A power grid side green hydrogen energy storage capacity optimization method and system

Abstract

The application is suitable for the field of power control, and provides a power grid side green hydrogen energy storage capacity optimization method and system.The method comprises the following steps: determining the load demand of a controlled regional power grid and a corresponding power supply demand response curve; constructing an optimization model of a green hydrogen energy storage system of the controlled regional power grid, and configuring adjustable parameters of the green hydrogen energy storage system for planning evolution in the optimization model; setting constraint conditions and an objective function of the optimization model; running the optimization model of the green hydrogen energy storage system, iterating the adjustable parameters, outputting capacity parameters of the green hydrogen energy storage system, and obtaining a capacity optimization set; the application can establish a power supply demand response curve according to the obtained related data, and based on this, establish an optimization model of green hydrogen energy storage for capacity prediction of power supply, obtain optimal capacity parameter optimization of the capacity of the green hydrogen energy storage system, and realize the consideration of the cost and benefit of green hydrogen energy storage charging and discharging.

Description

Technical Field

[0001] The present invention belongs to the field of power control, and in particular relates to a method and system for optimizing green hydrogen energy storage capacity on the grid side. Background Art

[0002] With the emergence of environmental issues such as climate change and pollution, most countries and regions are increasingly focusing on clean energy. my country has also made significant progress in photovoltaic power generation, while also developing sustainable energy sources such as wind and hydropower. However, the grid's integration of clean energy places stringent demands on the stability and security of power supply. Photovoltaic and wind power generation are subject to significant fluctuations and rapid power supply fluctuations. To address this, the development of green hydrogen energy storage has helped improve the security, stability, and reliability of the grid's power system, enhancing the quality of power supply.

[0003] Although green hydrogen energy storage has the advantages of being clean, efficient, and sustainable, it is of great significance for promoting energy transformation and achieving the "dual carbon" goals; however, green hydrogen energy storage mainly includes three links: green hydrogen production, hydrogen storage, and hydrogen utilization. It will bring high costs in green hydrogen production and hydrogen storage. If large-scale application is not considered in the context of construction costs, the cost-effectiveness of unit power supply will be low. Therefore, it is necessary to design a grid-side green hydrogen energy storage capacity optimization method and system that can balance the stability of power replenishment and construction costs. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for optimizing green hydrogen energy storage capacity on the grid side to address at least one of the defects mentioned in the background technology.

[0005] The first aspect of the present invention is achieved by providing a method for optimizing green hydrogen energy storage capacity on the grid side, the method comprising:

[0006] Determine the load demand of the power grid in the controlled area and the corresponding supplementary power demand response curve;

[0007] Constructing an optimization model for the green hydrogen energy storage system of the controlled regional power grid, and configuring adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model;

[0008] Setting the constraints and objective function of the optimization model, where the constraint is that the discharge amount of the green hydrogen energy storage system at any time t is greater than or equal to the value corresponding to the power replenishment demand response curve at that time t while meeting the load demand, and the objective function is to minimize the cost per kilowatt-hour;

[0009] running the optimization model of the green hydrogen energy storage system, iterating the adjustable parameters, outputting capacity parameters of the green hydrogen energy storage system, and obtaining a capacity optimization set; wherein the number of iterations of the adjustable parameters is preset;

[0010] According to the capacity optimization set, optimal capacity parameters are selected to optimize the capacity of the green hydrogen energy storage system.

[0011] Furthermore, the method further comprises:

[0012] Obtaining operating data of the green hydrogen energy storage system after capacity optimization;

[0013] Exploring the energy conversion potential of the electrolyzer and fuel cell included in the green hydrogen energy storage system and quantifying it as a potential factor;

[0014] The adjustable parameters are screened or supplemented based on the potential factor.

[0015] Furthermore, the method further comprises:

[0016] Analyze the operating data of the green hydrogen energy storage system and predict the future battery capacity of the current fuel cell;

[0017] The selection rule of the optimal capacity parameter is optimized according to the battery capacity condition.

