Dynamic simulation modeling method for light storage charging and discharging integrated charging station

Abstract

The invention relates to the technical field of dynamic simulation modeling, in particular to a dynamic simulation modeling method for an optical storage charging and discharging integrated charging station, and the method comprises the steps: determining a power utilization demand rate corresponding to each target subarea according to the load power change condition of an electric appliance of each target subarea in the optical storage charging and discharging integrated charging station in a current time period; determining the power generation efficiency reduction degree corresponding to each target partition; determining the scheduling opportunity rationality corresponding to each target partition; according to the scheduling opportunity rationality corresponding to each target partition and the outward scheduling power and load power distribution condition in the current time period, determining an electric energy loss value corresponding to each target partition; and performing dynamic simulation modeling based on the electric energy loss values corresponding to all the target partitions. According to the method, dynamic simulation modeling is realized by quantifying the electric energy loss value, and the rationality of electric energy loss quantification is improved, so that the rationality of dynamic simulation modeling of the optical storage charging and discharging integrated charging station is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic simulation modeling, and particularly relates to a dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging power station. Background Art

[0002] Photovoltaic power generation technology converts solar energy into electrical energy through photovoltaic modules. With the rapid development of the solar energy industry, photovoltaic power generation has become one of the important clean energy sources; the energy storage system mainly stores excess electrical energy through batteries and releases electrical energy during peak power demand periods or when photovoltaic power generation is insufficient; through power electronic devices, such as inverters, DC / DC converters, and charge and discharge control systems, etc., to achieve the interconnection and interoperability of photovoltaic power generation and the energy storage system; the simulation technology of the power system simulates the operating state and response behavior of the power system by establishing a dynamic mathematical model.

[0003] In the integrated photovoltaic energy storage charging and discharging power station, for dynamic simulation modeling of the power loss in different partitions, the traditional method obtains the power loss by collecting the difference between the input power and the output power in the partition during the operation process. However, in the actual operation process of the power station, the difference between the input power and the output power often cannot directly represent the power loss. For example, the different power generation situations between different photovoltaic panels in the partition often also affect the power loss. Therefore, when obtaining the power loss by collecting the difference between the input power and the output power in the partition during the operation process, the rationality of the power loss quantification is often poor, resulting in poor rationality of the dynamic simulation modeling of the integrated photovoltaic energy storage charging and discharging power station. Summary of the Invention

[0004] In order to solve the technical problem of poor rationality of the dynamic simulation modeling of the integrated photovoltaic energy storage charging and discharging power station caused by poor rationality of the power loss quantification, the present invention proposes a dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging power station.

[0005] In a first aspect, the present invention provides a dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging power station, and the method includes: Determine the power demand rate corresponding to each target partition according to the change of the electrical appliance load power in each target partition of the integrated photovoltaic energy storage charging and discharging power station during the current time period; Determine the degree of reduction in power generation efficiency corresponding to each target partition according to the power demand rate corresponding to each target partition and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within it during the current time period; Determine the rationality of the dispatching timing corresponding to each target partition according to the degree of reduction in power generation efficiency corresponding to each target partition and the change of the outward dispatching power and the output power of the energy storage battery within it during the current time period; Determine the power loss value corresponding to each target partition according to the rationality of the scheduling timing corresponding to each target partition and the distribution of the external scheduling power and load power within the current time period; Perform dynamic simulation modeling based on the power loss values corresponding to all target partitions.

[0006] Combined with the first aspect above, in a possible implementation manner, the determining the power demand rate corresponding to each target partition according to the change in the electrical appliance load power of each target partition in the integrated photovoltaic-storage-charging station within the current time period includes: Determine any one target partition in the integrated photovoltaic-storage-charging station as a marked partition, and obtain the electrical appliance load power at each moment within the current time period of the marked partition, forming the electrical appliance load power sequence corresponding to the marked partition; Determine the power fluctuation factor corresponding to the marked partition according to the electrical appliance load power sequence corresponding to the marked partition; Determine the power demand rate corresponding to the marked partition according to the mean value of all the electrical appliance load powers in the electrical appliance load power sequence corresponding to the marked partition and the power fluctuation factor corresponding to the marked partition.

[0007] Combined with the first aspect above, in a possible implementation manner, the determining the power fluctuation factor corresponding to the marked partition according to the electrical appliance load power sequence corresponding to the marked partition includes: Perform curve fitting on the electrical appliance load power sequence corresponding to the marked partition to obtain the electrical appliance load power curve corresponding to the marked partition; Determine the difference between each adjacent maximum value and minimum value in the electrical appliance load power curve corresponding to the marked partition as the undulation difference, obtaining the undulation difference sequence corresponding to the marked partition; Determine the mean value of all the undulation differences in the undulation difference sequence corresponding to the marked partition as the power fluctuation factor corresponding to the marked partition.

[0008] Combined with the first aspect above, in a possible implementation manner, the determining the degree of reduction in power generation efficiency corresponding to each target partition according to the power demand rate corresponding to each target partition and the distribution of the output power, output current, and output voltage of different photovoltaic panels in different photovoltaic panel strings within it within the current time period includes: Obtain the output power, output current, and output voltage of each photovoltaic panel at each moment within the current time period, respectively forming the output power sequence, output current sequence, and output voltage sequence corresponding to each photovoltaic panel; Determine the power generation rate corresponding to each photovoltaic panel according to the mean value and variance of all the output powers in the output power sequence corresponding to each photovoltaic panel; The minimum value among the output currents of all photovoltaic panels in each photovoltaic panel string at each moment within the current time period is denoted as the minimum representative current value, and a minimum representative current value sequence corresponding to each photovoltaic panel string is formed; According to the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which it belongs, a current difference sequence corresponding to each photovoltaic panel is constructed; According to the output voltage sequence and the current difference sequence corresponding to each photovoltaic panel, a loss power sequence corresponding to each photovoltaic panel is determined; According to the loss power sequences and power generation rates corresponding to different photovoltaic panels in each photovoltaic panel string, and the power consumption demand rate corresponding to the target partition to which it belongs, the series influence degree corresponding to each photovoltaic panel string is determined; According to the average value of the series influence degrees corresponding to all photovoltaic panel strings in each target partition, and the output voltage sequences corresponding to different photovoltaic panels in all photovoltaic panel strings in each target partition, the degree of reduction in power generation efficiency corresponding to each target partition is determined.

[0009] Combined with the above first aspect, in a possible implementation manner, the determining the series influence degree corresponding to each photovoltaic panel string according to the loss power sequences and power generation rates corresponding to different photovoltaic panels in each photovoltaic panel string, and the power consumption demand rate corresponding to the target partition to which it belongs, includes: According to the average value of all loss powers in the loss power sequence corresponding to each photovoltaic panel and its corresponding power generation rate, an initial series influence factor corresponding to each photovoltaic panel is determined; According to the average value of the initial series influence factors corresponding to all photovoltaic panels in each photovoltaic panel string, and the power consumption demand rate corresponding to the target partition to which the same photovoltaic panel string belongs, the series influence degree corresponding to each photovoltaic panel string is determined.

