Energy storage capacity configuration method and system for wind-solar power generation based on pem electrolyzer

Abstract

This invention discloses a method and system for configuring energy storage capacity in wind and solar power generation based on PEM electrolyzers. The method includes: determining the maximum deployable photovoltaic capacity P. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation; setting the photovoltaic installed capacity P. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​is used to form a preset range of capacity combinations; for each capacity combination, the total energy storage capacity W under the set objective is calculated. 储 and the real-time power vector P for hydrogen production 制氢实 Based on the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination. 总 Compare the total cost C of all capacity combinations 总 This invention outputs the target capacity combination with the lowest total cost. It solves the problem that existing energy storage capacity configuration methods cannot simultaneously achieve both hydrogen production capacity and economic efficiency.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and in particular to a method and system for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer. Background Technology

[0002] With the rapid development of the new energy industry, the scale of development and utilization of renewable energy sources such as wind and solar power is constantly expanding. Off-grid wind and solar power generation systems, because they are not restricted by grid access, have broad application prospects in remote areas, islands and other scenarios. Furthermore, integrated projects combining PEM (proton exchange membrane) electrolyzers for hydrogen production can convert unstable wind and solar energy into hydrogen energy storage, achieving efficient energy utilization.

[0003] However, off-grid wind and solar power generation inherently suffers from intermittency and high volatility, leading to unstable power output. Meanwhile, although the hydrogen production capacity of PEM electrolyzers can be adjusted within the range of 0-100%, it requires precise matching with wind and solar power output and energy storage systems; otherwise, problems such as high curtailment rates, insufficient hydrogen production, or excessive system costs may occur. Existing capacity configuration methods are mostly based on typical daily data or simplified models, failing to fully consider the all-time output characteristics throughout the 8760 hours of the year, making it difficult to reflect the realities of long-term system operation. Furthermore, optimization objectives often focus solely on hydrogen production or cost, failing to achieve a synergistic balance between the two, resulting in insufficient practicality of the configuration results.

[0004] Therefore, there is an urgent need for a capacity configuration method that can combine all-time output data and take into account both hydrogen production and economic efficiency to solve the capacity matching problem of off-grid wind-solar-PEM electrolyzer-energy storage integrated projects and improve system operation stability and economy. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for configuring energy storage capacity for wind and solar power generation based on PEM electrolyzers, which aims to solve the problem that the energy storage capacity configuration method for wind and solar power generation in the prior art cannot take into account both hydrogen production and economy.

[0006] The embodiments of the present invention are implemented as follows:

[0007] A method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer, characterized in that the method includes:

[0008] By acquiring photovoltaic (PV) power output data and wind power output data for all time periods, the maximum deployable PV capacity P can be determined. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation;

[0009] Set photovoltaic installed capacity P光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 Divide them into N groups to form a preset capacity combination;

[0010] For each capacity combination, the MATLAB-CPLEX solver was used to calculate the total energy storage capacity W under the set objective. 储 and the real-time power vector P for hydrogen production 制氢实 ;

[0011] Based on the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 Output the target capacity combination with the lowest total cost.

[0012] Furthermore, in the above-mentioned method for configuring energy storage capacity for wind and solar power generation based on PEM electrolyzers, the total energy storage capacity W under each capacity combination is calculated using the MATLAB-CPLEX solver under a set objective. 储 and the real-time power vector P for hydrogen production 制氢实 The steps include:

[0013] Based on photovoltaic power output data, wind power output data, and photovoltaic installed capacity P 光 Wind power installed capacity P 风 Determine the real-time photovoltaic output vector P respectively 光实 Real-time wind power output vector P 风实 And define the abandoned power vector P 弃电 Real-time power vector of energy storage P 储实 Total energy storage capacity (W) 储 Real-time capacity vector of energy storage W 储实 Real-time power vector P for hydrogen production 制氢实 ;

[0014] Set constraints, including energy conservation conditions, real-time power limits for energy storage, hydrogen production load limits, hydrogen production limits, power curtailment limits, energy storage capacity limits, and constraints that the energy storage capacity is 0 at the initial and final moments of energy storage.

[0015] The energy conservation condition is: P 光实 +P 风实 +P 储实 -P制氢实 -P 弃电 =0;

[0016] The real-time power limit for energy storage is: -P 储 ≤P 储实 ≤P 储 ;

[0017] Hydrogen production load limit is: 0 ≤ P 制氢实 ≤P 制氢 ;

[0018] Hydrogen production is limited to: Q≤ΣP 制氢实 / hydrogen production coefficient;

[0019] The limit for the amount of electricity to be abandoned is: 0 ≤ P 弃电 ≤P 光实 +P 风实 ;

[0020] Energy storage capacity is limited to: 0 ≤ W 储实 ≤W 储 ;

[0021] in, k is the time step index;

[0022] The objective is to maximize hydrogen production, which is then transformed into a problem of minimizing hydrogen production. The objective function is Obj = Q - ΣP. 制氢实 / hydrogen production coefficient, solve for the total energy storage capacity W under this combination. 储 and the real-time power vector P for hydrogen production 制氢实 .