[0018] Furthermore, the step of determining the load demand of the controlled regional power grid and the corresponding supplementary power demand response curve specifically includes:

[0019] Obtain historical power supply data of the power grid in the controlled area;

[0020] Based on big data or empirical rules, the load demand of the power grid in the controlled area for a period of time in the future is estimated according to the historical power supply data;

[0021] The power supplement demand on the grid side is determined according to the load demand and the power output on the grid side, and then a power supplement demand response curve is formulated according to the power supplement demand.

[0022] Furthermore, the steps of constructing an optimization model for the green hydrogen energy storage system of the controlled regional power grid and configuring adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model specifically include:

[0023] Obtaining planning data of the power grid in the controlled area, and determining the topology of the green hydrogen energy storage system based on the planning data;

[0024] Establishing an optimization model of the green hydrogen energy storage system according to the topological structure of the green hydrogen energy storage system;

[0025] Input the optimization model into the simulation software to optimize the parameters;

[0026] Conduct a systematic evaluation of the optimization results through simulation software; the systematic evaluation includes technical indicators, environmental indicators and economic indicators;

[0027] Select two or more green hydrogen energy storage system planning evolution routes based on the systematic evaluation results;

[0028] The planned evolution path is vectorized and compiled into adjustable parameters in the optimization model, which can characterize the planned evolution path of the green hydrogen energy storage system.

[0029] Furthermore, the method also includes: creating data samples and training the optimization model of the green hydrogen energy storage system.

[0030] A second aspect of the present invention is a grid-side green hydrogen energy storage capacity optimization system, which is used in the above method, and includes:

[0031] The load and supplementary power demand determination module is used to determine the load demand of the controlled area power grid and the corresponding supplementary power demand response curve;

[0032] An optimization model construction module is used to construct an optimization model of the green hydrogen energy storage system of the controlled regional power grid and configure adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model;

[0033] A model parameter setting module is used to set the constraints and objective function of the optimization model, where the constraint is that the discharge amount of the green hydrogen energy storage system at any time t is greater than or equal to the value corresponding to the power replenishment demand response curve at that time t while meeting the load demand, and the objective function is to minimize the cost per kilowatt-hour;

[0034] an energy storage capacity prediction module, configured to run an optimization model of the green hydrogen energy storage system, iterate the adjustable parameters, output capacity parameters of the green hydrogen energy storage system, and obtain a capacity optimization set; wherein the number of iterations of the adjustable parameters is preset;

[0035] A capacity optimization module is used to select optimal capacity parameters according to the capacity optimization set to optimize the capacity of the green hydrogen energy storage system.

[0036] Furthermore, the system further comprises:

[0037] An operation data acquisition module, configured to acquire operation data based on the green hydrogen energy storage system after capacity optimization;

[0038] A deep mining module is used to explore the energy conversion potential of the electrolyzer and fuel cell included in the green hydrogen energy storage system and quantify it into a potential factor;

[0039] The parameter adjustment module is used to screen or complete the adjustable parameters based on the potential factor.

[0040] Furthermore, the system further comprises:

[0041] A battery degradation prediction module is used to analyze the operating data of the green hydrogen energy storage system and predict the future battery capacity of the current fuel cell;

[0042] A rule optimization module is used to optimize the selection rule of the optimal capacity parameter according to the battery capacity.

[0043] According to a third aspect of the present invention, a computer device includes a processor and a memory, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.

[0044] A fourth aspect of the present invention is a storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method when executed by a processor.

