A wind-solar-hydrogen storage system capacity matching calculation method based on power distribution

Abstract

The application discloses a kind of wind and light hydrogen storage system capacity matching calculation method based on power distribution.The matching calculation method disclosed in the application relates to the capacity matching of wind and light hydrogen storage system in two cases of given wind power, photovoltaic resource and given user hydrogen demand.Capacity matching method of wind and light hydrogen storage system in the case of given wind power, photovoltaic resource includes wind and light resource data statistical processing, curve polynomial fitting, calculating power interval energy value, processing boundary condition, writing optimization model, hydrogen storage scale calculation, determining optimal installed capacity ratio.Capacity matching method of wind and light hydrogen storage system in the case of given user hydrogen demand includes determining new energy configuration ratio, calculating new energy total demand, forming power distribution curve, processing boundary condition, writing optimization model, determining optimal installed capacity ratio.The application solves the problem that the prior art scheme uses full time scale optimization algorithm in wind and light hydrogen storage system capacity matching calculation, resulting in too long calculation time.

Description

Technical Field

[0001] This invention relates to the field of renewable energy-based green hydrogen production technology, and more specifically, to a method for calculating the capacity ratio of wind-solar-hydrogen storage systems based on power distribution. Background Technology

[0002] Renewable hydrogen can be obtained through renewable energy production, and it can be applied in automotive, refining, steel, and construction industries. It can also be further synthesized into green hydrogen-based fuels or feedstocks for use in chemical, shipping, and maritime sectors. A renewable energy hydrogen production system comprises renewable energy power generation, transmission and distribution, energy storage, hydrogen production, and storage. In grid-connected renewable energy hydrogen production systems, energy storage is configured according to demand. In off-grid renewable energy systems, due to the lack of regulation and support from the main power grid, energy storage facilities are typically required to provide frequency and voltage support and peak-shaving capabilities. Since large-scale renewable energy hydrogen production projects currently all adopt grid-connected methods, this technical solution only applies to grid-connected projects.

[0003] In the development or consulting phase of renewable energy hydrogen production projects, a crucial task is determining the project scale and the capacity matching relationships between its components. These matching relationships are formed under certain boundary conditions; that is, as the boundary conditions change, the system capacity matching relationships also change. For different boundary conditions, the configuration results of the technical solutions all involve the capacity matching relationships of each component. Therefore, under general economic objectives, the basic principles of system matching remain consistent. Generally, various optimization or production simulation methods can be used to configure renewable energy hydrogen production systems based on long-term data. However, optimization or production simulation requires establishing relatively complex optimization models and consumes significant computational resources and time, which is not conducive to rapid calculation or obtaining matching results under conditions of limited computational resources.

[0004] The basic principle of system matching is to determine the capacity scale of major equipment such as hydrogen production, energy storage, and hydrogen storage under certain boundary conditions, such as the proportion of electricity fed into the grid, the proportion of electricity discharged from the grid, the green electricity consumption rate, the green electricity curtailment rate, the grid connection price, and the time-of-use price, based on certain renewable energy conditions. Wind power and photovoltaic resources are typically used as input conditions to determine the scale of hydrogen production, energy storage, and hydrogen storage, in order to obtain the lowest unit cost of hydrogen production. (Hydrogen storage is determined by both hydrogen production and consumption; what is related to hydrogen production may be related to energy storage, but generally speaking, the demand for hydrogen storage arises from the mismatch between hydrogen supply and consumption.) In practical projects, given a user's hydrogen demand, it may be necessary to determine the scale of wind power, solar power, energy storage, and hydrogen storage. In this case, the user's hydrogen demand yields a stable hydrogen demand over a longer timescale and an annual hydrogen demand, which in turn allows for a series of relationships between electrolyzer installed capacity and utilization hours. Since the user's hydrogen demand is the input condition, the goal is to obtain the optimal wind, solar, and energy storage capacity to minimize the user's unit hydrogen cost. Different wind and solar installed capacities and different wind-solar ratios result in different power distribution curves, requiring different ratios or installed capacities, leading to different investment and operation and maintenance costs, and ultimately affecting the final unit hydrogen production cost. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method for calculating the capacity ratio of wind-solar-hydrogen storage system based on power distribution.

[0006] This invention provides a method for calculating the capacity allocation of a wind-solar-hydrogen storage system based on power distribution. This method is implemented through the following technical solution: the input conditions are divided into two cases: given wind power and photovoltaic resources, and given user hydrogen demand. System configuration schemes are proposed for each of these two cases.

[0007] (1) Given wind power and photovoltaic resources ① Statistical processing of wind and solar resource data Obtain at least one year of wind and solar resources and output data for a certain region, with a time resolution of 5 minutes, 10 minutes, or 15 minutes. Then, overlay the wind power output data and photovoltaic output data moment by moment to form the annual wind and solar output data. This data contains the relevant information of wind and solar resources.