[0010] Combined with the above first aspect, in a possible implementation manner, the determining the degree of reduction in power generation efficiency corresponding to each target partition according to the average value of the series influence degrees corresponding to all photovoltaic panel strings in each target partition, and the output voltage sequences corresponding to different photovoltaic panels in all photovoltaic panel strings in each target partition, includes: The cumulative value of the output voltages collected at the same moment in the output voltage sequences corresponding to all photovoltaic panels in each photovoltaic panel string is determined as the overall output voltage of each photovoltaic panel string at the same moment, and an overall output voltage sequence corresponding to each photovoltaic panel string is obtained; The minimum value among the overall output voltages of all photovoltaic panel strings in each target partition at each moment within the current time period is denoted as the minimum representative voltage value, and a minimum representative voltage value sequence corresponding to each target partition is formed; Determine the voltage difference sequence corresponding to each photovoltaic panel string by taking the difference between the overall output voltage sequence corresponding to each photovoltaic panel string and the minimum representative voltage value sequence corresponding to its target partition; Determine the overall voltage difference corresponding to each photovoltaic panel string according to the voltage difference sequence corresponding to each photovoltaic panel string; Determine the degree of reduction in power generation efficiency corresponding to each target partition according to the mean value of the overall voltage differences corresponding to all photovoltaic panel strings in each target partition and the mean value of the series influence degrees corresponding to all photovoltaic panel strings in each target partition.

[0011] Combined with the first aspect above, in a possible implementation manner, the determining the overall voltage difference corresponding to each photovoltaic panel string according to the voltage difference sequence corresponding to each photovoltaic panel string includes: Determine the mean value of all voltage differences in the voltage difference sequence corresponding to each photovoltaic panel string as the overall voltage difference corresponding to each photovoltaic panel string.

[0012] Combined with the first aspect above, in a possible implementation manner, the determining the rationality of the scheduling timing corresponding to each target partition according to the degree of reduction in power generation efficiency corresponding to each target partition and the changes in the external scheduling power and the output power of the energy storage battery within the current time period includes: Obtain the external scheduling power at each moment within the current time period for each target partition to form an external scheduling power sequence corresponding to each target partition; Obtain the output power of the energy storage battery in each target partition at each moment within the current time period to form an energy storage battery power sequence corresponding to each target partition; Determine the overlapping moments corresponding to each target partition according to the external scheduling power sequence and the energy storage battery power sequence corresponding to each target partition; Determine the rationality of the scheduling timing corresponding to each target partition according to the total number of all overlapping moments corresponding to each target partition, the mean value of the output powers collected at all overlapping moments in the energy storage battery power sequence corresponding to each target partition, and the degree of reduction in power generation efficiency corresponding to each target partition.

[0013] Combined with the first aspect above, in a possible implementation manner, the determining the overlapping moments corresponding to each target partition according to the external scheduling power sequence and the energy storage battery power sequence corresponding to each target partition includes: Determine the collection moments corresponding to the non-zero external scheduling powers in the external scheduling power sequence corresponding to each target partition as reference moments to obtain a set of reference moments corresponding to each target partition; Determine the acquisition moments corresponding to the non-zero output powers in the energy storage battery power sequence corresponding to each target partition as temporary moments, and obtain the set of temporary moments corresponding to each target partition; Determine the intersection of the set of reference moments and the set of temporary moments corresponding to each target partition as the target intersection corresponding to each target partition; Determine each moment in the target intersection corresponding to each target partition as the overlapping moment corresponding to each target partition.

[0014] Combined with the above first aspect, in a possible implementation manner, the determining the power loss value corresponding to each target partition according to the scheduling timing rationality corresponding to each target partition and its external scheduling power and load power distribution conditions during the current time period includes: Determine any one target partition in the integrated photovoltaic and energy storage charging and discharging station as the marked partition; Obtain the external scheduling power of the marked partition at each moment during the current time period, and form the external scheduling power sequence corresponding to the marked partition; Obtain the total load power of the target partition to which the marked partition is externally scheduled at each moment during the current time period as load power data, and form the load power data sequence corresponding to the marked partition; Determine the power loss value corresponding to the marked partition according to the external scheduling power sequence, load power data sequence and scheduling timing rationality corresponding to the marked partition.

[0015] Combined with the above first aspect, in a possible implementation manner, the determining the power loss value corresponding to the marked partition according to the external scheduling power sequence, load power data sequence and scheduling timing rationality corresponding to the marked partition includes: Determine the target similarity corresponding to the marked partition according to the external scheduling power sequence and load power data sequence corresponding to the marked partition; Obtain the input power of the energy storage battery in the marked partition at each moment during the current time period, and form the input power sequence corresponding to the marked partition; Determine the input fluctuation factor corresponding to the marked partition according to the input power sequence corresponding to the marked partition; Determine the power loss value corresponding to the marked partition according to the mean value of all load power data in the load power data sequence corresponding to the marked partition, and its corresponding target similarity, input fluctuation factor and scheduling timing rationality.

[0016] Combined with the above first aspect, in a possible implementation manner, the determining the target similarity corresponding to the marked partition according to the external scheduling power sequence and load power data sequence corresponding to the marked partition includes: Normalize the cosine similarity between the outward dispatch power sequence corresponding to the marked partition and the load power data sequence to obtain the target similarity corresponding to the marked partition.

[0017] Combined with the first aspect above, in a possible implementation manner, the determining the input fluctuation factor corresponding to the marked partition according to the input power sequence corresponding to the marked partition includes: Construct an input power curve corresponding to the marked partition according to the input power sequence corresponding to the marked partition; Determine the absolute value of the slope corresponding to each data point in the input power curve corresponding to the marked partition as the target slope, and obtain the target slope sequence corresponding to the marked partition; Determine the average value of all the target slopes in the target slope sequence corresponding to the marked partition as the input fluctuation factor corresponding to the marked partition.

[0018] Combined with the first aspect above, in a possible implementation manner, the constructing a current difference sequence corresponding to each photovoltaic panel according to the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which the photovoltaic panel belongs includes: Determine the difference between the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which the photovoltaic panel belongs as the current difference sequence corresponding to each photovoltaic panel.

[0019] Combined with the first aspect above, in a possible implementation manner, the determining a loss power sequence corresponding to each photovoltaic panel according to the output voltage sequence and the current difference sequence corresponding to each photovoltaic panel includes: Determine the product of the output voltage sequence corresponding to each photovoltaic panel and the current difference sequence as the loss power sequence corresponding to each photovoltaic panel.

