Optical storage and charging integrated charging station operation optimization system and method

Abstract

The invention discloses an optical storage and charging integrated charging station operation optimization system and method, and relates to the technical field of charging stations, and the system comprises an energy storage module, a charging pile module, a photovoltaic module, and an optical storage and charging management platform. The energy storage module comprises a plurality of energy storage battery packs, the charging pile module comprises a plurality of charging piles, the photovoltaic module comprises a plurality of photovoltaic panel groups, the plurality of photovoltaic panel groups form a photovoltaic array, and the photovoltaic storage and charging management platform comprises a data acquisition module, a data processing center and an optimization control module. According to the scheme, by accurately analyzing the power generation power change curve and the power generation efficiency of the photovoltaic module and combining the state of the energy storage module and the requirements of the charging pile, photovoltaic and energy storage cooperative power supply is achieved, photovoltaic power generation can be fully utilized, the light abandoning phenomenon is reduced, meanwhile, the energy storage charging and discharging strategy is optimized, the overall energy utilization rate is increased, and dependence on power grid electric energy is reduced; and energy utilization is more reasonable and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of charging stations, and in particular to a system and method for optimizing the operation of a charging station with integrated photovoltaic storage and charging. Background Art

[0002] There are some integrated photovoltaic storage and charging station systems on the market. They have been able to achieve basic integration of photovoltaic power generation, energy storage and electric vehicle charging, which can improve energy utilization efficiency, reduce electricity costs and enhance the stability and reliability of power supply.

[0003] However, during the operation of the existing photovoltaic storage and charging integrated charging station, there are problems such as inaccurate and comprehensive monitoring and analysis of the operating data of each module and insufficient optimization of the power supply strategy, which leads to energy waste and high operating costs. In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] The purpose of the present invention is to solve the problems raised in the above background technology and to propose a system and method for optimizing the operation of a photovoltaic storage and charging integrated charging station.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A photovoltaic storage and charging integrated charging station operation optimization system, comprising an energy storage module, a charging pile module, a photovoltaic module and a photovoltaic storage and charging management platform; the energy storage module comprises a plurality of energy storage battery groups, the charging pile module comprises a plurality of charging piles, the photovoltaic module comprises a plurality of photovoltaic panel groups, the plurality of photovoltaic panel groups constitute a photovoltaic array, and the photovoltaic storage and charging management platform comprises a data acquisition module, a data processing center and an optimization control module; A data acquisition module is used to collect real-time operation data of the energy storage module, charging pile module and photovoltaic module and corresponding external environment data through a sensor network; The data processing center is used to analyze the photovoltaic module, energy storage module and charging pile module respectively to obtain the analysis results of each module; it is also used to build a predictive power generation model; it is also used to analyze the real-time electricity price information of the power grid to obtain the total required electricity price cost; the analysis results of each module include power change curve related data, power generation efficiency related information, energy storage analysis results, and power supply information; the total required electricity price cost, energy storage comprehensive evaluation value and model power value are weighted to obtain the supply value; set the supply signaling group, including photovoltaic storage charging signaling, energy storage charging signaling, power purchase and power supply signaling, and match the supply value with the value range of the supply signaling group to generate the corresponding signaling; The optimization control module is used to receive corresponding signaling to perform corresponding operations, specifically: When the photovoltaic storage charging signal is received, the collaborative power supply mechanism is started; the collaborative power supply mechanism is used to control the photovoltaic module and the energy storage module to collaboratively provide the required electrical energy for the charging pile according to the predicted power generation power and the charging power provided by the energy storage; when the energy storage charging signal is received, the energy storage module is used to provide power for the vehicle with the maximum charging rated power corresponding to the charging pile; when the power purchase signal is received, the electricity purchased from the power grid is allocated to the charging pile to charge the vehicle.

