A dynamic simulation modeling method for integrated photovoltaic, storage and charging-discharging charging stations

Abstract

The present invention relates to the technical field of dynamic simulation modeling, and specifically to a dynamic simulation modeling method for a photovoltaic, storage, charging and discharging integrated charging station, the method comprising: determining the electricity demand rate corresponding to each target partition according to the load power changes of electrical appliances in each target partition in the current time period in the photovoltaic, storage, charging and discharging integrated charging station; determining the degree of reduction in power generation efficiency corresponding to each target partition; determining the rationality of the scheduling timing corresponding to each target partition; determining the electric energy loss value corresponding to each target partition according to the rationality of the scheduling timing corresponding to each target partition and its outward scheduling power and load power distribution in the current time period; and performing dynamic simulation modeling based on the electric energy loss values ​​corresponding to all target partitions. The present invention realizes dynamic simulation modeling by quantifying the electric energy loss value, and improves the rationality of the quantification of electric energy loss, thereby improving the rationality of the dynamic simulation modeling of the photovoltaic, storage, charging and discharging integrated charging station.

Description

Technical Field

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

[0002] Photovoltaic power generation technology converts solar energy into electricity through photovoltaic modules. With the rapid development of the solar industry, photovoltaic power generation has become a major clean energy source. Energy storage systems primarily use batteries to store excess electricity and release it during peak power demand periods or when photovoltaic power generation is insufficient. The interconnection between photovoltaic power generation and energy storage systems is achieved through power electronic equipment such as inverters, DC / DC converters, and charge and discharge control systems. Power system simulation technology simulates the operating state and response behavior of power systems by establishing dynamic mathematical models.

[0003] In the integrated photovoltaic, storage and charging and discharging charging station, the power loss in different zones is dynamically simulated and modeled. The traditional method obtains the power loss by collecting the difference between the input power and output power in the zone during operation. However, in the actual operation 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 conditions between different photovoltaic panels in the zone often affect the power loss. Therefore, when the power loss is obtained by collecting the difference between the input power and the output power in the zone during operation, the rationality of the power loss quantification is often poor, which leads to the poor rationality of the dynamic simulation modeling of the integrated photovoltaic, storage and charging and discharging charging station. Summary of the Invention

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

[0005] In a first aspect, the present invention provides a dynamic simulation modeling method for an integrated photovoltaic storage charging and discharging charging station, the method comprising:

[0006] Determine the power demand rate corresponding to each target zone in the integrated solar-storage-charging-discharging charging station based on the load power changes of electrical appliances in each target zone during the current time period.

[0007] Determine the degree of reduction in power generation efficiency for each target zone based on the power demand rate corresponding to each target zone and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within the zone during the current time period;

[0008] Determine the rationality of the dispatch timing for each target partition based on the degree of reduction in power generation efficiency corresponding to each target partition and the changes in outward dispatch power and energy storage battery output power in the current time period;

[0009] Determine the power loss value corresponding to each target partition based on the rationality of the scheduling timing corresponding to each target partition and the distribution of outward scheduling power and load power in the current time period;

[0010] Dynamic simulation modeling is performed based on the power loss values ​​corresponding to all target partitions.

[0011] In conjunction with the first aspect above, in one possible implementation, determining the power demand rate corresponding to each target zone in the integrated solar-storage-charging-discharging charging station based on the load power changes of electrical appliances in each target zone within the current time period includes:

[0012] Determine any target zone in the integrated solar-storage-charging-discharging charging station as a marked zone, and obtain the load power of electrical appliances in the marked zone at each moment in the current time period to form a load power sequence of electrical appliances corresponding to the marked zone;

[0013] Determining the power consumption fluctuation factor corresponding to the marked partition according to the electrical load power sequence corresponding to the marked partition;

[0014] The power demand rate corresponding to the marked partition is determined according to the average value of all electrical load powers in the electrical load power sequence corresponding to the marked partition and the power fluctuation factor corresponding to the marked partition.

[0015] In combination with the first aspect above, in a possible implementation, determining the power consumption fluctuation factor corresponding to the marked partition according to the electrical load power sequence corresponding to the marked partition includes:

[0016] Performing curve fitting on the electrical load power sequence corresponding to the marked partition to obtain the electrical load power curve corresponding to the marked partition;

[0017] Determine the difference between each adjacent maximum value and minimum value in the load power curve of the electrical appliance corresponding to the marked partition as a fluctuation difference, and obtain a fluctuation difference sequence corresponding to the marked partition;

[0018] The average of all fluctuation differences in the fluctuation difference sequence corresponding to the marked partition is determined as the power consumption fluctuation factor corresponding to the marked partition.

[0019] In conjunction with the first aspect above, in one possible implementation, determining the degree of reduction in power generation efficiency corresponding to each target zone based on the power demand rate corresponding to each target zone and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within the zone during the current time period includes:

[0020] Obtain the output power, output current, and output voltage of each photovoltaic panel at each moment in the current time period, and construct the output power sequence, output current sequence, and output voltage sequence corresponding to each photovoltaic panel respectively;

[0021] Determine the power generation rate corresponding to each photovoltaic panel based on the mean and variance of all output powers in the output power sequence corresponding to each photovoltaic panel;

[0022] The minimum value of the output current of all photovoltaic panels in each photovoltaic panel string at each moment in the current time period is recorded as the minimum representative current value, forming a minimum representative current value sequence corresponding to each photovoltaic panel string;

[0023] 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;

[0024] Determine the power loss sequence corresponding to each photovoltaic panel based on the output voltage sequence and current difference sequence corresponding to each photovoltaic panel;

[0025] Determine the series connection impact of each photovoltaic panel string based on the power loss sequence and power generation rate of different photovoltaic panels in each photovoltaic panel string, as well as the power demand rate of the target partition to which it belongs;

[0026] The degree of reduction in power generation efficiency corresponding to each target partition is determined based on the mean value of the series influence degree corresponding to all photovoltaic panel strings in each target partition and the output voltage sequence corresponding to different photovoltaic panels in all photovoltaic panel strings in each target partition.