[0008] The annual wind and solar power output data are processed, with a power interval ΔP as the step size, where ΔP is taken as 1MW, and the initial power is... P For zero power, count the data points in the annual wind and solar power output data that are greater than each power value. The number of counts is converted into a statistical duration in hours (h) according to the data time resolution. Plot each power value on the x-axis and each statistical duration on the y-axis. T (h) is the vertical axis, and the relationship between power and statistical duration composed of several discrete points is obtained. The processing process is shown in equation (1). The points in the above relationship graph decrease monotonically as the power value increases. In addition, due to the certain regularity of wind and solar power characteristics, the curve will be different depending on the wind and solar installed capacity and ratio. Different relationship curves will affect the system matching results.

[0009] (1) ② Curve polynomial fitting The power value obtained in step ① P and statistical duration TThe discrete points of the (h) relationship are fitted with a cubic polynomial with derivative constraints to obtain a continuous and smooth curve relating power value and statistical duration (h), the analytical expression of which is given by equation (2). From this smooth curve, it can be seen that the statistical duration is greater than any power value, and the curve is monotonically decreasing and has the condition for differentiation. The higher the time resolution of the wind and solar power output data (the smaller the time interval), the more accurate the fitted smooth curve and relational function.

[0010] (2) In the formula, a , b , c , d These are the coefficients of the variables of each degree in a cubic polynomial.

[0011] If the electrolytic cell power is configured as follows P e If the power value is greater than that, the energy cannot be used by the electrolyzer. The electrolyzer can only convert wind power and photovoltaic power less than that into hydrogen production power. The utilization hours of the hydrogen production electrolyzer can be calculated through the above smooth curve, as shown in equation (3). At the same time, it can be seen from the curve that if the power of the electrolyzer is increased, more wind and solar power will be used, but its utilization hours may decrease. And from the shape of the curve, it can be seen that when the power of the electrolyzer increases to a certain range, the utilization hours will decrease rapidly. This will result in the new energy power used by increasing the scale of the electrolyzer being insufficient to offset the cost increase brought about by the decrease in the utilization hours of the electrolyzer, which will affect the economic efficiency.

[0012] (3) In the formula, P e For the power configuration of the electrolytic cell, U h Configure the electrolytic cell power as follows P e The number of hours used.

[0013] ③ Calculate the energy value for a certain power range. The energy value covered is calculated by integrating between the two power values ​​using the smooth curve described above. The integration variable is... P The lower and upper limits of points are respectively P 1. P 2, then the annual renewable energy power generation can be calculated to be between P 1. P The total energy value between 2, which can be used to calculate the change in utilization hours of each device due to the change in this part of energy. The energy value of a certain power range is calculated as shown in equation (4); (4) In the formula, △E Power ( P 1. P 2) The energy value covered by the interval, P 1. P 2 represents the lower limit and upper limit of the points, respectively; ④ Handling boundary conditions The boundary conditions involved include the proportion of electricity fed into the grid, the proportion of electricity removed from the grid, the green electricity consumption rate, the grid connection price, and the time-of-use grid connection price. The above boundary conditions need to be processed in the calculation. Electricity fed into the grid comes from periods when wind and solar power output exceeds that of the electrolyzers, but typically, the proportion of electricity fed into the grid should not exceed 20% of wind and solar power generation. Electricity supplied to the grid typically accounts for no more than 10% of total electricity consumption; this may be used for hydrogen production or charging of energy storage. The overall effect of this energy is to increase the utilization hours of the electrolyzers, but the grid connection incurs electricity costs, usually settled using time-of-use pricing. The green electricity absorption rate mainly affects energy storage configuration, which in turn affects the utilization hours of the electrolyzers, influencing both the configuration of electrolyzers and energy storage. However, this can generally be addressed within the framework of the aforementioned fitted curve.

[0014] It should be noted that if the boundary conditions such as the proportion of electricity fed into the grid, the proportion of electricity removed from the grid, and the green electricity consumption rate are relatively lenient, then configuring energy storage may reduce economic benefits. This is mainly because the increased utilization hours of the electrolyzers due to energy storage configuration are insufficient to compensate for the cost of energy storage configuration. In this case, the optimal choice is to reduce the configuration of energy storage facilities to avoid the safety and maintenance issues associated with energy storage. In summary, the boundary conditions can be expressed by the following formula.

[0015] (5) In the formula, R on The percentage of electricity used for internet access. R off For the proportion of electricity sold offline, R con For green electricity consumption rate, u , v , w These are the limits for the proportion of electricity supplied to the grid, the proportion of electricity sold off the grid, and the green electricity consumption rate.