[0023] Furthermore, in the above-mentioned method for configuring energy storage capacity for wind and solar power generation based on PEM electrolyzers, the cost data includes the photovoltaic construction cost C. 光 Wind power construction cost C 风 Electrolytic cell construction cost C 氢 Energy storage construction cost C 储 Photovoltaic operating cost M 光 Wind power operating cost M 风 Electrolytic cell operating cost M 氢 Energy storage operating cost M 储 The price of hydrogen S 氢 .

[0024] Furthermore, in the above-mentioned method for configuring energy storage capacity for wind and solar power generation based on PEM electrolyzers, the step of configuring energy storage capacity according to the total energy storage capacity W... 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 The calculation formula is:

[0025] C总 =C 光 ×P 光 +C 风 ×P 风 +C 氢 ×P 制氢 +C 储 ×W 储 +M 光 ×P 光 +M 风 ×P 风 +M 氢 ×P 制氢 +M 储 ×W 储 -S 氢 ×ΣP 制氢实 / hydrogen production coefficient;

[0026] Among them, C 光 For photovoltaic construction costs, P 光 For photovoltaic installed capacity, C 风 For wind power construction costs, P 风 For wind power installed capacity, C 氢 For the construction cost of the electrolytic cell, P 制氢 For hydrogen production load, C 储 For energy storage construction costs, W 储 M represents the total energy storage capacity. 光 M represents the operating cost of photovoltaic power and the operating cost of wind power. 风 M 氢 For the operating cost of electrolytic cells, M 储 For energy storage operating costs, S 氢 This refers to the price of hydrogen.

[0027] Furthermore, in the above-mentioned energy storage capacity configuration method for wind and solar power generation based on PEM electrolyzers, the hydrogen production coefficient is the power consumption for producing one standard cubic meter of hydrogen.

[0028] Furthermore, in the above-mentioned method for configuring energy storage capacity for wind and solar power generation based on PEM electrolyzers, the photovoltaic installed capacity P... 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for are as follows:

[0029] 0≤P 光 ≤P 光max ;

[0030] 0≤P 风 ≤P 风max ;

[0031] 0≤P 储 ≤P 光 +P 风 .

[0032] Furthermore, in the above-mentioned method for configuring energy storage capacity for wind and solar power generation based on PEM electrolyzers, the photovoltaic installed capacity P is configured using an arithmetic progression. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The steps for dividing the species into N groups to form a predetermined capacity combination include:

[0033] Based on the minimum annual hydrogen production requirement Q, the hydrogen production coefficient, and all-time wind and solar power output data, the minimum synergistic threshold range between photovoltaic and wind power installed capacity is calculated:

[0034] ;

[0035] Remove invalid intervals from the original value range that are below the threshold. To meet the minimum installed capacity required for minimum hydrogen production using photovoltaic power alone, The minimum installed capacity required to meet the minimum hydrogen production capacity using wind power output alone;

[0036] Calculate the photovoltaic installed capacity P respectively 光 Wind power installed capacity P 风 Energy storage capacity P 储 Within their respective valid value ranges, the total cost C is... 总 The marginal impact coefficient is used to divide the effective value range of each parameter into high-sensitivity, medium-sensitivity and low-sensitivity segments based on the marginal impact coefficient. The marginal impact coefficient is obtained by taking the partial derivative of the formula for calculating total cost. The range where the absolute value of the marginal impact coefficient is greater than the preset threshold is the high-sensitivity segment, the range between the two preset thresholds is the medium-sensitivity segment, and the range less than the minimum preset threshold is the low-sensitivity segment.

[0037] A dynamic step-size arithmetic sequence division strategy is adopted for different sensitive segments. The highly sensitive segments are divided into N1 groups according to the first preset step size, the medium sensitive segments are divided into N2 groups according to the second preset step size, and the low sensitive segments are divided into N3 groups according to the third preset step size. The first preset step size < the second preset step size < the third preset step size, and N1 + N2 + N3 = N.

[0038] The photovoltaic installed capacity P after division 光 The values ​​of each group and the installed wind power capacity P 风 Each group's values ​​were paired and scored. The scoring index was the ratio of the standard deviation to the mean of the combined wind and solar power output over the entire time period. The smaller the ratio, the stronger the complementarity. Photovoltaic installed capacity P with a score below the preset qualified threshold was removed. 光 And wind power installed capacity P 风 combination;

[0039] The qualified photovoltaic installed capacity P after screening光 And wind power installed capacity P 风 Combined and partitioned energy storage capacity P 储 The values ​​from each group are cross-combined to form the final preset capacity combination.