[0027] In conjunction with the first aspect above, in one possible implementation, determining the series connection influence corresponding to each photovoltaic panel string based on the loss power sequence and power generation rate corresponding to different photovoltaic panels in each photovoltaic panel string, and the power demand rate corresponding to the target zone to which it belongs, includes:

[0028] Determine the initial series influence factor corresponding to each photovoltaic panel based on the mean value of all power losses in the power loss sequence corresponding to each photovoltaic panel and its corresponding power generation rate;

[0029] The series connection influence degree of each photovoltaic panel string is determined based on the average of the initial series connection influence factors corresponding to all photovoltaic panels in each photovoltaic panel string and the power demand rate corresponding to the target partition to which the same photovoltaic panel string belongs.

[0030] In conjunction with the first aspect above, in one possible implementation, determining the degree of reduction in power generation efficiency corresponding to each target partition based on the average of the series influence degrees corresponding to all photovoltaic panel strings within each target partition and the output voltage sequence corresponding to different photovoltaic panels in all photovoltaic panel strings within each target partition includes:

[0031] The output voltage sequence corresponding to each photovoltaic panel string is obtained by summing the output voltages collected at the same time in the output voltage sequence corresponding to all photovoltaic panels in each photovoltaic panel string to determine the overall output voltage of each photovoltaic panel string at the same time, thereby obtaining the overall output voltage sequence corresponding to each photovoltaic panel string;

[0032] The minimum value of the overall output voltage of all photovoltaic panels in each target partition at each moment in the current time period is recorded as the minimum representative voltage value, forming a minimum representative voltage value sequence corresponding to each target partition;

[0033] 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;

[0034] Determine the overall voltage difference corresponding to each photovoltaic panel string based on the voltage difference sequence corresponding to each photovoltaic panel string;

[0035] The degree of reduction in power generation efficiency corresponding to each target partition is determined based on the mean value of the overall voltage difference corresponding to all photovoltaic panel strings in each target partition and the mean value of the series influence degree corresponding to all photovoltaic panel strings in each target partition.

[0036] In conjunction with the first aspect above, in one possible implementation, determining the overall voltage difference corresponding to each photovoltaic panel string according to the voltage difference sequence corresponding to each photovoltaic panel string includes:

[0037] The average value of all 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.

[0038] In conjunction with the first aspect above, in one possible implementation, determining the rationality of the scheduling timing corresponding to each target zone based on the degree of reduction in power generation efficiency corresponding to each target zone and changes in outbound dispatch power and energy storage battery output power in the current time period includes:

[0039] Obtain the outbound scheduling power of each target partition at each time in the current time period to form an outbound scheduling power sequence corresponding to each target partition;

[0040] Obtain the output power of the energy storage batteries in each target partition at each moment in the current time period to form a power sequence of the energy storage batteries corresponding to each target partition;

[0041] Determine the overlapping time corresponding to each target partition based on the outward scheduling power sequence and energy storage battery power sequence corresponding to each target partition;

[0042] The rationality of the scheduling timing corresponding to each target partition is determined based on the total number of all overlapping moments corresponding to each target partition, the average 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.

[0043] In conjunction with the first aspect above, in one possible implementation, determining the overlapping time corresponding to each target partition according to the outbound scheduling power sequence and the energy storage battery power sequence corresponding to each target partition includes:

[0044] Determine the collection time corresponding to the outward scheduling power that is not equal to 0 in the outward scheduling power sequence corresponding to each target partition as the reference time, and obtain a reference time set corresponding to each target partition;

[0045] Determine the collection time corresponding to the output power that is not equal to 0 in the energy storage battery power sequence corresponding to each target partition as a temporary time, and obtain a temporary time set corresponding to each target partition;

[0046] Determine the intersection of the reference time set and the temporary time set corresponding to each target partition as the target intersection corresponding to each target partition;

[0047] Each moment in the target intersection corresponding to each target partition is determined as the overlapping moment corresponding to each target partition.

[0048] In combination with the first aspect above, in one possible implementation, determining the power loss value corresponding to each target partition based on the rationality of the scheduling timing corresponding to each target partition and the distribution of the outward scheduling power and load power in the current time period includes:

[0049] Determine any target zone in the integrated photovoltaic storage and charging station as a marked zone;

[0050] Obtaining the outbound scheduling power of the marked partition at each time in the current time period to form an outbound scheduling power sequence corresponding to the marked partition;

[0051] Obtaining the total load power of the target partition outwardly scheduled by the marked partition at each moment in the current time period as load power data to form a load power data sequence corresponding to the marked partition;

[0052] The electric energy loss value corresponding to the marked partition is determined according to the outward dispatching power sequence, the load power data sequence and the rationality of the dispatching timing corresponding to the marked partition.

[0053] In combination with the first aspect above, in one possible implementation, 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:

[0054] Determining a target similarity corresponding to the marked partition according to an outward scheduling power sequence and a load power data sequence corresponding to the marked partition;

[0055] Obtaining the input power of the energy storage battery in the marked partition at each moment in the current time period to form an input power sequence corresponding to the marked partition;

[0056] Determining an input fluctuation factor corresponding to the marked partition according to an input power sequence corresponding to the marked partition;

[0057] The power loss value corresponding to the marked partition is determined according to the mean value of all load power data in the load power data sequence corresponding to the marked partition, as well as its corresponding target similarity, input fluctuation factor and scheduling timing rationality.

[0058] In conjunction with the first aspect above, in a possible implementation, determining the target similarity corresponding to the marked partition according to the outbound scheduling power sequence and the load power data sequence corresponding to the marked partition includes:

[0059] The cosine similarity between the outward scheduling power sequence and the load power data sequence corresponding to the marked partition is normalized to obtain the target similarity corresponding to the marked partition.

[0060] In combination with the first aspect above, in a possible implementation, determining the input fluctuation factor corresponding to the marked partition according to the input power sequence corresponding to the marked partition includes:

[0061] constructing an input power curve corresponding to the marked partition according to the input power sequence corresponding to the marked partition;

[0062] Determining 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 obtaining a target slope sequence corresponding to the marked partition;

[0063] The average of all target slopes in the target slope sequence corresponding to the marked partition is determined as the input fluctuation factor corresponding to the marked partition.

[0064] In conjunction with the first aspect above, in one possible implementation, constructing a current difference sequence corresponding to 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 includes:

[0065] 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 is determined as the current difference sequence corresponding to each photovoltaic panel.