[0016] ⑤ List the optimization model By fitting the duration-power curve in step ②, obtaining the power range energy value in step ③, and processing the boundary conditions in step ④, the problem is transformed into an objective function for minimizing the levelized cost of hydrogen production (LCOH), as shown in equation (6), and the boundary conditions shown in equation (5) are satisfied. The variables to be optimized are the installed power of the electrolyzer and the installed power of the energy storage. The installed energy of the energy storage is related to the installed power of the energy storage and is not used as a separate optimization variable. (6) In the formula, d The discount rate is... C o This is the annual operation and maintenance cost coefficient. P eN The installed power of the electrolytic cell, P sN For energy storage installed capacity, V H For hydrogen storage volume, C E Cost per unit power of the electrolytic cell C SP Cost per unit power of energy storage C SE The unit energy cost of energy storage C H For the unit cost of hydrogen storage, E H Energy consumption per unit of hydrogen; ⑥ Calculation of hydrogen storage capacity The total annual hydrogen production in equation (6) meets the user's annual hydrogen demand. Based on the user's hydrogen demand characteristics, the hydrogen storage scale is determined. V H The characteristics of user hydrogen demand include the scale of continuous hydrogen consumption fluctuations, the scale of intermittent hydrogen consumption, and the period of intermittent consumption. ⑦ Determine the optimal installed capacity ratio Regarding equation (6) concerning the installed power of the electrolytic cell P eN Energy storage installed capacity P sN By finding the extreme values, we can obtain the electrolyzer power, energy storage power and energy storage capacity at the lowest levelized unit hydrogen production cost. In equation (6), the energy storage scale and hydrogen storage cost are constant values ​​and do not affect the calculation results.

[0017] (2) Given the user's hydrogen demand ① Clarify the allocation ratio of new energy sources There are four conditions for the allocation of new energy sources: First, the allocation ratio of wind power and photovoltaic power is known; second, the installed capacity of wind power is known, and the installed capacity of photovoltaic power needs to be determined; third, the installed capacity of photovoltaic power is known, and the installed capacity of wind power needs to be determined; fourth, there are no restrictions on the scale and ratio of wind power and photovoltaic power installations. The fourth condition is more complex: determining the optimal wind and solar power capacity under certain conditions. This is essentially a problem of determining the optimal capacity ratio, which is actually coupled with the hydrogen production and energy storage processes. Using complex calculations cannot guarantee computational efficiency. From the perspective of wind and solar power utilization hours, one of the power generation methods will inevitably have a lower unit cost per kilowatt-hour. The core issue lies in maximizing the utilization hours of the electrolyzers through a reasonable wind and solar power ratio. Therefore, the problem can be transformed into finding the ratio of electrolyzers with a certain installed capacity that maximizes their utilization hours. This is equivalent to finding the wind and solar power ratio that maximizes the utilization hours. However, the installed capacity of the electrolyzers may vary, requiring the calculation of a series of extreme values, which can then be used to determine an average.

[0018] ② Calculate the total demand for new energy sources By calculating the user's total annual hydrogen demand, the renewable energy demand can be obtained. Combined with local wind and solar power resources, the utilization hours of wind and solar power generation can be determined. Assuming that hydrogen production is primarily powered by renewable energy generation, the installed capacity of wind and solar power generation can be calculated based on the renewable energy allocation ratio determined in step ①. The three indicators of curtailed electricity, grid-connected electricity, and grid-disconnected electricity all affect the installed capacity of new energy sources. If there is curtailed or grid-connected electricity, grid-disconnected electricity is needed to make up the shortfall. Curtailed, grid-connected, and grid-disconnected electricity can all be restricted. When curtailment and grid connection are both impossible, energy storage regulation is required. Similarly, when there are restrictions on grid-disconnected electricity, energy storage regulation is also necessary. Considering that the limits for these three indicators are usually relatively small, they have some impact on energy storage configuration, but not a significant impact on the overall configuration of new energy sources. Therefore, the impact of these three indicators on wind and solar power installed capacity is not considered here, and the installed capacity of wind and solar power generation is calculated using the following formula. If grid-connected and grid-connected electricity are substantial and constitute a certain proportion, the impact of net electricity exchange with the grid can be considered to calculate the total demand for new energy sources.

[0019] (7) In the formula, P wN For wind power installed capacity, P sN For photovoltaic installed capacity, U w For wind power utilization hours, U s For photovoltaic utilization hours, S H Based on the user's annual hydrogen demand, E up For internet access power consumption, E down For offline power consumption, R crFor the curtailment rate limit, k The ratio of wind power to photovoltaic installations.

[0020] ③ Forming a power distribution curve Based on step ②, wind power and photovoltaic installed capacity are obtained. The annual output curves of wind power and photovoltaic are generated using the scenario generation method, and the power-time distribution curves are calculated.