[0020] In a second aspect, the present invention provides a dynamic simulation modeling system for an integrated photovoltaic-storage-charging-discharging charging station, and the system includes: An electricity demand rate determination module, configured to determine the electricity demand rate corresponding to each target partition according to the change in the load power of the electrical appliances in each target partition in the integrated photovoltaic-storage-charging-discharging charging station during the current time period; A power generation efficiency reduction degree determination module, configured to determine the power generation efficiency reduction degree corresponding to each target partition according to the electricity demand rate corresponding to each target partition and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within each target partition during the current time period; A scheduling opportunity rationality determination module, configured to determine the scheduling opportunity rationality corresponding to each target partition according to the degree of reduction in power generation efficiency corresponding to each target partition, the outbound scheduling power, and the change in the output power of the energy storage battery within the current time period; An electric energy loss value determination module, configured to determine the electric energy loss value corresponding to each target partition according to the scheduling opportunity rationality corresponding to each target partition, the outbound scheduling power, and the load power distribution within the current time period; A dynamic simulation modeling module, configured to perform dynamic simulation modeling based on the electric energy loss values corresponding to all target partitions.

[0021] In a third aspect, a server is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0022] In a fourth aspect, a computer program product is provided, including: computer program code, when the computer program code runs on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0023] In a fifth aspect, a computer-readable storage medium is provided, storing computer program code, when the computer program code runs on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0024] The present invention has the following beneficial effects: A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station of the present invention realizes dynamic simulation modeling by quantifying the electric energy loss value, solves the technical problem of poor rationality of dynamic simulation modeling of the integrated photovoltaic energy storage charging and discharging charging station caused by poor rationality of electric energy loss quantification, and improves the rationality of electric energy loss quantification, thereby improving the rationality of dynamic simulation modeling of the integrated photovoltaic energy storage charging and discharging charging station. Specifically, the present invention comprehensively considers multiple factors related to electric energy loss, such as the change in the load power of electrical appliances, the distribution of output power, output current, and output voltage, the change in outbound scheduling power and the output power of the energy storage battery, and the load power distribution, etc., thereby quantifying the power consumption demand rate, the degree of reduction in power generation efficiency, and the scheduling opportunity rationality corresponding to different target partitions in the integrated photovoltaic energy storage charging and discharging charging station, and then more accurately quantifying the electric energy loss value, improving the rationality of electric energy loss quantification, and thus improving the rationality of dynamic simulation modeling of the integrated photovoltaic energy storage charging and discharging charging station. Description of the Drawings

[0025] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of a dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station of the present invention; Figure 2 It is a schematic diagram of the composition structure of a dynamic simulation modeling system for an integrated photovoltaic energy storage charging and discharging charging station of the present invention; Figure 3 It is a schematic diagram of the structure of a computer device of the present invention. Detailed implementation manners

[0027] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific implementation manners, structures, features and effects of the technical solutions proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0029] In order to perform simulation modeling on the power loss situation in each partition of the integrated photovoltaic energy storage charging and discharging charging station, current sensors and voltage sensors can be used to monitor the output voltage and current data of each photovoltaic panel in the photovoltaic array of each partition; monitor the input voltage and current at the input end and the output voltage and current at the output end of the local energy storage battery in each partition; monitor the load power of the electrical appliances in each partition; access the dispatching system in the power station to monitor the power dispatching situation between each partition in real time, including dispatching from one partition to another and the power data during the dispatching process.

[0030] Reference Figure 1 , which shows the flow of some embodiments of a dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to the present invention. The dynamic simulation modeling method for the integrated photovoltaic energy storage charging and discharging charging station includes the following steps: Step S1, determine the corresponding power consumption demand rate for each target partition according to the change in the load power of the electrical appliances in each target partition of the integrated photovoltaic energy storage charging and discharging charging station during the current time period.

[0031] It should be noted that the integrated photovoltaic energy storage charging and discharging power station is generally designed in zones, with independent photovoltaic arrays and local energy storage deployed in each zone; and the photovoltaic arrays in each zone are formed by connecting several different photovoltaic panels in series and parallel; the photovoltaic panels within a photovoltaic panel string are connected in series. The target zone can be a zone in the integrated photovoltaic energy storage charging and discharging power station. The current time period can be a period of time with the current moment as the end moment. The duration corresponding to the current time period can be preset, and it can be 1 minute.

[0032] Secondly, after the photovoltaic device generates electricity, the generated electric energy will be preferentially used to supply energy to the power grid of the power station, and the remaining excess electric energy will be used to charge the energy storage battery to supply power when the power generation of the photovoltaic device cannot meet the load demand of the electrical appliances in the zone; the photovoltaic power generation in each zone preferentially supplies the loads in that zone. If the photovoltaic power is greater than the demand of the load, the excess electric energy will enter energy storage or be cross-zonally dispatched; therefore, in order to analyze the electric energy loss in each zone during the electric energy distribution and dispatching process of the power station, it is often necessary to obtain the electricity demand levels of each zone in the power station during the current monitoring time period. Among them, the current monitoring time period is the current time period.

[0033] As an example, this step may include the following steps: The first step is to determine any target zone in the integrated photovoltaic energy storage charging and discharging power station as the marked zone, and obtain the electrical appliance load power at each moment within the current time period for the above-mentioned marked zone, forming the electrical appliance load power sequence corresponding to the above-mentioned marked zone.

[0034] Among them, the electrical appliance load power sequence can be a time series. The electrical appliance load power of the marked zone at a certain moment can represent the total electrical appliance load power of the marked zone at that moment.

[0035] The second step, determining the power consumption fluctuation factor corresponding to the above-mentioned marked zone according to the electrical appliance load power sequence corresponding to the above-mentioned marked zone may include the following sub-steps: The first sub-step is to perform curve fitting on the electrical appliance load power sequence corresponding to the above-mentioned marked zone to obtain the electrical appliance load power curve corresponding to the above-mentioned marked zone.

[0036] Among them, the electrical appliance load power curve can be a curve with the acquisition moment as the abscissa and the electrical appliance load power as the ordinate.

[0037] The second sub-step is to determine the difference between each adjacent maximum value and minimum value in the electrical appliance load power curve corresponding to the above-mentioned marked zone as the undulation difference, obtaining the undulation difference sequence corresponding to the above-mentioned marked zone.

[0038] Among them, a minimum value adjacent to the maximum value can be the minimum value closest to the maximum value on one side of the maximum value.

[0039] The third sub-step is to determine the mean value of all the undulation differences in the undulation difference sequence corresponding to the above-mentioned marked partition as the power consumption fluctuation factor corresponding to the above-mentioned marked partition.

[0040] The third step is to determine the power consumption demand rate corresponding to the above-mentioned marked partition according to the mean value of all the electrical appliance load powers in the electrical appliance load power sequence corresponding to the above-mentioned marked partition and the power consumption fluctuation factor corresponding to the above-mentioned marked partition.