[0066] In combination with the first aspect above, in one possible implementation, determining the power loss sequence corresponding to each photovoltaic panel based on the output voltage sequence and current difference sequence corresponding to each photovoltaic panel includes:

[0067] The product of the output voltage sequence corresponding to each photovoltaic panel and the current difference sequence is determined as the loss power sequence corresponding to each photovoltaic panel.

[0068] In a second aspect, the present invention provides a dynamic simulation modeling system for an integrated photovoltaic storage and charging station, the system comprising:

[0069] The power demand rate determination module is used to determine the power demand rate corresponding to each target zone in the integrated photovoltaic storage charging and discharging charging station based on the changes in the load power of electrical appliances in each target zone during the current time period;

[0070] A module for determining the degree of reduction in power generation efficiency is used to determine the degree of reduction in power generation efficiency corresponding to each target zone based on the power demand rate corresponding to each target zone and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within the zone during the current time period;

[0071] The scheduling timing rationality determination module is used to determine the rationality of the scheduling timing corresponding to each target partition based on the degree of reduction in power generation efficiency corresponding to each target partition and the changes in the outward scheduling power and energy storage battery output power in the current time period;

[0072] The power loss value determination module is used to determine the power loss value corresponding to each target partition based on the rationality of the scheduling opportunity corresponding to each target partition and the distribution of outward scheduling power and load power in the current time period;

[0073] The dynamic simulation modeling module is used to perform dynamic simulation modeling based on the power loss values ​​corresponding to all target partitions.

[0074] In a third aspect, a server is provided, comprising a memory and a processor. The memory is configured to store executable program code, and the processor is configured to call and execute the executable program code from the memory, so that the device executes the method of the first aspect or any possible implementation of the first aspect.

[0075] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0076] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation of the first aspect.

[0077] The present invention has the following beneficial effects:

[0078] The present invention provides a method for dynamic simulation modeling of a photovoltaic, storage, and charging-discharging integrated charging station. This method realizes dynamic simulation modeling by quantifying the value of electric energy loss, thereby solving the technical problem of poor rationality of dynamic simulation modeling of photovoltaic, storage, and charging-discharging integrated charging stations caused by poor rationality of electric energy loss quantification, and improving the rationality of electric energy loss quantification, thereby improving the rationality of dynamic simulation modeling of photovoltaic, storage, and charging-discharging integrated charging stations. Specifically, the present invention comprehensively considers multiple factors related to electric energy loss, such as the change in load power of electrical appliances, the distribution of output power, output current, and output voltage, the change in outward dispatching power and output power of energy storage batteries, and the load power distribution, thereby quantifying the power demand rate, the degree of reduction in power generation efficiency, and the rationality of dispatching timing corresponding to different target partitions in the photovoltaic, storage, and charging-discharging integrated charging station, and then more accurately quantifying the value of electric energy loss, improving the rationality of electric energy loss quantification, thereby improving the rationality of dynamic simulation modeling of photovoltaic, storage, and charging-discharging integrated charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0080] Figure 1 This is a flow chart of a dynamic simulation modeling method for a photovoltaic, storage, charging, and discharging integrated charging station according to the present invention;

[0081] Figure 2 This is a schematic diagram of the structure of a dynamic simulation modeling system for a photovoltaic, storage, charging, and discharging integrated charging station according to the present invention;

[0082] Figure 3 The figure is a structural diagram of a computer device of the present invention. DETAILED DESCRIPTION

[0083] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementations, structures, features, and effects of the technical solutions proposed by the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0084] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0085] In order to simulate and model the energy loss in each partition of the integrated photovoltaic storage and 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 of the input end of the local energy storage battery in each partition and the output voltage and current of the output end; monitor the load power of electrical appliances in each partition; connect to the dispatching system in the power station, and monitor the power dispatching between each partition in real time, including dispatching from one partition to another, and power data during the dispatching process.

[0086] refer to Figure 1 , shows the process of some embodiments of a method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to the present invention. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station includes the following steps:

[0087] Step S1: Determine the power demand rate corresponding to each target zone in the integrated photovoltaic, storage and charging station according to the load power change of electrical appliances in each target zone within the current time period.

[0088] It should be noted that integrated solar-powered, energy-storage-charge-and-discharge charging stations are typically designed with zones, each deploying an independent photovoltaic array and local energy storage. Each zone's photovoltaic array consists of several different photovoltaic panels connected in series and parallel, and the photovoltaic panels within a photovoltaic string are connected in series. The target zone can be a zone within the integrated solar-powered, energy-storage-charge-and-discharge charging station. The current time period can be a period ending at the current time. The duration of the current time period can be pre-set, such as 1 minute.

[0089] Secondly, when photovoltaic equipment generates electricity, the generated energy is primarily used to supply the power grid of the power plant. Any excess energy is used to charge energy storage batteries, allowing for power supply when the photovoltaic equipment's power generation cannot meet the load demand of the sub-area electrical appliances. Each sub-area's photovoltaic power generation is prioritized for the load in that area. If the photovoltaic power exceeds the load demand, the excess energy is transferred to energy storage or dispatched across sub-areas. Therefore, in order to analyze the power loss in each sub-area during the power distribution and scheduling process of the power plant, it is often necessary to obtain the power demand level of each sub-area within the power plant during the current monitoring period. The current monitoring period is also referred to as the current time period.

[0090] As an example, this step may include the following steps:

[0091] In the first step, any target partition in the integrated photovoltaic storage and charging station is identified as a marked partition, and the load power of electrical appliances in the marked partition at each moment in the current time period is obtained to form a load power sequence of electrical appliances corresponding to the marked partition.

[0092] The electrical load power sequence may be a time series, and the electrical load power of the marked partition at a certain moment may represent the total electrical load power of the marked partition at that moment.

[0093] The second step, based on the load power sequence of the electrical appliances corresponding to the marked partitions, determines the power consumption fluctuation factor corresponding to the marked partitions, which may include the following sub-steps:

[0094] In the first sub-step, curve fitting is performed on the electrical load power sequence corresponding to the marked partition to obtain the electrical load power curve corresponding to the marked partition.

[0095] The electrical load power curve may be a curve with the collection time as the horizontal coordinate and the electrical load power as the vertical coordinate.

[0096] In the second sub-step, the difference between each adjacent maximum value and minimum value in the load power curve of the electrical appliance corresponding to the marked partition is determined as a fluctuation difference, and a fluctuation difference sequence corresponding to the marked partition is obtained.