[0040] Another object of the present invention is to provide an energy storage capacity configuration system for wind and solar power generation based on a PEM electrolyzer, the system comprising:

[0041] The acquisition module is used to acquire photovoltaic (PV) power output data and wind power output data throughout the entire time period to determine the maximum deployable PV capacity P. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation;

[0042] The setting module is used to set the photovoltaic installed capacity P. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 Divide them into N groups to form a preset capacity combination;

[0043] The combination module is used to calculate the total energy storage capacity W under each capacity combination using the MATLAB-CPLEX solver, under a set objective. 储 and the real-time power vector P for hydrogen production 制氢实 ;

[0044] Configuration module, used to configure according to the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 Output the target capacity combination with the lowest total cost.

[0045] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0046] Another object of the present invention is to provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0047] This invention determines the maximum deployable photovoltaic capacity P by acquiring photovoltaic power output data and wind power output data throughout the entire time period. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation; setting the photovoltaic installed capacity P. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The system is divided into N groups to form a preset capacity combination. For each capacity combination, the MATLAB-CPLEX solver is used to calculate the total energy storage capacity W under the set target. 储 and the real-time power vector P for hydrogen production 制氢实 Based on the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 This approach outputs the target capacity combination with the lowest total cost. Capacity configuration no longer relies on one-sided analysis of typical daily data or simplified models, but rather on the year-round, all-time output characteristics. Through multi-dimensional capacity combination traversal, it achieves precise matching of wind, solar, energy storage, and hydrogen production systems across all scenarios. This is achieved through all-time data acquisition to reconstruct the system's long-term real-time operating status; multiple capacity combination traversal to cover potential matching schemes; precise calculation of energy storage capacity and real-time hydrogen production power by a professional solver; total cost calculation to achieve a synergistic balance between minimum hydrogen production requirements and economic efficiency; and optimal combination selection to output an efficient matching scheme. The synergistic effect of multiple links avoids situations such as high curtailment rates, insufficient hydrogen production, or excessively high system costs. This solves the problem in existing technologies where the lack of full consideration of all-time output characteristics makes it difficult to balance hydrogen production and economic efficiency. Attached Figure Description

[0048] Figure 1 A flowchart illustrating the energy storage capacity configuration method for wind and solar power generation based on a PEM electrolyzer, provided in the first embodiment of the present invention.

[0049] Figure 2 This is a structural block diagram of the energy storage capacity configuration system for wind and solar power generation based on a PEM electrolyzer, as shown in the third embodiment of the present invention.

[0050] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0051] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0052] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] Example 1

[0055] Please see Figure 1 The figure shows a method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer in the first embodiment of the present invention, the method including steps S10 to S13.

[0056] Step S10: Obtain photovoltaic power output data and wind power output data for the entire time period to determine the maximum deployable photovoltaic capacity P. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for obtaining wind and solar power generation.

[0057] This requires collecting photovoltaic (PV) and wind power output data throughout the entire time period. In specific implementation, "entire time period" specifically refers to 8760 hours, which can fully cover the intermittent and fluctuating characteristics of wind and solar power generation throughout the year, avoiding the limitations of typical daily data. Simultaneously, key project constraints and cost data must be clearly defined, including the maximum deployable PV capacity P. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for obtaining wind and solar power generation.

[0058] Specifically, cost data includes photovoltaic construction cost C. 光 Wind power construction cost C风 Electrolytic cell construction cost C 氢 Energy storage construction cost C 储 Photovoltaic operating cost M 光 Wind power operating cost M 风 Electrolytic cell operating cost M 氢 Energy storage operating cost M 储 The price of hydrogen S 氢 .

[0059] Step S11, set the photovoltaic installed capacity P 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 Divide them into N groups to form a preset capacity combination.

[0060] Next, the capacity range is set and the combination is generated. First, the photovoltaic installed capacity P is determined. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The value range is determined by dividing the three values ​​into N groups using an arithmetic sequence, forming a preset number of capacity combinations. Setting a value range is to limit the reasonable configuration interval and avoid invalid configurations (such as exceeding the site's carrying capacity), while the arithmetic sequence division can systematically and comprehensively cover all possible configuration schemes, ensuring that no potential optimal solution is missed. The number of groups N can be adjusted according to the project's accuracy requirements, balancing computational efficiency and result accuracy.

[0061] Step S12: For each capacity combination, use the MATLAB-CPLEX solver to calculate the total energy storage capacity W under the set objective. 储 and the real-time power vector P for hydrogen production 制氢实 .

[0062] The simulation process involves using the MATLAB-CPLEX solver for each capacity combination (this solver excels at handling linear programming optimization problems and is well-suited to the constraints and objective function requirements of this method). Under the core objective of "maximizing hydrogen production," by defining variables and setting constraints, the total energy storage capacity and real-time hydrogen production power vector corresponding to the combination are finally obtained. This step is crucial for achieving precise matching between wind, solar, and energy storage systems and PEM electrolyzers, directly determining the hydrogen production capacity and energy storage requirements of each configuration.