[0006] Furthermore, the data processing center includes a monitoring setting module, a photovoltaic energy storage analysis module, an energy storage status analysis module, a charging demand analysis module, a prediction model generation module and a comprehensive processing judgment module; A monitoring setting module, used to mark the time range of the preset photovoltaic power generation monitoring time before the current moment as the photovoltaic power generation monitoring time zone; Photovoltaic analysis module, used to analyze the real-time operation data of photovoltaic modules and obtain relevant data of power change curve; it also evaluates and analyzes the power generation efficiency of photovoltaic modules to obtain information related to power generation efficiency; the information related to power generation efficiency includes actual power generation ratio, environmental shadow value, wind shadow value, power generation comprehensive value, group power value, model power value, and wind impact value; The energy storage status analysis module is used to analyze the status of the energy storage module and obtain the energy storage analysis results; wherein the energy storage analysis results include the remaining power average value of the energy storage battery group, the battery group evaluation value, the number of charge and discharge times, the factory time, the energy storage evaluation value and the energy storage comprehensive evaluation value; The charging demand analysis module is used to analyze the demand corresponding to the charging pile and obtain the power supply information; wherein the power supply information provided includes the predicted power generation power of the photovoltaic module and the charging power provided by the energy storage module; The prediction model generation module is used to obtain the historical real-time operation data of the energy storage module, the charging pile module and the photovoltaic module, the historical external environment data and the photovoltaic characteristic information as historical data; the support vector regression model is trained using the historical data, the mean square error is calculated, and the model generalization ability is evaluated by cross-validation to obtain the model evaluation value; the model is generalized according to the model evaluation value and its corresponding threshold value, and finally the prediction power generation model is obtained; The power grid electricity price analysis module is used to analyze the real-time electricity price information of the power grid and obtain the required charging amount of the vehicle corresponding to any charging pile; obtain the real-time electricity price of the power grid and calculate the electricity price cost based on the real-time electricity price and the required charging amount; traverse all charging piles in the charging pile module and obtain the electricity price cost of the vehicle corresponding to each charging pile one by one; sum up the electricity price costs of all charging piles in the charging pile module to obtain the total required electricity price cost; The comprehensive processing and judgment module is used to perform weighted processing on the total required electricity price cost, the comprehensive evaluation value of energy storage and the analog electricity value to obtain the supply value; set the supply signaling group, including the photovoltaic storage charging signaling, the energy storage charging signaling, and the power purchase and supply signaling, and match the supply value with the value range of the supply signaling group to generate the corresponding signaling.

[0007] Furthermore, the real-time operation data of the photovoltaic modules is analyzed, specifically: Obtain the current and voltage of each photovoltaic panel group of the photovoltaic module at any time in the photovoltaic power generation monitoring time zone, and calculate the real-time power generation power of each photovoltaic panel group at the corresponding time; Add up the power generation of all photovoltaic panel groups to obtain the total power generation of the photovoltaic array in the photovoltaic power generation monitoring time zone; construct a curve graph of the total power generation changing with time, with time as the horizontal axis and the total power generation as the vertical axis, and plot the total power generation calculated in each photovoltaic power generation monitoring time zone to obtain a power change curve graph; divide the power change curve graph into power change curve graphs corresponding to the cycle according to the set cycle group; the set cycle group includes daily cycle, weekly cycle, monthly cycle, seasonal cycle, and annual cycle group; Obtain a power change curve diagram of any period group in the set period group in chronological order, extract the power change curve in the power change curve diagram, and use the mean square error method to calculate the similarity values ​​of the power change curves of adjacent periods; sort the similarity values ​​in chronological order, construct a similarity change line diagram, input the similarity values ​​and the numbers of their corresponding periods into the similarity change line diagram, mark the positions of the similarity values ​​in the similarity change line diagram as similar points, and connect adjacent similar points to obtain adjacent phase lines; calculate the slope of the adjacent phase line, mark the slope with a positive value as an ascending slope value, and mark the slope with a negative value as a descending slope value; The total value of the rising slope and the total value of the falling slope are obtained by summing up all the rising slope values ​​and the falling slope values ​​in the same period group, and the total value of the rising slope and the total value of the falling slope are weighted to obtain the oblique shadow value; Calculate the mean and standard deviation of similar values ​​of adjacent periods in the same period group to obtain similar mean and similar fluctuation value; perform weighted processing on the oblique shadow value, similar mean and similar difference to obtain the period shadow value; The shadow values ​​of all cycle groups in the set cycle group are weighted to obtain the comprehensive shadow value; the rising slope value, falling slope value, shadow value, similar mean, similar fluctuation value, shadow value and comprehensive shadow value are marked as the power change curve related data of the corresponding cycle.