[0045] The present invention provides a grid-side green hydrogen energy storage capacity optimization method. The method can establish a power replenishment demand response curve for the power replenishment demand on the grid side in the future based on the acquired relevant data, and based on this, establish a green hydrogen energy storage system, conduct a preliminary design of the power replenishment capacity, and use an optimization model to predict the capacity to obtain the optimal capacity parameters to optimize the capacity of the green hydrogen energy storage system, achieve a balance between the cost and benefit of green hydrogen energy storage charging and discharging, and provide better power supply stability compared to photovoltaic power generation and wind power generation. The energy storage conversion equipment in the green hydrogen energy storage system can be flexibly connected to and adjusted from the grid-side input, that is, grid distribution, actively participate in the demand-side response of the grid, and enhance the stability and safety of the grid-side power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of a method for optimizing grid-side green hydrogen energy storage capacity provided by an embodiment of the present invention;

[0047] Figure 2 This is a first sub-flowchart of an embodiment of the present invention;

[0048] Figure 3 This is a second sub-flowchart of an embodiment of the present invention;

[0049] Figure 4 This is a third sub-flowchart of an embodiment of the present invention;

[0050] Figure 5 A structural block diagram of a grid-side green hydrogen energy storage capacity optimization system provided by an embodiment of the present invention;

[0051] Figure 6 A structural block diagram of another grid-side green hydrogen energy storage capacity optimization system provided by an embodiment of the present invention;

[0052] Figure 7 A structural block diagram of another grid-side green hydrogen energy storage capacity optimization system provided by an embodiment of the present invention;

[0053] Figure 8 A block diagram of the internal structure of a computer device is provided in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] Figure 1 A flowchart of a method for optimizing grid-side green hydrogen energy storage capacity is provided in an embodiment of the present invention, which may specifically include the following steps:

[0056] S101: Determine the load demand of the power grid in the controlled area and the corresponding supplementary power demand response curve;

[0057] The controlled area can be an industrial park, a town, a development zone, etc.

[0058] S102: Constructing an optimization model for the green hydrogen energy storage system of the controlled regional power grid, and configuring adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model;

[0059] Exemplarily, a green hydrogen energy storage system includes an electrolyzer, a hydrogen storage container, and a fuel cell; wherein the hydrogen produced after the electrolyzer is powered on is stored in the hydrogen storage container, and the hydrogen storage container supplies hydrogen to the fuel cell for power generation; the maximum output power of the electrolyzer is determined by the smaller value between its own power and the hydrogen energy stored in the hydrogen storage container; the maximum output power of the fuel cell is jointly affected by its own power output characteristics and the energy that can be stored in the hydrogen storage container, and the smaller value between the two is used as the maximum output power of the fuel cell.

[0060] Among them, the main factors affecting the performance of electrolyzers include: electrolyte materials, temperature, power supply, cathode and anode materials, etc.; the main factors affecting the performance of hydrogen storage containers include: pressure, temperature, etc.; the main factors affecting the performance of fuel cells include: catalysts, electrode structure, electrolytes, working environment, hydrogen supply conditions, etc.

[0061] As mentioned above, for example, for the electrolyzer, the adjustable parameters therein can be set and remain unchanged; for the hydrogen storage container, the adjustable parameters therein can change with the development of technology, such as the hydrogen storage pressure and hydrogen storage temperature of the hydrogen storage container; for the fuel cell, the adjustable parameters therein include: catalyst, working environment, and hydrogen supply conditions; among them, the working environment includes the temperature, pressure, humidity and gas flow during combustion; the hydrogen supply conditions include the purity and humidity of hydrogen; therefore, the total adjustable parameters include: hydrogen storage pressure, hydrogen storage temperature, catalyst, temperature, pressure, humidity and gas flow during combustion, as well as the purity and humidity of hydrogen.

[0062] Exemplarily, the hydrogen storage container adopts a hydrogen storage tank commonly available on the market.

[0063] S103: Setting constraints and an objective function for the optimization model, wherein the constraint is that the discharge amount of the green hydrogen energy storage system at any time t is greater than or equal to the value corresponding to the power replenishment demand response curve at that time t while meeting the load demand, and the objective function is to minimize the cost per kilowatt-hour;

[0064] Exemplarily, time t is a value on the time scale, generally any future time from the current time.

[0065] When determining the cost per kilowatt-hour (KWh), the construction cost of the green hydrogen energy storage system, assuming the capacity remains unchanged, will decrease if the proportion of charging the green hydrogen energy storage system during the off-peak hours of the power grid is greater.

