Capacity configuration method for new energy storage system

Abstract

The invention relates to a new energy storage system capacity configuration method, and belongs to the technical field of new energy hydrogen production. Comprising the following steps: searching a minimum load time period when the new energy generation power is lower than the minimum load of an electrolytic cell from new energy historical data; obtaining new energy generating capacity in the minimum load time period according to the new energy generating power in the minimum load time period; and obtaining the economically optimal capacity of the new energy storage system according to the power generation amount required by the minimum load of the electrolytic cell in the minimum load time period and the new energy power generation amount in the minimum load time period. Through the steps of the method, the problem that an existing algorithm is too high in complexity can be solved.

Description

TECHNICAL FIELD

[0001] The application relates to a new energy storage system capacity configuration method and belongs to the technical field of new energy hydrogen production. BACKGROUND

[0002] With the progress of social level, people's electricity demand is also diversified. On this basis, new energy power generation systems are favored. New energy is environmentally friendly and has huge energy. A new energy system uses new energy to drive a generator to generate electricity and transmits it to the power grid through an electrical system to provide clean and renewable power resources for people.

[0003] The existing typical new energy system includes a new energy power station and an electrolytic hydrogen production system. The electricity generated by the new energy power station is converted into chemical energy of hydrogen and stored by the electrolytic cell of the electrolytic hydrogen production system. However, the electrolytic cell will be shut down during the period when the power supply power is lower than the minimum load. Therefore, a storage system such as a battery can be reconfigured. When the new energy power generation is surplus, in addition to electrolytic hydrogen production, the additional electricity is stored in the storage system. When the new energy power generation power is lower than the minimum load of the electrolytic cell, the storage system is discharged to meet the minimum load of the electrolytic cell to avoid the shutdown of the electrolytic cell. The cost of the new energy storage system is not low. Therefore, from the perspective of economic benefits, it is very important to consider the capacity configuration of the new energy storage system.

[0004] In recent years, technology personnel in various places have researched the capacity configuration of the new energy storage system from the economic aspect and have achieved certain results. For example, the Chinese patent application publication file with the publication number CN117252308B discloses a kind of optimal parameter planning method of offshore wind power hydrogen production system considering grid-connected power flow. The method establishes a steady-state operation model of each device based on the historical data of maximum active power generation power, establishes an operation constraint model considering grid-connected power flow for the steady-state operation model, and integrates the steady-state operation model and the operation constraint model into a sea wind power hydrogen production system model. The economic configuration condition is used to determine the full life cycle optimization objective function of the sea wind power hydrogen production system model, and an economic configuration problem is formed.

[0005] Although the above-mentioned method considers the capacity configuration problem of the wind power storage system from the economic configuration aspect, the algorithm involved in the steady-state operation model, the operation constraint model and the economic configuration problem in the scheme has the problem of too high complexity. SUMMARY

[0006] The purpose of the present application is to provide a new energy storage system capacity configuration method to solve the problem of too high complexity of the algorithm in the prior art.

[0007] To achieve the above-mentioned purpose, the scheme of the present application includes:

[0008] The application discloses a new energy storage system capacity configuration method, which comprises the following steps: searching for a minimum load time period in which new energy power is lower than the minimum load of an electrolytic cell from new energy historical data; obtaining new energy power in the minimum load time period according to new energy power in the minimum load time period; and obtaining an economically optimal new energy storage system capacity according to the minimum load of the electrolytic cell required power in the minimum load time period and the new energy power in the minimum load time period.

[0009] Further, before the step 1), the extreme time period in which the power generation power is lower than the minimum load of the electrolytic cell for a long time is further screened out from the new energy historical data.

[0010] Further, in the step 2), after the edge quantization processing of the new energy power in the minimum load time period, a starting time point at which the wind power is lower than the minimum load of the electrolytic cell and an ending time point at which the wind power is higher than the minimum load of the electrolytic cell are obtained, and the new energy power between the starting time point and the ending time point is integrated to obtain the new energy power in the minimum load time period.

