Comprehensive energy scheduling method and device for offshore wind power hydrogen production, computer equipment, storage medium and computer program product

Abstract

The invention relates to a comprehensive energy scheduling method and device for offshore wind power hydrogen production, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring current system data associated with the offshore wind power hydrogen production system; according to the current system data, constructing a target function and a current constraint condition corresponding to the offshore wind power hydrogen production system; according to the objective function and the current constraint condition, constructing an energy scheduling model corresponding to the offshore wind power hydrogen production system; obtaining current unit data corresponding to the offshore wind power hydrogen production system, and inputting the current unit data and the current system data into the energy scheduling model to obtain a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system; and according to the current energy scheduling instruction, performing corresponding energy scheduling processing on the offshore wind power hydrogen production system. By adopting the method, the energy scheduling accuracy of the offshore wind power hydrogen production system can be improved.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a comprehensive energy scheduling method, apparatus, computer equipment, computer-readable storage medium, and computer program product for offshore wind power hydrogen production. Background Art

[0002] In the power grid system, in order to ensure the stability of energy supply of offshore wind power hydrogen production system, it is crucial to accurately dispatch the energy of offshore wind power hydrogen production system.

[0003] Traditionally, manual scheduling is used to dispatch energy for offshore wind power hydrogen production systems. However, this manual scheduling method is subjective and prone to errors, resulting in low energy dispatch accuracy for offshore wind power hydrogen production systems. Summary of the Invention

[0004] Based on this, it is necessary to provide a comprehensive energy scheduling method, device, computer equipment, computer-readable storage medium and computer program product for offshore wind power hydrogen production, which can improve the energy scheduling accuracy of offshore wind power hydrogen production systems in response to the above technical problems.

[0005] In a first aspect, the present application provides a comprehensive energy scheduling method for offshore wind power hydrogen production, comprising:

[0006] Obtain current system data associated with offshore wind power hydrogen production systems;

[0007] Constructing an objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data;

[0008] Constructing an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and the current constraint conditions;

[0009] Obtaining current unit data corresponding to the offshore wind power hydrogen production system, inputting the current unit data and the current system data into the energy scheduling model, and obtaining a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system;

[0010] According to the current energy dispatch instruction, corresponding energy dispatch processing is performed on the offshore wind power hydrogen production system.

[0011] In one embodiment, constructing the objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data includes:

[0012] Extracting carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system from the current system data;

[0013] Based on the carbon trading data, a carbon trading cost function corresponding to the offshore wind power hydrogen production system is constructed; based on the green certificate trading data, a green certificate trading cost function corresponding to the offshore wind power hydrogen production system is constructed;

[0014] According to the carbon transaction cost function and the green certificate transaction cost function, an objective function corresponding to the offshore wind power hydrogen production system is constructed.

[0015] In one embodiment, constructing a carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon trading data includes:

[0016] Extracting carbon quota data, carbon emission data, and unit carbon trading price data corresponding to the offshore wind power hydrogen production system from the carbon trading data;

[0017] A carbon transaction cost function corresponding to the offshore wind power hydrogen production system is constructed according to the carbon quota data, the carbon emission data and the unit carbon transaction price data.

[0018] In one embodiment, constructing a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system based on the green certificate transaction data includes:

[0019] Determine the current number of green certificates corresponding to the offshore wind power hydrogen production system based on the green certificate transaction data, and extract the current unit price of the green certificate corresponding to the offshore wind power hydrogen production system from the green certificate transaction data;

[0020] According to the current number of green certificates and the current unit price of green certificates, a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system is constructed.

[0021] In one embodiment, obtaining current unit data corresponding to the offshore wind power hydrogen production system includes:

[0022] Determining the hydrogen-blended gas turbine unit, hydrogen fuel cell, and methane reactor associated with the offshore wind power hydrogen production system;

[0023] Acquiring first unit data of the hydrogen-blended gas generator set, second unit data of the hydrogen fuel cell set, and third unit data of the methane reactor set;

[0024] The first unit data, the second unit data and the third unit data are all used as current unit data corresponding to the offshore wind power hydrogen production system.

[0025] In one embodiment, inputting the current unit data and the current system data into the energy scheduling model to obtain the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system includes:

[0026] Determining a first data type corresponding to the current unit data and a second data type corresponding to the current system data;

[0027] querying the correspondence between data types and feature extraction models to obtain the feature extraction model corresponding to the first data type as the first feature extraction model corresponding to the current unit data, and querying the correspondence to obtain the feature extraction model corresponding to the second data type as the second feature extraction model corresponding to the current system data;

[0028] Inputting the current unit data into the first feature extraction model to obtain a first feature vector of the current unit data, and inputting the current system data into the second feature extraction model to obtain a second feature vector of the current system data;

[0029] fusing the first eigenvector and the second eigenvector to obtain a fused eigenvector corresponding to the offshore wind power hydrogen production system;

[0030] Inputting the fused feature vector into the energy scheduling model to obtain the predicted probability of the offshore wind power hydrogen production system under each preset energy scheduling instruction;

[0031] The preset energy scheduling instruction with the greatest predicted probability is selected from the preset energy scheduling instructions as the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system.

[0032] In a second aspect, the present application also provides a comprehensive energy dispatching device for offshore wind power hydrogen production, comprising:

[0033] A data acquisition module, used to acquire current system data associated with the offshore wind power hydrogen production system;

[0034] An information construction module, configured to construct an objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data;

[0035] A model building module, configured to build an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and the current constraint conditions;

[0036] an instruction prediction module, configured to obtain current unit data corresponding to the offshore wind power hydrogen production system, input the current unit data and the current system data into the energy scheduling model, and obtain a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system;

[0037] The energy scheduling module is used to perform corresponding energy scheduling processing on the offshore wind power hydrogen production system according to the current energy scheduling instruction.

[0038] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0039] Obtain current system data associated with offshore wind power hydrogen production systems;

[0040] Constructing an objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data;

[0041] Constructing an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and the current constraint conditions;

[0042] Obtaining current unit data corresponding to the offshore wind power hydrogen production system, inputting the current unit data and the current system data into the energy scheduling model, and obtaining a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system;

[0043] According to the current energy dispatch instruction, corresponding energy dispatch processing is performed on the offshore wind power hydrogen production system.

[0044] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0045] Obtain current system data associated with offshore wind power hydrogen production systems;

[0046] Constructing an objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data;

[0047] Constructing an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and the current constraint conditions;

[0048] Obtaining current unit data corresponding to the offshore wind power hydrogen production system, inputting the current unit data and the current system data into the energy scheduling model, and obtaining a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system;

[0049] According to the current energy dispatch instruction, corresponding energy dispatch processing is performed on the offshore wind power hydrogen production system.