[0063] Specifically, based on photovoltaic power output data, wind power output data, and photovoltaic installed capacity P... 光Wind power installed capacity P 风 Determine the real-time photovoltaic output vector P respectively 光实 Real-time wind power output vector P 风实 And define the abandoned power vector P 弃电 Real-time power vector of energy storage P 储实 Total energy storage capacity (W) 储 Real-time capacity vector of energy storage W 储实 Real-time power vector P for hydrogen production 制氢实 ;

[0064] Set constraints, including energy conservation conditions, real-time power limits for energy storage, hydrogen production load limits, hydrogen production limits, power curtailment limits, energy storage capacity limits, and constraints that the energy storage capacity is 0 at the initial and final moments of energy storage.

[0065] The objective is to maximize hydrogen production, which is then transformed into a problem of minimizing hydrogen production. The objective function is Obj = Q - ΣP. 制氢实 / hydrogen production coefficient, solve for the total energy storage capacity W under this combination. 储 and the real-time power vector P for hydrogen production 制氢实 .

[0066] First, the variables are defined. Based on the previously acquired full-time photovoltaic (PV) and wind power (Wind) output data, combined with the current combined PV and Wind power installed capacities, the following variables are calculated: PV real-time output vector (actual output at each moment = base output data at that moment × PV installed capacity, totaling 8760 values, accurately reflecting the temporal distribution characteristics of PV output) and Wind power real-time output vector (calculation logic is consistent with PV, reflecting the temporal fluctuations of Wind power output). Simultaneously, the following variables are defined: curtailment vector (recording excess electricity not utilized at each moment), energy storage real-time power vector (charging and discharging power of the energy storage system at each moment, positive values ​​for discharging and negative values ​​for charging), total energy storage capacity (total energy storage scale of the energy storage system), energy storage real-time capacity vector (actual remaining capacity of the energy storage system at each moment), and hydrogen production real-time power vector (actual hydrogen production power of the PEM electrolyzer at each moment). These variables comprehensively cover the core energy flow and state parameters during system operation, laying the foundation for subsequent constraint setting and objective solving.

[0067] Next are the constraint settings, which ensure the rationality and stability of system operation through multi-dimensional constraints: energy conservation condition, ensuring that the energy balance within the system is maintained at any time, with no energy being created or lost out of thin air; real-time power limit of energy storage, limiting the charging and discharging power of the energy storage system to not exceed its rated power capacity, avoiding equipment overload damage; hydrogen production load limit, adapting to the power adjustment range of PEM electrolyzer from 0 to 100%, ensuring the safe and stable operation of the electrolyzer; hydrogen production limit, ensuring that the annual hydrogen production reaches the minimum requirement, where the hydrogen production coefficient is the power consumption for producing one standard cubic meter of hydrogen, and the hydrogen production is quantified through power integration and coefficient conversion; power curtailment limit, ensuring the rationality of power curtailment records, the power curtailment cannot be negative and cannot exceed the total wind and solar power output at the current moment; energy storage capacity limit, avoiding overcharging or over-discharging of the energy storage system, extending equipment life; constraint that the energy storage capacity is 0 at the initial and final moments of energy storage, ensuring the periodic stable operation of the system throughout the year, and avoiding the accumulation of capacity in the initial or final state from affecting the next round of operation simulation.

[0068] For example, the energy conservation condition is: P 光实 +P 风实 +P 储实 -P 制氢实 -P 弃电 =0;

[0069] The real-time power limit for energy storage is: -P 储 ≤P 储实 ≤P 储 ;

[0070] Hydrogen production load limit is: 0 ≤ P 制氢实 ≤P 制氢 ;

[0071] Hydrogen production is limited to: Q≤ΣP 制氢实 / hydrogen production coefficient;

[0072] The limit for the amount of electricity to be abandoned is: 0 ≤ P 弃电 ≤P 光实 +P 风实 ;

[0073] Energy storage capacity is limited to: 0 ≤ W 储实 ≤W 储 ;

[0074] in, k is the time step index.

[0075] Finally, the objective function is constructed and solved, with maximizing hydrogen production as the core objective. Since the MATLAB-CPLEX solver only supports minimization, the objective function is transformed into a minimization problem, and the objective function is constructed as Obj=Q-ΣP. 制氢实 / Hydrogen production coefficient. By minimizing Obj, the actual hydrogen production can be indirectly maximized. Finally, by solving this objective function, the optimal total energy storage capacity and real-time hydrogen production power vector under the current capacity combination can be obtained.

[0076] Step S13, based on the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 Output the target capacity combination with the lowest total cost.

