Capacity configuration method and device for electrochemical hydrogen composite energy storage of wind-solar hydrogen production

Abstract

The present application relates to the technical field of new energy storage technology, and more particularly to a capacity configuration method and device for electrochemical hydrogen composite energy storage of wind-solar hydrogen production. The present application constructs an annual comprehensive cost function of the electrochemical hydrogen composite energy storage system; under the premise of meeting the comprehensive constraints, a preset solver is called to solve the annual comprehensive cost function to obtain the minimum cost comprehensive capacity configuration of the electrochemical hydrogen composite energy storage system, which includes the installed capacity of wind-solar power generation, the rated power of two types of electrolytic cells, the rated capacity of the battery, the rated capacity of the PCS, the rated capacity of the fuel cell and the configuration capacity of the hydrogen storage tank; the comprehensive constraints include wind-solar power generation equipment modeling constraints, electrolytic cell equipment modeling constraints, battery modeling constraints, fuel cell modeling constraints, compressor modeling constraints, hydrogen storage tank modeling constraints, methanol synthesis modeling constraints and power balance constraints, so that the present application can more economically configure the capacity of the electrochemical hydrogen composite energy storage system.

Description

Technical Field

[0001] This invention relates to the field of new energy storage technology, and in particular to a capacity configuration method and apparatus for electrochemical hydrogen composite energy storage using wind and solar hydrogen production. Background Technology

[0002] With the increasing penetration rate of new energy sources, wind and solar power are playing an increasingly important role in the energy supply system of off-grid and weak-grid areas. However, wind and solar resources are characterized by significant intermittency and random fluctuations. Their output power is affected by various factors such as seasonal changes, meteorological conditions, and day / night cycles, making it difficult to achieve a stable and continuous energy supply. In off-grid or weak-grid application scenarios far from the main power grid, these characteristics are further amplified, placing higher demands on system operational stability, energy utilization efficiency, and power supply reliability. To improve the absorption of renewable energy in off-grid scenarios, wind and solar hydrogen production technology is gradually becoming an important development direction. By using surplus wind and solar power for water electrolysis to produce hydrogen, the conversion and storage of electrical energy into hydrogen energy across time periods can be realized, alleviating the problem of wind and solar curtailment while providing a clean and dispatchable energy carrier for remote areas.

[0003] In off-grid environments with frequent fluctuations in wind and solar power output, electrolyzers inevitably experience frequent start-ups and shutdowns, as well as significant power fluctuations. This not only leads to a decrease in hydrogen production efficiency but also significantly accelerates the aging and lifespan degradation of key components. In particular, the operational characteristics during the start-up phase, such as power ramp-up, minimum stable load, and start-up / shutdown frequency constraints, have a significant impact on system economy and reliability, but these are often simplified or ignored in existing research. Secondly, electrochemical energy storage systems play a crucial role in smoothing wind and solar power fluctuations, peak shaving, valley filling, and maintaining system power balance. However, their operation is limited by capacity, maximum charge / discharge power, and bidirectional conversion efficiency, and their charge / discharge strategies directly affect the internal energy flow path and equipment operating status. Meanwhile, while hydrogen energy storage offers advantages such as large capacity and long-cycle energy storage, its operation is constrained by multiple factors, including compression power consumption, upper limits on hydrogen storage tank capacity, and hydrogen charging / discharging rates, making the scheduling and management strategies of hydrogen storage systems more complex. Based on this, fuel cells are introduced as a key device for converting hydrogen energy into electricity, enabling the system to achieve stable power supply through hydrogen storage even when wind and solar power output is insufficient. However, fuel cells, electrolyzers and compressors are tightly coupled at the power level, and their operating status is significantly time-dependent with the charging and discharging behavior of electrochemical energy storage, power curtailment, and external load demand, which further increases the difficulty of system coordination control and capacity configuration.

[0004] Based on this, the present invention proposes a capacity configuration method and device for electrochemical hydrogen composite energy storage using wind and solar hydrogen production to solve the problem of how to configure the capacity of electrochemical hydrogen composite energy storage system more economically. Summary of the Invention

[0005] To address the issue of how to configure the capacity of an electrochemical hydrogen composite energy storage system more economically, embodiments of the present invention provide a method and apparatus for configuring the capacity of an electrochemical hydrogen composite energy storage system based on wind and solar hydrogen production.

[0006] In a first aspect, embodiments of the present invention provide a capacity configuration method for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production. The method is applied to an electrochemical hydrogen composite energy storage system, which includes a wind and solar power generation unit, two types of electrolyzers, a hydrogen storage tank, a fuel cell, and a methanol synthesis device, comprising: Construct an average annual comprehensive cost function for an electrochemical hydrogen composite energy storage system; wherein, the average annual comprehensive cost function includes average annual investment cost, average annual operation and maintenance cost, annual carbon emission cost, and cost of consuming biomass raw materials; Under the premise of satisfying comprehensive constraints, the preset solver is invoked to solve the annual comprehensive cost function to obtain the comprehensive capacity configuration with the minimum cost of the electrochemical hydrogen composite energy storage system. The comprehensive capacity configuration includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks; the comprehensive constraints include modeling constraints for wind and solar power generation equipment, modeling constraints for electrolyzer equipment, modeling constraints for batteries, modeling constraints for fuel cells, modeling constraints for compressors, modeling constraints for hydrogen storage tanks, modeling constraints for methanol synthesis, and power balance constraints.