[0066] If the green hydrogen energy storage system is charged during the off-peak period of the power grid, then we only need to consider whether the difference between the increase in the capacity of the green hydrogen energy storage system and the corresponding increase in construction costs is positive;

[0067] S104: Running the optimization model of the green hydrogen energy storage system, iterating the adjustable parameters, outputting capacity parameters of the green hydrogen energy storage system, and obtaining a capacity optimization set; wherein the number of iterations of the adjustable parameters is preset;

[0068] For example, the optimization model of the green hydrogen energy storage system can be built and run in the HOMER software. In addition, the HOMER software supports parameter adjustment. Adjustable parameters can be configured in the HOMER software and set to automatic iteration. The number of iterations can be pre-set, for example: 10 4 times, 10 5 Second and so on.

[0069] Among them, the optimization model of the green hydrogen energy storage system is a deep learning model, which can be obtained by transfer learning based on the conventional deep learning model, which is an existing technology; the capacity parameters of the green hydrogen energy storage system mainly need to determine the minimum and maximum values ​​of the capacity.

[0070] S105: According to the capacity optimization set, select optimal capacity parameters to optimize the capacity of the green hydrogen energy storage system.

[0071] Exemplarily, the selection rule for the optimal capacity parameter may be the minimum cost per kilowatt-hour method;

[0072] In this embodiment, the method is mainly aimed at the power grid side. It can establish a power replenishment demand response curve for the power replenishment demand on the power grid side in the future based on the acquired relevant data, and based on this, establish a green hydrogen energy storage system, conduct a preliminary design of the power replenishment capacity, and use the optimization model to predict the capacity to obtain the optimal capacity parameters to optimize the capacity of the green hydrogen energy storage system, achieve a balance between the cost and benefit of charging and discharging of the green hydrogen energy storage system, and provide better power supply stability compared to photovoltaic power generation and wind power generation. The energy storage conversion equipment in the green hydrogen energy storage system can flexibly access and adjust the input of the power grid side, that is, to replenish the power grid, actively participate in the demand-side response of the power grid, and enhance the stability and security of the power supply on the power grid side.

[0073] In one embodiment, a photovoltaic power generation system and / or a wind power generation system can be added as a power source for the green hydrogen energy storage system, while the power supply of the power grid can be reduced, thereby further improving the new energy consumption rate.

[0074] In one example of this embodiment, taking a town as an example, the photovoltaic power generation system is distributed and can be installed on the roofs of various buildings (households) or other spaces. The photovoltaic power generation of each household can be regulated according to the electricity consumption of each household. In addition, the photovoltaic power generation system has a distributed structure and its control is also distributed. In this way, single households, multiple households and villages in the town can be regulated synchronously and independently.

[0075] In one example of this embodiment, Figure 2 As shown, the method further includes:

[0076] S201: Acquire operating data of the green hydrogen energy storage system after capacity optimization;

[0077] On the grid side, the operating data of the green hydrogen energy storage system mainly includes charging and discharging power, temperature, current, and voltage;

[0078] S202: Exploring the energy conversion potential of the electrolyzer and fuel cell included in the green hydrogen energy storage system and quantifying it as a potential factor;

[0079] The energy conversion potentials of electrolyzers and fuel cells are: when the adjustable parameters (i.e., temperature, power supply, etc.) of the electrolyzer are iterated to what values, the maximum hydrogen production efficiency is achieved; when the adjustable parameters (i.e., catalyst, working environment, hydrogen supply conditions) of the fuel cell are iterated to what values, the maximum discharge efficiency is achieved;

[0080] Identify the adjustable parameters that affect the hydrogen production power of the electrolyzer and rank them according to the degree of influence; Identify the adjustable parameters that affect the power generation power of the fuel cell and rank them according to the degree of influence. Quantify the values ​​of the adjustable parameters and obtain the potential factors.

[0081] In the quantization process, parameters of different units and orders of magnitude can be normalized.