[0097] A minimum value adjacent to a maximum value may be a minimum value on one side of the maximum value that is closest to the maximum value.

[0098] In the third sub-step, the average of all fluctuation differences in the fluctuation difference sequence corresponding to the marked partition is determined as the power consumption fluctuation factor corresponding to the marked partition.

[0099] In the third step, the power demand rate corresponding to the marked partition is determined based on the average of all electrical load powers in the electrical load power sequence corresponding to the marked partition and the power fluctuation factor corresponding to the marked partition.

[0100] For example, the formula for determining the power demand rate corresponding to the marked partition can be:

[0101] ;

[0102] in, LY is the electricity demand rate corresponding to the marked partition. is the hyperbolic tangent function. w It is the mean value of all electrical load powers in the electrical load power sequence corresponding to the marked partition. It is the power consumption fluctuation factor corresponding to the marked partition. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, which can be 0.0001.

[0103] It should be noted that when w The larger the value is, the higher the load of the electrical appliances in the marked area is. The smaller the value is, the smaller the fluctuation of the load of the electrical appliances in the marked area is. LY The larger it is, the more likely the load of electrical appliances in the marked zone is to be at a larger level, which often means the electricity demand rate in the marked zone is greater.

[0104] Step S2: determining the degree of reduction in power generation efficiency corresponding to each target partition based on 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 the target partition in the current time period.

[0105] It should be noted that photovoltaic modules often generate electricity through light energy to meet the electricity demand in each zone, but due to the unstable power generation in the photovoltaic modules, the power generation efficiency of the photovoltaic modules may be reduced; there are several photovoltaic panels in the integrated photovoltaic storage and charging station. The power generation efficiency of different photovoltaic panels may vary due to their orientation and cloud cover, and photovoltaic panels with different power generation efficiencies may affect each other, resulting in a reduction in the power generation efficiency of the modules.

[0106] As an example, this step may include the following steps:

[0107] The first step is to obtain the output power, output current and output voltage of each photovoltaic panel at each moment in the current time period, and respectively form the output power sequence, output current sequence and output voltage sequence corresponding to each photovoltaic panel.

[0108] The output power sequence, the output current sequence and the output voltage sequence may be time series.

[0109] In the second step, the power generation rate corresponding to each photovoltaic panel is determined based on the mean and variance of all output powers in the output power sequence corresponding to each photovoltaic panel.

[0110] For example, the formula for determining the power generation rate corresponding to a photovoltaic panel can be:

[0111] ;

[0112] in, x is the power generation rate corresponding to the photovoltaic panel. is the normalization function. s It 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. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, which can be 0.0001.

[0113] It should be noted that when x The larger it is, the greater the output power of the photovoltaic panel in the current time period and the more stable its output power is; it often means that the power generation rate of the photovoltaic panel is relatively higher.

[0114] In the third step, the minimum value of the output current of all photovoltaic panels in each photovoltaic panel string at each moment in the current time period is recorded as the minimum representative current value, forming a minimum representative current value sequence corresponding to each photovoltaic panel string.

[0115] The minimum representative current value sequence may be a time series, and the minimum representative current value at a certain moment may represent the minimum output current corresponding to all photovoltaic panels in the photovoltaic panel string at that moment.

[0116] It's important to note that because multiple PV strings exist within a zone, the PV panels within each string are connected in series and have varying efficiencies, which can reduce the overall efficiency of the string. Specifically, because the panels within a string are connected in series and the current in the series circuit is the same, inefficient panels within the string often limit the overall current to a lower level. Therefore, the minimum current in the string is often the actual output current.

[0117] The fourth step is to construct a current difference sequence corresponding to 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.

[0118] 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 may be determined as the current difference sequence corresponding to each photovoltaic panel.

[0119] It should be noted that the difference between two sequences may be a sequence formed by taking the difference between elements at corresponding positions in the two sequences.

[0120] The fifth step is to determine the loss power sequence corresponding to each photovoltaic panel based on the output voltage sequence and current difference sequence corresponding to each photovoltaic panel.

[0121] For example, the product of the output voltage sequence corresponding to each photovoltaic panel and the current difference sequence may be determined as the loss power sequence corresponding to each photovoltaic panel.

[0122] It should be noted that the product of two sequences may be a sequence formed by multiplying elements at corresponding positions in the two sequences.

[0123] The sixth step is to determine the series connection impact of each photovoltaic panel string based on the power loss sequence and power generation rate of different photovoltaic panels in each photovoltaic panel string, as well as the power demand rate of the target zone to which it belongs. The following sub-steps may be included:

[0124] In the first sub-step, the initial series connection impact factor corresponding to each photovoltaic panel is determined according to the average value of all power losses in the power loss sequence corresponding to each photovoltaic panel and its corresponding power generation rate.

[0125] In the second sub-step, the series connection influence degree corresponding to each photovoltaic panel string is determined based on the average of the initial series connection influence factors corresponding to all photovoltaic panels in each photovoltaic panel string and the power demand rate corresponding to the target partition to which the same photovoltaic panel string belongs.

[0126] For example, the formula for determining the series connection influence of a photovoltaic panel string can be:

[0127] ;

[0128] in, It is i The series influence degree corresponding to each photovoltaic panel string. i It is the serial number of different photovoltaic panel strings in the target zone. is the hyperbolic tangent function. It is i The electricity demand rate corresponding to the target zone to which each photovoltaic panel string belongs. It is i The number of different photovoltaic panels in a photovoltaic panel string. j It is i The serial numbers of different photovoltaic panels in a photovoltaic panel string. It isi The first j The power generation rate corresponding to each photovoltaic panel. It is i The first j The mean of all power losses in the power loss sequence corresponding to each photovoltaic panel. It is i The first j The initial series impact factor corresponding to each photovoltaic panel.

[0129] It should be noted that when The larger the i The first j The higher the power generation rate of each photovoltaic panel, the higher the power generation rate of each photovoltaic panel. The larger the i The more likely the load of the electrical appliances in the target zone of the photovoltaic panel string is to be at a larger level, the higher the load of the electrical appliances in the target zone of the photovoltaic panel string is. i The greater the power demand rate in the target zone to which the photovoltaic panel string belongs, the greater the power demand rate in the target zone to which the photovoltaic panel string belongs. The larger the i The first j The greater the power loss of each photovoltaic panel, the greater the The larger the i The greater the power generation efficiency and the higher the power loss of the photovoltaic panels in a photovoltaic panel string, the greater the influence of the series connection. The larger the i The greater the degree of series connection within a photovoltaic panel string, the greater the impact.