[0077] Based on cost accounting and optimal selection, the total cost of each capacity combination is calculated according to the total energy storage capacity and real-time hydrogen production power vector obtained from the solution, combined with the cost data obtained in the early stage. By comparing the total cost of all combinations, the target capacity combination with the lowest total cost is output.

[0078] For example, the total cost C 总 The calculation formula is:

[0079] C 总 =C 光 ×P 光 +C 风 ×P 风 +C 氢 ×P 制氢 +C 储 ×W 储 +M 光 ×P 光 +M 风 ×P 风 +M 氢 ×P 制氢 +M 储 ×W 储 -S 氢 ×ΣP 制氢实 / hydrogen production coefficient;

[0080] Among them, C 光 For photovoltaic construction costs, P 光 For photovoltaic installed capacity, C 风 For wind power construction costs, P 风 For wind power installed capacity, C 氢 For the construction cost of the electrolytic cell, P 制氢 For hydrogen production load, C 储 For energy storage construction costs, W 储 M represents the total energy storage capacity. 光 M represents the operating cost of photovoltaic power and the operating cost of wind power. 风 M 氢 For the operating cost of electrolytic cells, M 储For energy storage operating costs, S 氢 This refers to the price of hydrogen.

[0081] In summary, the energy storage capacity configuration method for wind and solar power generation based on PEM electrolyzers in the above embodiments of the present invention determines the maximum deployable photovoltaic capacity P by acquiring photovoltaic power output data and wind power output data throughout the entire time period. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation; setting the photovoltaic installed capacity P. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The system is divided into N groups to form a preset capacity combination. For each capacity combination, the MATLAB-CPLEX solver is used to calculate the total energy storage capacity W under the set target. 储 and the real-time power vector P for hydrogen production 制氢实 Based on the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 This approach outputs the target capacity combination with the lowest total cost. Capacity configuration no longer relies on one-sided analysis of typical daily data or simplified models, but rather on the year-round, all-time output characteristics. Through multi-dimensional capacity combination traversal, it achieves precise matching of wind, solar, energy storage, and hydrogen production systems across all scenarios. This is achieved through all-time data acquisition to reconstruct the system's long-term real-time operating status; multiple capacity combination traversal to cover potential matching schemes; precise calculation of energy storage capacity and real-time hydrogen production power by a professional solver; total cost calculation to achieve a synergistic balance between minimum hydrogen production requirements and economic efficiency; and optimal combination selection to output an efficient matching scheme. The synergistic effect of multiple links avoids situations such as high curtailment rates, insufficient hydrogen production, or excessively high system costs. This solves the problem in existing technologies where the lack of full consideration of all-time output characteristics makes it difficult to balance hydrogen production and economic efficiency.

[0082] Example 2

[0083] This embodiment also proposes a method for configuring energy storage capacity for wind and solar power generation based on PEM electrolyzers. The difference between the energy storage capacity configuration method for wind and solar power generation based on PEM electrolyzers in this embodiment and the energy storage capacity configuration method for wind and solar power generation based on PEM electrolyzers in Embodiment 1 is as follows:

[0084] Photovoltaic installed capacity P 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for are as follows:

[0085] 0≤P 光 ≤P 光max ;

[0086] 0≤P 风 ≤P 风max ;

[0087] 0≤P 储 ≤P 光 +P 风 .

[0088] The photovoltaic installed capacity P is arranged in an arithmetic sequence manner. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The steps for dividing the species into N groups to form a predetermined capacity combination include:

[0089] Based on the minimum annual hydrogen production requirement Q, the hydrogen production coefficient, and all-time wind and solar power output data, the minimum synergistic threshold range between photovoltaic and wind power installed capacity is calculated:

[0090] ;

[0091] Remove invalid intervals from the original value range that are below the threshold. To meet the minimum installed capacity required for minimum hydrogen production using photovoltaic power alone, The minimum installed capacity required to meet the minimum hydrogen production capacity using wind power output alone;

[0092] Calculate the photovoltaic installed capacity P respectively 光 Wind power installed capacity P 风 Energy storage capacity P 储 Within their respective valid value ranges, the total cost C is... 总 The marginal impact coefficient is used to divide the effective value range of each parameter into high-sensitivity, medium-sensitivity and low-sensitivity segments based on the marginal impact coefficient. The marginal impact coefficient is obtained by taking the partial derivative of the formula for calculating total cost. The range where the absolute value of the marginal impact coefficient is greater than the preset threshold is the high-sensitivity segment, the range between the two preset thresholds is the medium-sensitivity segment, and the range less than the minimum preset threshold is the low-sensitivity segment.

[0093] A dynamic step-size arithmetic sequence division strategy is adopted for different sensitive segments. The highly sensitive segments are divided into N1 groups according to the first preset step size, the medium sensitive segments are divided into N2 groups according to the second preset step size, and the low sensitive segments are divided into N3 groups according to the third preset step size. The first preset step size < the second preset step size < the third preset step size, and N1 + N2 + N3 = N.