[0007] Secondly, embodiments of the present invention provide a capacity configuration device for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production. The capacity configuration device is applied to an electrochemical hydrogen composite energy storage system, which includes a wind and solar power generation unit, two types of electrolyzers, a hydrogen storage tank, a fuel cell, and a methanol synthesis unit. The first data processing module is used to construct the annual comprehensive cost function of the electrochemical hydrogen composite energy storage system; wherein, the annual comprehensive cost function includes the annual investment cost, the annual operation and maintenance cost, the annual carbon emission cost, and the cost of consuming biomass raw materials; The second data processing module is used to call a preset solver to solve the annual comprehensive cost function under the premise of satisfying comprehensive constraints, so as to obtain the comprehensive capacity configuration with the minimum cost of the electrochemical hydrogen composite energy storage system. The comprehensive capacity configuration includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks; the comprehensive constraints include modeling constraints for wind and solar power generation equipment, modeling constraints for electrolyzer equipment, modeling constraints for batteries, modeling constraints for fuel cells, modeling constraints for compressors, modeling constraints for hydrogen storage tanks, modeling constraints for methanol synthesis, and power balance constraints.

[0008] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of the present invention.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of the present invention.

[0010] This invention provides a capacity configuration method and apparatus for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production. First, an annual comprehensive cost function for the electrochemical hydrogen composite energy storage system is constructed, including annual investment cost, annual operation and maintenance cost, annual carbon emission cost, and the cost of consuming biomass raw materials. Second, under the premise of satisfying comprehensive constraints, a preset solver is invoked to optimize the constructed annual comprehensive cost function, ultimately obtaining a comprehensive capacity configuration scheme that minimizes the cost of the electrochemical hydrogen composite energy storage system. This comprehensive capacity configuration is the result of multi-parameter collaborative optimization, including the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of the batteries, the rated capacity of the PCS (Potentially Modulated Cell System), the rated capacity of the fuel cells, and the configuration capacity of the hydrogen storage tank. Comprehensive constraints include modeling constraints for wind and solar power generation equipment, electrolyzer equipment, batteries, fuel cells, compressors, hydrogen storage tanks, methanol synthesis, and power balance. These multi-dimensional constraints ensure the feasibility and scientific validity of the configuration scheme. Through the above method, the present invention can balance system cost and operational stability, and configure the capacity of the electrochemical hydrogen composite energy storage system more economically. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating the capacity configuration method for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production according to one embodiment is shown. Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention; Figure 3 A structural diagram of a capacity configuration device for electrochemical hydrogen combined energy storage based on wind and solar hydrogen production is shown according to one embodiment. Figure 4A framework diagram of an electrochemical hydrogen composite energy storage system according to one embodiment is shown; Figure 5 A graph showing the hydrogen production rate of an electrolyzer according to one embodiment is provided. Figure 6 A per-unit graph of wind power output for the entire year is shown according to one embodiment; Figure 7 A per-unit diagram of annual photovoltaic power output is shown according to one embodiment. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] Please refer to Figure 1 This invention provides a capacity configuration method for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production. The method is applied to an electrochemical hydrogen composite energy storage system, which includes a wind and solar power generation unit, two types of electrolyzers, a hydrogen storage tank, a fuel cell, and a methanol synthesis device. Step 100: Construct the annual comprehensive cost function of the electrochemical hydrogen composite energy storage system; wherein, the annual comprehensive cost function includes the annual investment cost, the annual operation and maintenance cost, the annual carbon emission cost, and the cost of consuming biomass raw materials; Step 102: Under the premise of satisfying the comprehensive constraints, call the preset solver to solve the annual comprehensive cost function and obtain the comprehensive capacity configuration with the minimum cost of the electrochemical hydrogen composite energy storage system. The comprehensive capacity configuration includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks; the comprehensive constraints include modeling constraints for wind and solar power generation equipment, modeling constraints for electrolyzer equipment, modeling constraints for batteries, modeling constraints for fuel cells, modeling constraints for compressors, modeling constraints for hydrogen storage tanks, modeling constraints for methanol synthesis, and power balance constraints.

[0015] In this embodiment, firstly, an annual comprehensive cost function for the electrochemical hydrogen composite energy storage system is constructed. This function includes annual investment cost, annual operation and maintenance cost, annual carbon emission cost, and the cost of consuming biomass raw materials. Secondly, under the premise of satisfying comprehensive constraints, a preset solver is invoked to optimize the constructed annual comprehensive cost function, ultimately obtaining a comprehensive capacity configuration scheme that minimizes the cost of the electrochemical hydrogen composite energy storage system. This comprehensive capacity configuration is the result of multi-parameter collaborative optimization, including the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of the battery, the rated capacity of the PCS, the rated capacity of the fuel cell, and the configuration capacity of the hydrogen storage tank. Comprehensive constraints include modeling constraints for wind and solar power generation equipment, electrolyzer equipment, battery, fuel cell, compressor, hydrogen storage tank, methanol synthesis, and power balance. These multi-dimensional constraints ensure the feasibility and scientific validity of the configuration scheme. Through the above method, this invention can balance system cost and operational stability, enabling more economical capacity configuration for the electrochemical hydrogen composite energy storage system.