[0021] ④ Handling boundary conditions The boundary conditions involved include the proportion of electricity fed into the grid, the proportion of electricity discharged from the grid, the green electricity consumption rate, the green electricity curtailment rate, the grid-connected electricity price, the time-of-use electricity price, and the energy storage configuration ratio. The handling of the above boundary conditions is the same as the handling of boundary conditions for given wind power and photovoltaic resources. It should be noted that the process of handling boundary conditions is essentially the process of defining the solution range of the main variables to be optimized. Therefore, in principle, after handling the boundary conditions, it is not necessary to consider the above boundary conditions again in the subsequent optimization solution process.

[0022] ⑤ List the optimization model By using the power-time distribution curve obtained from step ③ and the boundary conditions processed in step ④, the problem is transformed into a problem that varies with 2-3 optimization variables for a single objective (such as an economic objective) or multiple objectives (such as economic objectives, curtailment rate objectives, hydrogen production objectives, etc., with certain weights, and is transformed into a single objective problem through weighting).

[0023] The variables to be optimized in this step are hydrogen production, energy storage, and hydrogen storage capacity. We can still treat hydrogen production and energy storage capacity separately from hydrogen storage capacity to simplify the solution process. Hydrogen production and energy storage capacity are mainly determined by economic and other objectives, while the demand for hydrogen storage capacity is mainly caused by the mismatch between hydrogen consumption and hydrogen production. It seems that the scale of hydrogen storage can be calculated by considering the long-term process of hydrogen production and consumption.

[0024] ⑥ Determine the optimal installed capacity ratio Solve the optimization model from step ⑤, including the objective function and constraints, to obtain the optimal installed capacity ratio of the system. While obtaining the optimal installed capacity ratio, further calculate the key performance indicators to better demonstrate the optimization results.

[0025] Finally, by combining the given wind power and photovoltaic resources with the given user hydrogen demand, a complete technical solution for the configuration of an integrated wind-solar-hydrogen-storage system is formed. The technical solution provides a simplified calculation method that uses non-production simulation and complex optimization processes.

[0026] Compared with the prior art, the beneficial effects of the present invention are: The solution addresses the capacity matching calculation in existing integrated wind-solar-hydrogen-storage systems by employing a full-time-scale optimization algorithm, thereby reducing computation time.

[0027] The solution addresses the problem of existing technical solutions that only consider wind power and photovoltaic power output without taking into account their correlation, and incorporates information on the correlation between wind power and photovoltaic power output.

[0028] This addresses the issue that existing technical solutions can only calculate capacity matching in integrated wind-solar-hydrogen-storage systems that operate for 8760 hours per year, thereby improving the computational capabilities for calculating renewable energy output over multiple years.

[0029] The solution addresses the limitation of existing technologies that employ full-time-scale optimization calculations, which cannot conveniently output calculation results based on different wind and solar power outputs, thereby improving the algorithm's portability and adaptability. Attached Figure Description

[0030] Figure 1 It is a process for allocating the capacity of wind-solar-hydrogen-storage systems given wind and solar power resources. Figure 2 It is a process for allocating the capacity of wind, solar and hydrogen storage systems to meet the given user's hydrogen demand. Detailed Implementation

[0031] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in detail below with reference to specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. The described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the present invention.

[0032] A method for calculating the capacity allocation of a wind-solar-hydrogen storage system based on power distribution is implemented through the following technical solution. This method divides the input conditions into two cases: given wind power and photovoltaic resources, and given user hydrogen demand. System configuration schemes are proposed for each of these two cases.

[0033] (1) such as Figure 1 As shown, given wind power and photovoltaic resources ① Statistical processing of wind and solar resource data Obtain at least one year of wind and solar resources and output data for a certain region, with a time resolution of 5 minutes, 10 minutes, or 15 minutes. Then, overlay the wind power output data and photovoltaic output data moment by moment to form the annual wind and solar output data. This data contains the relevant information of wind and solar resources.

[0034] The annual wind and solar power output data are processed, with a power interval ΔP as the step size, where ΔP is taken as 1MW, and the initial power is... P For zero power, count the data points in the annual wind and solar power output data that are greater than each power value. The number of counts is converted into a statistical duration in hours (h) according to the data time resolution. Plot each power value on the x-axis and each statistical duration on the y-axis. T (h) is the vertical axis, and the relationship between power and statistical duration composed of several discrete points is obtained. The processing process is shown in equation (1). The points in the above relationship graph decrease monotonically as the power value increases. In addition, due to the certain regularity of wind and solar power characteristics, the curve will be different depending on the wind and solar installed capacity and ratio. Different relationship curves will affect the system matching results.

[0035] (1) ② Curve polynomial fitting The power value obtained in step ① P and statistical duration T The discrete points of the (h) relationship are fitted with a cubic polynomial with derivative constraints to obtain a continuous and smooth curve relating power value and statistical duration (h), the analytical expression of which is given by equation (2). From this smooth curve, it can be seen that the statistical duration is greater than any power value, and the curve is monotonically decreasing and has the condition for differentiation. The higher the time resolution of the wind and solar power output data (the smaller the time interval), the more accurate the fitted smooth curve and relational function.