[0130] In the seventh step, the degree of reduction in power generation efficiency corresponding to each target partition is determined based on the average of the series influence degrees corresponding to all photovoltaic panel strings in each target partition and the output voltage sequence corresponding to different photovoltaic panels in all photovoltaic panel strings in each target partition.

[0131] It should be noted that the photovoltaic panels in the photovoltaic module are often connected in parallel and aggregated into a unified partitioned power system after string output. When there are differences in voltages in different branches during the parallel process, reverse current will often appear in the parallel circuit, resulting in power loss.

[0132] For example, determining the degree of reduction in power generation efficiency corresponding to each target zone may include the following sub-steps:

[0133] In the first sub-step, the cumulative value of the output voltage collected at the same time in the output voltage sequence 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 time, thereby obtaining the overall output voltage sequence corresponding to each photovoltaic panel string.

[0134] The overall output voltage sequence may be a time sequence.

[0135] It should be noted that the overall output voltage of the photovoltaic panel string at a certain moment may be the accumulated value of the output voltages of all photovoltaic panels in the photovoltaic panel string at that moment, which may represent the overall output voltage of the photovoltaic panel string.

[0136] In the second sub-step, the minimum value of the overall output voltage of all photovoltaic panel strings in each target partition at each moment in the current time period is recorded as the minimum representative voltage value, forming a minimum representative voltage value sequence corresponding to each target partition.

[0137] The minimum representative voltage value at a certain moment may represent the minimum output voltage corresponding to all photovoltaic panel strings in the target partition at that moment.

[0138] 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 the target partition to which it belongs is determined as the voltage difference sequence corresponding to each photovoltaic panel string.

[0139] The fourth sub-step is to determine the overall voltage difference corresponding to each photovoltaic panel string according to the voltage difference sequence corresponding to each photovoltaic panel string.

[0140] For example, the average of all voltage differences in the voltage difference sequence corresponding to each photovoltaic panel string may be determined as the overall voltage difference corresponding to each photovoltaic panel string.

[0141] The fifth sub-step is to determine the degree of reduction in power generation efficiency corresponding to each target partition based on the average of the overall voltage differences corresponding to all photovoltaic panel strings in each target partition and the average of the series influence degrees corresponding to all photovoltaic panel strings in each target partition.

[0142] For example, the formula for determining the degree of reduction in power generation efficiency corresponding to the target partition can be:

[0143] ;

[0144] in, GZ is the degree of reduction in power generation efficiency corresponding to the target partition. is the hyperbolic tangent function. c It is the mean of the overall voltage differences corresponding to all PV panel strings in the target zone. CS It is the mean value of the series influence corresponding to all photovoltaic panel strings in the target zone.

[0145] It should be noted that when CS The larger the value is, the greater the impact of the series connection of photovoltaic panels in the target zone is. cThe larger the value is, the greater the overall voltage difference of the photovoltaic panel strings in the target zone is. GZ When the value is larger, it often indicates that the mean value of the difference defects of the strings in the target partition is larger, which often indicates that the power loss caused by the voltage difference is greater, and the power generation efficiency is more reduced.

[0146] Step S3, determining the rationality of the scheduling timing corresponding to each target partition based on the degree of reduction in power generation efficiency corresponding to each target partition and the changes in the outward scheduling power and energy storage battery output power in the current time period.

[0147] It should be noted that when the photovoltaic modules in different zones of a power station experience a reduction in power generation efficiency, the generated electricity needs to be dispatched, resulting in power loss during the dispatch process. Therefore, it is necessary to analyze the dispatch loss under the current power generation efficiency reduction rate. Specifically, after power generation, different zones in a power station will give priority to supplying electrical appliances. If there is surplus power, it can be allocated to other zones for charging. However, in actual operation, due to delays in the dispatch system, there may be problems with the scheduling timing of different zones. As a result, when the surplus power in the current zone is relatively small, the power dispatched outward is still large. At this time, because the zone does not give priority to meeting the power demand of the electrical appliances in the zone, power loss occurs.

[0148] As an example, this step may include the following steps:

[0149] The first step is to obtain the outbound scheduling power of each target partition at each time in the current time period, and form an outbound scheduling power sequence corresponding to each target partition.

[0150] The outward dispatching power sequence may be a time sequence. The outward dispatching power of the target partition at a certain moment may represent the power of the target partition for dispatching surplus power to other areas.

[0151] The second step is to obtain the output power of the energy storage battery in each target partition at each moment in the current time period to form the energy storage battery power sequence corresponding to each target partition.

[0152] The energy storage battery power sequence may be a time series.

[0153] The third step, based on the outbound scheduling power sequence and the energy storage battery power sequence corresponding to each target partition, determines the overlapping time corresponding to each target partition, which may include the following sub-steps:

[0154] In the first sub-step, the collection time corresponding to the outward scheduling power that is not equal to 0 in the outward scheduling power sequence corresponding to each target partition is determined as the reference time, and a reference time set corresponding to each target partition is obtained.

[0155] In the second sub-step, the collection time corresponding to the output power not equal to 0 in the energy storage battery power sequence corresponding to each target partition is determined as a temporary time, and a temporary time set corresponding to each target partition is obtained.

[0156] In the third sub-step, the intersection of the reference time set and the temporary time set corresponding to each target partition is determined as the target intersection corresponding to each target partition.

[0157] In the fourth sub-step, each moment in the target intersection corresponding to each target partition is determined as the overlapping moment corresponding to each target partition.

[0158] The fourth step is to determine the rationality of the scheduling timing corresponding to each target partition based on the total number of all overlapping moments corresponding to each target partition, the average 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.

[0159] For example, the formula for determining the rationality of the scheduling timing corresponding to the target partition can be:

[0160] ;

[0161] in, GH It is the rationality of the scheduling timing corresponding to the target partition. is a natural exponential function. GZ is the degree of reduction in power generation efficiency corresponding to the target partition. t is the total number of overlapping moments corresponding to the target partition. D It is the mean value of the output power collected at all overlapping moments in the energy storage battery power sequence corresponding to the target partition.