[0082] S203: Screening or completing the adjustable parameters based on the potential factor;

[0083] The purpose of screening or completion is to eliminate unnecessary parameters, enhance influential parameters, and further enhance the accuracy and reliability of the capacity optimization results of the green hydrogen energy storage system based on adjustable parameters;

[0084] In one example of this embodiment, Figure 3 As shown, the method further includes:

[0085] S301: Analyze the operating data of the green hydrogen energy storage system to predict the future battery capacity of the current fuel cell;

[0086] For example, a machine learning prediction model can be established to predict the capacity attenuation of a fuel cell, and further predict the future battery capacity of the current fuel cell.

[0087] S302: Optimizing the selection rule of the optimal capacity parameter according to the battery capacity.

[0088] The selected rules are optimized mainly based on technical indicators, environmental indicators and economic indicators. For example, weights are assigned to these three indicators and the weights of the three indicators are adjusted according to the battery capacity to achieve new optimal capacity parameters.

[0089] In one example, economic indicators are preferred; however, for different controlled areas, flexible selection may be made among technical indicators, environmental indicators, and economic indicators based on local conditions.

[0090] In one example of this embodiment, Figure 4 As shown, the step of determining the load demand of the controlled area power grid and the corresponding supplementary power demand response curve specifically includes:

[0091] S401: Obtain historical power supply data of the controlled area power grid;

[0092] S402: Based on big data or empirical rules, infer the load demand of the power grid in the controlled area for a period of time in the future according to the historical power supply data;

[0093] Among them, big data can be used to predict the load demand of the power grid in the controlled area in the future;

[0094] It is also possible to establish a prediction equation based on experience, and infer the load demand of the power grid in the controlled area in the future from the prediction equation;

[0095] S403: Determine the power supplement demand on the grid side according to the load demand and the power output on the grid side, and then formulate a power supplement demand response curve according to the power supplement demand.

[0096] In one example of this embodiment, the steps of constructing an optimization model for the green hydrogen energy storage system of the controlled regional power grid and configuring adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model specifically include:

[0097] Obtaining planning data of the power grid in the controlled area, and determining the topology of the green hydrogen energy storage system based on the planning data;

[0098] Establishing an optimization model of the green hydrogen energy storage system according to the topological structure of the green hydrogen energy storage system;

[0099] Input the optimization model into the simulation software to optimize the parameters;

[0100] Conduct a systematic evaluation of the optimization results through simulation software; the systematic evaluation includes technical indicators, environmental indicators and economic indicators;

[0101] Select two or more green hydrogen energy storage system planning evolution routes based on the systematic evaluation results;

[0102] The planned evolution path is vectorized and compiled into adjustable parameters in the optimization model, which can characterize the planned evolution path of the green hydrogen energy storage system.

[0103] The evaluation of the above technical indicators, environmental indicators and economic indicators are all existing technologies and will not be described in detail here.

[0104] In an example of this embodiment, the method further includes: generating data samples and training the optimization model of the green hydrogen energy storage system.

[0105] Among them, the data samples are used to train a migrated deep learning model to obtain an optimized model of the green hydrogen energy storage system. The migration of the model is an existing technology and will not be described in detail here.

[0106] Exemplarily, the data samples mainly include operating data of the electrolyzer, the hydrogen storage container, and the fuel cell.

[0107] In another embodiment, Figure 5As shown, a grid-side green hydrogen energy storage capacity optimization system is used in the above method, and the system includes:

[0108] The load and supplementary power demand determination module 100 is used to determine the load demand of the controlled area power grid and the corresponding supplementary power demand response curve;

[0109] An optimization model building module 200 is used to build an optimization model of the green hydrogen energy storage system of the controlled regional power grid and configure adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model;

[0110] A model parameter setting module 300 is used to set the constraints and objective function of the optimization model, wherein the constraint is that the discharge amount of the green hydrogen energy storage system at any time t is greater than or equal to the value corresponding to the power replenishment demand response curve at that time t while meeting the load demand, and the objective function is to minimize the cost per kilowatt-hour;

[0111] an energy storage capacity prediction module 400 for running the optimization model of the green hydrogen energy storage system, iterating the adjustable parameters, outputting capacity parameters of the green hydrogen energy storage system, and obtaining a capacity optimization set; wherein the number of iterations of the adjustable parameters is preset;

[0112] The capacity optimization module 500 is configured to select optimal capacity parameters according to the capacity optimization set to optimize the capacity of the green hydrogen energy storage system.