[0036] (2) In the formula, a , b , c , d These are the coefficients of the variables of each degree in a cubic polynomial.

[0037] If the electrolytic cell power is configured as follows P eIf the power value is greater than that, the energy cannot be used by the electrolyzer. The electrolyzer can only convert wind power and photovoltaic power less than that into hydrogen production power. The utilization hours of the hydrogen production electrolyzer can be calculated through the above smooth curve, as shown in equation (3). At the same time, it can be seen from the figure that if the power of the electrolyzer is increased, more wind and solar power will be used, but its utilization hours may decrease. And from the shape of the curve, it can be seen that when the power of the electrolyzer increases to a certain range, the utilization hours will decrease rapidly. This will result in the new energy power used by increasing the scale of the electrolyzer being insufficient to offset the cost increase brought about by the decrease in the utilization hours of the electrolyzer, which will affect the economic efficiency.

[0038] (3) In the formula, P e For the power configuration of the electrolytic cell, U h Configure the electrolytic cell power as follows P e The number of hours used.

[0039] ③ Calculate the energy value of a certain power range. The energy value covered is calculated by integrating between the two power values ​​using the smooth curve described above. The integration variable is... P The lower and upper limits of points are respectively P 1. P 2, then the annual renewable energy power generation can be calculated to be between P 1. P The total energy value between 2, which can be used to calculate the change in utilization hours of each device due to the change in this part of energy. The energy value of a certain power range is calculated as shown in equation (4); (4) In the formula, △ E Power ( P 1. P 2) The energy value covered by the interval, P 1. P 2 represents the lower limit and upper limit of the points, respectively; ④ Handling boundary conditions The boundary conditions involved include the proportion of electricity fed into the grid, the proportion of electricity removed from the grid, the green electricity consumption rate, the grid connection price, and the time-of-use grid connection price. The above boundary conditions need to be processed in the calculation. Electricity fed into the grid comes from periods when wind and solar power output exceeds that of the electrolyzers, but typically, the proportion of electricity fed into the grid should not exceed 20% of wind and solar power generation. Electricity supplied to the grid typically accounts for no more than 10% of total electricity consumption; this may be used for hydrogen production or charging of energy storage. The overall effect of this energy is to increase the utilization hours of the electrolyzers, but the grid connection incurs electricity costs, usually settled using time-of-use pricing. The green electricity absorption rate mainly affects energy storage configuration, which in turn affects the utilization hours of the electrolyzers, influencing both the configuration of electrolyzers and energy storage. However, this can generally be addressed within the framework of the aforementioned fitted curve.

[0040] It should be noted that if the boundary conditions such as the proportion of electricity fed into the grid, the proportion of electricity removed from the grid, and the green electricity consumption rate are relatively lenient, then configuring energy storage may reduce economic benefits. This is mainly because the increased utilization hours of the electrolyzers due to energy storage configuration are insufficient to compensate for the cost of energy storage configuration. In this case, the optimal choice is to reduce the configuration of energy storage facilities to avoid the safety and maintenance issues associated with energy storage. In summary, the boundary conditions can be expressed by the following formula.

[0041] (5) In the formula, R on The percentage of electricity used for internet access. R off For the proportion of electricity sold offline, R con For green electricity consumption rate, u , v , w These are the limits for the proportion of electricity supplied to the grid, the proportion of electricity sold off the grid, and the green electricity consumption rate.

[0042] ⑤ List the optimization model By fitting the duration-power curve in step ②, obtaining the power range energy value in step ③, and processing the boundary conditions in step ④, the problem is transformed into an objective function for minimizing the levelized cost of hydrogen production (LCOH), as shown in equation (6), and the boundary conditions shown in equation (5) are satisfied. The variables to be optimized are the installed power of the electrolyzer and the installed power of the energy storage. The installed energy of the energy storage is related to the installed power of the energy storage and is not used as a separate optimization variable. (6) In the formula, d The discount rate is... C o This is the annual operation and maintenance cost coefficient. P eN The installed power of the electrolytic cell, P sN For energy storage installed capacity, V H For hydrogen storage volume,C E Cost per unit power of the electrolytic cell C SP Cost per unit power of energy storage C SE The unit energy cost of energy storage C H For the unit cost of hydrogen storage, E H Energy consumption per unit of hydrogen; ⑥ Calculation of hydrogen storage capacity The total annual hydrogen production in equation (6) meets the user's annual hydrogen demand. Based on the user's hydrogen demand characteristics, the hydrogen storage scale is determined. V H The characteristics of user hydrogen demand include the scale of continuous hydrogen consumption fluctuations, the scale of intermittent hydrogen consumption, and the period of intermittent consumption. ⑦ Determine the optimal installed capacity ratio Regarding equation (6) concerning the installed power of the electrolytic cell P eN Energy storage installed capacity P sN By finding the extreme values, we can obtain the electrolyzer power, energy storage power and energy storage capacity at the lowest levelized unit hydrogen production cost. In equation (6), the energy storage scale and hydrogen storage cost are constant values ​​and do not affect the calculation results.