[0050] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0051] Obtain current system data associated with offshore wind power hydrogen production systems;

[0052] Constructing an objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data;

[0053] Constructing an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and the current constraint conditions;

[0054] Obtaining current unit data corresponding to the offshore wind power hydrogen production system, inputting the current unit data and the current system data into the energy scheduling model, and obtaining a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system;

[0055] According to the current energy dispatch instruction, corresponding energy dispatch processing is performed on the offshore wind power hydrogen production system.

[0056] The above-mentioned comprehensive energy scheduling method, device, computer equipment, storage medium and computer program product for offshore wind power hydrogen production first obtains current system data associated with the offshore wind power hydrogen production system, and then constructs the objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data. Then, based on the objective function and the current constraints, an energy scheduling model corresponding to the offshore wind power hydrogen production system is constructed. Then, current unit data corresponding to the offshore wind power hydrogen production system is obtained, and the current unit data and the current system data are input into the energy scheduling model to obtain the current energy scheduling instructions corresponding to the offshore wind power hydrogen production system. Finally, the offshore wind power hydrogen production system is subjected to corresponding energy scheduling processing according to the current energy scheduling instructions. In this way, in the process of energy dispatching of the offshore wind power hydrogen production system, the objective function and current constraints corresponding to the offshore wind power hydrogen production system can be constructed more accurately through the current system data associated with the offshore wind power hydrogen production system, so that the energy dispatching model corresponding to the offshore wind power hydrogen production system can be constructed more accurately, and then the current unit data and current system data corresponding to the offshore wind power hydrogen production system can be integrated to more accurately obtain the current energy dispatching instructions corresponding to the offshore wind power hydrogen production system, which is conducive to improving the accuracy of determining the current energy dispatching instructions, and thus improving the energy dispatching accuracy of the offshore wind power hydrogen production system; moreover, the entire process does not require human intervention, avoiding the subjective factors and prone to errors in the manual dispatching method, which leads to the defect of low energy dispatching accuracy of the offshore wind power hydrogen production system, and further improves the energy dispatching accuracy of the offshore wind power hydrogen production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 Schematic diagram of a flow chart of a comprehensive energy scheduling method for offshore wind power hydrogen production in one embodiment;

[0059] Figure 2 This is a flow chart of a comprehensive energy scheduling method for offshore wind power hydrogen production in another embodiment;

[0060] Figure 3 A schematic diagram of the principle of offshore wind power hydrogen production in one embodiment;

[0061] Figure 4 This is a structural block diagram of a comprehensive energy dispatching device for offshore wind power hydrogen production in one embodiment;

[0062] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0065] In an exemplary embodiment, Figure 1 As shown, a comprehensive energy scheduling method for offshore wind power hydrogen production is provided. This embodiment uses the method applied to a server as an example for illustration; it is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablet computers; the server can be implemented as an independent server or a server cluster consisting of multiple servers. In this embodiment, the method includes the following steps:

[0066] Step S101: Acquire current system data associated with the offshore wind power hydrogen production system.

[0067] Among them, the offshore wind power hydrogen production system refers to an integrated energy system that combines offshore wind power generation with hydrogen production technology. It generates electricity by utilizing the abundant wind energy resources at sea, and then converts the electrical energy into chemical energy and stores it in hydrogen to achieve efficient utilization and storage of energy.

[0068] Among them, the current system data refers to the system data of the offshore wind power hydrogen production system at the current time, including the carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system.

[0069] Exemplarily, the server obtains the carbon trading data corresponding to the offshore wind power hydrogen production system through the carbon trading platform associated with the offshore wind power hydrogen production system, and obtains the green certificate trading data corresponding to the offshore wind power hydrogen production system through the green certificate trading platform associated with the offshore wind power hydrogen production system; then, the server combines and processes the carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system according to a preset combination method to obtain the current system data associated with the offshore wind power hydrogen production system.

[0070] Step S102: constructing the objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data.

[0071] The objective function is used to represent a function associated with the cost of the offshore wind power hydrogen production system.

[0072] Among them, the current constraints are used to represent the constraints of the offshore wind power hydrogen production system at the current time, including spare capacity opportunity constraints, equipment operation constraints and power balance constraints.

[0073] Exemplarily, the server identifies the noise type corresponding to the noise in the current system data, queries the correspondence between the noise type and the denoising method, obtains the denoising method corresponding to the current system data, and denoises the current system data according to the denoising method to obtain the denoised system data; then, the server constructs the objective function and current constraints corresponding to the denoised system data based on the denoised system data, as the objective function and current constraints corresponding to the offshore wind power hydrogen production system.

[0074] Step S103: constructing an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and current constraints.

[0075] Among them, the energy scheduling model refers to a network model that can obtain energy scheduling instructions corresponding to the offshore wind power hydrogen production system, such as a convolutional neural network model.

[0076] Exemplarily, the server constructs an initial energy scheduling model corresponding to the offshore wind power hydrogen production system based on the objective function and current constraints; then, the server obtains historical unit data and historical system data corresponding to the offshore wind power hydrogen production system, and inputs the historical unit data and historical system data into the initial energy scheduling model to obtain predicted energy scheduling instructions corresponding to the offshore wind power hydrogen production system; then, the server obtains the actual energy scheduling instructions corresponding to the offshore wind power hydrogen production system, and obtains a loss value based on the difference between the predicted energy scheduling instructions and the actual energy scheduling instructions; then, the server adjusts the model parameters of the initial energy scheduling model based on the loss value; then, the server re-trains the initial energy scheduling model after the model parameters are adjusted until the loss value obtained by the trained initial energy scheduling model is less than the loss value threshold, then stops training, and uses the trained initial energy scheduling model as the energy scheduling model corresponding to the offshore wind power hydrogen production system.

[0077] Step S104: obtaining current unit data corresponding to the offshore wind power hydrogen production system, inputting the current unit data and current system data into the energy scheduling model, and obtaining the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system.

[0078] Among them, the current unit data is used to represent the unit data of the IES (Integrated Energy System) hydrogen equipment (including hydrogen-blended gas units, hydrogen fuel cells and methane reactors) corresponding to the offshore wind power hydrogen production system at the current time.

[0079] Among them, the current energy dispatch instruction refers to the energy dispatch instruction corresponding to the offshore wind power hydrogen production system at the current time.

[0080] Exemplarily, the server determines the identification information of the hydrogen-using equipment corresponding to the offshore wind power hydrogen production system, and obtains the current unit data corresponding to the offshore wind power hydrogen production system based on the identification information; then, the server normalizes the current unit data and the current system data to obtain processed unit data and processed system data; then, the server inputs the processed unit data and the processed system data into the energy scheduling model, and obtains the current energy scheduling instructions corresponding to the offshore wind power hydrogen production system through the energy scheduling model.

[0081] Step S105 : performing corresponding energy dispatch processing on the offshore wind power hydrogen production system according to the current energy dispatch instruction.

[0082] Exemplarily, the server performs integrity verification on the current energy dispatching instruction to obtain an integrity verification result corresponding to the current energy dispatching instruction; when the integrity verification result indicates that the current energy dispatching instruction is complete, the server performs corresponding energy dispatching processing on the offshore wind power hydrogen production system according to the current energy dispatching instruction.