[0008] Furthermore, the power generation efficiency of the photovoltaic modules is evaluated and analyzed, specifically: The rated power and actual light intensity of any photovoltaic panel in each photovoltaic panel group in the photovoltaic module in the photovoltaic power generation monitoring time zone are obtained, and the theoretical power generation power at any time in the photovoltaic power generation monitoring time zone is calculated using the standard photovoltaic cell conversion efficiency formula; the theoretical power generation power at any time in the photovoltaic power generation monitoring time zone is divided by the actual power generation power to obtain the actual power generation ratio; Obtain external environmental data of photovoltaic panels, including environmental information and meteorological information. Environmental information includes temperature, humidity, and atmospheric pressure. Meteorological information includes air density, wind direction, and wind speed. Set a baseline value for any parameter in the environmental information, calculate the difference between the value of any parameter in the environmental information and the baseline value corresponding to the parameter to obtain a baseline difference value; set an environmental monitoring time zone, calculate the average value and standard deviation of the baseline difference values ​​in the environmental monitoring time zone to obtain a baseline difference mean value and a baseline difference fluctuation value; perform weighted calculation on the baseline difference values, baseline difference mean values, and baseline difference fluctuation values ​​of all parameters in the environmental information to obtain an environmental shadow value; Obtain the tilt direction of the photovoltaic panel, analyze the tilt direction of the photovoltaic panel and meteorological information to obtain the wind impact value; calculate the average value and standard deviation of the wind impact value at any time in the environmental monitoring time zone to obtain the wind mean value and wind fluctuation value; perform weighted calculation on the wind impact value, wind mean value and wind fluctuation value to obtain the wind shadow value; The actual power generation ratio, the ambient shadow value, and the wind shadow value are weighted to obtain the total power generation value of the photovoltaic panel; the total power generation value of all photovoltaic panels in the photovoltaic panel group is weighted to obtain the group power value corresponding to the photovoltaic panel group; the group power values ​​of all photovoltaic panel groups in the photovoltaic module are weighted to obtain the module power value; The actual power generation ratio, environmental shadow value, wind shadow value, total power generation value, group power value, model power value, and wind influence value are marked as information related to power generation efficiency.

[0009] Furthermore, the process of establishing the predictive power generation model is as follows: Acquire historical real-time operation data, historical external environment data and photovoltaic characteristic information of energy storage modules, charging pile modules and photovoltaic modules as historical data; divide the historical data into training sets and test sets according to a set ratio; A support vector regression model is selected and initialized, and the support vector regression model is trained using the training set data. The support vector regression model with radial basis function as the kernel function is used to calculate the mean square error between the power generation predicted by the photovoltaic module and the actual power generation; Cross-validation is then used to evaluate the generalization ability of the model. The training set is divided into k subsets, where k is a preset integer. For each round of validation, any subset is selected as the validation set, and the remaining k-1 subsets are selected as the training set. This process is repeated k times. In each round of training and validation, the mean square error on the validation set is calculated. The mean square error of the k-round validation set is calculated to obtain the model evaluation value, and the model evaluation threshold is set. If the model evaluation value is less than the model evaluation threshold, it means that the prediction error on the model validation set data is small, indicating that the model has good generalization ability; conversely, if the model evaluation value is greater than or equal to the model evaluation threshold, the data expansion method is used to increase the training data; the model after training, verification and evaluation is marked as a predictive power generation model; Among them, photovoltaic characteristic information includes data related to power change curves of different set periods and information related to power generation efficiency. Data related to power change curves of different set periods include rising slope value, falling slope value, oblique shadow value, similar mean value, similar fluctuation value, period shadow value and comprehensive period shadow value. Information related to power generation efficiency includes actual power generation ratio, ring shadow value, wind shadow value, comprehensive power generation value, group power value and model power value.

[0010] Furthermore, the status of the energy storage module is analyzed, specifically: Obtain the current power value of each energy storage battery group in the energy storage module, identify the rated power of the energy storage battery group, divide the current power value by the rated power to obtain the remaining power percentage value; average the remaining power percentage values ​​of all energy storage battery groups to obtain the remaining power average value; perform weighted calculation on the remaining power percentage value of the energy storage battery group and the remaining power average value of its energy storage module to obtain the remaining power average value corresponding to the energy storage battery group; Obtain the battery information of each energy storage battery group in the energy storage module, including terminal voltage, charge and discharge power, and battery temperature; count and calculate the statistical indicators of any information in the battery information, including the average value, maximum value, minimum value, average value and median, mode, and standard deviation; perform weighted calculation on the parameters in the statistical indicators to obtain the battery group evaluation value; Obtain the number of charge and discharge times of the energy storage battery pack; Identify the time when the energy storage battery pack was shipped from the factory, and calculate the time difference between the time when the battery was shipped from the factory and the current time to obtain the length of time when the battery was shipped from the factory; The remaining power average value, battery group evaluation value, charge and discharge times, and factory time are weighted to obtain the energy storage evaluation value; The energy storage evaluation values ​​of all energy storage battery groups in the energy storage module are weighted and calculated to obtain a comprehensive energy storage evaluation value; the remaining power average value of the energy storage battery group, the battery group evaluation value, the number of charge and discharge times, the factory time, the energy storage evaluation value and the comprehensive energy storage evaluation value are marked as the energy storage analysis results.