[0094] The photovoltaic installed capacity P after division 光 The values ​​of each group and the installed wind power capacity P 风 Each group's values ​​were paired and scored. The scoring index was the ratio of the standard deviation to the mean of the combined wind and solar power output over the entire time period. The smaller the ratio, the stronger the complementarity. Photovoltaic installed capacity P with a score below the preset qualified threshold was removed. 光 And wind power installed capacity P 风 combination;

[0095] The qualified photovoltaic installed capacity P after screening 光 And wind power installed capacity P 风 Combined and partitioned energy storage capacity P 储 The values ​​from each group are cross-combined to form the final preset capacity combination.

[0096] First, the minimum collaborative threshold range is calculated. Based on the minimum annual hydrogen production requirement, the hydrogen production coefficient, and all-time wind and solar power output data, the minimum thresholds for photovoltaic installed capacity and wind power installed capacity are determined separately, forming an effective value range. Invalid ranges below the threshold are eliminated from the original value range. This avoids invalid combinations where photovoltaic or wind power capacity is too small to meet the minimum hydrogen production requirements even with energy storage, thus reducing the amount of subsequent calculations from the source.

[0097] Next, the sensitive sections are divided, and the photovoltaic installed capacity P is calculated for each section. 光 Wind power installed capacity P 风 Energy storage capacity P 储The marginal impact coefficient on total cost within their respective effective value ranges represents the degree of impact of each additional 1kW of photovoltaic capacity on total cost. Based on the absolute value of the marginal impact coefficient, three segments are defined: high-sensitivity, medium-sensitivity, and low-sensitivity. The segment with an absolute value greater than a preset high threshold is considered high-sensitivity (the quantity change within this segment has a significant impact on total cost and needs precise coverage); the segment between the high and low thresholds is medium-sensitivity (moderate impact); and the segment less than the low threshold is low-sensitivity (weak impact). The core of this segmentation is to identify the configuration range most sensitive to cost, providing a basis for subsequent dynamic step-size division. Then, an arithmetic progression strategy with dynamic step sizes is adopted, setting different step sizes for different sensitivity segments: high-sensitivity segments are divided into N1 groups according to the first preset step size, medium-sensitivity segments into N2 groups according to the second preset step size, and low-sensitivity segments into N3 groups according to the third preset step size, satisfying the condition: first preset step size < second preset step size < third preset step size, and N1 + N2 + N3 = N. The step size is smaller for high-sensitivity segments, which can cover values ​​more densely and ensure that the optimal solution is not missed; the step size is larger for medium- and low-sensitivity segments, which reduces the number of groups, balances accuracy and efficiency, and avoids excessive computational resources being used for segments with little impact on cost.

[0098] Then, a combination screening was conducted to determine the photovoltaic installed capacity P after the division. 光 The values ​​of each group and the installed wind power capacity P 风 Each group's values ​​were paired and scored. The scoring index was the ratio of the standard deviation to the mean of the combined wind and solar power output over the entire time period (i.e., the coefficient of variation). The smaller the ratio, the less volatile the combined wind and solar power output and the stronger the complementarity. Strong wind and solar complementarity can reduce the regulation pressure on energy storage systems and lower the curtailment rate and energy storage costs. Therefore, photovoltaic installed capacity P with a score below the preset qualified threshold was removed. 光 With wind power installed capacity P 风 Combining these elements further filters out combinations with practical application value, reducing invalid crossovers.

[0099] Finally, the final capacity combination is formed, which includes the qualified photovoltaic installed capacity P after screening. 光 With wind power installed capacity P 风 Combined with the divided energy storage capacity P 储 The values ​​from each group are cross-combined to form the final preset capacity combination.

[0100] This process employs a four-step strategy of "threshold elimination - sensitive segment division - dynamic step size - complementary screening" to significantly reduce the number of invalid combinations and improve overall computational efficiency while ensuring coverage of the optimal configuration range. At the same time, complementary screening enhances the practicality of the combinations, making the subsequent solution results more in line with actual operational needs.

[0101] In summary, the energy storage capacity configuration method for wind and solar power generation based on PEM electrolyzers in the above embodiments of the present invention determines the maximum deployable photovoltaic capacity P by acquiring photovoltaic power output data and wind power output data throughout the entire time period. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation; setting the photovoltaic installed capacity P. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The system is divided into N groups to form a preset capacity combination. For each capacity combination, the MATLAB-CPLEX solver is used to calculate the total energy storage capacity W under the set target. 储 and the real-time power vector P for hydrogen production 制氢实 Based on the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 This approach outputs the target capacity combination with the lowest total cost. Capacity configuration no longer relies on one-sided analysis of typical daily data or simplified models, but rather on the year-round, all-time output characteristics. Through multi-dimensional capacity combination traversal, it achieves precise matching of wind, solar, energy storage, and hydrogen production systems across all scenarios. This is achieved through all-time data acquisition to reconstruct the system's long-term real-time operating status; multiple capacity combination traversal to cover potential matching schemes; precise calculation of energy storage capacity and real-time hydrogen production power by a professional solver; total cost calculation to achieve a synergistic balance between minimum hydrogen production requirements and economic efficiency; and optimal combination selection to output an efficient matching scheme. The synergistic effect of multiple links avoids situations such as high curtailment rates, insufficient hydrogen production, or excessively high system costs. This solves the problem in existing technologies where the lack of full consideration of all-time output characteristics makes it difficult to balance hydrogen production and economic efficiency.