[0083] In the above-mentioned comprehensive energy scheduling method for offshore wind power hydrogen production, the current system data associated with the offshore wind power hydrogen production system is first obtained, and then the objective function and current constraints corresponding to the offshore wind power hydrogen production system are constructed based on the current system data. Then, based on the objective function and the current constraints, an energy scheduling model corresponding to the offshore wind power hydrogen production system is constructed. Then, the current unit data corresponding to the offshore wind power hydrogen production system is obtained, and the current unit data and the current system data are input into the energy scheduling model to obtain the current energy scheduling instructions corresponding to the offshore wind power hydrogen production system. Finally, according to the current energy scheduling instructions, the offshore wind power hydrogen production system is subjected to corresponding energy scheduling processing. In this way, in the process of energy dispatching of the offshore wind power hydrogen production system, the objective function and current constraints corresponding to the offshore wind power hydrogen production system can be constructed more accurately through the current system data associated with the offshore wind power hydrogen production system, so that the energy dispatching model corresponding to the offshore wind power hydrogen production system can be constructed more accurately, and then the current unit data and current system data corresponding to the offshore wind power hydrogen production system can be integrated to more accurately obtain the current energy dispatching instructions corresponding to the offshore wind power hydrogen production system, which is conducive to improving the accuracy of determining the current energy dispatching instructions, and thus improving the energy dispatching accuracy of the offshore wind power hydrogen production system; moreover, the entire process does not require human intervention, avoiding the subjective factors and prone to errors in the manual dispatching method, which leads to the defect of low energy dispatching accuracy of the offshore wind power hydrogen production system, and further improves the energy dispatching accuracy of the offshore wind power hydrogen production system.

[0084] In an exemplary embodiment, the above step S102 constructs the objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data, specifically including the following contents: extracting the carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system from the current system data; constructing the carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon trading data, and constructing the green certificate trading cost function corresponding to the offshore wind power hydrogen production system based on the green certificate trading data; constructing the objective function corresponding to the offshore wind power hydrogen production system based on the carbon trading cost function and the green certificate trading cost function.

[0085] Among them, carbon trading data includes carbon quota data, carbon emission data and unit carbon trading price data corresponding to offshore wind power hydrogen production systems.

[0086] Among them, the carbon trading cost function is used to represent the relationship between the carbon trading cost and various related factors of the offshore wind power hydrogen production system in the carbon trading market, also known as the CET (Carbon Emission Trading) cost function.

[0087] Among them, the green certificate trading data includes the current green certificate unit price corresponding to the offshore wind power hydrogen production system.

[0088] Among them, the green certificate transaction cost function is used to represent the relationship between the green certificate transaction cost and related factors in the green certificate trading process of the offshore wind power hydrogen production system, also known as the GCT (Green Certificate Trading) cost function.

[0089] Exemplarily, the server identifies the noise type corresponding to the noise in the current system data, queries the correspondence between the noise type and the denoising method, obtains the denoising method corresponding to the current system data, and denoises the current system data according to the denoising method to obtain the denoised system data; then, the server inputs the denoised system data into the trained named entity recognition model, and extracts the carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system from the denoised system data through the named entity recognition model; then, the server constructs the initial carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon trading data, and obtains the carbon tax data (such as carbon tax rate) corresponding to the offshore wind power hydrogen production system, and fine-tunes the initial carbon trading cost function based on the carbon tax data to obtain the carbon trading cost function corresponding to the offshore wind power hydrogen production system; then, the server constructs the carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the green certificate trading data. The server obtains the initial green certificate transaction cost function corresponding to the offshore wind power hydrogen production system, and obtains the economic data (such as inflation rate) corresponding to the offshore wind power hydrogen production system. According to the economic data, the initial green certificate transaction cost function is fine-tuned to obtain the green certificate transaction cost function corresponding to the offshore wind power hydrogen production system; then, the server determines the operation and maintenance and equipment depreciation cost function, energy purchase cost function, hydrogen energy utilization equipment and energy storage equipment operation and maintenance cost function, offshore power transmission loss cost function, wind abandonment cost function, risk cost function and backup cost function corresponding to the offshore wind power hydrogen production system; then, the server sums the carbon trading cost function, green certificate transaction cost function, operation and maintenance and equipment depreciation cost function, energy purchase cost function, hydrogen energy utilization equipment and energy storage equipment operation and maintenance cost function, offshore power transmission loss cost function, wind abandonment cost function, risk cost function and backup cost function to obtain the objective function corresponding to the offshore wind power hydrogen production system.

[0090] In this embodiment, carbon trading and green certificate trading data are specifically extracted from the system data, and corresponding cost functions are constructed respectively, which can accurately reflect the cost situation of the offshore wind power hydrogen production system in the two specific areas of carbon trading and green certificate trading, thereby more accurately showing the cost structure of the system at different transaction levels, and providing accurate basic data for in-depth analysis of the economic characteristics of the system.

[0091] In an exemplary embodiment, a carbon trading cost function corresponding to an offshore wind power hydrogen production system is constructed based on carbon trading data, specifically including the following contents: extracting carbon quota data, carbon emission data and unit carbon trading price data corresponding to the offshore wind power hydrogen production system from the carbon trading data; constructing a carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon quota data, carbon emission data and unit carbon trading price data.

[0092] Among them, carbon quota data refers to the total amount of greenhouse gases such as carbon dioxide that are allowed to be emitted by offshore wind power hydrogen production systems within a certain period of time.

[0093] Among them, carbon emission data refers to the amount of greenhouse gases such as carbon dioxide actually emitted by offshore wind power hydrogen production systems during production and operation.

[0094] Among them, the unit carbon trading price data is the trading price of each unit of carbon emission rights of the offshore wind power hydrogen production system in the carbon trading market.

[0095] Exemplarily, the server extracts the carbon quota data, carbon emission data and unit carbon trading price data corresponding to the offshore wind power hydrogen production system from the carbon trading data according to preset data extraction rules (such as preset data types); then, the server constructs an initial carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon quota data, carbon emission data and unit carbon trading price data, and obtains the carbon tax data (such as carbon tax rate) corresponding to the offshore wind power hydrogen production system. Based on the carbon tax data, the server fine-tunes the initial carbon trading cost function to obtain the carbon trading cost function corresponding to the offshore wind power hydrogen production system.

[0096] In this embodiment, by directly extracting relevant information from carbon trading data to construct a carbon trading cost function, the actual cost of the offshore wind power hydrogen production system in carbon trading can be accurately reflected, so that the costs caused by carbon emissions and carbon quota usage can be accurately calculated, avoiding the ambiguity and uncertainty of cost estimation.

[0097] In an exemplary embodiment, a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system is constructed based on the green certificate transaction data, which specifically includes the following contents: determining the current number of green certificates corresponding to the offshore wind power hydrogen production system based on the green certificate transaction data, and extracting the current green certificate unit price corresponding to the offshore wind power hydrogen production system from the green certificate transaction data; constructing a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system based on the current number of green certificates and the current unit price of green certificates.