[0011] Further, the starting time point is the time point at which the intersection point of a line between a first edge sampling point which is the earliest sampling time in the minimum load time period and a first adjacent sampling point adjacent to the first edge sampling point and higher than the minimum load of the electrolytic cell and the minimum load of the electrolytic cell, and the ending time point is calculated in proportion according to the relationship between the minimum load of the electrolytic cell and the sampling value of the first edge sampling point and the sampling value of the first adjacent sampling point and according to the sampling time of the first edge sampling point and the sampling time of the first adjacent sampling point.

[0012] Further, the ending time point is the time point at which the intersection point of a line between a second edge sampling point which is the latest sampling time in the minimum load time period and a second adjacent sampling point adjacent to the second edge sampling point and higher than the minimum load of the electrolytic cell and the minimum load of the electrolytic cell, and the starting time point is calculated in proportion according to the relationship between the minimum load of the electrolytic cell and the sampling value of the second edge sampling point and the sampling value of the second adjacent sampling point and according to the sampling time of the second edge sampling point and the sampling time of the second adjacent sampling point.

[0013] Further, the integration of the new energy power between the starting time point and the ending time point adopts the following simple rectangular method: the minimum load time period is divided into sampling point time periods corresponding to all sampling points in the minimum load time period; a rectangle is constructed based on all sampling points in the minimum load time period, the length of the rectangle is the sampling value of the corresponding sampling point, the width of the rectangle is the sampling point time period of the corresponding sampling point, and the areas of all rectangles are summed to complete the integration.

[0014] Further, in step 2), the new energy sampling points in the minimum load time period are fitted and then integrated to obtain the new energy power generation in the minimum load time period.

[0015] Further, in step 3), the new energy power generation in the minimum load time period is subtracted from the power generation required to meet the minimum load of the electrolytic cell in the minimum load time period to obtain the economic optimal new energy storage system capacity.

[0016] Further, in step 1), in the new energy historical data, the time period with the lowest new energy power generation is taken as the minimum load time period.

[0017] The beneficial effects of the present application are:

[0018] The present application is an open-type invention. The present application finds the minimum load time period in which the new energy power generation is lower than the minimum load of the electrolytic cell from historical data. The new energy storage system is used to provide additional power when the new energy power generation cannot meet the minimum load of the electrolytic cell, so as to ensure that the electrolytic cell does not stop. Therefore, the present application can take the difference between the power required by the electrolytic cell in the minimum load time period and the new energy power generation in the time period, a set value greater than or less than the difference as the capacity of the new energy storage system according to different needs. After obtaining the difference between the power required by the electrolytic cell in the minimum load time period and the new energy power generation in the time period, selecting a new energy storage system with different capacity according to different needs will try to meet the required power of the electrolytic cell when the new energy power generation is low, without exceeding the demand too much, which is optimal in economy, effectively avoids the problem of too high algorithm complexity, and has the advantages of being applicable to actual engineering and accurate calculation. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a flow chart of a wind power storage system capacity configuration method according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of wind power lower than the minimum load of an electrolytic cell according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of using a simple rectangular method to integrate wind power generation according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] To make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below with reference to the drawings and embodiments.

[0023] The application provides a new energy storage system capacity configuration method, which is used for reasonable capacity configuration on the storage side. The concept of the application is to find a minimum load time period in which the new energy power generation is lower than the minimum load of the electrolytic cell from the new energy historical data; to obtain the new energy power generation in the minimum load time period according to the new energy power generation in the minimum load time period; and to obtain the economically optimal new energy storage system capacity by the power generation amount required to meet the minimum load of the electrolytic cell in the minimum load time period and the new energy power generation in the minimum load time period.

[0024] Embodiment 1

[0025] This embodiment takes wind power generation as an example to illustrate the new energy storage system capacity configuration method of the application.