[0016] In one embodiment of the present invention, the annual average comprehensive cost function is constructed by the following formula: In the formula, The average annual comprehensive cost, The average annual investment cost, As the average annual operation and maintenance cost, Annual carbon emission cost, To consume the cost of biomass raw materials, For the first The unit investment price of the equipment, For the first Rated capacity of the equipment For the system planning life, For the discount rate, set This includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks. For the first The average annual unit investment and maintenance cost of the equipment for The flow rate of carbon dioxide discharged by the system at all times. For carbon quotas, T Total duration Cost per unit of annual carbon emissions Cost per unit of biomass; for Real-time biomass usage.

[0017] In this embodiment, the formula aims to minimize the average annual comprehensive cost over the entire lifecycle. The formula converts the one-time investment in equipment into an equivalent annual cost, resolving the issue of cost comparability across different equipment lifecycles over a long planning period. Compared to the traditional "direct amortization of total investment" model, this approach aligns better with actual financial calculation logic, significantly improving the engineering feasibility of the optimization results. Simultaneously, by using set H, the entire industry chain of equipment, including wind and solar power generation, electrolyzers, batteries, PCS, fuel cells, and hydrogen storage tanks, is uniformly included in cost accounting. This comprehensive coverage of equipment from energy production to chemical synthesis provides a clear and comprehensive cost boundary for system-level capacity optimization. The formula uses the same equipment set H as the investment cost, maintaining the uniformity of the cost accounting object. This allows for a simultaneous consideration of the dual impact of equipment capacity configuration on investment and operation and maintenance during the optimization process, avoiding optimization biases caused by the fragmented equipment dimension in traditional models. This results in a more accurate cost balance that better reflects the actual operating patterns of the system. This formula dynamically calculates costs based on the difference between time-of-use carbon dioxide flow rate and carbon quota, reflecting the carbon emission surplus or deficit of the system at different times. This achieves refined and dynamic measurement of annual carbon emission costs, rather than traditional static estimation. Simultaneously, it directly embeds carbon quotas (CEA) and unit annual carbon emission costs, rigidly binding low-carbon goals with economic costs. Furthermore, the formula quantifies biomass feedstock usage in a time-series format, directly linking feedstock consumption to the system's time-of-use operating status, achieving dynamic linkage optimization between biomass resource costs, methanol production, and carbon emission levels. This design provides a unique resource efficiency optimization dimension for biomass-to-methanol systems, enabling precise balancing of feedstock costs with other cost items and production targets during optimization, thus improving the overall rationality of system resource utilization.

[0018] In one embodiment of the present invention, the rated power of the two types of electrolyzers includes the rated power of PEM electrolyzers and the rated power of AEL electrolyzers, and the installed capacity of wind and solar power generation includes the installed capacity of wind turbine power generation and the installed capacity of photovoltaic power generation.

[0019] In one embodiment of the present invention, the modeling constraints for the electrolytic cell equipment are constructed using the following formula: In the formula, This is the normal operating mode of the AEL electrolyzer. This is the backup operating mode for the AEL electrolyzer. This is the shutdown mode for the AEL electrolyzer. This is the startup mode for the AEL electrolytic cell. This is the normal operating mode of the PEM electrolyzer. This is the shutdown mode for the PEM electrolyzer. To start the PEM electrolyzer variable, To disable the PEM electrolyzer variables; For the start-up variables of the AEL electrolytic cell, For the off-action variables of the AEL electrolytic cell; For the start-up variables of PEM electrolyzer, For the PEM electrolyzer off action variable, For the first Taiwan equipment, For a moment, This refers to the number of AEL electrolytic cells installed. This refers to the number of PEM electrolyzers installed. No. Taiwan AEL Electrolyzer Hydrogen production capacity that is always in normal working condition. No. Taiwan PEM electrolyzer Hydrogen production capacity that is always in normal working condition. For the first Taiwan AEL Electrolyzer Power that consumes electricity but does not produce hydrogen and is always in a hot standby state; Rated power of a single AEL electrolytic cell This refers to the rated power of a single PEM electrolytic cell; The rated power of the AEL electrolytic cell, This refers to the rated power of the PEM electrolyzer. The total hydrogen production rate at any given time in the AEL electrolyzer is [value missing]. This represents the total hydrogen production rate at any given time in the PEM electrolyzer. The hydrogen production efficiency of the AEL electrolyzer. The hydrogen production efficiency of the PEM electrolyzer. Hydrogen has a high calorific value. For a unit of time, This represents the total power consumption of the two types of electrolytic cells.