[0113] In one example of this embodiment, Figure 6 As shown, the system further includes:

[0114] An operating data acquisition module 600 is configured to acquire operating data of the green hydrogen energy storage system after optimizing the capacity;

[0115] A deep mining module 700 is used to mine the energy conversion potential of the electrolyzer and fuel cell included in the green hydrogen energy storage system and quantify it into a potential factor;

[0116] The parameter adjustment module 800 is used to screen or complete the adjustable parameters based on the potential factor.

[0117] In one example of this embodiment, Figure 7 As shown, the system further includes:

[0118] A battery degradation prediction module 900 is used to analyze the operating data of the green hydrogen energy storage system and predict the future battery capacity of the current fuel cell;

[0119] The rule optimization module 1000 is used to optimize the selection rule of the optimal capacity parameter according to the battery capacity.

[0120] In another embodiment, Figure 8 As shown, a computer device includes a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method are implemented;

[0121] In another embodiment, a storage medium stores a computer program thereon, wherein the computer program implements the steps of the method when executed by a processor.

[0122] An embodiment of the present invention provides a grid-side green hydrogen energy storage capacity optimization method, and based on this method, provides a grid-side green hydrogen energy storage capacity optimization system. The method can predict the required output on the grid side based on the obtained relevant data, such as the output power of green hydrogen energy storage and the required power of the electricity load, and perform capacity prediction through a preset optimization model to obtain optimal capacity parameters, optimize the capacity of the green hydrogen energy storage system, and achieve a balance between the cost and benefit of charging and discharging of the green hydrogen energy storage system, providing better power supply stability compared to photovoltaic power generation and wind power generation. The energy storage conversion equipment (i.e., fuel cell) in the green hydrogen energy storage system can flexibly access and adjust the input on the grid side, that is, supplement the power of the grid, actively participate in the demand-side response of the grid, and enhance the stability and security of the grid-side power supply.

[0123] Figure 8 The figure shows the internal structure of a computer device in one embodiment. The computer device includes a processor, a memory, a network interface, an input device and a display screen connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for optimizing the green hydrogen energy storage capacity on the grid side. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement a method for optimizing the green hydrogen energy storage capacity on the grid side. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0124] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0125] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0126] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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.

[0127] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing green hydrogen energy storage capacity on the grid side, characterized in that: The method comprises: Determine the load demand of the power grid in the controlled area and the corresponding supplementary power demand response curve; Constructing an optimization model for the green hydrogen energy storage system of the controlled regional power grid, and configuring adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model; specifically including: Obtaining planning data of the power grid in the controlled area, and determining the topology of the green hydrogen energy storage system based on the planning data; Establishing an optimization model of the green hydrogen energy storage system according to the topological structure of the green hydrogen energy storage system; Input the optimization model into the simulation software to optimize the parameters; Conduct a systematic evaluation of the optimization results through simulation software; the systematic evaluation includes technical indicators, environmental indicators and economic indicators; Select two or more green hydrogen energy storage system planning evolution routes based on the systematic evaluation results; The planned evolution path is vectorized and compiled into adjustable parameters in the optimization model, which can characterize the planned evolution path of the green hydrogen energy storage system; Setting the constraints and objective function of the optimization model, where the constraint is that the discharge amount of the green hydrogen energy storage system at any time t is greater than or equal to the value corresponding to the power replenishment demand response curve at that time t while meeting the load demand, and the objective function is to minimize the cost per kilowatt-hour; running the optimization model of the green hydrogen energy storage system, iterating the adjustable parameters, outputting capacity parameters of the green hydrogen energy storage system, and obtaining a capacity optimization set; wherein the number of iterations of the adjustable parameters is preset; According to the capacity optimization set, optimal capacity parameters are selected to optimize the capacity of the green hydrogen energy storage system.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining operating data of the green hydrogen energy storage system after capacity optimization; Exploring the energy conversion potential of the electrolyzer and fuel cell included in the green hydrogen energy storage system and quantifying it as a potential factor; The adjustable parameters are screened or supplemented based on the potential factor.