[0026] As shown in a wind power storage system capacity configuration method flow chart in Figure 1 , the method comprises the following steps:

[0027] 1) The historical data of the wind power storage system is processed, and the extreme time period in which the wind power is lower than the minimum load of the electrolytic cell for a long time is filtered out.

[0028] Because the extreme time period in which the wind power is lower than the minimum load of the electrolytic cell for a long time has a very low probability of occurrence in actual engineering, such extreme data can be filtered out when processing the historical data, so as to avoid that the abnormal operation data makes the capacity configuration of the storage system too high and the cost increases.

[0029] 2) The minimum load time period in which the wind power is lower than the minimum load of the electrolytic cell is found from the filtered data.

[0030] 3) The wind turbine power generation in the minimum load time period is obtained according to the wind turbine power generation in the minimum load time period;

[0031] 4) The economically optimal wind power storage hydrogen system capacity is obtained according to the power generation amount required to meet the minimum power of the electrolytic cell in the minimum load time period and the wind turbine power generation in the minimum load time period.

[0032] As shown in a wind power lower than the minimum load of the electrolytic cell schematic diagram in Figure 2 , the horizontal axis is time and the vertical axis is power. The straight line parallel to the horizontal axis is the minimum load of the electrolytic cell, and the curve is the wind power curve changing with time. In actual engineering, the time interval of the wind turbine power sampling point is relatively long, generally 3-5 minutes. As can be seen from the figure, the wind power is sometimes higher than the minimum load of the electrolytic cell and sometimes lower than the minimum load of the electrolytic cell. When the wind power is higher than the minimum load of the electrolytic cell, the power other than the electrolytic power consumption can be used for the energy storage system to store energy; when the wind power is lower than the minimum load of the electrolytic cell, the energy storage system needs to be discharged to meet the minimum load of the electrolytic cell.

[0033] In step 3), after fitting the wind turbine sampling points, the magnitude of the sampling points is compared with that of the minimum load of the electrolyzer to obtain the wind power curve during the minimum load period when the minimum load is lower than that of the electrolyzer. The wind turbine power generation is obtained by integrating the wind power curve during the minimum load period.

[0034] Due to the long sampling interval, it is difficult to determine the specific time when the wind power is less than or exceeds the minimum load of the electrolyzer, resulting in a large error in the integral calculation after direct fitting. As an alternative implementation, the wind power falling below the minimum load of the electrolyzer is first subjected to edge quantization processing before integral calculation, specifically including the following methods:

[0035] like Figure 3 The diagram shown illustrates the calculation of wind turbine power generation using the simple rectangular integral method. Sampling points c, e, f, and m represent wind power falling below the minimum load of the electrolyzer, while edge sampling points c and m represent wind power falling below the minimum load. The purpose of edge quantization is to calculate the x-axis coordinate of the intersection point a of the line connecting sampling point c and the adjacent sampling point g (which is above the minimum load of the electrolyzer), which represents the time point when the wind power begins to fall below the minimum load of the electrolyzer. Simultaneously, the x-axis coordinate of the intersection point b of the line connecting sampling point m and the adjacent sampling point h (which is above the minimum load of the electrolyzer), which also represents the time point when the wind power begins to exceed the minimum load of the electrolyzer, is calculated. First, the ordinates of sampling points a, c, and g, and the abscissas of sampling points c and g are known. Based on the position of the ordinate of sampling point a between the ordinates of sampling points c and g, the abscissa of the intersection point a is calculated using the same proportions from the abscissas of sampling points c and g. For example, first calculate the ratio of the distance from the ordinate of intersection point a to the ordinate of sampling point g to the distance from the ordinate of sampling point g to the ordinate of sampling point c (i.e., the ratio of the longitudinal distance from sampling point a to g to the longitudinal distance from sampling point g to c). Then, the distance from the abscissa of intersection point a to the abscissa of sampling point g also has the same proportion as the distance from the abscissa of sampling point g to the abscissa of sampling point c. Therefore, the abscissa of the point a where the load begins to fall below the minimum load of the electrolytic cell can be calculated.