[0020] In this embodiment, the modeling constraints of the electrolyzer equipment couple the operational transitions and start-up / shutdown of various operating conditions of the electrolyzer. Furthermore, for the modeling constraints of the two types of electrolyzers in the system (AEL alkaline electrolyzers and PEM proton exchange membrane electrolyzers), a differentiated yet collaborative constraint system is constructed from multi-mode operating logic, action variable constraints, power range limits, and hydrogen production coupling. Firstly, at the operating mode level, a single electrolyzer is creatively decomposed into four states: normal operation, standby, shutdown, and start-up. Orderly switching between states is achieved through variable constraints. For the AEL electrolyzer, the sum of the variables for each mode is defined as the start / shutdown variable. The start-up action variable is limited to not exceeding the standby mode, and the shutdown action variable is limited to not exceeding the previous start-up mode. Simultaneously, the start-up and shutdown action variables are constrained to be mutually exclusive to prevent simultaneous start-up and shutdown of the equipment. For the PEM electrolyzer, it is simplified to two modes: normal operation and shutdown. Variable constraints limit the power change amplitude and action mutual exclusivity, adapting to the differences in physical characteristics between the two types of electrolyzers while ensuring the stability and controllability of the system operation. Secondly, at the power constraint level, an innovative range-based power limitation rule was introduced: for AEL electrolyzers, the normal operating power is constrained to 0.45~1.15 times the rated power, and the standby power is fixed at 0.15 times the rated power; for PEM electrolyzers, the normal operating power is constrained to 0.25~1.1 times the rated power. This not only closely matches the actual operating efficiency range of the electrolyzers but also avoids equipment damage caused by power overload or excessively low operation, while providing flexible adjustment space for wind and solar power fluctuations. Finally, at the coupling level of hydrogen production and total power consumption, parameters such as the number of electrolyzers, rated power, hydrogen production efficiency, and hydrogen calorific value are incorporated into the calculation constraints of hydrogen production rate and total power consumption. This modeling method not only reflects the differences in hydrogen production characteristics between the two types of electrolyzers but also deeply binds the operation of the electrolyzers with constraints such as system power balance and methanol synthesis material balance, effectively improving the feasibility and practicality of system optimization configuration.

[0021] In one embodiment of the present invention, the modeling constraints for wind and solar power generation equipment are constructed using the following formula: In the formula, Let be the actual power generation of the wind turbine at time t. Let be the actual power generation of the photovoltaic system at time t. This is a matrix of per-unit values ​​for the annual power generation of the wind turbine. This is a matrix of per-unit values ​​for the annual power generation of photovoltaic systems. For the installed capacity of the wind turbine, This refers to the installed capacity of photovoltaic power.

[0022] In this embodiment, traditional modeling often relies on typical meteorological year data or simplified linear relationships, making it difficult to accurately match the real-time fluctuation characteristics of wind and solar resources. This invention innovatively introduces a product of historical data per-unit matrix and installed capacity, modeling wind and solar power output as follows: actual wind power output is determined by the historical per-unit matrix of wind resources and the installed capacity of wind turbines; actual solar power output is determined by the historical per-unit matrix of solar resources and the installed capacity of solar power. This modeling approach preserves the temporal fluctuation characteristics of wind and solar resources while directly embedding installed capacity as a core optimization variable into the constraints, achieving coupling between resource characteristics and equipment capacity. Compared to traditional output estimation under a fixed capacity assumption, this constraint more realistically reflects the dynamic characteristics of wind and solar power generation, improving output prediction accuracy and strengthening the linkage between modeling and optimization objectives.

[0023] In one embodiment of the present invention, the battery modeling constraints are constructed using the following formula: In the formula, for The capacity of the storage battery at all times. for The charging power of the battery at all times for The discharge power of the battery at all times; For the charging efficiency of the storage battery, This refers to the battery's discharge efficiency. For time intervals, This is the battery charging status bit. This is the battery discharge state position. The rated capacity of the battery, Rated capacity of PCS; The constraints for fuel cell modeling are constructed using the following formula: In the formula, For fuel cells Power generation at any given moment Hydrogen consumption rate of fuel cells For the efficiency of fuel cells, This refers to the rated capacity of the fuel cell; The compressor modeling constraints are constructed using the following formula: In the formula, This refers to the power consumption of the compressor. This refers to the compressor's operating coefficient. The constraints for modeling the hydrogen storage tank are constructed using the following formula: In the formula, This represents the real-time hydrogen capacity within the hydrogen storage tank. The real-time rate of hydrogen supply to the external hydrogen storage tank. Configure the capacity for the hydrogen storage tank.

[0024] In this embodiment, to protect the battery from deep discharge to excessively low energy levels and to prevent it from remaining in a high state of charge for extended periods, the minimum state of charge (SOC) of the battery is limited to 10%. Configure the capacity of the storage battery. This is the battery charging status bit. For the battery discharge state, which is a 0-1 variable, an innovative dynamic capacity iteration formula incorporating charge and discharge efficiency is introduced at the battery modeling constraint level. This directly correlates the capacity at time t with the capacity at the previous time, charge and discharge power, time interval, and efficiency, while limiting the capacity to within 10% to 100% of the rated capacity to avoid overcharging and over-discharging. Mutual exclusion constraints on charge and discharge state positions ensure that devices cannot charge and discharge simultaneously, which not only conforms to the physical degradation characteristics of batteries but also provides energy storage regulation for the system to mitigate wind and solar power fluctuations. At the fuel cell modeling constraint level, the power generation is creatively directly linked to the hydrogen consumption rate, efficiency, and the high calorific value of hydrogen. This reflects the physical laws of hydrogen-to-electricity conversion and strictly limits the fuel cell output within the rated capacity range to avoid overload operation. At the compressor modeling constraint level, a linear proportional relationship is innovatively used to correlate the compressor power consumption with the total power consumption of the electrolyzer, achieving synchronous mapping of "hydrogen production - compression energy consumption" through a fixed operating coefficient. At the level of hydrogen storage tank modeling constraints, a dynamic hydrogen capacity iteration formula was creatively constructed, which directly correlates the real-time capacity with the capacity at the previous moment, the hydrogen production rate of the two types of electrolyzers, the hydrogen consumption rate of the fuel cell, and the external hydrogen supply rate. At the same time, the capacity is limited to the range of 0 to the rated configuration, which realizes the full material balance of "hydrogen production-hydrogen consumption-hydrogen storage" and enhances the controllability and predictability of hydrogen energy flow in the system.