[0098] Among them, the current number of green certificates refers to the number of green electricity certificates owned by the offshore wind power hydrogen production system.

[0099] Among them, the current unit price of green certificates refers to the unit price of the green certificates corresponding to the offshore wind power hydrogen production system at the time of transaction.

[0100] Exemplarily, the server determines the change value of the number of green certificates corresponding to the offshore wind power hydrogen production system (for example, the number of green certificates has been increased) based on the green certificate trading data; then, the server obtains the initial number of green certificates corresponding to the offshore wind power hydrogen production system, and determines the current number of green certificates corresponding to the offshore wind power hydrogen production system based on the initial number of green certificates and the change value of the number of green certificates; then, the server extracts the historical green certificate unit price corresponding to the offshore wind power hydrogen production system from the green certificate trading data, and selects the historical green certificate unit price with the most recent transaction time from each historical green certificate unit price as the current green certificate unit price corresponding to the offshore wind power hydrogen production system; then, the server constructs the initial green certificate transaction cost function corresponding to the offshore wind power hydrogen production system based on the current number of green certificates and the current green certificate unit price, and obtains the economic data corresponding to the offshore wind power hydrogen production system (such as the inflation rate), and fine-tunes the initial green certificate transaction cost function based on the economic data to obtain the green certificate transaction cost function corresponding to the offshore wind power hydrogen production system.

[0101] In this embodiment, by determining the current number and unit price of green certificates corresponding to the offshore wind power hydrogen production system from the green certificate trading data, the cost situation of the system in green certificate trading can be accurately determined, and the impact of market price fluctuations on costs can be reflected in a timely manner, avoiding cost accounting distortion caused by price fluctuations.

[0102] In an exemplary embodiment, the above-mentioned step S104, obtaining the current unit data corresponding to the offshore wind power hydrogen production system, specifically includes the following contents: determining the hydrogen-blended gas unit, hydrogen fuel cell and methane reactor associated with the offshore wind power hydrogen production system; obtaining the first unit data of the hydrogen-blended gas unit, the second unit data of the hydrogen fuel cell, and the third unit data of the methane reactor; and using the first unit data, the second unit data and the third unit data as the current unit data corresponding to the offshore wind power hydrogen production system.

[0103] Among them, a hydrogen-blended gas unit refers to a power generation equipment that uses hydrogen mixed with natural gas or other fuel as fuel, such as a hydrogen-blended gas turbine.

[0104] Among them, hydrogen fuel cell refers to a power generation equipment that directly converts the chemical energy of hydrogen and oxygen into electrical energy.

[0105] Among them, a methane reactor refers to a power generation equipment that converts methane into hydrogen.

[0106] Among them, the first unit data refers to the unit data of the hydrogen-blended gas-fired unit, including the hydrogen blending ratio of the hydrogen-blended gas-fired unit, the natural gas power and hydrogen power input to the hydrogen-blended gas-fired unit, the volume flow rate of natural gas and the volume flow rate of hydrogen input to the hydrogen-blended gas-fired unit, the output electrical power of the hydrogen-blended gas-fired unit, the output thermal power electrical conversion efficiency and thermal conversion efficiency.

[0107] Among them, the second unit data refers to the unit data of the hydrogen fuel cell, including the hydrogen fuel cell's electrical energy conversion efficiency, thermal energy conversion efficiency, hydrogen input power, electrical power generation, and thermal power generation.

[0108] Among them, the third unit data refers to the unit data of the methane reactor, including the methanation reaction efficiency, input hydrogen power and output gas power of the methane reactor.

[0109] Exemplarily, the server obtains an energy flow topology map corresponding to the offshore wind power hydrogen production system, and determines, based on the energy flow topology map, the hydrogen-blended gas turbine unit, hydrogen fuel cell and methane reactor connected to the offshore wind power hydrogen production system as the hydrogen-blended gas turbine unit, hydrogen fuel cell and methane reactor associated with the offshore wind power hydrogen production system; then, the server obtains the first unit data of the hydrogen-blended gas turbine unit, the second unit data of the hydrogen fuel cell, and the third unit data of the methane reactor; then, the server extracts the first key unit data from the first unit data, the second key unit data from the second unit data, and the third key unit data from the third unit data; then, the server splices the first key unit data, the second key unit data and the third key unit data in the third unit data according to a preset splicing order to obtain the current unit data corresponding to the offshore wind power hydrogen production system.

[0110] In this embodiment, by identifying the hydrogen-blended gas turbines, hydrogen fuel cells, and methane reactors associated with the system and obtaining their unit data, the operating conditions of different parts of the system can be fully understood, reflecting the performance, status, and operating parameters of each device from different perspectives, thereby enabling a more accurate and comprehensive understanding of the overall operating status of the offshore wind power hydrogen production system.

[0111] In an exemplary embodiment, the above-mentioned step S104, inputting the current unit data and the current system data into the energy scheduling model to obtain the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system, specifically includes the following contents: determining the first data type corresponding to the current unit data, and the second data type corresponding to the current system data; querying the correspondence between the data type and the feature extraction model, obtaining the feature extraction model corresponding to the first data type, as the first feature extraction model corresponding to the current unit data, and querying the correspondence, obtaining the feature extraction model corresponding to the second data type, as the second feature extraction model corresponding to the current system data; inputting the current unit data into the first feature extraction model to obtain the first feature vector of the current unit data, and inputting the current system data into the second feature extraction model to obtain the second feature vector of the current system data; fusing the first feature vector and the second feature vector to obtain the fused feature vector corresponding to the offshore wind power hydrogen production system; inputting the fused feature vector into the energy scheduling model to obtain the predicted probability of the offshore wind power hydrogen production system under each preset energy scheduling instruction; from each preset energy scheduling instruction, screening out the preset energy scheduling instruction with the largest predicted probability as the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system.

[0112] The first data type refers to the data type corresponding to the current unit data.

[0113] The second data type refers to the data type corresponding to the current system data.

[0114] The correspondence between data types and feature extraction models is used to represent the association between the data types and feature extraction models. For example, if the data type is text, the corresponding feature extraction model is the bag-of-words model; if the data type is numeric, the corresponding feature extraction model is the principal component analysis model.

[0115] The first feature extraction model refers to a feature extraction model that matches the current unit data.

[0116] The second feature extraction model refers to a feature extraction model that matches the current system data.

[0117] The first eigenvector refers to the eigenvector of the current unit data.

[0118] The second eigenvector refers to the eigenvector of the current system data.

[0119] The fused feature vector refers to a feature vector obtained by fusing the first feature vector and the second feature vector.

[0120] The preset energy scheduling instruction refers to a pre-set energy scheduling instruction. It should be noted that the preset energy scheduling instruction may be determined according to the circumstances.