[0043] (2) such as Figure 2 As shown, given a user's hydrogen demand ① Clarify the allocation ratio of new energy sources There are four conditions for the allocation of new energy sources: First, the allocation ratio of wind power and photovoltaic power is known; second, the installed capacity of wind power is known, and the installed capacity of photovoltaic power needs to be determined; third, the installed capacity of photovoltaic power is known, and the installed capacity of wind power needs to be determined; fourth, there are no restrictions on the scale and ratio of wind power and photovoltaic power installations. The fourth condition is more complex: determining the optimal wind and solar power capacity under certain conditions. This is essentially a problem of determining the optimal capacity ratio, which is actually coupled with the hydrogen production and energy storage processes. Using complex calculations cannot guarantee computational efficiency. From the perspective of wind and solar power utilization hours, one of the power generation methods will inevitably have a lower unit cost per kilowatt-hour. The core issue lies in maximizing the utilization hours of the electrolyzers through a reasonable wind and solar power ratio. Therefore, the problem can be transformed into finding the ratio of electrolyzers with a certain installed capacity that maximizes their utilization hours. This is equivalent to finding the wind and solar power ratio that maximizes the utilization hours. However, the installed capacity of the electrolyzers may vary, requiring the calculation of a series of extreme values, which can then be used to determine an average.

[0044] ② Calculate the total demand for new energy sources By calculating the user's total annual hydrogen demand, the renewable energy demand can be obtained. Combined with local wind and solar power resources, the utilization hours of wind and solar power generation can be determined. Assuming that hydrogen production is primarily powered by renewable energy generation, the installed capacity of wind and solar power generation can be calculated based on the renewable energy allocation ratio determined in step ①. The three indicators of curtailed electricity, grid-connected electricity, and grid-disconnected electricity all affect the installed capacity of new energy sources. If there is curtailed or grid-connected electricity, grid-disconnected electricity is needed to make up the shortfall. Curtailed, grid-connected, and grid-disconnected electricity can all be restricted. When curtailment and grid connection are both impossible, energy storage regulation is required. Similarly, when there are restrictions on grid-disconnected electricity, energy storage regulation is also necessary. Considering that the limits for these three indicators are usually relatively small, they have some impact on energy storage configuration, but not a significant impact on the overall configuration of new energy sources. Therefore, the impact of these three indicators on wind and solar power installed capacity is not considered here, and the installed capacity of wind and solar power generation is calculated using the following formula. If grid-connected and grid-connected electricity are substantial and constitute a certain proportion, the impact of net electricity exchange with the grid can be considered to calculate the total demand for new energy sources.

[0045] (7) In the formula, P wN For wind power installed capacity, P sN For photovoltaic installed capacity, U w For wind power utilization hours, U s The number of hours of photovoltaic utilization. S H For the user's annual hydrogen demand, E up For internet access power consumption, E down For offline power consumption, R cr For the curtailment rate limit, k The ratio of wind power to photovoltaic installations.

[0046] ③ Forming a power distribution curve Based on step ②, wind power and photovoltaic installed capacity are obtained. The annual output curves of wind power and photovoltaic are generated using the scenario generation method, and the power-time distribution curves are calculated.

[0047] ④ Handling boundary conditions The boundary conditions involved include the proportion of electricity fed into the grid, the proportion of electricity discharged from the grid, the green electricity consumption rate, the green electricity curtailment rate, the grid-connected electricity price, the time-of-use electricity price, and the energy storage configuration ratio. The handling of the above boundary conditions is the same as the handling of boundary conditions for given wind power and photovoltaic resources. It should be noted that the process of handling boundary conditions is essentially the process of defining the solution range of the main variables to be optimized. Therefore, in principle, after handling the boundary conditions, it is not necessary to consider the above boundary conditions again in the subsequent optimization solution process.

[0048] ⑤ List the optimization model By using the power-time distribution curve obtained from step ③ and the boundary conditions processed in step ④, the problem is transformed into a problem that varies with 2-3 optimization variables for a single objective (such as an economic objective) or multiple objectives (such as economic objectives, curtailment rate objectives, hydrogen production objectives, etc., with certain weights, and is transformed into a single objective problem through weighting).

[0049] The variables to be optimized in this step are hydrogen production, energy storage, and hydrogen storage capacity. We can still treat hydrogen production and energy storage capacity separately from hydrogen storage capacity to simplify the solution process. Hydrogen production and energy storage capacity are mainly determined by economic and other objectives, while the demand for hydrogen storage capacity is mainly caused by the mismatch between hydrogen consumption and hydrogen production. It seems that the scale of hydrogen storage can be calculated by considering the long-term process of hydrogen production and consumption.