[0025] like Figure 6As shown in this embodiment, traditional K-means clustering leads to the loss of data temporal sequence and fails to reflect the cross-seasonal energy storage advantages of hydrogen energy storage; simple time-series clustering cannot consider the delayed start-up of electrolyzers for hot standby. Therefore, this invention adopts monthly hierarchical clustering. First, the year is divided into 12 months, and the number of typical days is determined based on the number of days in each month. Second, for each month, the daily curves of wind power and photovoltaic power are standardized. Then, the Ward hierarchical clustering method is used to cluster all daily curves within the month. The Ward method, based on the principle of minimizing variance, ensures that the curve shapes of samples within a cluster are most similar. From each cluster, the medoid (i.e., the true day with the smallest Euclidean distance from the cluster center) is selected as the typical day of that cluster to preserve the authenticity of the actual observation data. The clustering results are sorted by the original date to maintain the time order within the month. Finally, the typical days selected for each month are arranged in monthly order, and their beginnings and ends are connected to reconstruct a typical daily power generation sequence of 744 hours. At the same time, the number of original days represented by each typical day is recorded as a weight to reflect the statistical importance of that typical day throughout the year.

[0026] In one embodiment of the present invention, the power balance constraint is constructed by the following formula; The constraints for methanol synthesis modeling are constructed using the following formula: In the formula, Gasification of corn stalks Flow rate Gasification of corn stalks Flow rate Gasification of corn stalks Flow rate In the syngas produced by a unit biomass gasification reaction The content, In the syngas produced by a unit biomass gasification reaction The content, In the syngas produced by a unit biomass gasification reaction The content, The gasification rate of biomass. For entry into the methanol synthesis unit Flow rate; To participate in the water-gas shift reaction Flow rate The product generated by the water-gas shift reaction Flow rate The product generated by the water-gas shift reaction Flow rate For system emissions Flow rate The rate of methanol synthesis; The efficiency of methanol synthesis. This represents the total amount of methanol synthesized. The rate of methanol synthesis; The efficiency of methanol synthesis. The power consumption of the biomass processing device; The power consumption per unit of biomass processing This refers to the power consumption of the methanol synthesis unit; Here is the molar mass of methanol; The electricity consumption is expressed as a unit of methanol. This refers to the installed capacity of the methanol synthesis unit.

[0027] In this embodiment, the power balance constraint requires that the total output power at the generator end must equal the total power consumption at the load end. As can be seen from the system structure, the wind and solar power output at any given time, plus the discharge power of the battery and fuel cell, must equal the power consumption of the electrolyzer, compressor, battery charging power, abandoned power, and the power consumption of the biomass gasification unit and methanol synthesis unit. Methanol is synthesized from carbon monoxide and hydrogen. Biomass gasification produces a certain proportion of carbon monoxide, hydrogen, and carbon dioxide. A portion of the carbon monoxide enters the water gas reaction to generate hydrogen to adjust the carbon-hydrogen ratio, which then enters the methanol synthesis unit. Any remaining hydrogen is supplemented by the wind and solar hydrogen production system. The carbon dioxide obtained from the water gas reaction and the gasified carbon dioxide are directly discharged. The equations for the methanol reaction and the water gas reaction are as follows: In this embodiment, the device data is shown in Table 1: Table 1 System Configuration Parameters The battery charge / discharge efficiency is taken as 0.93; the fuel cell efficiency as 0.6; the efficiencies of the alkaline electrolyzer and PEM electrolyzer are taken as 0.62 and 0.67 respectively; the carbon dioxide emission penalty is 0.1 yuan / kg; and the average annual operation and maintenance cost is taken as 2% of the investment cost. Corn stalks are used as the biomass feedstock, and the gasification data is derived from experience: 1 kg of corn stalks produces 14.341 mol / L gas. 14.662 mol With 14.866 mol At the same time, a minimum annual methanol production of 100,000 tons is set; the carbon quota is 0.

[0028] In this embodiment, the optimization results are analyzed as shown in Table 2: Table 2 Table 2 shows that the system is configured with 100.71MW of wind power and 107.04MW of photovoltaic capacity. While maintaining a high renewable energy output, a synergistic regulation mechanism with both short- and long-term time scales is formed by simultaneously configuring 40.61MW / 162.44MWh of electrochemical energy storage and 6.10MW of fuel cells. The configuration results for the two types of electrolyzers are 10 AEL electrolyzers and 0 PEM electrolyzers. The high unit price of PEM electrolyzers is the main reason for not configuring PEM electrolyzers in the system. However, as the cost of AEL electrolyzers stabilizes in the future and metal substitutes appear in the reactor of PEM electrolyzers, the proportion of PEM will gradually increase.

[0029] Table 3 Cost Analysis As shown in Table 3, the average annual comprehensive cost of an electricity-hydrogen-biomass system for producing 100,000 tons of methanol per year is 210 million yuan, with a static investment of 978 million yuan. In the future, the levelized cost of methanol can be calculated by adding investments in chemical synthesis, biomass treatment, and water resources used in the system reaction.