[0121] The predicted probability is used to indicate the possibility that the energy scheduling model determines that the preset energy scheduling instruction is correct.

[0122] Exemplarily, the server extracts the first data feature corresponding to the current unit data and the second data feature corresponding to the current system data; then, the server determines the first data type corresponding to the current unit data based on the first data feature corresponding to the current unit data, and determines the second data type corresponding to the current system data based on the second data feature corresponding to the current system data; then, the server queries the correspondence between the data type and the feature extraction model, obtains the feature extraction model corresponding to the first data type, as the first feature extraction model corresponding to the current unit data, and queries the correspondence between the data type and the feature extraction model, obtains the feature extraction model corresponding to the second data type, as the second feature extraction model corresponding to the current system data; then, the server inputs the current unit data into the first feature extraction model, obtains the first feature vector of the current unit data through the first feature extraction model, and inputs the current system data into The second feature extraction model obtains the second feature vector of the current system data through the second feature extraction model; then, the server inputs the current unit data into the importance prediction model to obtain the first importance of the current unit data as the first weight of the first feature vector, and inputs the current system data into the importance prediction model to obtain the second importance of the current system data as the second weight of the second feature vector; then, the server fuses the first feature vector and the second feature vector according to the first weight and the second weight to obtain the fused feature vector corresponding to the offshore wind power hydrogen production system; then, the server inputs the fused feature vector into the energy scheduling model to obtain the predicted probability of the offshore wind power hydrogen production system under each preset energy scheduling instruction; then, the server screens out the preset energy scheduling instruction with the largest predicted probability from each preset energy scheduling instruction, and uses the preset energy scheduling instruction as the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system.

[0123] In this embodiment, by determining different data types and matching a special feature extraction model for each data type, key features can be extracted from different data sources in a more targeted manner, thereby obtaining feature vectors that more accurately reflect the system status; on this basis, these feature vectors are fused and processed and input into the energy scheduling model, which can more accurately predict the results under each preset energy scheduling instruction, so that the current energy scheduling instructions screened out are more in line with the actual situation, which is conducive to improving the accuracy of energy scheduling.

[0124] In an exemplary embodiment, Figure 2 As shown, another integrated energy scheduling method for offshore wind power hydrogen production is provided. This method is described by taking the application of the method to a server as an example. Specifically, the method includes the following steps:

[0125] Step S201: Acquire current system data associated with the offshore wind power hydrogen production system.

[0126] Step S202: extract the carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system from the current system data.

[0127] Step S203: extract the carbon quota data, carbon emission data and unit carbon trading price data corresponding to the offshore wind power hydrogen production system from the carbon trading data; and construct a carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon quota data, carbon emission data and unit carbon trading price data.

[0128] Step S204: Determine the current number of green certificates corresponding to the offshore wind power hydrogen production system based on the green certificate trading data, and extract the current unit price of green certificates corresponding to the offshore wind power hydrogen production system from the green certificate trading data; construct a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system based on the current number of green certificates and the current unit price of green certificates.

[0129] Step S205: construct an objective function corresponding to the offshore wind power hydrogen production system based on the carbon transaction cost function and the green certificate transaction cost function.

[0130] Step S206: construct an energy scheduling model corresponding to the offshore wind power hydrogen production system based on the objective function and current constraints.

[0131] Step S207, determine the hydrogen-blended gas turbine unit, hydrogen fuel cell, and methane reactor associated with the offshore wind power hydrogen production system; obtain first unit data of the hydrogen-blended gas turbine unit, second unit data of the hydrogen fuel cell, and third unit data of the methane reactor; and use the first unit data, the second unit data, and the third unit data as current unit data corresponding to the offshore wind power hydrogen production system.

[0132] Step S208: Determine the first data type corresponding to the current unit data and the second data type corresponding to the current system data.

[0133] Step S209, query the correspondence between the data type and the feature extraction model, obtain the feature extraction model corresponding to the first data type, as the first feature extraction model corresponding to the current unit data, and query the correspondence to obtain the feature extraction model corresponding to the second data type, as the second feature extraction model corresponding to the current system data.

[0134] In step S210 , the current unit data is input into a first feature extraction model to obtain a first feature vector of the current unit data, and the current system data is input into a second feature extraction model to obtain a second feature vector of the current system data.

[0135] In step S211, the first eigenvector and the second eigenvector are fused to obtain a fused eigenvector corresponding to the offshore wind power hydrogen production system; the fused eigenvector is input into the energy scheduling model to obtain the predicted probability of the offshore wind power hydrogen production system under each preset energy scheduling instruction.

[0136] Step S212 , selecting the preset energy dispatching instruction with the highest predicted probability from the preset energy dispatching instructions as the current energy dispatching instruction corresponding to the offshore wind power hydrogen production system.

[0137] Step S213: performing corresponding energy dispatch processing on the offshore wind power hydrogen production system according to the current energy dispatch instruction.

[0138] In the above-mentioned comprehensive energy scheduling method for offshore wind power hydrogen production, in the process of energy scheduling of the offshore wind power hydrogen production system, the current system data associated with the offshore wind power hydrogen production system can be used to more accurately construct the objective function and current constraints corresponding to the offshore wind power hydrogen production system, thereby more accurately constructing the energy scheduling model corresponding to the offshore wind power hydrogen production system, and then the current unit data and current system data corresponding to the offshore wind power hydrogen production system can be integrated to more accurately obtain the current energy scheduling instructions corresponding to the offshore wind power hydrogen production system, which is conducive to improving the accuracy of determining the current energy scheduling instructions, and thus improving the energy scheduling accuracy of the offshore wind power hydrogen production system; moreover, the entire process does not require human intervention, avoiding the subjective factors and prone to errors in the manual scheduling method, which leads to the defect of low energy scheduling accuracy of the offshore wind power hydrogen production system, and further improves the energy scheduling accuracy of the offshore wind power hydrogen production system.

[0139] In an exemplary embodiment, in order to more clearly illustrate the comprehensive energy scheduling method for offshore wind power hydrogen production provided by the embodiment of the present application, the comprehensive energy scheduling method for offshore wind power hydrogen production is specifically described below with a specific embodiment. In one embodiment, the present application also provides a comprehensive energy scheduling method for offshore wind power hydrogen production that takes into account the grid carrying capacity constraint. In the process of energy scheduling of the offshore wind power hydrogen production system, the current system data associated with the offshore wind power hydrogen production system is first obtained, and then the objective function and current constraints corresponding to the offshore wind power hydrogen production system are constructed based on the current system data. Then, according to the objective function and the current constraints, an energy scheduling model corresponding to the offshore wind power hydrogen production system is constructed. Then, the current unit data corresponding to the offshore wind power hydrogen production system is obtained, and the current unit data and the current system data are input into the energy scheduling model to obtain the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system. Finally, the offshore wind power hydrogen production system is subjected to corresponding energy scheduling processing according to the current energy scheduling instruction. Specifically including the following contents:

[0140] 1. Offshore wind power hydrogen production system model:

[0141] (1) Seawater desalination system:

[0142] Seawater desalination technology can be divided into reverse osmosis membrane method and distillation method. The seawater desalination system adopts reverse osmosis membrane method, in which the expression of power consumption and water production volume of the unit is shown in formula (1):

[0143] , formula (1)

[0144] Where, is the power consumption of the seawater desalination system during period t; is the power consumption of the hydrogen electrolysis equipment in period t; It is the ratio of the power consumption of the desalination system to the amount of electricity required for water electrolysis, which is 0.07% here.