[0050] ⑥ Determine the optimal installed capacity ratio Solve the optimization model from step ⑤, including the objective function and constraints, to obtain the optimal installed capacity ratio of the system. While obtaining the optimal installed capacity ratio, further calculate the key performance indicators to better demonstrate the optimization results.

[0051] Finally, by combining the given wind power and photovoltaic resources with the given user hydrogen demand, a complete technical solution for the configuration of an integrated wind-solar-hydrogen-storage system is formed. The technical solution provides a simplified calculation method that uses non-production simulation and complex optimization processes.

[0052] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for calculating the capacity allocation of a wind-solar-hydrogen storage system based on power distribution, characterized in that, The method divides the input conditions into two cases: given wind power and photovoltaic resources, and given user hydrogen demand. The system configuration schemes for these two cases are proposed below. (1) Given wind power and photovoltaic resources ①Statistical processing of wind and solar resource data Obtain at least one year of wind and solar resources and output data for a certain region, with a time resolution of 5 minutes, 10 minutes, or 15 minutes. Then, overlay the wind power output data and photovoltaic output data moment by moment to form the annual wind and solar output data. This data contains the relevant information of wind and solar resources. The annual wind and solar power output data are processed, with a power interval ΔP as the step size, where ΔP is taken as 1MW, and the initial power is... P For zero power, count the data points in the annual wind and solar power output data that are greater than each power value. The number of counts is converted into a statistical duration in hours (h) according to the data time resolution. Plot each power value on the x-axis and each statistical duration on the y-axis. T (h) is the vertical axis, and the relationship between power and statistical duration composed of several discrete points is obtained. The processing process is shown in equation (1). （1） ② Curve polynomial fitting The power value obtained in step ① P and statistical duration T The discrete points of the (h) relationship are fitted with a cubic polynomial with derivative constraints to obtain a continuous and smooth power value and statistical duration (h) relationship curve, the analytical expression of which is given by equation (2); （2） In the formula, a , b , c , d These are the coefficients of the variables of each degree in a cubic polynomial; If the electrolytic cell power is configured as follows P e If the energy is greater than the power value, the electrolyzer cannot be used. The electrolyzer can only convert wind power and photovoltaic power less than the power value into hydrogen production power. The utilization hours of the hydrogen production electrolyzer are calculated through the above smooth curve, as shown in formula (3). （3） In the formula, P e For the power configuration of the electrolytic cell, U h Configure the electrolytic cell power as follows P e Hours of use; ③ Calculate the energy value of the power range The energy value covered is calculated by integrating between the two power values ​​using the smooth curve described above. The integration variable is... P The lower and upper limits of points are respectively P 1. P 2. Then calculate the annual renewable energy power generation between P 1. P The total energy value between 2 is used to calculate the change in utilization hours of each device due to the change in this part of energy. The energy value of a certain power range is calculated as shown in equation (4). （4） In the formula, △ E Power ( P 1. P 2) The energy value covered by the interval, P 1. P 2 represents the lower limit and upper limit of the points, respectively; ④ Handling boundary conditions The boundary conditions involved include the proportion of electricity fed into the grid, the proportion of electricity removed from the grid, the green electricity consumption rate, the grid connection price, and the time-of-use grid connection price. The above boundary conditions need to be processed in the calculation. Electricity fed into the grid comes from periods when wind and solar power output exceeds that of the electrolyzers. The proportion of grid-connected electricity should not exceed 20% of total wind and solar power generation, while the proportion of electricity supplied to the grid should not exceed 10% of total electricity consumption. The overall effect of this energy is to increase the utilization hours of the electrolyzers, but the grid connection will incur electricity costs, which will be settled using time-of-use pricing. The green electricity absorption rate affects energy storage configuration, which in turn affects the utilization hours of the electrolyzers, thus influencing the configuration of both the electrolyzers and energy storage. This can be addressed in the aforementioned fitted curve. In summary, the boundary conditions are expressed by the following formula. （5） In the formula, R on The percentage of electricity used for internet access. R off For the proportion of electricity sold offline, R con For green electricity consumption rate, u , v , w These are the limits for the proportion of electricity fed into the grid, the proportion of electricity removed from the grid, and the green electricity consumption rate. ⑤ List the optimization model By fitting the duration-power curve in step ②, obtaining the power range energy value in step ③, and processing the boundary conditions in step ④, the problem is transformed into an objective function for minimizing the levelized hydrogen production cost (LCOH), as shown in equation (6), and the boundary conditions shown in equation (5) are satisfied. The variables to be optimized are the installed power of the electrolyzer and the installed power of the energy storage. The installed energy of the energy storage is related to the