[0030] like Figure 4 As shown in the figure, in this embodiment, the new energy power generation unit is equivalent to the wind and solar power generation unit in the system, and the water electrolysis hydrogen production unit is equivalent to the two types of electrolyzers in the system. The two types of electrolyzers include PEM electrolyzers and AEL electrolyzers. In addition, the system also includes a biomass hydrogen production unit and a composite energy storage unit.

[0031] like Figure 2 , Figure 3As shown, this embodiment of the invention provides a capacity configuration device for electrochemical hydrogen composite energy storage combining wind and solar hydrogen production. The device embodiment can be implemented through software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for a capacity configuration device of an electrochemical hydrogen composite energy storage system based on wind and solar hydrogen production, provided in an embodiment of the present invention. Except for... Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0032] like Figure 3 As shown in this embodiment, a capacity configuration device for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production is provided. This capacity configuration device is applied to an electrochemical hydrogen composite energy storage system, which includes a wind and solar power generation unit, two types of electrolyzers, a hydrogen storage tank, a fuel cell, and a methanol synthesis unit. The first data processing module is used to construct the annual comprehensive cost function of the electrochemical hydrogen composite energy storage system; wherein, the annual comprehensive cost function includes the annual investment cost, the annual operation and maintenance cost, the annual carbon emission cost, and the cost of consuming biomass raw materials; The second data processing module is used to call a preset solver to solve the annual comprehensive cost function under the premise of satisfying comprehensive constraints, so as to obtain the comprehensive capacity configuration with the minimum cost of the electrochemical hydrogen composite energy storage system. The comprehensive capacity configuration includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks; the comprehensive constraints include modeling constraints for wind and solar power generation equipment, modeling constraints for electrolyzer equipment, modeling constraints for batteries, modeling constraints for fuel cells, modeling constraints for compressors, modeling constraints for hydrogen storage tanks, modeling constraints for methanol synthesis, and power balance constraints.

[0033] In one embodiment of the present invention, the average annual comprehensive cost function is constructed by the following formula: In the formula, The average annual comprehensive cost, The average annual investment cost is... The average annual operation and maintenance cost is mentioned above. The annual carbon emission cost is... The cost of consuming biomass raw materials, For the first The unit investment price of the equipment, For the first Rated capacity of the equipment For the system planning life, For the discount rate, set This includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks. For the first The average annual unit investment and maintenance cost of the equipment for The flow rate of carbon dioxide discharged by the system at all times. For carbon quotas, T Total duration Cost per unit of annual carbon emissions Cost per unit of biomass; for Real-time biomass usage.

[0034] In one embodiment of the present invention, the rated power of the two types of electrolyzers includes the rated power of PEM electrolyzers and the rated power of AEL electrolyzers, and the installed capacity of wind and solar power generation includes the installed capacity of wind turbine power generation and the installed capacity of photovoltaic power generation.

[0035] In one embodiment of the present invention, the modeling constraints of the electrolytic cell equipment are constructed using the following formula: In the formula, This is the normal operating mode of the AEL electrolyzer. This is the backup operating mode for the AEL electrolyzer. This is the shutdown mode for the AEL electrolyzer. This is the startup mode for the AEL electrolytic cell. This is the normal operating mode of the PEM electrolyzer. This is the shutdown mode for the PEM electrolyzer. To start the PEM electrolyzer variable, To disable the PEM electrolyzer variables; For the start-up variables of the AEL electrolytic cell, For the off-action variables of the AEL electrolytic cell; For the start-up variables of PEM electrolyzer, For the PEM electrolyzer off action variable, For the first Taiwan equipment, For a moment, This refers to the number of AEL electrolytic cells installed. This refers to the number of PEM electrolyzers installed. No. Taiwan AEL Electrolyzer Hydrogen production capacity that is always in normal working condition. No. Taiwan PEM electrolyzer Hydrogen production capacity that is always in normal working condition. For the first Taiwan AEL Electrolyzer Power that consumes electricity but does not produce hydrogen and is always in a hot standby state; Rated power of a single AEL electrolytic cell This refers to the rated power of a single PEM electrolytic cell; The rated power of the AEL electrolytic cell, This refers to the rated power of the PEM electrolyzer. The total hydrogen production rate at any given time in the AEL electrolyzer is [value missing]. This represents the total hydrogen production rate at any given time in the PEM electrolyzer. The hydrogen production efficiency of the AEL electrolyzer. The hydrogen production efficiency of the PEM electrolyzer. Hydrogen has a high calorific value. For a unit of time, This represents the total power consumption of the two types of electrolytic cells.

[0036] In one embodiment of the present invention, the modeling constraints for the wind and solar power generation equipment are constructed using the following formula: In the formula, Let be the actual power generation of the wind turbine at time t. Let be the actual power generation of the photovoltaic system at time t. This is a matrix of per-unit values ​​for the annual power generation of the wind turbine. This is a matrix of per-unit values ​​for the annual power generation of photovoltaic systems. For the installed capacity of the wind turbine, This refers to the installed capacity of photovoltaic power.