[0011] Furthermore, the corresponding requirements of charging piles are analyzed, specifically: The charging pile determines the vehicle connection status by detecting the connection signal of the charging interface. The vehicle connection status includes the vehicle connected to the charging pile state and the vehicle not connected state; Obtain vehicle information of a vehicle connected to a charging station, including vehicle type, battery capacity, and maximum charging rated power; Subtract the current charge from the vehicle's battery capacity to get the required charge; The connection time between the charging pile and the vehicle is recorded as the vehicle charging time; the predicted charging time is calculated based on the maximum charging rated power of the vehicle and the required charging amount; the time area of ​​the predicted charging time after the vehicle charging time is marked as the predicted charging time zone; the predicted power generation model is used to obtain the predicted power generation power at any time in the predicted charging time zone, and the maximum charging rated power of the vehicle is subtracted from the predicted power generation power to obtain the charging power provided by the energy storage; Then analyze the difference between the prediction and the actual charging process of the vehicle at the charging pile. During the vehicle charging process, obtain the charged amount of the vehicle at any time within the predicted charging time zone, and subtract the sum of the predicted power generation power and the charging power provided by the energy storage from the charged amount to obtain the insufficient charge amount at that moment; calculate the mean and standard deviation of the insufficient charge amount between the current moment and the vehicle charging moment to obtain the insufficient charge mean and insufficient charge fluctuation value; The current moment's undercharged amount, undercharged average value, and undercharged fluctuation value are weighted to obtain the charging control coefficient; the charging control coefficient is used to dynamically update the predicted power generation power and the charging power provided by the energy storage; the predicted power generation power of the photovoltaic module and the charging power provided by the energy storage module are marked as providing power supply information.

[0012] Furthermore, the inclination direction and meteorological information of the photovoltaic panel are analyzed, specifically: the surface area and surface roughness of the photovoltaic panel are obtained, and the surface area and surface roughness are weighted to obtain the drag coefficient of the photovoltaic panel; the projected area of ​​the photovoltaic panel in the direction perpendicular to the wind direction is obtained, the force of the wind on the photovoltaic panel is obtained, and the force of the wind on the photovoltaic panel in the parallel direction is calculated according to the drag coefficient, force, air density, wind speed, and projected area, and marked as the flat value; the pressure coefficient of the wind direction is obtained, and the force of the wind on the photovoltaic panel in the vertical direction is calculated according to the pressure coefficient, force, air density, wind speed, and projected area, and marked as the vertical value; the flat value and the vertical value are weighted to obtain the wind force impact value.

[0013] Furthermore, the data processing center includes an electricity purchase analysis module and a purchase execution module, wherein: The power purchase analysis module is used to perform comprehensive calculation and analysis based on the off-peak electricity price data, the remaining power capacity information of the energy storage module, the power generation data of the photovoltaic module, and the power transmission loss data from the power grid to the energy storage module to obtain the electricity value; when the electricity value is less than the preset electricity price threshold, it is determined that the current off-peak power grid electricity price is at an appropriate level, and a power grid purchase and charging signal is generated; when the electricity value is greater than or equal to the preset electricity price threshold, it is determined that charging the energy storage module is not economical at present or the power generation of the energy storage module and the photovoltaic module can meet the system operation requirements, and there is no need to perform power purchase operations on the power grid, and a stop power purchase signal is generated; The purchase execution module is used to receive the signaling sent by the power purchase analysis module and perform corresponding operations.