installed power of the energy storage and is not used as a separate optimization variable. （6） In the formula, d The discount rate is... C o This is the annual operation and maintenance cost coefficient. P eN The installed power of the electrolytic cell, P sN For energy storage installed capacity, V H For hydrogen storage volume, C E Cost per unit power of the electrolytic cell C SP Cost per unit power of energy storage C SE The unit energy cost of energy storage C H For the unit cost of hydrogen storage, E H Energy consumption per unit of hydrogen; ⑥ Hydrogen storage capacity calculation The total annual hydrogen production in equation (6) meets the user's annual hydrogen demand. Based on the user's hydrogen demand characteristics, the hydrogen storage scale is determined. V H The characteristics of user hydrogen demand include the scale of continuous hydrogen consumption fluctuations, the scale of intermittent hydrogen consumption, and the period of intermittent consumption. ⑦ Determine the optimal installed capacity ratio Regarding equation (6) concerning the installed power of the electrolytic cell P eN Energy storage installed capacity P sN By finding the extreme values, we can obtain the electrolyzer power, energy storage power and energy storage capacity at which the levelized unit hydrogen production cost is the lowest. In equation (6), the energy storage scale and hydrogen storage cost are constants and do not affect the calculation results. (2) Given the user's hydrogen demand ① Clarify the allocation ratio of new energy sources There are four conditions for the allocation of new energy sources: First, the allocation ratio of wind power and photovoltaic power is known; second, the installed capacity of wind power is known, and the installed capacity of photovoltaic power needs to be determined; third, the installed capacity of photovoltaic power is known, and the installed capacity of wind power needs to be determined; fourth, the installed capacity and allocation ratio of wind power and photovoltaic power are both unknown. For the fourth condition, the optimal wind power and photovoltaic installation capacity and ratio are determined by the following methods: First, determine the power generation form with the lowest unit cost per kilowatt-hour; second, while keeping the demand for new energy power unchanged, gradually reduce wind power installation and increase photovoltaic installation, with a power change step size of 10MW, and take 10-20 wind power and photovoltaic installation combinations; third, for each wind power and photovoltaic installation combination, calculate the minimum LCOH according to formula (6); finally, take the wind power and photovoltaic installation combination that minimizes LCOH as the wind power and photovoltaic installation ratio. ② Calculate the total demand for new energy sources By calculating the total annual hydrogen demand of users, the renewable energy demand can be obtained. Combined with local wind and solar power resources, the utilization hours of wind and solar power generation can be determined. Assuming that hydrogen production is primarily powered by renewable energy generation, the installed capacity of wind and solar power generation can be calculated based on the renewable energy allocation ratio determined in step ①. The three indicators of abandoned electricity, grid-connected electricity, and grid-off electricity have an impact on the installed capacity of new energy. If there is abandoned electricity or grid-connected electricity, grid-off electricity is needed to make up for the shortfall. Abandoned electricity, grid-connected electricity, and grid-off electricity may all be restricted. If abandoned electricity cannot be abandoned and grid-connected electricity cannot be connected, energy storage regulation is required. If there is a restriction on grid-off electricity, energy storage regulation is also required. Considering the sufficiency of electricity, the installed capacity of wind power and photovoltaic power generation is calculated according to the following formula. （7） In the formula, P wN For wind power installed capacity, P sN For photovoltaic installed capacity, U w For wind power utilization hours, U s The number of hours of photovoltaic utilization. S H For the user's annual hydrogen demand, E up For internet access power consumption, E down For offline power consumption, R cr For the curtailment rate limit, k The ratio of wind power to photovoltaic installations. ③ Forming a power distribution curve Based on the wind power and photovoltaic installed capacity obtained in step ②, the annual output curves of wind power and photovoltaic are generated using the scene generation method, and the power-time distribution curves are calculated. ④ Handling boundary conditions The boundary conditions involved include the proportion of electricity fed into the grid, the proportion of electricity discharged from the grid, the green electricity consumption rate, the green electricity curtailment rate, the grid-connected electricity price, the time-of-use electricity price, and the energy storage configuration ratio. The handling of the above boundary conditions is the same as the handling of boundary conditions for given wind power and photovoltaic resources. ⑤ List the optimization model By using the power-time distribution curve obtained from step ③ and the boundary conditions processed in step ④, the problem is transformed into a problem that varies with 2-3 optimization variables for a single or multiple objectives. The variables to be optimized in this step are hydrogen production capacity, energy storage power, and energy storage energy. ⑥ Determine the optimal installed capacity ratio Solve the optimization model in step ⑤, including the objective function and constraints, to obtain the optimal installed capacity ratio of the system; Finally, by combining the given wind and solar power resources with the given user hydrogen demand, the hydrogen storage scale is determined, and an integrated wind-solar-hydrogen-storage system configuration scheme is formed.

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