[0145] (2) PEM (Proton Exchange Membrane) hydrogen electrolysis equipment model:

[0146] Special environmental factors such as low temperature and salt spray at sea have a significant impact on the operating characteristics of PEM electrolysis hydrogen equipment, and the equipment ages quickly. Therefore, a dynamic production efficiency PEM electrolysis hydrogen equipment model is established, and the hydrogen production of the PEM equipment in time period t is:

[0147] , formula (2)

[0148] Where HHV (High Heating Value) is the high heating value of hydrogen, expressed in kWh / kg, and the power consumption of the equipment during period t is:

[0149] , formula (3)

[0150] Where, is the output power of the wind farm in period t; and They are the conversion efficiency of AC / DC (Alternating Current / Direct Current) converter and DC / DC converter respectively.

[0151] , formula (4)

[0152] in, is the electrical power input into the PEM; 、 and are the efficiency function coefficients of PEM respectively; The rated value of the PEM input electrical power; is the amount of hydrogen-producing substance in PEM; is the rated capacity of the PEM.

[0153] (3) Hydrogen compressor model:

[0154] The hydrogen compressor completes the compression and transportation of hydrogen by changing the volume of hydrogen.

[0155] , formula (5)

[0156] in, is the electric power consumed by the compressor during period t; is the specific heat capacity constant of hydrogen; is the injected hydrogen temperature; The working efficiency of the compressor; is the hydrogen isentropic index; is the compression ratio; and The upper and lower limits of the compressor's electric power consumption.

[0157] (4) Hydrogen pipeline model:

[0158] The hydrogen pipeline is used to transport hydrogen to the hydrogen storage tank, and its model can be expressed as:

[0159] , formula (6)

[0160] Where, is the accumulated amount of hydrogen in the pipeline; is the flow rate of hydrogen input into the pipeline; is the flow rate of the hydrogen output pipeline; is the loss of hydrogen in the pipeline; and are the minimum and maximum values ​​of the accumulated hydrogen in the pipeline respectively.

[0161] (5) Hydrogen storage tank model:

[0162] , formula (7)

[0163] Where: and are the pressure in the hydrogen energy storage (HES) tank and its upper limit; is the molar gas constant; is the density of hydrogen; It is the hydrogen storage gas of HES; is the volume of the HES tank; is the relative molecular mass of hydrogen; is the gas temperature in HES; and They are HES hydrogen charging speed and hydrogen discharging speed respectively; is the calorific value of hydrogen; and They are HES hydrogen charging power and hydrogen discharging power respectively; is the hydrogen storage state of HES.

[0164] (6) Principle of hydrogen production from offshore wind power:

[0165] like Figure 3 As shown, electricity generated by an offshore wind farm is used to produce hydrogen in an electrolyzer. This compressed hydrogen is then transported via a submarine hydrogen pipeline to an onshore hydrogen storage facility, where it is supplied to the hydrogen equipment used in the Electric Heated Hydrogen IES (EHIES). Proton exchange membrane electrolyzers are used because of their fast start-up and shutdown speeds and high reaction efficiency.

[0166] 2. Modeling of multi-link utilization model of hydrogen energy:

[0167] (1) Hydrogen-blended gas generator sets:

[0168] Natural gas doped with hydrogen can not only further enrich the utilization of hydrogen energy, but also effectively reduce the system's carbon emissions and make the fuel source cleaner. The hydrogen doped gas turbine (HGT) model can be expressed as:

[0169] , formula (8)

[0170] , formula (9)

[0171] in, is the hydrogen doping ratio of HGT; and are the natural gas power and hydrogen power input to HGT respectively; and are the volume flow rates of natural gas and hydrogen fed into HGT, respectively; and are the calorific values ​​of natural gas and hydrogen, respectively; and are the output electrical and thermal power of the HGT, respectively; and are the electrical and thermal conversion efficiencies respectively.

[0172] Since excessive hydrogen addition will affect the stable operation of the gas-fired unit, the hydrogen addition ratio must not exceed 20% to prevent hydrogen embrittlement. Similarly, the mathematical model of the hydrogen-doped gas boiler (HGB) can be expressed in the same way.

[0173] (2) Hydrogen fuel cell model:

[0174] Hydrogen fuel cells (HFCs) can use hydrogen energy to convert electricity into heat. Considering the nonlinear relationship between the power generation and heat generation efficiency of HFCs and the load rate, their model can be expressed as:

[0175] , formula (10)

[0176] in, and are the electrical energy and thermal energy conversion efficiency respectively; is the hydrogen input power of HFC; i is the polynomial order, and are the polynomial coefficients of the electric energy and thermal energy conversion efficiency functions respectively; and They are respectively the power generation and heat generation.

[0177] (3) Methane reactor model:

[0178] , formula (11)

[0179] in, is the reaction efficiency of methanation; and are the input hydrogen power and output gas power respectively; is the mass of methane per unit volume; is the molar conversion factor of hydrogen methanation.

[0180] 3. Carbon-Green Certificate Joint Trading Mechanism:

[0181] (1) Carbon trading mechanism:

[0182] The baseline method is usually used to allocate carbon emission sources. The IES carbon emission quota and actual carbon emissions can be expressed as:

[0183] , formula (12)

[0184] , formula (13)

[0185] in, is the total amount of IES carbon allowances; and are the carbon emission quota coefficients per unit electricity and per unit heat respectively; Purchase electrical power for the system; 、 and is the carbon emission calculation coefficient of coal-fired units; 、 and is the carbon emission calculation coefficient of the gas-fired unit; is the electricity and heat conversion coefficient; is the total output of the gas unit; and are the actual carbon emissions of coal-fired units and gas-fired units respectively; The efficiency of MR (Methanol Reforming) in absorbing carbon dioxide; is the amount of carbon dioxide absorbed by MR; is the actual total carbon emissions of IES.

[0186] Therefore, the CET cost of participating in the carbon trading market can be expressed as:

[0187] , formula (14)

[0188] Among them, C CET CET costs borne by IES; is the unit carbon trading price.

[0189] (2) Green Certificate Trading Mechanism:

[0190] The green certificate trading (GCT) mechanism refers to the proportion of renewable energy generation used in energy consumption, promoting the system to absorb a certain proportion of green electricity. The GCT mechanism is usually combined with the renewable energy quota system and can be expressed as:

[0191] , formula (15)

[0192] Where: G IES The number of green certificates required for IES; is the new energy quota ratio; The actual power load of the user; The amount of green electricity generated for renewable energy; The number of green certificates required by the user; and The electricity and quantity of green certificates voluntarily purchased by IES; The number of green certificates traded for IES. Therefore, the GCT cost It can be expressed as:

[0193] , formula (16)

[0194] in, and The unit prices of green certificates sold and purchased respectively; The penalty unit price.

