the corresponding meteorological data and geographical information data, into the wind and solar power output prediction model for model training until the model converges to obtain the optimal wind and solar power output prediction model;

[0041] The prediction module is used to input the collected real-time data into the optimal wind and solar power output prediction model to obtain the prediction result.

[0042] In this embodiment, the Pearson correlation coefficient can be used to measure the correlation between different data. A positive value indicates that the feature is positively correlated with the wind and solar power output, and a negative value indicates that the feature is negatively correlated with the wind and solar power output. By combining the data pairwise, obtaining the output power of the same type of energy / different types of energy within the same area / different areas, and performing model training on this basis, the accuracy of model prediction can be improved.

[0043] Furthermore, the wind and solar power output prediction model adopts a three-layer BP neural network, including an input layer, an output layer, and a hidden layer. Each layer is fully connected, and there is a weight value for each connection;

[0044] Among them, the input layer nodes correspond to the input variables of the model, that is, the historical data of wind and solar power output, as well as the corresponding meteorological data and geographical information data;

[0045] The number of output layer nodes is 1, and the output vector is the wind and solar power output on the prediction day.

[0046] Further, when training the wind and light power output prediction model, the historical data of wind and light power output and the corresponding meteorological data and geographical information data are input into the model, and the predicted value is obtained through the forward propagation algorithm; the mean square error function of the quadratic cost function is used as the loss function to obtain the gap between the predicted value and the true value; the backpropagation optimization algorithm is used to calculate the gradient of the loss function with respect to each grid parameter; according to the gradient and the learning rate, each grid parameter is updated through the gradient descent algorithm; the backpropagation optimization algorithm is repeatedly run to complete the training of the BP neural network.

[0047] Further, the wind and light prediction unit further includes an evaluation and correction module, which evaluates and corrects the wind and light power output prediction results through the coefficient of determination to obtain the final wind and light power output prediction system at the preset moment, improving the accuracy of the prediction results.

[0048] Further, the hydrogen production regulation unit includes one or more electrolytic cell hydrogen production systems; the electrolytic cell hydrogen production system is a device that uses electricity as the input energy to produce hydrogen.

[0049] In actual use, the output ports of the photovoltaic power generation system and the wind power generation system are connected to the transformer, which can convert wind energy and solar energy into direct current or alternating current and transmit it to the hydrogen production system, and the electric energy is used by the hydrogen production system to electrolyze water to produce hydrogen. In this embodiment, the hydrogen production system is designed in the form of multiple parallel branches, which can reduce the impact of a single branch failure on the overall system.

[0050] Further, the energy storage device includes a number of energy storage batteries, and the number of energy storage batteries are connected in series or in parallel. When the volatility and uncertainty of wind and light power generation cause energy fluctuations in the system, the energy storage device can effectively maintain the stable operation of the hydrogen production system and improve the reliability of the hydrogen production system. In addition, with the goal of maximizing economic benefits, on the basis of maintaining the stable operation of the system, the optimal operation mode of the hydrogen production system can be confirmed, making the operation strategy more scientific and reasonable and improving the energy utilization rate.

[0051] The various embodiments in this specification are described in a progressive manner, and the key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other.

[0052] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A wind and solar power generation intelligent hydrogen production system, characterized in that: include: Wind and solar prediction unit, hydrogen production regulation unit, and dispatch matching unit; The wind and solar power prediction unit trains the constructed wind and solar power output prediction model based on the historical wind and solar power output data and the corresponding meteorological data and geographic information data, and obtains the wind and solar power output prediction result for a certain period of time in the future according to the obtained optimal wind and solar power output prediction model; A hydrogen production regulating unit is used to adjust the load state and operation range of the hydrogen production device according to the wind and solar power output prediction results; The dispatching and matching unit is used to store excess electric energy through energy storage equipment or to maintain the normal operation of the hydrogen production device when the wind and solar power output forecast results cannot meet the demand.

2. According to claim 1, a wind-solar power generation intelligent hydrogen production system is characterized in that: Meteorological data include at least one of ambient temperature, humidity, air pressure, wind speed, wind direction, precipitation, cloud cover, sunshine hours and light radiation intensity; geographic information data include at least one of topography, terrain, longitude and latitude, altitude and water resources distribution.

3. The wind-solar power generation intelligent hydrogen production system according to claim 1 is characterized in that: The historical data of wind and solar power output include: wind farm data and photovoltaic power station data in the preset area, as well as wind power and photovoltaic power at different times.

4. The wind-solar power generation intelligent hydrogen production system according to claim 1 is characterized in that: The wind and solar prediction unit includes: analysis module, model building module, model training module, and prediction module; The analysis module is used to combine the collected historical data of wind and solar power output in pairs, and perform wind and solar coupling analysis through the Pearson correlation coefficient to obtain the analysis results; Model building module, used to preliminarily build a wind and solar output prediction model through artificial neural network algorithm; The model training module is used to input the historical data of wind and solar power output and the corresponding meteorological data and geographic information data into the wind and solar power output prediction model for model training until the model converges to obtain the optimal wind and solar power output prediction model; The prediction module is used to input the collected real-time data into the optimal wind and solar power output prediction model to obtain the prediction results.

5. The wind-solar power generation intelligent hydrogen production system according to claim 1 is characterized in that: The wind and solar power output prediction model uses a three-layer BP neural network, including an input layer, an output layer, and a hidden layer. Each layer is fully connected, and each connection has a weighted value. Among them, the input layer nodes correspond to the input variables of the model, namely the historical data of wind and solar power output and the corresponding meteorological data and geographic information data; The number of nodes in the output layer is 1, and the output vector is the wind and solar power output on the predicted day.

6. The wind-solar power generation intelligent hydrogen production system according to claim 1 is characterized in that: The wind and solar power prediction unit also includes an evaluation and correction module, which evaluates and corrects the wind and solar power output prediction result through a determination coefficient to obtain a wind and solar power output prediction system at a final preset time.

7. The wind-solar power generation intelligent hydrogen production system according to claim 1 is characterized in that: The hydrogen production regulating unit includes one or more electrolyzer hydrogen production systems; The electrolyzer hydrogen production system is a device that uses electricity as input energy to produce hydrogen.

8. The wind-solar power generation intelligent hydrogen production system according to claim 1 is characterized in that: The energy storage device includes a plurality of energy storage batteries, and the plurality of energy storage batteries are connected in series or in parallel.

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