[0014] Furthermore, the present invention proposes a method for optimizing the operation of a photovoltaic storage and charging integrated charging station, comprising the following steps: S1, data collection step: collecting real-time operation data of energy storage modules, charging pile modules and photovoltaic modules and corresponding external environment data through the sensor network; S2, data processing step: respectively analyze the photovoltaic module, energy storage module and charging pile module to obtain the analysis results of each module, including power change curve related data, power generation efficiency related information, energy storage analysis results, and power supply information. At the same time, analyze the real-time power price information of the power grid to obtain the total required power price cost, and weight the total required power price cost, energy storage comprehensive evaluation value and model power value to obtain the supply value, set the supply signaling group and match the supply value with the signaling group value range to generate the corresponding signaling; S3, optimization control steps: execute corresponding operations according to the received signaling, start the collaborative power supply mechanism when receiving the photovoltaic storage charging signaling, so that the photovoltaic module and the energy storage module collaboratively supply power to the charging pile according to the predicted power generation power and the charging power provided by the energy storage; when receiving the energy storage charging signaling, use the energy storage module to supply power to the vehicle corresponding to the maximum charging rated power of the vehicle; when receiving the power purchase and power supply signaling, distribute the grid power to the charging pile for charging the vehicle.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes the coordinated power supply of photovoltaic and energy storage by accurately analyzing the power generation curve and power generation efficiency of the photovoltaic module, combining the status of the energy storage module and the demand of the charging pile, which can make full use of photovoltaic power generation and reduce the phenomenon of abandoned light. At the same time, it optimizes the energy storage charging and discharging strategy, improves the overall energy utilization rate, reduces the dependence on grid electricity, and makes energy utilization more reasonable and efficient.

[0016] The present invention predicts the power generation and energy storage provided by real-time monitoring and analysis of the operating data of each module, and responds to power supply fluctuations in advance. When the power grid is out of power or the photovoltaic output is insufficient, the energy storage module can replenish power in time to ensure stable power supply of the charging pile. At the same time, the optimization control module flexibly allocates the power supply source according to the signaling, adapts to various working conditions, and ensures the continuity and reliability of the power supply service. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 is a system block diagram of the present invention; Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0021] Example 1

[0022] like Figure 1-Figure 2 As shown, a photovoltaic storage and charging integrated charging station operation optimization system includes an energy storage module, a charging pile module, a photovoltaic module and a photovoltaic storage and charging management platform; the energy storage module includes multiple energy storage battery groups, the charging pile module includes multiple charging piles, the photovoltaic module includes multiple photovoltaic panel groups, and the multiple photovoltaic panel groups form a photovoltaic array. The photovoltaic storage and charging management platform includes a data acquisition module, a data processing center and an optimization control module; A data acquisition module is used to collect real-time operating data of the energy storage module, charging pile module and photovoltaic module and corresponding external environment data through a sensor network; wherein the real-time operating data of the energy storage module includes the power, voltage, current, charging and discharging power, and battery temperature of the energy storage battery group; the real-time operating data of the charging pile module includes the connection status, charging power, charging time, and charged amount of each charging pile; and the photovoltaic module includes the light intensity, power generation power, voltage, and current of the photovoltaic panel group; The data processing center is used to analyze the photovoltaic module, energy storage module and charging pile module respectively to obtain the analysis results of each module; it is also used to build a predictive power generation model; it is also used to analyze the real-time electricity price information of the power grid to obtain the total required electricity price cost; the analysis results of each module include power change curve related data, power generation efficiency related information, energy storage analysis results, and power supply information; the total required electricity price cost, energy storage comprehensive evaluation value and model power value are weighted to obtain the supply value; set the supply signaling group, including photovoltaic storage charging signaling, energy storage charging signaling, power purchase and power supply signaling, and match the supply value with the value range of the supply signaling group to generate the corresponding signaling; The optimization control module is used to receive corresponding signaling to perform corresponding operations, specifically: When the photovoltaic storage charging signal is received, the collaborative power supply mechanism is started; the collaborative power supply mechanism is used to control the photovoltaic module and the energy storage module to collaboratively provide the required electrical energy for the charging pile according to the predicted power generation power and the charging power provided by the energy storage; when the energy storage charging signal is received, the energy storage module is used to provide power for the vehicle with the maximum charging rated power corresponding to the charging pile; when the power purchase signal is received, the electricity purchased from the power grid is allocated to the charging pile to charge the vehicle.