[0041] For example, the formula for determining the power consumption demand rate corresponding to the marked partition can be: ; where LY is the power consumption demand rate corresponding to the marked partition. is the hyperbolic tangent function. w is the mean value of all the electrical appliance load powers in the electrical appliance load power sequence corresponding to the marked partition. is the power consumption fluctuation factor corresponding to the marked partition. is a preset factor greater than 0, mainly used to prevent the denominator from being 0, and it can be 0.0001.

[0042] It should be noted that when w is larger, it often indicates that the load of the electrical appliances in the marked partition is relatively higher. When is smaller, it often indicates that the fluctuation degree of the load of the electrical appliances in the marked partition is smaller. Therefore, when LY is larger, it often indicates that the load of the electrical appliances in the marked partition is more likely to be at a relatively large level value, and it often indicates that the power consumption demand rate in the marked partition is larger.

[0043] Step S2: Determine the degree of reduction in power generation efficiency corresponding to each target partition according to the power consumption demand rate corresponding to each target partition and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within the target partition during the current time period.

[0044] It should be noted that photovoltaic modules often generate electricity through light energy to meet the power consumption needs in each partition. However, due to the unstable power generation in photovoltaic modules, it may cause a reduction in the power generation efficiency of photovoltaic modules; there are several photovoltaic panels in the integrated photovoltaic energy storage charging and discharging station. Different photovoltaic panels may have different power generation efficiencies due to factors such as orientation and cloud occlusion, and photovoltaic panels with different power generation efficiencies may affect each other, resulting in a reduction in the power generation efficiency of the module.

[0045] As an example, this step may include the following steps: First step, obtain the output power, output current, and output voltage of each photovoltaic panel at each moment within the current time period, and respectively form an output power sequence, an output current sequence, and an output voltage sequence corresponding to each photovoltaic panel.

[0046] Among them, the output power sequence, output current sequence, and output voltage sequence can be time series.

[0047] Second step, determine the power generation rate corresponding to each photovoltaic panel according to the mean and variance of all output powers in the output power sequence corresponding to each photovoltaic panel.

[0048] For example, the formula for determining the power generation rate corresponding to a photovoltaic panel can be: ; Among them, x is the power generation rate corresponding to the photovoltaic panel. is the normalization function. s is the mean of all output powers in the output power sequence corresponding to the photovoltaic panel. is the variance of all output powers in the output power sequence corresponding to the photovoltaic panel. is a preset factor greater than 0, mainly used to prevent the denominator from being 0, and it can be 0.0001.

[0049] It should be noted that when x is larger, it often indicates that the output power of the photovoltaic panel within the current time period is larger and its output power is more stable; it often indicates that the power generation rate of the photovoltaic panel is relatively higher.

[0050] Third step, record the minimum value among the output currents of all photovoltaic panels in each photovoltaic panel string at each moment within the current time period as the minimum representative current value, and form a minimum representative current value sequence corresponding to each photovoltaic panel string.

[0051] Among them, the minimum representative current value sequence can be a time series. The minimum representative current value at a certain moment can characterize the minimum output current corresponding to all photovoltaic panels in the photovoltaic panel string at that moment.

[0052] It should be noted that since there are several photovoltaic string groups in the partition, the photovoltaic panels in the string group are connected in series with each other and there are differences in their efficiencies, which will cause the power generation efficiency of the entire photovoltaic string group to decline. Specifically, since the photovoltaic panels in the photovoltaic panel string group are connected in series and the current in the series circuit is the same, the inefficient panels in the string group often affect the overall current and limit it to a relatively low level. Therefore, the minimum current in the string group is often the actual output current of the string group.

[0053] Step 4: Construct a current difference sequence for each photovoltaic panel based on the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which it belongs.

[0054] For example, the difference between the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which it belongs can be determined as the current difference sequence corresponding to each photovoltaic panel.

[0055] It should be noted that the difference between two sequences can be a sequence formed by taking the difference between the elements at the corresponding positions in these two sequences.

[0056] Step 5: Determine the loss power sequence corresponding to each photovoltaic panel based on the output voltage sequence and the current difference sequence corresponding to each photovoltaic panel.

[0057] For example, the product of the output voltage sequence corresponding to each photovoltaic panel and the current difference sequence can be determined as the loss power sequence corresponding to each photovoltaic panel.

[0058] It should be noted that the product of two sequences can be a sequence formed by multiplying the elements at the corresponding positions in these two sequences.

[0059] Step 6: Determining the series influence degree corresponding to each photovoltaic panel string based on the loss power sequence and the power generation rate corresponding to different photovoltaic panels in each photovoltaic panel string, and the power consumption demand rate corresponding to the target partition to which it belongs may include the following sub-steps: The first sub-step: Determine the initial series influence factor corresponding to each photovoltaic panel according to the mean value of all loss powers in the loss power sequence corresponding to each photovoltaic panel and its corresponding power generation rate.

[0060] The second sub-step: Determine the series influence degree corresponding to each photovoltaic panel string according to the mean value of the initial series influence factors corresponding to all photovoltaic panels in each photovoltaic panel string and the power consumption demand rate corresponding to the target partition to which the same photovoltaic panel string belongs.

[0061] For example, the formula for determining the series influence degree corresponding to a photovoltaic panel string can be: ; Wherein, is the series influence degree corresponding to the i th photovoltaic panel string. i is the serial number of different photovoltaic panel strings in the target partition. is the hyperbolic tangent function. is the i th power consumption demand rate corresponding to the target partition to which the th photovoltaic panel string belongs. i is the number of different photovoltaic panels in the jis the serial number of different photovoltaic panels in the i th photovoltaic panel string. is the i th photovoltaic panel string, and j is the power generation rate corresponding to the th photovoltaic panel. i is the j th photovoltaic panel string, and is the average value of all the loss powers in the loss power sequence corresponding to the i th photovoltaic panel. j is the initial series influence factor corresponding to the

[0062] It should be noted that when is larger, it often indicates that the power generation rate of the i th photovoltaic panel string and the j th photovoltaic panel is relatively higher. When is larger, it often indicates that the load of the electrical appliances in the target partition to which the i th photovoltaic panel string belongs is more likely to be at a relatively large level value, and it often indicates that the power demand rate in the target partition to which the i th photovoltaic panel string belongs is larger. When is larger, it often indicates that the loss power of the i th photovoltaic panel string and the j th photovoltaic panel is relatively larger. When is larger, it often indicates that the power generation efficiency of the photovoltaic panels in the i th photovoltaic panel string is higher and the loss power is also higher, and it often indicates that the series influence degree is larger. Therefore, when is larger, it often indicates that the series influence degree within the i th photovoltaic panel string is larger.

[0063] Step 7: Determine the degree of reduction in power generation efficiency corresponding to each target partition according to the average value of the series influence degrees corresponding to all the photovoltaic panel strings in each target partition, and the output voltage sequences corresponding to different photovoltaic panels in all the photovoltaic panel strings in each target partition.