[0102] Example 3

[0103] Please see Figure 2 The figure shows an energy storage capacity configuration system for wind and solar power generation based on a PEM electrolyzer, as proposed in the third embodiment of the present invention. The system includes:

[0104] The acquisition module 100 is used to acquire photovoltaic power output data and wind power output data throughout the entire time period to determine the maximum deployable photovoltaic capacity P. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation;

[0105] Setting module 200 is used to set the photovoltaic installed capacity P. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 Divide them into N groups to form a preset capacity combination;

[0106] The combination module 300 is used to calculate the total energy storage capacity W under each capacity combination using the MATLAB-CPLEX solver, under a set objective. 储 and the real-time power vector P for hydrogen production 制氢实 ;

[0107] Configuration module 400, used to configure according to the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 Output the target capacity combination with the lowest total cost.

[0108] The functions or operation steps implemented by the above modules are largely the same as those in the above method embodiments, and will not be repeated here.

[0109] Example 4

[0110] In another aspect, the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method described in Embodiments 1 to 2 above.

[0111] Example 5

[0112] In another aspect, the present invention provides an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in Embodiments 1 to 2 above.

[0113] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0115] More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable storage media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0116] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0117] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0118] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer, characterized in that, The method includes: By acquiring photovoltaic (PV) power output data and wind power output data for all time periods, the maximum deployable PV capacity P can be determined. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation; Set photovoltaic installed capacity P 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 Divide them into N groups to form a preset capacity combination; For each capacity combination, the MATLAB-CPLEX solver was used to calculate the total energy storage capacity W under the set objective. 储 and the real-time power vector P for hydrogen production 制氢实 ; Based on the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 Output the target capacity combination with the lowest total cost; For each capacity combination, the MATLAB-CPLEX solver is used to calculate the total energy storage capacity W under the set objective. 储 and the real-time power vector P for hydrogen production 制氢实 The steps include: Based on photovoltaic power output data, wind power output data, and photovoltaic installed capacity P 光 Wind power installed capacity P 风 Determine the real-time photovoltaic output vector P respectively 光实 Real-time wind power output vector P 风实 And define the abandoned power vector P 弃电 Real-time power vector of energy storage P 储实 Total energy storage capacity (W) 储 Real-time capacity vector of energy storage W 储实 Real-time power vector P for hydrogen production 制氢实 ; Set constraints, including energy conservation conditions, real-time power limits for energy storage, hydrogen production load limits, hydrogen production limits, power curtailment limits, energy storage capacity limits, and constraints that the energy storage capacity is 0 at the initial and final moments of energy storage. The energy conservation condition is: P 光实 +P 风实 +P 储实 -P 制氢实 -P 弃电 =0; The real-time power limit for energy storage is: -P 储 ≤P 储实 ≤P 储 ; Hydrogen production load limit is: 0 ≤ P 制氢实 ≤P 制氢 ; Hydrogen production is limited to: Q≤ΣP 制氢实 / hydrogen production coefficient; The limit for the amount of electricity to be abandoned is: 0 ≤ P 弃电 ≤P 光实 +P 风实 ; Energy storage capacity is limited to: 0 ≤ W 储实 ≤W 储 ; in, k is the time step index; The objective is to maximize hydrogen production, which is then transformed into a problem of minimizing hydrogen production. The objective function is Obj = Q - ΣP. 制氢实 / hydrogen production coefficient, solve for the total energy storage capacity W under this combination. 储 and the real-time power vector P for hydrogen production 制氢实 .

2. The method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer according to claim 1, characterized in that, Cost data includes photovoltaic construction cost C 光 Wind power construction cost C 风 Electrolytic cell construction cost C 氢 Energy storage construction cost C 储 Photovoltaic operating cost M 光 Wind power operating cost M 风 Electrolytic cell operating cost M 氢 Energy storage operating cost M 储 The price of hydrogen S 氢 .