[0023] In this application, the data processing center includes a monitoring setting module, a photovoltaic energy storage analysis module, an energy storage status analysis module, a charging demand analysis module, a prediction model generation module and a comprehensive processing judgment module; A monitoring setting module, used to mark the time range of the preset photovoltaic power generation monitoring time before the current moment as the photovoltaic power generation monitoring time zone; Photovoltaic analysis module, used to analyze the real-time operation data of photovoltaic modules and obtain relevant data of power change curve; it also evaluates and analyzes the power generation efficiency of photovoltaic modules to obtain information related to power generation efficiency; the information related to power generation efficiency includes actual power generation ratio, environmental shadow value, wind shadow value, power generation comprehensive value, group power value, model power value, and wind impact value; The energy storage status analysis module is used to analyze the status of the energy storage module and obtain the energy storage analysis results; wherein the energy storage analysis results include the remaining power average value of the energy storage battery group, the battery group evaluation value, the number of charge and discharge times, the factory time, the energy storage evaluation value and the energy storage comprehensive evaluation value; The charging demand analysis module is used to analyze the demand corresponding to the charging pile and obtain the power supply information; wherein the power supply information provided includes the predicted power generation power of the photovoltaic module and the charging power provided by the energy storage module; The prediction model generation module is used to obtain the historical real-time operation data of the energy storage module, the charging pile module and the photovoltaic module, the historical external environment data and the photovoltaic characteristic information as historical data; the support vector regression model is trained using the historical data, the mean square error is calculated, and the model generalization ability is evaluated by k-fold cross validation to obtain the model evaluation value; the model is generalized according to the model evaluation value and its corresponding threshold value, and finally the prediction power generation model is obtained; The power grid electricity price analysis module is used to analyze the real-time electricity price information of the power grid and obtain the required charging amount of the vehicle corresponding to any charging pile; obtain the real-time electricity price of the power grid and calculate the electricity price cost based on the real-time electricity price and the required charging amount; traverse all charging piles in the charging pile module and obtain the electricity price cost of the vehicle corresponding to each charging pile one by one; sum up the electricity price costs of all charging piles in the charging pile module to obtain the total required electricity price cost; The comprehensive processing and judgment module is used to perform weighted processing on the total electricity price cost, energy storage comprehensive evaluation value and analog electricity value to obtain the supply value.

[0024] In this application, the real-time operating data of the photovoltaic module is analyzed, specifically: Get the current I and voltage U of each photovoltaic panel group of the photovoltaic module at any time in the photovoltaic power generation monitoring time zone, and use the formula Calculate the real-time power generation P of each photovoltaic panel group at the corresponding moment; Add up the power generation of all photovoltaic panel groups to get the total power generation of the photovoltaic array in the photovoltaic power generation monitoring time zone ; Construct a curve graph of total power generation changing with time, with time as the horizontal axis and total power generation as the vertical axis, and plot the total power generation calculated in each photovoltaic power generation monitoring time zone to obtain a power change curve graph; divide the power change curve graph into power change curve graphs corresponding to the cycle according to the set cycle group; the set cycle group includes daily cycle, weekly cycle, monthly cycle, seasonal cycle, and annual cycle group; Obtain a power change curve diagram of any period group in the set period group in chronological order, extract the power change curve in the power change curve diagram, and use the mean square error method to calculate the similarity values ​​of the power change curves of adjacent periods; sort the similarity values ​​in chronological order, construct a similarity change line diagram, input the similarity values ​​and the numbers of their corresponding periods into the similarity change line diagram, mark the positions of the similarity values ​​in the similarity change line diagram as similar points, and connect adjacent similar points to obtain adjacent phase lines; calculate the slope of the adjacent phase line, mark the slope with a positive value as an ascending slope value, and mark the slope with a negative value as a descending slope value; The total value of the rising slope and the total value of the falling slope are obtained by summing up all the rising slope values ​​and the falling slope values ​​in the same period group, and the total value of the rising slope and the total value of the falling slope are weighted to obtain the oblique shadow value; Calculate the mean and standard deviation of similar values ​​of adjacent periods in the same period group to obtain similar mean and similar fluctuation value; perform weighted processing on the oblique shadow value, similar mean and similar difference to obtain the period shadow value; The shadow values ​​of all cycle groups in the set cycle group are weighted to obtain the comprehensive shadow value; the rising slope value, falling slope value, shadow value, similar mean, similar fluctuation value, shadow value and comprehensive shadow value are marked as the power change curve related data of the corresponding cycle.