[0064] It should be noted that after the photovoltaic panels in the photovoltaic module are strung and output, they are often paralleled and aggregated into a unified partition power system. When there are voltage differences in different branches during the parallel process, it often leads to reverse current in the parallel circuit and thus power loss.

[0065] For example, determining the degree of reduction in power generation efficiency corresponding to each target partition may include the following sub-steps: In the first sub-step, the cumulative value of the output voltages collected at the same moment in the output voltage sequences corresponding to all the photovoltaic panels in each photovoltaic panel string is determined as the overall output voltage of each photovoltaic panel string at the same moment, and an overall output voltage sequence corresponding to each photovoltaic panel string is obtained.

[0066] Among them, the overall output voltage sequence can be a time sequence.

[0067] It should be noted that the overall output voltage of a photovoltaic panel string at a certain moment can be the cumulative value of the output voltages of all the photovoltaic panels in the photovoltaic panel string at that moment, which can represent the overall output voltage situation of the photovoltaic panel string.

[0068] In the second sub-step, the minimum value among the overall output voltages of all the photovoltaic panel strings in each target partition at each moment within the current time period is recorded as the minimum representative voltage value, and a minimum representative voltage value sequence corresponding to each target partition is formed.

[0069] Among them, the minimum representative voltage value at a certain moment can characterize the minimum output voltage corresponding to all the photovoltaic panel strings in the target partition at that moment.

[0070] In the third sub-step, the difference between the overall output voltage sequence corresponding to each photovoltaic panel string and the minimum representative voltage value sequence corresponding to its target partition is determined as the voltage difference sequence corresponding to each photovoltaic panel string.

[0071] In the fourth sub-step, the overall voltage difference corresponding to each photovoltaic panel string is determined according to the voltage difference sequence corresponding to each photovoltaic panel string.

[0072] For example, the mean value of all the voltage differences in the voltage difference sequence corresponding to each photovoltaic panel string can be determined as the overall voltage difference corresponding to each photovoltaic panel string.

[0073] In the fifth sub-step, the degree of reduction in power generation efficiency corresponding to each target partition is determined according to the mean value of the overall voltage differences corresponding to all the photovoltaic panel strings in each target partition and the mean value of the series influence degrees corresponding to all the photovoltaic panel strings in each target partition.

[0074] For example, the formula for determining the degree of reduction in power generation efficiency corresponding to the target partition can be: ; Among them, GZ is the degree of reduction in power generation efficiency corresponding to the target partition. is the hyperbolic tangent function. c is the mean value of the overall voltage differences corresponding to all the photovoltaic panel strings in the target partition. CS is the mean value of the series influence degrees corresponding to all the photovoltaic panel strings in the target partition.

[0075] It should be noted that when CS is larger, it often indicates that the series influence degree of the photovoltaic panel strings in the target partition is larger. When c is larger, it often indicates that the overall voltage difference of the photovoltaic panel strings in the target partition is larger. Therefore, when GZ is larger, it often indicates that the average value of the difference defects of the strings in the target partition is larger, often indicating that the power loss caused by the voltage difference is larger, and often indicating that the degree of reduction in power generation efficiency is larger.

[0076] Step S3: Determine the rationality of the scheduling timing corresponding to each target partition according to the degree of reduction in power generation efficiency corresponding to each target partition and the changes in the external scheduling power and the output power of the energy storage battery in the current time period.

[0077] It should be noted that after the photovoltaic modules in different partitions of the power station have a reduction in power generation efficiency, due to the need to schedule the electric energy generated by power generation, there is power loss during the scheduling process. Therefore, it is necessary to analyze the scheduling loss at the current power generation efficiency reduction rate. Specifically, after power generation in different partitions of the power station, the electrical appliances will be given priority. If there is surplus power afterwards, it can be distributed to other partitions for charging. However, in the actual operation process, due to the delay of the scheduling system, there may be problems with the scheduling timing in different partitions, which may result in a relatively large external scheduling power when the surplus power in the current partition is small. At this time, since the partition does not give priority to meeting the electrical appliance power demand in the partition, power loss occurs.

[0078] As an example, this step may include the following steps: The first step: Obtain the external scheduling power of each target partition at each moment in the current time period to form an external scheduling power sequence corresponding to each target partition.

[0079] Among them, the external scheduling power sequence can be a time sequence. The external scheduling power of the target partition at a certain moment can represent the power of the target partition for surplus power scheduling to other regions.

[0080] The second step: Obtain the output power of the energy storage battery in each target partition at each moment in the current time period to form an energy storage battery power sequence corresponding to each target partition.

[0081] Among them, the energy storage battery power sequence can be a time sequence.

[0082] The third step: According to the external scheduling power sequence and the energy storage battery power sequence corresponding to each target partition, determining the overlapping moments corresponding to each target partition may include the following sub-steps: The first sub-step: Determine the acquisition moments corresponding to the non-zero outward scheduling powers in the outward scheduling power sequence corresponding to each target partition as reference moments, and obtain the reference moment set corresponding to each target partition.

[0083] The second sub-step: Determine the acquisition moments corresponding to the non-zero output powers in the energy storage battery power sequence corresponding to each target partition as temporary moments, and obtain the temporary moment set corresponding to each target partition.

[0084] The third sub-step: Determine the intersection of the reference moment set and the temporary moment set corresponding to each target partition as the target intersection corresponding to each target partition.

[0085] The fourth sub-step: Determine each moment in the target intersection corresponding to each target partition as the overlapping moment corresponding to each target partition.

[0086] The fourth step: Determine the rationality of the scheduling opportunity corresponding to each target partition according to the total number of all overlapping moments corresponding to each target partition, the average value of the output powers collected at all overlapping moments in the energy storage battery power sequence corresponding to each target partition, and the degree of reduction in the power generation efficiency corresponding to each target partition.

[0087] For example, the formula for determining the rationality of the scheduling opportunity corresponding to the target partition can be: ; Wherein, GH is the rationality of the scheduling opportunity corresponding to the target partition. is the natural exponential function. GZ is the degree of reduction in the power generation efficiency corresponding to the target partition. t is the total number of all overlapping moments corresponding to the target partition. D is the average value of the output powers collected at all overlapping moments in the energy storage battery power sequence corresponding to the target partition.

[0088] It should be noted that if there is an output situation of the energy storage battery in the partition and the partition is still performing surplus power scheduling for other regions, it often indicates that the surplus power scheduling within the partition is unreasonable at this time. And when performing surplus power scheduling, the higher the output of the energy storage battery within the partition, the lower the rationality of the partition scheduling. Therefore, the more overlapping moments corresponding to the target partition, and the higher the output power of the energy storage battery within the target partition at the overlapping moments, the more unreasonable the surplus power scheduling within the partition is often indicated. That is, when is larger, it often indicates that there is still a situation of battery charge replenishment in the target partition during the scheduling process, and the more the replenished power, the lower the rationality of the scheduling opportunity at this time. When GZThe larger it is, it often indicates that the average difference defect of the strings in the target partition is larger, often indicating that the power loss caused by voltage difference is larger, and often indicating that the degree of reduction in power generation efficiency is larger. Therefore, when GH The larger it is, it often indicates that the rationality of the scheduling timing of the target partition is greater.