[0037] In one embodiment of the present invention, the battery modeling constraints are constructed using the following formula: In the formula, for The capacity of the storage battery at all times. for The charging power of the battery at all times for The discharge power of the battery at all times; For the charging efficiency of the storage battery, This refers to the battery's discharge efficiency. For time intervals, This is the battery charging status bit. This is the battery discharge state position. The rated capacity of the battery, Rated capacity of PCS; The fuel cell modeling constraints are constructed using the following formula: In the formula, For fuel cells Power generation at any given moment Hydrogen consumption rate of fuel cells For the efficiency of fuel cells, This refers to the rated capacity of the fuel cell; The compressor modeling constraints are constructed using the following formula: In the formula, This refers to the power consumption of the compressor. This refers to the compressor's operating coefficient. The modeling constraints for the hydrogen storage tank are constructed using the following formula: In the formula, This represents the real-time hydrogen capacity within the hydrogen storage tank. The real-time rate of hydrogen supply to the external hydrogen storage tank. Configure the capacity for the hydrogen storage tank.

[0038] In one embodiment of the present invention, the power balance constraint is constructed by the following formula; The methanol synthesis modeling constraints are constructed using the following formula: In the formula, Gasification of corn stalks Flow rate Gasification of corn stalks Flow rate Gasification of corn stalks Flow rate In the syngas produced by a unit biomass gasification reaction The content, In the syngas produced by a unit biomass gasification reaction The content, In the syngas produced by a unit biomass gasification reaction The content, For the gasification rate of biomass For entry into the methanol synthesis unit Flow rate; To participate in the water-gas shift reaction Flow rate The product generated by the water-gas shift reaction Flow rate The product generated by the water-gas shift reaction Flow rate For system emissions Flow rate The rate of methanol synthesis; The efficiency of methanol synthesis. This represents the total amount of methanol synthesized. The rate of methanol synthesis; The efficiency of methanol synthesis. The power consumption of the biomass processing device; The power consumption per unit of biomass processing This refers to the power consumption of the methanol synthesis unit; Here is the molar mass of methanol; The electricity consumption is expressed as a unit of methanol. This refers to the installed capacity of the methanol synthesis unit.

[0039] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the capacity configuration device of an electrochemical hydrogen composite energy storage system based on wind and solar hydrogen production. In other embodiments of the present invention, a capacity configuration device for an electrochemical hydrogen composite energy storage system based on wind and solar hydrogen production may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0040] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0041] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a capacity configuration method for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production, according to any embodiment of this invention.

[0042] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a capacity configuration method for electrochemical hydrogen composite energy storage based on wind and solar hydrogen production, according to any embodiment of this invention.

[0043] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or Mpu) of the system or apparatus may read and execute the program code stored in the storage medium.

[0044] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0045] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as Cd-ROM, Cd-R, Cd-Rw, DVD-ROM, DVD-Ram, DVD-Rw, DVD+Rw), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0046] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0047] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other device installed on the expansion board or expansion module executes some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0049] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for configuring the capacity of an electrochemical hydrogen composite energy storage system based on wind and solar hydrogen production, characterized in that, The method is applied to an electrochemical hydrogen composite energy storage system, which includes a wind and solar power generation unit, two types of electrolyzers, a hydrogen storage tank, a fuel cell, and a methanol synthesis unit, comprising: Construct an average annual comprehensive cost function for an electrochemical hydrogen composite energy storage system; wherein, the average annual comprehensive cost function includes average annual investment cost, average annual operation and maintenance cost, annual carbon emission cost, and cost of consuming biomass raw materials; Under the premise of satisfying comprehensive constraints, the preset solver is invoked to solve the annual comprehensive cost function to obtain the comprehensive capacity configuration with the minimum cost of the electrochemical hydrogen composite energy storage system. The comprehensive capacity configuration includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks; the comprehensive constraints include modeling constraints for wind and solar power generation equipment, modeling constraints for electrolyzer equipment, modeling constraints for batteries, modeling constraints for fuel cells, modeling constraints for compressors, modeling constraints for hydrogen storage tanks, modeling constraints for methanol synthesis, and power balance constraints. The rated power of the two types of electrolyzers includes the rated power of PEM electrolyzers and the rated power of AEL electrolyzers, and the installed capacity of wind and solar power generation includes the installed capacity of wind turbine power generation and the installed capacity of photovoltaic power generation. The modeling constraints for the electrolytic cell equipment are constructed using the following formula: In the formula, This is the normal operating mode of the AEL electrolyzer. This is the backup operating mode for the AEL electrolyzer. This is the shutdown mode for the AEL electrolyzer. This is the startup mode for the AEL electrolytic cell. This is the normal operating mode of the PEM electrolyzer. This is the shutdown mode for the PEM electrolyzer. To start the PEM electrolyzer variable, To disable the PEM electrolyzer variables; For the start-up variables of the AEL electrolytic cell, For the off-action variables of the AEL electrolytic cell; For the start-up variables of PEM electrolyzer, For the PEM electrolyzer off action variable, For the first Taiwan equipment, For a moment, This refers to the number of AEL electrolytic cells installed. This refers to the number of PEM electrolyzers installed. No. Taiwan AEL Electrolyzer Hydrogen production capacity that is always in normal working condition. No. Taiwan PEM electrolyzer Hydrogen production capacity that is always in normal working condition. For the first Taiwan AEL Electrolyzer Power that consumes electricity but does not produce hydrogen and is always in a hot standby state; Rated power of a single AEL electrolytic cell This refers to the rated power of a single PEM electrolytic cell; The rated power of the AEL electrolytic cell, This refers to the rated power of the PEM electrolyzer. The total hydrogen production rate at any given time in the AEL electrolyzer is [value missing]. This represents the total hydrogen production rate at any given time in the PEM electrolyzer. The hydrogen production efficiency of the AEL electrolyzer. The hydrogen production efficiency of the PEM electrolyzer. Hydrogen has a high calorific value. For a unit of time, This represents the total power consumption of the two types of electrolytic cells; The power balance constraint is constructed using the following formula; The methanol synthesis modeling constraints are constructed using the following formula: In the formula, Gasification of corn stalks Flow rate Gasification of corn stalks Flow rate Gasification of corn stalks Flow rate In the syngas produced by a unit biomass gasification reaction The content, In the syngas produced by a unit biomass gasification reaction The content, In the syngas produced by a unit biomass gasification reaction The content, The gasification rate of biomass. For entry into the methanol synthesis unit Flow rate; To participate in the water-gas shift reaction Flow rate The product generated by the water-gas shift reaction Flow rate The product generated by the water-gas shift reaction Flow rate For system emissions Flow rate The rate of methanol synthesis; The efficiency of methanol synthesis. This represents the total amount of methanol synthesized. The rate of methanol synthesis; The efficiency of methanol synthesis. The power consumption of the biomass processing device; The power consumption per unit of biomass processing This refers to the power consumption of the methanol synthesis unit; Here is the molar mass of methanol; The electricity consumption is expressed as a unit of methanol. This refers to the installed capacity of the methanol synthesis unit.