[0195] (3) Carbon-Green Certificate Joint Trading Mechanism:

[0196] By determining the emission reduction contribution of each megawatt-hour of renewable energy power generation through the baseline emission factor, the carbon emission reduction benefit behind the GCT mechanism can be calculated, as shown in formula (17).

[0197] , formula (17)

[0198] in, The carbon quota offset behind the green certificate; and are the marginal emission factors for electricity and capacity, respectively; and are the marginal emission factor weights for electricity and capacity respectively.

[0199] Recalculate the carbon trading cost of formula (14) and modify it to:

[0200] , formula (18)

[0201] 4. IES stochastic optimization scheduling considering uncertainty:

[0202] The uncertainty of offshore wind power makes it difficult to accurately predict its output. In this paper, an IES stochastic optimization model is established based on chance-constrained objective programming.

[0203] (1) Objective function:

[0204] The optimization is performed with 1 day as the scheduling cycle and 1 hour as the time scale, and the operation and maintenance and equipment depreciation costs of the offshore wind power hydrogen production system are used. , CET cost , energy purchase cost , Operation and maintenance costs of hydrogen energy utilization equipment and energy storage equipment GCT cost , Offshore Transmission Loss Cost , wind curtailment costs , risk cost and standby costs The goal is to minimize the sum of the two. The objective function can be expressed as:

[0205] , formula (19)

[0206] Among them, the operation and maintenance and equipment depreciation costs of the offshore wind power hydrogen production system are:

[0207] , formula (20)

[0208] in, and are the operation and maintenance cost and depreciation cost of PEM respectively; is the operation and maintenance cost coefficient of PEW; is the investment cost of PEM; and are the unit capacity investment cost and installed capacity of PEM respectively; γ is the discount rate, which is 5%; is the depreciation period of PEM; and They are the operation and maintenance cost and depreciation cost of HC (Hydrogen Compressor); is the investment cost of HC; is the operation and maintenance cost coefficient of HC; and are the unit capacity investment cost and installed capacity of HC respectively; The operation and maintenance costs of HES; is the operation and maintenance cost coefficient of HES; The loss cost of transporting hydrogen through hydrogen pipelines; is the unit price of hydrogen; is the length of the hydrogen pipeline; is the hydrogen loss coefficient per unit length of pipeline.

[0209] The energy purchase cost of IES includes the cost of electricity and gas, which can be expressed as:

[0210] , formula (21)

[0211] in, Time-of-use electricity price for the external power grid; The gas purchase price.

[0212] Among them, the operation and maintenance costs of hydrogen energy utilization equipment and energy storage equipment are:

[0213] , formula (22)

[0214] Where n is the type of energy supply equipment; 、 are the output power and operation and maintenance coefficient of energy supply equipment n, respectively; x is the type of energy storage equipment; is the operation and maintenance coefficient of energy storage equipment x; and are the charging and discharging power of the energy storage device respectively.

[0215] The cost of wind curtailment is:

[0216] , formula (23)

[0217] Among them, λ WT is the unit wind curtailment penalty cost coefficient; is the wind power absorption power; is the predicted wind power.

[0218] Among them, the offshore transmission loss cost is:

[0219] , formula (24)

[0220] Among them, l J is the length of the submarine cable; α loss is the transmission loss coefficient of the cable per unit length.

[0221] Among them, GCT and CET costs are shown in Equations (16) and (18), respectively.

[0222] (2) Constraints:

[0223] In the IES stochastic optimization scheduling model, the reserve capacity opportunity constraint is used to replace the deterministic constraint of electric power balance, which is expressed as:

[0224] , formula (25)

[0225] in, The electricity load demand of users; and are the charging and discharging power of the battery (BT) respectively; is the probability operation function; is the wind power forecast error function, which is fitted by a normal distribution; β is the confidence level of the reserve capacity opportunity constraint. The wind power forecast error is a random variable. When the confidence level is given, the opportunity constraint can be transformed from an uncertain constraint to a deterministic constraint, as shown in Equation (26).

[0226] , formula (26)

[0227] where Φ(·) is the probability distribution function.

[0228] The power balance constraint is:

[0229] , formula (27)

[0230] in, is the actual heat load; and They are the charging and discharging power of TST (Thermal Storage Tank) respectively; The power of gas purchase.

[0231] In the above embodiment, during the energy scheduling of the offshore wind power hydrogen production system, the current system data associated with the offshore wind power hydrogen production system can be used to more accurately construct the objective function and current constraints corresponding to the offshore wind power hydrogen production system, thereby more accurately constructing the energy scheduling model corresponding to the offshore wind power hydrogen production system, and then the current unit data and current system data corresponding to the offshore wind power hydrogen production system can be integrated to more accurately obtain the current energy scheduling instructions corresponding to the offshore wind power hydrogen production system, which is conducive to improving the accuracy of determining the current energy scheduling instructions, and thus can improve the energy scheduling accuracy of the offshore wind power hydrogen production system; moreover, the entire process does not require human intervention, avoiding the defect of subjective factors and prone to errors in manual scheduling, which leads to low energy scheduling accuracy of the offshore wind power hydrogen production system, and further improves the energy scheduling accuracy of the offshore wind power hydrogen production system.

[0232] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0233] Based on the same inventive concept, embodiments of the present application also provide an offshore wind power hydrogen production integrated energy scheduling device for implementing the aforementioned offshore wind power hydrogen production integrated energy scheduling method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the offshore wind power hydrogen production integrated energy scheduling device provided below can be found in the above-mentioned limitations of the offshore wind power hydrogen production integrated energy scheduling method, and will not be repeated here.

[0234] In an exemplary embodiment, Figure 4 As shown, a comprehensive energy scheduling device for offshore wind power hydrogen production is provided, including: a data acquisition module 401, an information construction module 402, a model construction module 403, an instruction prediction module 404 and an energy scheduling module 405, wherein:

[0235] The data acquisition module 401 is used to acquire current system data associated with the offshore wind power hydrogen production system.

[0236] The information construction module 402 is used to construct the objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data.

[0237] The model building module 403 is used to build an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and current constraints.

[0238] The instruction prediction module 404 is used to obtain the current unit data corresponding to the offshore wind power hydrogen production system, input the current unit data and the current system data into the energy scheduling model, and obtain the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system.

[0239] The energy scheduling module 405 is used to perform corresponding energy scheduling processing on the offshore wind power hydrogen production system according to the current energy scheduling instruction.