[0036] In the same way, the x-coordinate of the intersection point b of the line connecting sampling points m and h and the minimum load of the electrolyzer can be calculated, which is the time point when the wind power begins to exceed the minimum load of the electrolyzer.

[0037] By identifying the points in time when wind power begins to fall below the minimum load of the electrolyzer and when wind power begins to rise above the minimum load of the electrolyzer, the accurate minimum load time period can be obtained. This allows for the accurate wind power curve within the minimum load time period. Furthermore, by integrating the wind power curve within the minimum load time period over time, the wind turbine power generation within the minimum load time period can be calculated.

[0038] Specifically, the integral calculation of the wind turbine power generation amount in the minimum load period can adopt a simple rectangular method, as shown in the following formula: Figure 3 As shown, a rectangle between the sampling point and the horizontal axis is constructed for each sampling point. For the rectangle S1 of the left edge sampling point c, the left side is located at the horizontal coordinate of the intersection point a of the line connecting the sampling points c and g and the minimum load of the electrolytic cell, and the right side is located at the horizontal coordinate of the midpoint between the sampling point c and the sampling point e; for the rectangle S4 of the right edge sampling point m, the right side is located at the horizontal coordinate of the intersection point b of the line connecting the sampling points m and h and the minimum load of the electrolytic cell, and the left side is located at the horizontal coordinate of the midpoint between the sampling point m and the sampling point f. For the middle sampling points, the corresponding rectangles (S2, S3) are located at the horizontal coordinates of the midpoints between the adjacent sampling points on the left and right sides. For example, the rectangle S2 of the point e is located at the horizontal coordinates of the midpoints between the sampling points c and e on the left side and between the sampling points e and f on the right side; for the rectangle S3 of the point f, the left side is located at the horizontal coordinate of the midpoint between the sampling points e and f, and the right side is located at the horizontal coordinate of the midpoint between the sampling points f and m.

[0039] For the rectangle S1, the side length is the wind power of the sampling point c, and the width is the horizontal coordinate difference between the midpoint between the sampling points c and e and the time point a at which the wind power starts to be lower than the minimum load of the electrolytic cell; for the rectangle S4, the side length is the wind power of the sampling point m, and the width is the horizontal coordinate difference between the midpoint between the sampling points m and f and the time point b at which the wind power starts to be higher than the minimum load of the electrolytic cell; for the rectangle S2, the side length is the wind power of the sampling point e, and the width is the horizontal coordinate difference between the midpoints between the sampling points c and e and between the sampling points e and f; for the rectangle S3, the side length is the wind power of the sampling point f, and the width is the horizontal coordinate difference between the midpoints between the sampling points e and f and between the sampling points f and m. After the areas of the rectangles S1, S2, S3 and S4 are calculated respectively, they are added up to obtain the wind turbine power generation amount.

[0040] According to the time point at which the wind power starts to be lower than the minimum load of the electrolytic cell, the time point at which the wind power starts to be higher than the minimum load of the electrolytic cell, and the minimum load of the electrolytic cell, the required power generation amount to meet the minimum load of the electrolytic cell in the minimum load period is obtained. The difference between the required power generation amount to meet the minimum load of the electrolytic cell in the minimum load period and the wind turbine power generation amount is the difference in power generation amount when the wind power is lower than the minimum load of the electrolytic cell in the historical data. The wind power energy storage system capacity is determined according to the difference in power generation amount, for example, the wind power energy storage system capacity equal to the difference in power generation amount is the economically optimal wind power hydrogen storage system capacity to meet the non-stop of the electrolytic cell.