[0025] In this application, the power generation efficiency of photovoltaic modules is evaluated and analyzed, specifically: Obtain the rated power and actual light intensity of any photovoltaic panel in each photovoltaic panel group in the photovoltaic module in the photovoltaic power generation monitoring time zone, and use the standard photovoltaic cell conversion efficiency formula to calculate the theoretical power generation at any time in the photovoltaic power generation monitoring time zone; divide the theoretical power generation at any time in the photovoltaic power generation monitoring time zone by the actual power generation to obtain the actual power generation ratio SF1; Obtain external environmental data of photovoltaic panels, including environmental information and meteorological information. Environmental information includes temperature, humidity, and atmospheric pressure. Meteorological information includes air density, wind direction, and wind speed. Set a baseline value for any parameter in the environmental information, and perform difference calculation between the value of any parameter in the environmental information and the baseline value corresponding to the parameter to obtain a baseline difference value; set an environmental monitoring time zone, and perform average and standard deviation calculations on the baseline difference values ​​within the environmental monitoring time zone to obtain a baseline difference mean value and a baseline difference fluctuation value; perform weighted calculations on the baseline difference values, baseline difference mean values, and baseline difference fluctuation values ​​of all parameters in the environmental information to obtain an environmental shadow value SF2; Obtain the tilt direction of the photovoltaic panel, analyze the tilt direction of the photovoltaic panel and meteorological information to obtain the wind impact value; calculate the average value and standard deviation of the wind impact value at any time in the environmental monitoring time zone to obtain the wind mean value and wind fluctuation value; perform weighted calculation on the wind impact value, wind mean value and wind fluctuation value to obtain the wind shadow value SF3; The actual power generation ratio, ambient shadow value, and wind shadow value are weighted to obtain the total power generation value of the photovoltaic panel. The formula is: ; Where f1, f2, and f3 represent the weights corresponding to the actual power generation ratio, the ambient shadow value, and the wind shadow value, respectively; the power generation values ​​of all photovoltaic panels in the photovoltaic panel group are weighted to obtain the group power value corresponding to the photovoltaic panel group; the group power values ​​of all photovoltaic panel groups in the photovoltaic module are weighted to obtain the module power value; The actual power generation ratio, environmental shadow value, wind shadow value, total power generation value, group power value, model power value, and wind influence value are marked as information related to power generation efficiency.

[0026] In this application, the process of establishing the predictive power generation model is as follows: Acquire historical real-time operation data, historical external environment data and photovoltaic characteristic information of energy storage modules, charging pile modules and photovoltaic modules as historical data; divide the historical data into training sets and test sets according to a set ratio; The support vector regression model is selected and initialized, and the support vector regression model is trained using the training set data. The support vector regression model with radial basis function as the kernel function is used to calculate the mean square error between the power generation predicted by the photovoltaic module and the actual power generation. The formula is: ; Where n is the number of samples in the training set data, y is the actual power generation, The preset predicted power generation capacity; Then use k-fold cross validation to evaluate the generalization ability of the model. The specific operations are as follows: The training set is divided into k subsets, where k is a preset integer, usually 5 or 10, which is set according to actual needs; for each round of verification, any subset is selected as the verification set, and the remaining k-1 subsets are used as the training set, and this process is repeated k times; in each round of training and verification, the mean square error on the verification set is calculated; The mean square error of the k-round validation set is calculated to obtain the model evaluation value, and the model evaluation threshold is set. If the model evaluation value is less than the model evaluation threshold, it means that the prediction error on the model validation set data is small, indicating that the model has good generalization ability; conversely, if the model evaluation value is greater than or equal to the model evaluation threshold, the data expansion method is used to increase the training data; the model after training, verification and evaluation is marked as a predictive power generation model; Among them, photovoltaic characteristic information includes data related to power change curves of different set periods and information related to power generation efficiency. Data related to power change curves of different set periods include rising slope value, falling slope value, oblique shadow value, similar mean value, similar fluctuation value, period shadow value and comprehensive period shadow value. Information related to power generation efficiency includes actual power generation ratio, ring shadow value, wind shadow value, comprehensive power generation value, group power value and model power value.

[0027] In this application, the status of the energy storage module is analyzed, specifically: Obtain the current power value of each energy storage battery group in the energy storage module, identify the rated power of the energy storage battery group, divide the current power value by the rated power to obtain the remaining power percentage value; average the remaining power percentage values ​​of all energy storage battery groups to obtain the remaining power average value; perform weighted calculation on the remaining power percentage value of the energy storage battery group and the remaining power average value of its energy storage module to obtain the remaining power average value corresponding to the energy storage battery group; Obtain the battery information of each energy storage battery group in the energy storage module, including terminal voltage, charge and discharge power, and battery temperature; count and calculate the statistical indicators of any information in the battery information, including the average value, maximum value, minimum value, average value and median, mode, and standard deviation; perform weighted calculation on the parameters in the statistical indicators to obtain the battery group evaluation value; Obtain the number of charge and discharge times of the energy storage battery pack; Identify the time when the energy storage battery pack was shipped from the factory, and calculate the time difference between the time when the battery was shipped from the factory and the current time to obtain the length of time when the battery was shipped from the factory; The remaining power average value, battery group evaluation value, charge and discharge times, and factory time are weighted to obtain the energy storage evaluation value; The energy storage evaluation values ​​of all energy storage battery groups in the energy storage module are weighted and calculated to obtain a comprehensive energy storage evaluation value; the remaining power average value of the energy storage battery group, the battery group evaluation value, the number of charge and discharge times, the factory time, the energy storage evaluation value and the comprehensive energy storage evaluation value are marked as the energy storage analysis results.