3. The method according to claim 2, characterized in that The method further comprises: Analyze the operating data of the green hydrogen energy storage system and predict the future battery capacity of the current fuel cell; The selection rule of the optimal capacity parameter is optimized according to the battery capacity condition.

4. The method according to claim 1, wherein The step of determining the load demand of the controlled regional power grid and the corresponding supplementary power demand response curve specifically includes: Obtain historical power supply data of the power grid in the controlled area; Based on big data or empirical rules, the load demand of the power grid in the controlled area for a period of time in the future is estimated according to the historical power supply data; The power supplement demand on the grid side is determined according to the load demand and the power output on the grid side, and then a power supplement demand response curve is formulated according to the power supplement demand.

5. The method according to claim 1, wherein The method also includes: creating data samples and training the optimization model of the green hydrogen energy storage system.

6. A grid-side green hydrogen energy storage capacity optimization system, which is used in the method according to any one of claims 1 to 5, characterized in that: The system comprises: The load and supplementary power demand determination module is used to determine the load demand of the controlled area power grid and the corresponding supplementary power demand response curve; An optimization model construction module is used to construct an optimization model of the green hydrogen energy storage system of the controlled regional power grid and configure adjustable parameters for planning and evolving the green hydrogen energy storage system in the optimization model; specifically, it includes: Obtaining planning data of the power grid in the controlled area, and determining the topology of the green hydrogen energy storage system based on the planning data; Establishing an optimization model of the green hydrogen energy storage system according to the topological structure of the green hydrogen energy storage system; Input the optimization model into the simulation software to optimize the parameters; Conduct a systematic evaluation of the optimization results through simulation software; the systematic evaluation includes technical indicators, environmental indicators and economic indicators; Select two or more green hydrogen energy storage system planning evolution routes based on the systematic evaluation results; The planned evolution path is vectorized and compiled into adjustable parameters in the optimization model, which can characterize the planned evolution path of the green hydrogen energy storage system; A model parameter setting module is used to set the constraints and objective function of the optimization model, where the constraint is that the discharge amount of the green hydrogen energy storage system at any time t is greater than or equal to the value corresponding to the power replenishment demand response curve at that time t while meeting the load demand, and the objective function is to minimize the cost per kilowatt-hour; an energy storage capacity prediction module, configured to run an optimization model of the green hydrogen energy storage system, iterate the adjustable parameters, output capacity parameters of the green hydrogen energy storage system, and obtain a capacity optimization set; wherein the number of iterations of the adjustable parameters is preset; A capacity optimization module is used to select optimal capacity parameters according to the capacity optimization set to optimize the capacity of the green hydrogen energy storage system.

7. The system according to claim 6, characterized in that The system further comprises: An operation data acquisition module, configured to acquire operation data based on the green hydrogen energy storage system after capacity optimization; A deep mining module is used to explore the energy conversion potential of the electrolyzer and fuel cell included in the green hydrogen energy storage system and quantify it into a potential factor; The parameter adjustment module is used to screen or complete the adjustable parameters based on the potential factor.

8. The system according to claim 6, wherein: The system further comprises: A battery degradation prediction module is used to analyze the operating data of the green hydrogen energy storage system and predict the future battery capacity of the current fuel cell; A rule optimization module is used to optimize the selection rule of the optimal capacity parameter according to the battery capacity.

9. A computer device comprising a processor and a memory, 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 5 are implemented.

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