3. The method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer according to claim 2, characterized in that, The total energy storage capacity W is mentioned. 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 The calculation formula is: C 总 =C 光 ×P 光 +C 风 ×P 风 +C 氢 ×P 制氢 +C 储 ×W 储 +M 光 ×P 光 +M 风 ×P 风 +M 氢 ×P 制氢 +M 储 ×W 储 -S 氢 ×ΣP 制氢实 / hydrogen production coefficient; Among them, C 光 For photovoltaic construction costs, P 光 For photovoltaic installed capacity, C 风 For wind power construction costs, P 风 For wind power installed capacity, C 氢 For the construction cost of the electrolytic cell, P 制氢 For hydrogen production load, C 储 For energy storage construction costs, W 储 M represents the total energy storage capacity. 光 M represents the operating cost of photovoltaic power and the operating cost of wind power. 风 M 氢 For the operating cost of electrolytic cells, M 储 For energy storage operating costs, S 氢 This refers to the price of hydrogen.

4. The method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer according to claim 3, characterized in that, The hydrogen production coefficient is the electricity consumption required to produce one standard cubic meter of hydrogen.

5. The method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer according to claim 1, characterized in that, Photovoltaic installed capacity P 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for are as follows: 0≤P 光 ≤P 光max ; 0≤P 风 ≤P 风max ; 0≤P 储 ≤P 光 +P 风 。 6. The method for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer according to claim 5, characterized in that, The photovoltaic installed capacity P is arranged in an arithmetic sequence manner. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The steps for dividing the species into N groups to form a predetermined capacity combination include: Based on the minimum annual hydrogen production requirement Q, the hydrogen production coefficient, and all-time wind and solar power output data, the minimum synergistic threshold range between photovoltaic and wind power installed capacity is calculated: ; Remove invalid intervals from the original value range that are below the threshold. To meet the minimum installed capacity required for minimum hydrogen production using photovoltaic power alone, The minimum installed capacity required to meet the minimum hydrogen production capacity using wind power output alone; Calculate the photovoltaic installed capacity P respectively 光 Wind power installed capacity P 风 Energy storage capacity P 储 Within their respective valid value ranges, the total cost C is... 总 The marginal impact coefficient is used to divide the effective value range of each parameter into high-sensitivity, medium-sensitivity, and low-sensitivity segments based on the marginal impact coefficient. The marginal impact coefficient is obtained by taking the partial derivative of the formula for calculating total cost. The range where the absolute value of the marginal impact coefficient is greater than the preset high threshold is the high-sensitivity segment, the range between the preset high threshold and the preset low threshold is the medium-sensitivity segment, and the range less than the preset low threshold is the low-sensitivity segment. A dynamic step-size arithmetic sequence division strategy is adopted for different sensitive segments. The highly sensitive segments are divided into N1 groups according to the first preset step size, the medium sensitive segments are divided into N2 groups according to the second preset step size, and the low sensitive segments are divided into N3 groups according to the third preset step size. The first preset step size < the second preset step size < the third preset step size, and N1 + N2 + N3 = N. The photovoltaic installed capacity P after division 光 The values ​​of each group and the installed wind power capacity P 风 Each group's values ​​were paired and scored. The scoring index was the ratio of the standard deviation to the mean of the combined wind and solar power output over the entire time period. The smaller the ratio, the stronger the complementarity. Photovoltaic installed capacity P with a score below the preset qualified threshold was removed. 光 And wind power installed capacity P 风 combination; The qualified photovoltaic installed capacity P after screening 光 And wind power installed capacity P 风 Combined and partitioned energy storage capacity P 储 The values ​​from each group are cross-combined to form the final preset capacity combination.

7. A wind and solar power generation energy storage capacity configuration system based on a PEM electrolyzer, characterized in that, The system for configuring energy storage capacity for wind and solar power generation based on a PEM electrolyzer, as described in any one of claims 1 to 6, comprises: The acquisition module is used to acquire photovoltaic (PV) power output data and wind power output data throughout the entire time period to determine the maximum deployable PV capacity P. 光max The maximum wind power capacity that can be deployed is P 风max Hydrogen production load P 制氢 The minimum annual hydrogen production requirement Q, and the cost data for wind and solar power generation; The setting module is used to set the photovoltaic installed capacity P. 光 Wind power installed capacity P 风 Energy storage capacity P 储 The range of values ​​for P is given, and the photovoltaic installed capacity P is arranged in an arithmetic sequence. 光 Wind power installed capacity P 风 Energy storage capacity P 储 Divide them into N groups to form a preset capacity combination; The combination module is used to calculate the total energy storage capacity W under each capacity combination using the MATLAB-CPLEX solver, under a set objective. 储 and the real-time power vector P for hydrogen production 制氢实 ; Configuration module, used to configure according to the total energy storage capacity W 储 and the real-time power vector P for hydrogen production 制氢实 Calculate the total cost C for each combination of wind and solar power generation based on cost data. 总 Compare the total cost C of all capacity combinations 总 Output the target capacity combination with the lowest total cost.

8. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method as described in any one of claims 1 to 6.

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