[0162] It should be noted that if a storage battery output situation occurs in a partition, and the partition is still performing surplus power scheduling for other areas, it often indicates that the surplus power scheduling within the partition is unreasonable at this time. Moreover, when performing surplus power scheduling, the higher the output of the storage battery in 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 storage battery in the target partition at the overlapping moments, the more unreasonable the surplus power scheduling within the partition is. That is, when The larger the value is, the more likely it is that the target partition still needs to replenish its battery power during the scheduling process, and the more power is replenished, the lower the rationality of the scheduling opportunity. GZ When the value is larger, it often means that the average value of the difference defect of the strings in the target partition is larger, which often means that the power loss caused by the voltage difference is larger, and the power generation efficiency is reduced to a greater extent. GHThe larger it is, the more reasonable the timing of scheduling the target partition is.

[0163] Step S4: determining 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 outward scheduling power and load power in the current time period.

[0164] It should be noted that when each zone is being dispatched, the chemical reaction of the battery during the charging and discharging process of the energy storage battery often causes power loss, and the transportation line loss often occurs when the surplus power is dispatched between zones. Therefore, when the surplus power is regulated between different zones of the power station, it is necessary to minimize the number of cycles of the energy storage battery and reduce transportation losses. Therefore, it is often necessary to analyze the power loss generated by each zone under the rationality of the current dispatch timing.

[0165] As an example, this step may include the following steps:

[0166] The first step is to identify any target partition in the integrated photovoltaic storage and charging station as a marked partition.

[0167] The second step is to obtain the outbound scheduling power of the marked partition at each time in the current time period, and form an outbound scheduling power sequence corresponding to the marked partition.

[0168] The outward scheduling power sequence may be a time sequence.

[0169] The third step is to obtain the total load power of the target partition dispatched outward by the marked partition at each moment in the current time period as load power data to form a load power data sequence corresponding to the marked partition.

[0170] The load power data series may be a time series.

[0171] The fourth step, based on the outward dispatch power sequence, load power data sequence, and dispatch timing rationality corresponding to the marked partition, determines the energy loss value corresponding to the marked partition, which may include the following sub-steps:

[0172] In the first sub-step, the target similarity corresponding to the marked partition is determined according to the outward scheduling power sequence and the load power data sequence corresponding to the marked partition.

[0173] For example, the cosine similarity between the outward scheduling power sequence and the load power data sequence corresponding to the above-mentioned marked partition may be normalized to obtain the target similarity corresponding to the above-mentioned marked partition.

[0174] It should be noted that when scheduling excess power, partitions with high power demand need to be prioritized. This allows the load to consume excess power promptly after scheduling, thus avoiding the energy loss of secondary charging to a certain extent. Therefore, the more consistent the trend of outbound power changes with the trend of load power changes in the outbound scheduling partition, the lower the energy loss. Therefore, the greater the target similarity corresponding to the marked partition, the greater the similarity between the outbound scheduling power sequence corresponding to the marked partition and the load power data sequence, which often indicates that the energy loss is relatively small.

[0175] The second sub-step is to obtain the input power of the energy storage battery in the marked partition at each moment in the current time period to form an input power sequence corresponding to the marked partition.

[0176] The input power series may be a time series.

[0177] The third sub-step, determining the input fluctuation factor corresponding to the marked partition according to the input power sequence corresponding to the marked partition, may include the following steps:

[0178] First, based on the input power sequence corresponding to the marked partition, an input power curve corresponding to the marked partition is constructed.

[0179] The input power curve may be a curve with the acquisition time as the horizontal coordinate and the input power in the input power sequence as the vertical coordinate.

[0180] Next, the absolute value of the slope corresponding to each data point in the input power curve corresponding to the marked partition is determined as the target slope, and a target slope sequence corresponding to the marked partition is obtained.

[0181] Finally, the mean value of all target slopes in the target slope sequence corresponding to the marked partition is determined as the input fluctuation factor corresponding to the marked partition.

[0182] The fourth sub-step is to determine the power loss value corresponding to the above-mentioned marked partition based on the mean value of all 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 scheduling timing rationality.

[0183] For example, the formula for determining the power loss value corresponding to the target partition can be:

[0184] ;

[0185] in, DS is the power loss value corresponding to the target partition. is a natural exponential function. GH It is the rationality of the scheduling timing corresponding to the target partition. AS is the target similarity corresponding to the target partition. F It is the mean 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. It is a pre-set factor greater than 0, mainly used to prevent the denominator from being 0, which can be 0.0001.

[0186] It should be noted that when GH The smaller it is, the worse the rationality of the target partition scheduling timing is, and the greater the power loss of the target partition is. AS When the value is larger, it often indicates that the similarity between the outward dispatch power sequence and the load power data sequence corresponding to the target partition is greater, and it often indicates that the power loss of the target partition is relatively smaller. The larger the value, the greater the load power of the dispatched part when the partition is dispatched outwards. At this time, the smaller the power loss caused by the dispatch, the smaller the power loss. DS The larger it is, the greater the power loss in the target partition.

[0187] Step S5: Perform dynamic simulation modeling based on the power loss values ​​corresponding to all target partitions.

[0188] As an example, based on the power loss values ​​corresponding to different target partitions, dynamic simulation modeling can be achieved through MATLAB software tools.

[0189] It should be noted that MATLAB software tools can assist in creating electrical system models, performing dynamic simulations, and analyzing the dynamic response of the system.

[0190] For example, dynamic simulation modeling can include the following steps:

[0191] The first step is to initialize the status of each partition.

[0192] The second step is to input internal variables, such as photovoltaic panel output power, energy storage battery input and output power, and load power.

[0193] The third step is to add the acquired power loss to the input loss.

[0194] Step 4: Update the state variables and loop until the simulation ends.

[0195] Step 5: Visualize results and report.

[0196] Step 6: Generate a time-varying loss curve for each partition, and use an energy flow Sankey diagram to display the loss distribution.

[0197] Step 7: Output conclusions: Identify the link with the greatest loss and propose hardware upgrades or control strategy optimization suggestions.