[0089] Step S4: Determine the power loss value corresponding to each target partition according to the rationality of the scheduling timing corresponding to each target partition and its outgoing scheduling power and load power distribution in the current time period.

[0090] It should be noted that when each partition is being scheduled, during the charging and discharging processes of the energy storage battery, the chemical reaction of the battery often causes power loss, and there are often transmission line losses when surplus power is scheduled between partitions; therefore, when surplus power is regulated between different partitions of the power station, it is necessary to minimize the number of cycles of the energy storage battery and reduce the transmission loss. Therefore, it is often necessary to analyze the power loss generated by each partition under the current rationality of the scheduling timing.

[0091] As an example, this step may include the following steps: The first step: Designate any target partition in the integrated photovoltaic, energy storage, charging and discharging charging station as the marked partition.

[0092] The second step: Obtain the outgoing scheduling power of the above marked partition at each moment in the current time period to form the outgoing scheduling power sequence corresponding to the above marked partition.

[0093] Among them, the outgoing scheduling power sequence can be a time series.

[0094] The third step: Obtain the total load power of the target partition to which the above marked partition schedules outgoing power at each moment in the current time period as the load power data to form the load power data sequence corresponding to the above marked partition.

[0095] Among them, the load power data sequence can be a time series.

[0096] The fourth step: Determining the power loss value corresponding to the above marked partition according to the outgoing scheduling power sequence, load power data sequence and scheduling timing rationality corresponding to the above marked partition may include the following sub-steps: The first sub-step: Determine the target similarity corresponding to the above marked partition according to the outgoing scheduling power sequence and load power data sequence corresponding to the above marked partition.

[0097] For example, the cosine similarity between the outgoing scheduling power sequence and the load power data sequence corresponding to the above marked partition can be normalized to obtain the target similarity corresponding to the above marked partition.

[0098] It should be noted that during the process of surplus power scheduling for partitions, it is necessary to prioritize the scheduling of partitions with high power demand. In this way, after scheduling, the load will often consume the surplus power in a timely manner, thereby avoiding the power loss of secondary charging to a certain extent. Therefore, when the change trend of the outward scheduling power is more consistent with the change trend of the load power of the outward scheduling partition, it often indicates that the power loss is relatively smaller. Therefore, when the target similarity corresponding to the marked partition is larger, it often indicates that the similarity between the outward scheduling power sequence and the load power data sequence corresponding to the marked partition is larger, and it often indicates that the power loss is relatively smaller.

[0099] The second sub-step is to obtain the input power at each moment within the current time period of the energy storage battery in the above-mentioned marked partition, and form the input power sequence corresponding to the above-mentioned marked partition.

[0100] Among them, the input power sequence can be a time series.

[0101] The third sub-step of determining the input fluctuation factor corresponding to the above-mentioned marked partition according to the input power sequence corresponding to the above-mentioned marked partition may include the following steps: First, according to the input power sequence corresponding to the above-mentioned marked partition, construct the input power curve corresponding to the above-mentioned marked partition.

[0102] Among them, the input power curve can be a curve with the acquisition time as the abscissa and the input power in the input power sequence as the ordinate.

[0103] Next, determine the absolute value of the slope corresponding to each data point in the input power curve corresponding to the above-mentioned marked partition as the target slope, and obtain the target slope sequence corresponding to the above-mentioned marked partition.

[0104] Finally, determine the average value of all the target slopes in the target slope sequence corresponding to the above-mentioned marked partition as the input fluctuation factor corresponding to the above-mentioned marked partition.

[0105] The fourth sub-step is to determine the power loss value corresponding to the above-mentioned marked partition according to the average value of all the load power data in the load power data sequence corresponding to the above-mentioned marked partition, as well as its corresponding target similarity, input fluctuation factor, and the rationality of the scheduling timing.

[0106] For example, the formula for determining the power loss value corresponding to the target partition can be: ; Among them, DS is the power loss value corresponding to the target partition. is the natural exponential function. GH is the rationality of the scheduling timing corresponding to the target partition. AS is the target similarity corresponding to the target partition. Fis the average value of all load power data in the load power data sequence corresponding to the target partition. k is the input fluctuation factor corresponding to the target partition. is a factor greater than 0 set in advance, mainly used to prevent the denominator from being 0, and it can be 0.0001.

[0107] It should be noted that when GH is smaller, it often indicates that the rationality of the target partition scheduling timing is worse, and it often indicates that the power loss of the target partition is relatively larger. When AS is larger, it often indicates that the similarity between the external scheduling power sequence corresponding to the target partition and the load power data sequence is larger, and it often indicates that the power loss of the target partition is relatively smaller. When is larger, it indicates that the load power of the scheduling part is larger when the partition is externally scheduled. At this time, the power loss generated by the scheduling is smaller, and it often indicates that the power loss is smaller. Therefore, when DS is larger, it often indicates that the power loss of the target partition is larger.

[0108] Step S5, perform dynamic simulation modeling based on the power loss values corresponding to all target partitions.

[0109] As an example, based on the power loss values corresponding to different target partitions, dynamic simulation modeling can be implemented through the MATLAB software tool.

[0110] It should be noted that the MATLAB software tool can assist in creating an electrical system model, performing dynamic simulation, and analyzing the dynamic response of the system.

[0111] For example, the dynamic simulation modeling can include the following steps: The first step is to initialize the states of each partition.

[0112] The second step is to input internal variables, such as the output power of photovoltaic panels, the input and output power of energy storage batteries, and load power.

[0113] The third step is to bring the obtained power loss into the input loss.

[0114] The fourth step is to update the state variables and loop until the simulation ends.

[0115] The fifth step is to visualize the results and reports.

[0116] The sixth step is to generate the curve of the loss of each partition changing with time, and use the energy flow Sankey diagram to display the loss distribution.

[0117] The seventh step is to output the conclusion: identify the link with the largest loss and propose suggestions for hardware upgrade or control strategy optimization.

[0118] Reference Figure 2, based on the same inventive concept as the above method embodiments, the present invention provides a dynamic simulation modeling system for a photovoltaic-storage-charging-discharging integrated charging station. The system includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a dynamic simulation modeling method for a photovoltaic-storage-charging-discharging integrated charging station, which may specifically include: An electricity demand rate determination module 201, configured to determine the electricity demand rate corresponding to each target partition according to the change in the load power of electrical appliances in each target partition of the photovoltaic-storage-charging-discharging integrated charging station during the current time period; A power generation efficiency reduction degree determination module 202, configured to determine the power generation efficiency reduction degree corresponding to each target partition according to the electricity demand rate corresponding to each target partition and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within it during the current time period; A scheduling timing rationality determination module 203, configured to determine the scheduling timing rationality corresponding to each target partition according to the power generation efficiency reduction degree corresponding to each target partition and the changes in the outward scheduling power and energy storage battery output power within it during the current time period; An electric energy loss value determination module 204, configured to determine the electric energy loss value corresponding to each target partition according to the scheduling timing rationality corresponding to each target partition and the outward scheduling power and load power distribution within it during the current time period; A dynamic simulation modeling module 205, configured to perform dynamic simulation modeling based on the electric energy loss values corresponding to all target partitions.