[0041] In actual engineering, there are more than one wind power curve in which the wind power is lower than the minimum load of the electrolytic cell, and a suitable wind power curve in which the wind power is lower than the minimum load of the electrolytic cell can be selected according to actual conditions. For example, when it is necessary to ensure that the electrolytic cell works for a long time, the wind power curve in which the power generation is lowest in the time period in which the wind power is lower than the minimum load of the electrolytic cell can be selected to obtain the capacity configuration of the wind power energy storage system, wherein the power generation can be estimated according to the prior art method.

Claims

1. A method for configuring the capacity of a new energy storage system, characterized in that, Includes the following steps: 1) Identify the time periods when the power generation of new energy sources is lower than the minimum load of the electrolyzer from historical data on new energy sources; 2) Obtain the new energy power generation capacity within the minimum load period based on the new energy power generation capacity within the minimum load period. Power generation; 3) Based on the power generation required to meet the minimum load of the electrolyzer during the minimum load time period and the minimum load... The energy storage system capacity is economically optimal to generate new energy power within a given time period.

2. The capacity configuration method for a new energy storage system according to claim 1, characterized in that, In step 1), before finding the minimum load time period, extreme time periods where the power generation is lower than the minimum load of the electrolyzer are filtered out from the historical data of new energy sources.

3. The capacity configuration method for a new energy storage system according to claim 1, characterized in that, In step 2), after edge quantization processing of the new energy power generation during the minimum load time period, the starting time point when the wind power starts to fall below the minimum load of the electrolyzer and the ending time point when the wind power starts to rise above the minimum load of the electrolyzer are obtained. The new energy power generation during the minimum load time period is obtained by integrating the new energy power generation between the starting time point and the ending time point.

4. The capacity configuration method for a new energy storage system according to claim 3, characterized in that, The start time point is the time point at which the line connecting the first edge sampling point with the earliest sampling time within the minimum load time period and the first adjacent sampling point with a sampling time higher than the minimum load of the electrolytic cell intersects with the minimum load of the electrolytic cell; the start time point is calculated proportionally based on the relationship between the minimum load of the electrolytic cell, the sampling value of the first edge sampling point, and the sampling value of the first adjacent sampling point, according to the sampling time of the first edge sampling point and the sampling time of the first adjacent sampling point.

5. The capacity configuration method for a new energy storage system according to claim 3, characterized in that, The end time point is the time point at which the line connecting the second edge sampling point with the latest sampling time within the minimum load time period and the second adjacent sampling point that is adjacent to the second edge sampling point and has a sampling time higher than the minimum load of the electrolytic cell intersects with the minimum load of the electrolytic cell; the end time point is calculated proportionally based on the relationship between the minimum load of the electrolytic cell, the sampling value of the second edge sampling point, and the sampling value of the second adjacent sampling point, according to the sampling time of the second edge sampling point and the sampling time of the second adjacent sampling point.

6. The capacity configuration method for a new energy storage system according to claim 3, characterized in that, The integration of new energy power generation between the start time point and the end time point is performed using the following simple rectangular method: The minimum load time period is divided into sampling point time periods that correspond one-to-one with all sampling points within the minimum load time period; rectangles are constructed based on all sampling points within the minimum load time period, where the length of the rectangle is the sampled value of the corresponding sampling point and the width of the rectangle is the sampling point time period of the corresponding sampling point, and the integral is completed by summing the areas of all rectangles.

7. The capacity configuration method for a new energy storage system according to claim 1, characterized in that, In step 2), the renewable energy sampling points within the minimum load time period are fitted and then integrated to obtain the renewable energy power generation within the minimum load time period.

8. The capacity configuration method for a new energy storage system according to claim 1, characterized in that, In step 3), the economically optimal new energy storage system capacity is obtained by subtracting the amount of new energy generated during the minimum load period from the amount of new energy generated during the minimum load period.

9. The capacity configuration method for a new energy storage system according to claim 1, characterized in that, In step 1), among all the time periods in the historical data of new energy where the power generation of new energy is lower than the minimum load of the electrolyzer, the time period with the lowest power generation of new energy is taken as the minimum load time period.

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