2. The method according to claim 1, characterized in that, The annual average comprehensive cost function is constructed using the following formula: In the formula, The average annual comprehensive cost, The average annual investment cost is... The average annual operation and maintenance cost is mentioned above. The annual carbon emission cost is... The cost of consuming biomass raw materials, For the first The unit investment price of the equipment, For the first Rated capacity of the equipment For the system planning life, For the discount rate, set This includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks. For the first The average annual unit investment and maintenance cost of the equipment for The flow rate of carbon dioxide discharged by the system at all times. For carbon quotas, T Total duration Cost per unit of annual carbon emissions Cost per unit of biomass; for Real-time biomass usage.

3. The method according to claim 2, characterized in that, The modeling constraints for the wind and solar power generation equipment are constructed using the following formula: In the formula, Let be the actual power generation of the wind turbine at time t. Let be the actual power generation of the photovoltaic system at time t. This is a matrix of per-unit values ​​for the annual power generation of the wind turbine. This is a matrix of per-unit values ​​for the annual power generation of photovoltaic systems. The installed capacity for wind turbine power generation, This refers to the installed capacity of photovoltaic power.

4. The method according to claim 3, characterized in that, The battery modeling constraints are constructed using the following formula: In the formula, for The capacity of the storage battery at all times. for The charging power of the battery at all times for The discharge power of the battery at all times; For the charging efficiency of the storage battery, This refers to the battery's discharge efficiency. For time intervals, This is the battery charging status bit. This is the battery discharge state position. The rated capacity of the battery, Rated capacity of PCS; The fuel cell modeling constraints are constructed using the following formula: In the formula, For fuel cells Power generation at any given moment Hydrogen consumption rate of fuel cells For the efficiency of fuel cells, This refers to the rated capacity of the fuel cell; The compressor modeling constraints are constructed using the following formula: In the formula, This refers to the power consumption of the compressor. This refers to the compressor's operating coefficient. The modeling constraints for the hydrogen storage tank are constructed using the following formula: In the formula, This represents the real-time hydrogen capacity within the hydrogen storage tank. The real-time rate of hydrogen supply to the external hydrogen storage tank. Configure the capacity for the hydrogen storage tank.

5. A capacity configuration device for electrochemical hydrogen composite energy storage combining wind and solar hydrogen production, characterized in that, For performing the method as described in any one of claims 1-4, the capacity configuration device is applied to an electrochemical hydrogen composite energy storage system, the electrochemical hydrogen composite energy storage system comprising a wind and solar power generation unit, two types of electrolyzers, a hydrogen storage tank, a fuel cell, and a methanol synthesis unit, including: The first data processing module is used to construct the annual comprehensive cost function of the electrochemical hydrogen composite energy storage system; wherein, the annual comprehensive cost function includes the annual investment cost, the annual operation and maintenance cost, the annual carbon emission cost, and the cost of consuming biomass raw materials; The second data processing module is used to call a preset solver to solve the annual comprehensive cost function under the premise of satisfying comprehensive constraints, so as to obtain the comprehensive capacity configuration with the minimum cost of the electrochemical hydrogen composite energy storage system. The comprehensive capacity configuration includes the installed capacity of wind and solar power generation, the rated power of the two types of electrolyzers, the rated capacity of batteries, the rated capacity of PCS, the rated capacity of fuel cells, and the configuration capacity of hydrogen storage tanks; the comprehensive constraints include modeling constraints for wind and solar power generation equipment, modeling constraints for electrolyzer equipment, modeling constraints for batteries, modeling constraints for fuel cells, modeling constraints for compressors, modeling constraints for hydrogen storage tanks, modeling constraints for methanol synthesis, and power balance constraints.

6. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-4.

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