[0198] refer to Figure 2 Based on the same inventive concept as the above-mentioned method embodiment, the present invention provides a dynamic simulation modeling system for a photovoltaic, storage, charging, and discharging integrated charging station. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of a dynamic simulation modeling method for a photovoltaic, storage, charging, and discharging integrated charging station may specifically include:

[0199] The power demand rate determination module 201 is used to determine the power demand rate corresponding to each target zone in the integrated photovoltaic storage charging and discharging charging station based on the load power changes of the electrical appliances in each target zone during the current time period;

[0200] The power generation efficiency reduction degree determination module 202 is configured to determine the power generation efficiency reduction degree corresponding to each target zone based on the power demand rate corresponding to each target zone and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within the zone during the current time period;

[0201] The scheduling timing rationality determination module 203 is used to determine the rationality of the scheduling timing corresponding to each target partition based on the degree of reduction in power generation efficiency corresponding to each target partition and the changes in the outward scheduling power and energy storage battery output power in the current time period;

[0202] The power loss value determination module 204 is used to determine the power loss value corresponding to each target partition based on the rationality of the scheduling timing corresponding to each target partition and the distribution of the outbound scheduling power and load power in the current time period;

[0203] The dynamic simulation modeling module 205 is used to perform dynamic simulation modeling based on the power loss values ​​corresponding to all target partitions.

[0204] Figure 3 FIG. 1 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. For example, Figure 3 As 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, wherein when the processor 302 executes the computer program 303, the computer device can execute any one of the dynamic simulation modeling methods of the integrated photovoltaic storage and charging station introduced above.

[0205] Based on the same inventive concept as the above-described method embodiments, the present invention provides a server comprising a memory and a processor. The memory is configured to store executable program code, and the processor is configured to retrieve and execute the executable program code from the memory, thereby enabling the device to execute any of the above-described methods for dynamic simulation modeling of a photovoltaic-storage-charging-and-discharging integrated charging station.

[0206] Based on the same inventive concept as the above-mentioned method embodiment, the present invention provides a computer program product, which includes: computer program code, which, when running on a computer, enables the computer to execute any of the above-mentioned dynamic simulation modeling methods for integrated photovoltaic storage and charging and discharging charging stations.

[0207] Based on the same inventive concept as the above-mentioned method embodiment, the present invention provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes any of the above-mentioned dynamic simulation modeling methods for an integrated photovoltaic storage and charging station.

[0208] In summary, the present invention uses sensors to collect various parameters of the photovoltaic and energy storage integrated power station during operation, and obtains the electricity demand rate in each partition by analyzing the changes in 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 string and parallel connection between different photovoltaic panels in the partition, and in the case of power generation loss, the timing of surplus power scheduling between each partition and the power loss of surplus power scheduling are analyzed to finally obtain the power loss of each partition. Compared with the traditional analysis of the relationship between the input and output power of each partition to obtain the power loss, by analyzing the influence between photovoltaic panels and the scheduling relationship between different partitions, the result of power loss is more accurate, and the rationality of power loss quantification is improved, thereby improving the rationality of dynamic simulation modeling of photovoltaic and energy storage integrated charging stations.

[0209] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A dynamic simulation modeling method for a photovoltaic storage charging and discharging integrated charging station, characterized in that: The following steps are involved: Determine the power demand rate corresponding to each target zone in the integrated solar-storage-charging-discharging charging station based on the load power changes of electrical appliances in each target zone during the current time period. Determine the degree of reduction in power generation efficiency for each target zone based on the power demand rate corresponding to each target zone and the output power, output current, and output voltage distribution of different photovoltaic panels in different photovoltaic panel strings within the zone during the current time period; Determine the rationality of the dispatch timing for each target partition based on the degree of reduction in power generation efficiency corresponding to each target partition and the changes in outward dispatch power and energy storage battery output power in the current time period; Determine the power loss value corresponding to each target partition based on the rationality of the scheduling timing corresponding to each target partition and the distribution of outward scheduling power and load power in the current time period; Perform dynamic simulation modeling based on the power loss values ​​corresponding to all target partitions; The rationality of the scheduling timing corresponding to each target partition is determined based on the degree of reduction in power generation efficiency corresponding to each target partition and the changes in the outward dispatch power and energy storage battery output power in the current time period, including: Obtain the outbound scheduling power of each target partition at each time in the current time period to form an outbound scheduling power sequence corresponding to each target partition; Obtain the output power of the energy storage batteries in each target partition at each moment in the current time period to form a power sequence of the energy storage batteries corresponding to each target partition; Determine the overlapping time corresponding to each target partition based on the outward scheduling power sequence and energy storage battery power sequence corresponding to each target partition; Determine the rationality of the scheduling timing for each target partition based on the total number of all overlapping moments corresponding to each target partition, the average 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; The determining of the overlapping time 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: Determine the collection time corresponding to the outward scheduling power that is not equal to 0 in the outward scheduling power sequence corresponding to each target partition as the reference time, and obtain a reference time set corresponding to each target partition; Determine the collection time corresponding to the output power that is not equal to 0 in the energy storage battery power sequence corresponding to each target partition as a temporary time, and obtain a temporary time set corresponding to each target partition; Determine the intersection of the reference time set and the temporary time set corresponding to each target partition as the target intersection corresponding to each target partition; Each moment in the target intersection corresponding to each target partition is determined as the overlapping moment corresponding to each target partition.

2. The dynamic simulation modeling method of a photovoltaic storage charging and discharging integrated charging station according to claim 1 is characterized in that: The step of determining the power demand rate corresponding to each target zone in the integrated photovoltaic storage charging and discharging charging station according to the load power change of electrical appliances in each target zone within the current time period includes: Determine any target zone in the integrated solar-storage-charging-discharging charging station as a marked zone, and obtain the load power of electrical appliances in the marked zone at each moment in the current time period to form a load power sequence of electrical appliances corresponding to the marked zone; Determining the power consumption fluctuation factor corresponding to the marked partition according to the electrical load power sequence corresponding to the marked partition; The power demand rate corresponding to the marked partition is determined according to the average value of all electrical load powers in the electrical load power sequence corresponding to the marked partition and the power fluctuation factor corresponding to the marked partition.