[0119] Figure 3 is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Exemplarily, as Figure 3 shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. Among them, when the processor 302 executes the computer program 303, the computer device can execute any of the above-described dynamic simulation modeling methods for a photovoltaic-storage-charging-discharging integrated charging station.

[0120] Based on the same inventive concept as the above method embodiments, the present invention provides a server, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes any of the above-described dynamic simulation modeling methods for a photovoltaic-storage-charging-discharging integrated charging station.

[0121] Based on the same inventive concept as the above method embodiments, the present invention provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, enabling the computer to execute any of the above optical storage charging and discharging integrated charging station dynamic simulation modeling methods.

[0122] Based on the same inventive concept as the above method embodiments, the present invention provides a computer-readable storage medium, which stores computer program code, when the computer program code runs on a computer, enabling the computer to execute any of the above optical storage charging and discharging integrated charging station dynamic simulation modeling methods.

[0123] In summary, the present invention collects various parameters of the optical storage integrated power station during operation by using sensors, and obtains the electricity demand rate in each partition by analyzing the change of the load power of electrical appliances in each partition; in the case of the electricity demand rate in each partition, the power generation loss is obtained by analyzing the influence of the series and parallel connections of different photovoltaic panels in the partition, and in the case of the power generation loss, the timing of the surplus power scheduling between each partition and the power loss of the surplus power scheduling are analyzed, and finally the power loss of each partition is obtained. Compared with the traditional method of obtaining the power loss by analyzing the relationship between the input and output powers of each partition, by analyzing the influence between the photovoltaic panels and the scheduling relationship between different partitions, the result of the power loss is more accurate, the rationality of the power loss quantification is improved, and thus the rationality of the dynamic simulation modeling of the optical storage charging and discharging integrated charging station is improved.

[0124] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station, characterized in that Including the following steps: Determine the electricity demand rate corresponding to each target partition according to the change of the electrical appliance load power in each target partition of the integrated photovoltaic-storage-charging-discharging charging station during the current time period; Determine the degree of reduction in power generation efficiency corresponding to each target partition according to the electricity demand rate corresponding to each target partition and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within it during the current time period; Determine the rationality of the dispatching timing corresponding to each target partition according to the degree of reduction in power generation efficiency corresponding to each target partition and the change of the outward dispatching power and the output power of the energy storage battery in the current time period; Determine the value of power loss corresponding to each target partition according to the rationality of the dispatching timing corresponding to each target partition and the outward dispatching power and load power distribution in the current time period; Conduct dynamic simulation modeling based on the power loss values corresponding to all target partitions.

2. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 1, characterized in that, The step of determining the electricity demand rate corresponding to each target partition according to the change of the electrical appliance load power in each target partition of the integrated photovoltaic-storage-charging-discharging charging station during the current time period includes: Designate any one target partition in the integrated photovoltaic-storage-charging-discharging charging station as a marked partition, and obtain the electrical appliance load power at each moment in the current time period of the marked partition, forming the electrical appliance load power sequence corresponding to the marked partition; Determine the electricity fluctuation factor corresponding to the marked partition according to the electrical appliance load power sequence corresponding to the marked partition; Determine the electricity demand rate corresponding to the marked partition according to the mean value of all electrical appliance load powers in the electrical appliance load power sequence corresponding to the marked partition and the electricity fluctuation factor corresponding to the marked partition.

3. A dynamic simulation modeling method for an integrated photovoltaic-storage-charging-discharging charging station according to claim 2, characterized in that, The step of determining the electricity fluctuation factor corresponding to the marked partition according to the electrical appliance load power sequence corresponding to the marked partition includes: Perform curve fitting on the electrical appliance load power sequence corresponding to the marked partition to obtain the electrical appliance load power curve corresponding to the marked partition; Determine the difference between each adjacent maximum value and minimum value in the electrical appliance load power curve corresponding to the marked partition as the undulation difference, obtaining the undulation difference sequence corresponding to the marked partition; Determine the mean value of all undulation differences in the undulation difference sequence corresponding to the marked partition as the electricity fluctuation factor corresponding to the marked partition.

4. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 1, characterized in that, The step of determining the degree of reduction in power generation efficiency corresponding to each target partition according to the electricity demand rate corresponding to each target partition and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within it during the current time period includes: Obtain the output power, output current, and output voltage of each photovoltaic panel at each moment in the current time period, respectively forming the output power sequence, output current sequence, and output voltage sequence corresponding to each photovoltaic panel; Determine the power generation rate corresponding to each photovoltaic panel according to the mean value and variance of all output powers in the output power sequence corresponding to each photovoltaic panel; The minimum value of the output currents of all the photovoltaic panels in each photovoltaic panel string at each moment within the current time period is recorded as the minimum representative current value, and a minimum representative current value sequence corresponding to each photovoltaic panel string is formed. Based on the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which it belongs, a current difference sequence corresponding to each photovoltaic panel is constructed. Based on the output voltage sequence and the current difference sequence corresponding to each photovoltaic panel, a loss power sequence corresponding to each photovoltaic panel is determined. Based on the loss power sequences and power generation rates corresponding to different photovoltaic panels in each photovoltaic panel string, and the power consumption demand rate corresponding to the target partition to which it belongs, the series influence degree corresponding to each photovoltaic panel string is determined. Based on the mean value of the series influence degrees corresponding to all the photovoltaic panel strings in each target partition, and the output voltage sequences corresponding to different photovoltaic panels in all the photovoltaic panel strings in each target partition, the degree of reduction in power generation efficiency corresponding to each target partition is determined.

5. A dynamic simulation modeling method for an integrated photovoltaic-storage-charging and discharging charging station according to claim 4, characterized in that, The determining of the series influence degree corresponding to each photovoltaic panel string based on the loss power sequences and power generation rates corresponding to different photovoltaic panels in each photovoltaic panel string, and the power consumption demand rate corresponding to the target partition to which it belongs includes: Based on the mean value of all the loss powers in the loss power sequence corresponding to each photovoltaic panel and its corresponding power generation rate, an initial series influence factor corresponding to each photovoltaic panel is determined. Based on the mean value of the initial series influence factors corresponding to all the photovoltaic panels in each photovoltaic panel string, and the power consumption demand rate corresponding to the target partition to which the same photovoltaic panel string belongs, the series influence degree corresponding to each photovoltaic panel string is determined.

6. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 4, characterized in that The determining of the degree of reduction in power generation efficiency corresponding to each target partition based on the mean value of the series influence degrees corresponding to all the photovoltaic panel strings in each target partition, and the output voltage sequences corresponding to different photovoltaic panels in all the photovoltaic panel strings in each target partition includes: The cumulative value of the output voltages collected at the same moment in the output voltage sequences corresponding to all the photovoltaic panels in each photovoltaic panel string is determined as the overall output voltage of each photovoltaic panel string at the same moment, and an overall output voltage sequence corresponding to each photovoltaic panel string is obtained. The minimum value of the overall output voltages of all the photovoltaic panel strings in each target partition at each moment within the current time period is recorded as the minimum representative voltage value, and a minimum representative voltage value sequence corresponding to each target partition is formed. The difference between the overall output voltage sequence corresponding to each photovoltaic panel string and the minimum representative voltage value sequence corresponding to the target partition to which it belongs is determined as the voltage difference sequence corresponding to each photovoltaic panel string. Based on the voltage difference sequence corresponding to each photovoltaic panel string, the overall voltage difference corresponding to each photovoltaic panel string is determined. Based on the mean value of the overall voltage differences corresponding to all the photovoltaic panel strings in each target partition, and the mean value of the series influence degrees corresponding to all the photovoltaic panel strings in each target partition, the degree of reduction in power generation efficiency corresponding to each target partition is determined.

7. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 6, characterized in that, The determining of the overall voltage difference corresponding to each photovoltaic panel string based on the voltage difference sequence corresponding to each photovoltaic panel string includes: The mean value of all the voltage differences in the voltage difference sequence corresponding to each photovoltaic panel string is determined as the overall voltage difference corresponding to each photovoltaic panel string.

8. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 1, characterized in that, Determining the rationality of the scheduling timing corresponding to each target partition according to the degree of reduction in power generation efficiency corresponding to each target partition, its outward scheduling power and the change in the output power of the energy storage battery during the current time period, includes: Obtaining the outward scheduling power of each target partition at each moment during the current time period, and forming an outward scheduling power sequence corresponding to each target partition; Obtaining the output power of the energy storage battery in each target partition at each moment during the current time period, and forming an energy storage battery power sequence corresponding to each target partition; Determining the overlapping moments corresponding to each target partition according to the outward scheduling power sequence and the energy storage battery power sequence corresponding to each target partition; Determining the rationality of the scheduling timing corresponding to each target partition according to the total number of all overlapping moments corresponding to each target partition, the average value of the output power collected at all overlapping moments in the energy storage battery power sequence corresponding to each target partition, and the degree of reduction in power generation efficiency corresponding to each target partition.

9. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 8, characterized in that, The determining the overlapping moments corresponding to each target partition according to the outward scheduling power sequence and the energy storage battery power sequence corresponding to each target partition includes: Determining the acquisition moments corresponding to the outward scheduling power not equal to 0 in the outward scheduling power sequence corresponding to each target partition as reference moments, and obtaining a reference moment set corresponding to each target partition; Determining the acquisition moments corresponding to the output power not equal to 0 in the energy storage battery power sequence corresponding to each target partition as temporary moments, and obtaining a temporary moment set corresponding to each target partition; Determining the intersection of the reference moment set and the temporary moment set corresponding to each target partition as the target intersection corresponding to each target partition; Determining each moment in the target intersection corresponding to each target partition as the overlapping moment corresponding to each target partition.

10. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 1, characterized in that, Determining the power loss value corresponding to each target partition according to the rationality of the scheduling timing corresponding to each target partition, its outward scheduling power and the load power distribution during the current time period, includes: Determining any one target partition in the integrated photovoltaic and energy storage charging and discharging station as the marked partition; Obtaining the outward scheduling power of the marked partition at each moment during the current time period, and forming an outward scheduling power sequence corresponding to the marked partition; Obtaining the total load power of the target partition outwardly scheduled by the marked partition at each moment during the current time period as load power data, and forming a load power data sequence corresponding to the marked partition; Determining the power loss value corresponding to the marked partition according to the outward scheduling power sequence, the load power data sequence and the rationality of the scheduling timing corresponding to the marked partition.

11. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 10, characterized in that, The determining the power loss value corresponding to the marked partition according to the outward scheduling power sequence, the load power data sequence and the rationality of the scheduling timing corresponding to the marked partition includes: Determining the target similarity corresponding to the marked partition according to the outward scheduling power sequence and the load power data sequence corresponding to the marked partition; Obtaining the input power of the energy storage battery in the marked partition at each moment during the current time period, and forming an input power sequence corresponding to the marked partition; Determine the input fluctuation factor corresponding to the marked partition according to the input power sequence corresponding to the marked partition; Determine the power loss value corresponding to the marked partition according to the mean value of all load power data in the load power data sequence corresponding to the marked partition, and its corresponding target similarity, input fluctuation factor, and scheduling timing rationality.

12. A dynamic simulation modeling method for an integrated photovoltaic-storage-charging-discharging charging station according to claim 11, characterized in that The determining the target similarity corresponding to the marked partition according to the outward scheduling power sequence and the load power data sequence corresponding to the marked partition includes: Normalize the cosine similarity between the outward scheduling power sequence and the load power data sequence corresponding to the marked partition to obtain the target similarity corresponding to the marked partition.

13. A dynamic simulation modeling method for an integrated charging and discharging station of photovoltaic energy storage, according to claim 11, characterized in that, The determining the input fluctuation factor corresponding to the marked partition according to the input power sequence corresponding to the marked partition includes: Construct the input power curve corresponding to the marked partition according to the input power sequence corresponding to the marked partition; Determine the absolute value of the slope corresponding to each data point in the input power curve corresponding to the marked partition as the target slope to obtain the target slope sequence corresponding to the marked partition; Determine the mean value of all target slopes in the target slope sequence corresponding to the marked partition as the input fluctuation factor corresponding to the marked partition.

14. A dynamic simulation modeling method for an integrated photovoltaic energy storage charging and discharging charging station according to claim 4, characterized in that, The constructing the current difference sequence corresponding to each photovoltaic panel according to the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which it belongs includes: Determine the difference between the output current sequence corresponding to each photovoltaic panel and the minimum representative current value sequence corresponding to the photovoltaic panel string to which it belongs as the current difference sequence corresponding to each photovoltaic panel.

15. A dynamic simulation modeling method for an integrated photovoltaic-storage-charging-discharging charging station according to claim 4, characterized in that, The determining the loss power sequence corresponding to each photovoltaic panel according to the output voltage sequence and the current difference sequence corresponding to each photovoltaic panel includes: Determine the product of the output voltage sequence corresponding to each photovoltaic panel and the current difference sequence as the loss power sequence corresponding to each photovoltaic panel.

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