[0240] In an exemplary embodiment, the information construction module 402 is also used to extract the carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system from the current system data; construct a carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon trading data, and construct a green certificate trading cost function corresponding to the offshore wind power hydrogen production system based on the green certificate trading data; and construct an objective function corresponding to the offshore wind power hydrogen production system based on the carbon trading cost function and the green certificate trading cost function.

[0241] In an exemplary embodiment, the information construction module 402 is also used to extract the carbon quota data, carbon emission data and unit carbon trading price data corresponding to the offshore wind power hydrogen production system from the carbon trading data; and construct the carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon quota data, carbon emission data and unit carbon trading price data.

[0242] In an exemplary embodiment, the information construction module 402 is also used to determine the current number of green certificates corresponding to the offshore wind power hydrogen production system based on the green certificate trading data, and to extract the current green certificate unit price corresponding to the offshore wind power hydrogen production system from the green certificate trading data; and to construct a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system based on the current number of green certificates and the current green certificate unit price.

[0243] In an exemplary embodiment, the instruction prediction module 404 is also used to determine the hydrogen-blended gas turbine unit, hydrogen fuel cell and methane reactor associated with the offshore wind power hydrogen production system; obtain first unit data of the hydrogen-blended gas turbine unit, second unit data of the hydrogen fuel cell, and third unit data of the methane reactor; and use the first unit data, the second unit data and the third unit data as the current unit data corresponding to the offshore wind power hydrogen production system.

[0244] In an exemplary embodiment, the instruction prediction module 404 is also used to determine the first data type corresponding to the current unit data and the second data type corresponding to the current system data; query the correspondence between the data type and the feature extraction model to obtain the feature extraction model corresponding to the first data type as the first feature extraction model corresponding to the current unit data, and query the correspondence to obtain the feature extraction model corresponding to the second data type as the second feature extraction model corresponding to the current system data; input the current unit data into the first feature extraction model to obtain the first feature vector of the current unit data, and input the current system data into the second feature extraction model to obtain the second feature vector of the current system data; fuse the first feature vector and the second feature vector to obtain the fused feature vector corresponding to the offshore wind power hydrogen production system; input the fused feature vector into the energy scheduling model to obtain the predicted probability of the offshore wind power hydrogen production system under each preset energy scheduling instruction; and screen out the preset energy scheduling instruction with the largest predicted probability from each preset energy scheduling instruction as the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system.

[0245] Each module in the above-mentioned integrated energy dispatching device for offshore wind power hydrogen production can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0246] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store current system data, current unit data, etc. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a comprehensive energy scheduling method for offshore wind power hydrogen production is realized.

[0247] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0248] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0249] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0250] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0251] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0252] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0253] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A comprehensive energy scheduling method for offshore wind power hydrogen production, characterized in that: The method comprises: Obtain current system data associated with offshore wind power hydrogen production systems; Constructing an objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data; Constructing an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and the current constraint conditions; Obtaining current unit data corresponding to the offshore wind power hydrogen production system, inputting the current unit data and the current system data into the energy scheduling model, and obtaining a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system; According to the current energy dispatch instruction, corresponding energy dispatch processing is performed on the offshore wind power hydrogen production system.

2. The method according to claim 1, characterized in that The constructing of the objective function and current constraints corresponding to the offshore wind power hydrogen production system according to the current system data includes: Extracting carbon trading data and green certificate trading data corresponding to the offshore wind power hydrogen production system from the current system data; Based on the carbon trading data, a carbon trading cost function corresponding to the offshore wind power hydrogen production system is constructed; based on the green certificate trading data, a green certificate trading cost function corresponding to the offshore wind power hydrogen production system is constructed; According to the carbon transaction cost function and the green certificate transaction cost function, an objective function corresponding to the offshore wind power hydrogen production system is constructed.

3. The method according to claim 2, characterized in that The step of constructing a carbon trading cost function corresponding to the offshore wind power hydrogen production system based on the carbon trading data includes: Extracting carbon quota data, carbon emission data, and unit carbon trading price data corresponding to the offshore wind power hydrogen production system from the carbon trading data; A carbon transaction cost function corresponding to the offshore wind power hydrogen production system is constructed according to the carbon quota data, the carbon emission data and the unit carbon transaction price data.

4. The method according to claim 2, characterized in that The step of constructing a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system based on the green certificate transaction data includes: Determine the current number of green certificates corresponding to the offshore wind power hydrogen production system based on the green certificate transaction data, and extract the current unit price of the green certificate corresponding to the offshore wind power hydrogen production system from the green certificate transaction data; According to the current number of green certificates and the current unit price of green certificates, a green certificate transaction cost function corresponding to the offshore wind power hydrogen production system is constructed.

5. The method according to claim 1, wherein The obtaining of current unit data corresponding to the offshore wind power hydrogen production system includes: Determining the hydrogen-blended gas turbine unit, hydrogen fuel cell, and methane reactor associated with the offshore wind power hydrogen production system; Acquiring first unit data of the hydrogen-blended gas generator set, second unit data of the hydrogen fuel cell set, and third unit data of the methane reactor set; The first unit data, the second unit data and the third unit data are all used as current unit data corresponding to the offshore wind power hydrogen production system.

6. The method according to any one of claims 1 to 5, characterized in that The step of inputting the current unit data and the current system data into the energy scheduling model to obtain a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system includes: Determining a first data type corresponding to the current unit data and a second data type corresponding to the current system data; querying the correspondence between data types and feature extraction models to obtain the feature extraction model corresponding to the first data type as the first feature extraction model corresponding to the current unit data, and querying the correspondence to obtain the feature extraction model corresponding to the second data type as the second feature extraction model corresponding to the current system data; Inputting the current unit data into the first feature extraction model to obtain a first feature vector of the current unit data, and inputting the current system data into the second feature extraction model to obtain a second feature vector of the current system data; fusing the first eigenvector and the second eigenvector to obtain a fused eigenvector corresponding to the offshore wind power hydrogen production system; Inputting the fused feature vector into the energy scheduling model to obtain the predicted probability of the offshore wind power hydrogen production system under each preset energy scheduling instruction; The preset energy scheduling instruction with the greatest predicted probability is selected from the preset energy scheduling instructions as the current energy scheduling instruction corresponding to the offshore wind power hydrogen production system.

7. A comprehensive energy dispatching device for offshore wind power hydrogen production, characterized in that: The device comprises: A data acquisition module, used to acquire current system data associated with the offshore wind power hydrogen production system; An information construction module, configured to construct an objective function and current constraints corresponding to the offshore wind power hydrogen production system based on the current system data; A model building module, configured to build an energy scheduling model corresponding to the offshore wind power hydrogen production system according to the objective function and the current constraint conditions; an instruction prediction module, configured to obtain current unit data corresponding to the offshore wind power hydrogen production system, input the current unit data and the current system data into the energy scheduling model, and obtain a current energy scheduling instruction corresponding to the offshore wind power hydrogen production system; The energy scheduling module is used to perform corresponding energy scheduling processing on the offshore wind power hydrogen production system according to the current energy scheduling instruction.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images