3. The dynamic simulation modeling method of a photovoltaic storage charging and discharging integrated charging station according to claim 2 is characterized in that: The determining, based on the electrical load power sequence corresponding to the marked partition, the power consumption fluctuation factor corresponding to the marked partition includes: Performing curve fitting on the electrical load power sequence corresponding to the marked partition to obtain the electrical load power curve corresponding to the marked partition; Determine the difference between each adjacent maximum value and minimum value in the load power curve of the electrical appliance corresponding to the marked partition as a fluctuation difference, and obtain a fluctuation difference sequence corresponding to the marked partition; The average of all fluctuation differences in the fluctuation difference sequence corresponding to the marked partition is determined as the power consumption fluctuation factor corresponding to the marked partition.

4. The dynamic simulation modeling method of a photovoltaic storage charging and discharging integrated charging station according to claim 1 is characterized in that: The determining of 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 output power, output current, and output voltage of different photovoltaic panels in different photovoltaic panel strings within the target partition in 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, and construct the output power sequence, output current sequence, and output voltage sequence corresponding to each photovoltaic panel respectively; Determine the power generation rate corresponding to each photovoltaic panel based on the mean and variance of all output powers in the output power sequence corresponding to each photovoltaic panel; The minimum value of the output current of all photovoltaic panels in each photovoltaic panel string at each moment in the current time period is recorded as the minimum representative current value, forming a minimum representative current value sequence corresponding to each photovoltaic panel string; 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; Determine the power loss sequence corresponding to each photovoltaic panel based on the output voltage sequence and current difference sequence corresponding to each photovoltaic panel; Determine the series connection impact of each photovoltaic panel string based on the power loss sequence and power generation rate of different photovoltaic panels in each photovoltaic panel string, as well as the power demand rate of the target partition to which it belongs; The degree of reduction in power generation efficiency corresponding to each target partition is determined based on the mean value of the series influence degree corresponding to all photovoltaic panel strings in each target partition and the output voltage sequence corresponding to different photovoltaic panels in all photovoltaic panel strings in each target partition.

5. The dynamic simulation modeling method of a photovoltaic storage charging and discharging integrated charging station according to claim 4 is characterized in that: The determining of the series connection influence degree corresponding to each photovoltaic panel string according to the loss power sequence and power generation rate corresponding to different photovoltaic panels in each photovoltaic panel string and the power demand rate corresponding to the target partition to which it belongs includes: Determine the initial series influence factor corresponding to each photovoltaic panel based on the mean value of all power losses in the power loss sequence corresponding to each photovoltaic panel and its corresponding power generation rate; The series connection influence degree of each photovoltaic panel string is determined based on the average of the initial series connection influence factors corresponding to all photovoltaic panels in each photovoltaic panel string and the power demand rate corresponding to the target partition to which the same photovoltaic panel string belongs.

6. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to claim 4 is characterized in that: The method of 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 sequence corresponding to different photovoltaic panels in all photovoltaic panel strings in each target partition includes: The output voltage sequence corresponding to each photovoltaic panel string is obtained by summing the output voltages collected at the same time in the output voltage sequence corresponding to all photovoltaic panels in each photovoltaic panel string to determine the overall output voltage of each photovoltaic panel string at the same time, thereby obtaining the overall output voltage sequence corresponding to each photovoltaic panel string; The minimum value of the overall output voltage of all photovoltaic panels in each target partition at each moment in the current time period is recorded as the minimum representative voltage value, forming a minimum representative voltage value sequence corresponding to each target partition; 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; Determine the overall voltage difference corresponding to each photovoltaic panel string based on the voltage difference sequence corresponding to each photovoltaic panel string; The degree of reduction in power generation efficiency corresponding to each target partition is determined based on the mean value of the overall voltage difference corresponding to all photovoltaic panel strings in each target partition and the mean value of the series influence degree corresponding to all photovoltaic panel strings in each target partition.

7. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to claim 6, characterized in that: The determining of the overall voltage difference corresponding to each photovoltaic panel string according to the voltage difference sequence corresponding to each photovoltaic panel string includes: The average value of all 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. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to claim 1, characterized in that: The determining of 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 outward scheduling power and load power in the current time period includes: Determine any target zone in the integrated photovoltaic storage and charging station as a marked zone; Obtaining the outbound scheduling power of the marked partition at each time in the current time period to form an outbound 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 in the current time period as load power data to form a load power data sequence corresponding to the marked partition; The electric energy loss value corresponding to the marked partition is determined according to the outward dispatching power sequence, the load power data sequence and the rationality of the dispatching timing corresponding to the marked partition.

9. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to claim 8, characterized in that: The determining the electric energy loss value corresponding to the marked partition according to the outward dispatching power sequence, the load power data sequence, and the rationality of the dispatching timing corresponding to the marked partition includes: Determining a target similarity corresponding to the marked partition according to an outward scheduling power sequence and a 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 in the current time period to form an input power sequence corresponding to the marked partition; Determining an input fluctuation factor corresponding to the marked partition according to an input power sequence corresponding to the marked partition; The power loss value corresponding to the marked partition is determined according to the mean value of all load power data in the load power data sequence corresponding to the marked partition, as well as its corresponding target similarity, input fluctuation factor and scheduling timing rationality.

10. A dynamic simulation modeling method for a photovoltaic storage charging and discharging integrated charging station according to claim 9, characterized in that: The determining, according to the outward scheduling power sequence and the load power data sequence corresponding to the marked partition, the target similarity corresponding to the marked partition includes: The cosine similarity between the outward scheduling power sequence and the load power data sequence corresponding to the marked partition is normalized to obtain the target similarity corresponding to the marked partition.

11. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to claim 9, characterized in that: The determining, according to the input power sequence corresponding to the marked partition, the input fluctuation factor corresponding to the marked partition includes: constructing an input power curve corresponding to the marked partition according to the input power sequence corresponding to the marked partition; Determining 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 obtaining a target slope sequence corresponding to the marked partition; The average of all target slopes in the target slope sequence corresponding to the marked partition is determined as the input fluctuation factor corresponding to the marked partition.

12. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to claim 4, characterized in that: The method of 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 it belongs includes: 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 is determined as the current difference sequence corresponding to each photovoltaic panel.

13. The method for dynamic simulation modeling of a photovoltaic storage charging and discharging integrated charging station according to claim 4, characterized in that: The step of determining the power loss sequence corresponding to each photovoltaic panel based on the output voltage sequence and current difference sequence corresponding to each photovoltaic panel includes: The product of the output voltage sequence corresponding to each photovoltaic panel and the current difference sequence is determined as the loss power sequence corresponding to each photovoltaic panel.

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