[0028] In this application, the requirements for charging piles are analyzed, specifically: The charging pile determines the vehicle connection status by detecting the connection signal of the charging interface. The vehicle connection status includes the vehicle connected to the charging pile state and the vehicle not connected state; Obtain vehicle information of a vehicle connected to a charging station, including vehicle type, battery capacity, and maximum charging rated power; Subtract the current charge from the vehicle's battery capacity to get the required charge; The connection time between the charging pile and the vehicle is recorded as the vehicle charging time; the predicted charging time is calculated based on the maximum charging rated power of the vehicle and the required charging amount; the time area of ​​the predicted charging time after the vehicle charging time is marked as the predicted charging time zone; the predicted power generation model is used to obtain the predicted power generation power at any time in the predicted charging time zone, and the maximum charging rated power of the vehicle is subtracted from the predicted power generation power to obtain the charging power provided by the energy storage; Then analyze the difference between the prediction and the actual charging process of the vehicle at the charging pile. During the vehicle charging process, obtain the charged amount of the vehicle at any time within the predicted charging time zone, and subtract the sum of the predicted power generation power and the charging power provided by the energy storage from the charged amount to obtain the insufficient charge amount at that moment; calculate the mean and standard deviation of the insufficient charge amount between the current moment and the vehicle charging moment to obtain the insufficient charge mean and insufficient charge fluctuation value; The current moment's undercharged amount, undercharged average value, and undercharged fluctuation value are weighted to obtain the charging control coefficient; the charging control coefficient is used to dynamically update the predicted power generation power and the charging power provided by the energy storage; the predicted power generation power of the photovoltaic module and the charging power provided by the energy storage module are marked as providing power supply information.

[0029] In this application, the tilt direction and meteorological information of the photovoltaic panel are analyzed, specifically: the surface area and surface roughness of the photovoltaic panel are obtained, and the surface area and surface roughness are weighted to obtain the resistance coefficient Cd of the photovoltaic panel; the projection area A of the photovoltaic panel in the direction perpendicular to the wind direction is obtained, and the force F of the wind on the photovoltaic panel is obtained. According to the resistance coefficient, force, air density, wind speed, and projection area, the force of the wind on the photovoltaic panel in the parallel direction is calculated and marked as the flat value. , the formula is: ; Then obtain the pressure coefficient Cp of the wind direction, and calculate the vertical force of the wind on the photovoltaic panel based on the pressure coefficient, force, air density, wind speed, and projection area, marked as the sag value, and the formula is: ; The wind impact value is obtained by weighted calculation of the flat value and the vertical value.

[0030] In this application, the data processing center includes an electricity purchase analysis module and a purchase execution module, wherein: The power purchase analysis module is used to perform comprehensive calculation and analysis based on the off-peak electricity price data, the remaining power capacity information of the energy storage module, the power generation data of the photovoltaic module, and the power transmission loss data from the power grid to the energy storage module. The specific detailed process is as follows: Get the electricity price DJ1 during the off-peak period; Obtain the current remaining power of the energy storage module and the rated capacity Qe of the energy storage module, and mark the ratio of the current remaining power to the rated capacity as the remaining power ratio DJ2; Obtain the time zone of the idle period and input it into the prediction power generation model to obtain the estimated idle power generation DJ3 of the photovoltaic module during the idle period; obtain the pre-sale electricity price Ps when the energy storage module is discharged; Set the power transmission loss rate L between the power obtained by the grid and the power expected to be stored in the energy storage module; calculate the electricity price, remaining power ratio, rated capacity, idle power generation, power transmission loss rate and pre-sale power price, and use the formula , get the electricity value DJ; when the electricity value is less than the preset electricity price threshold, determine that the current grid electricity price during the off-peak period is at an appropriate level, and generate a grid electricity purchase and charging signal; when the electricity value is greater than or equal to the preset electricity price threshold, determine that it is not economical to charge the energy storage module at present or the power generation of the energy storage module and the photovoltaic module can meet the system operation requirements, and there is no need to purchase electricity from the grid, and generate a stop electricity purchase signal; The purchase execution module is used to receive the signaling sent by the power purchase analysis module and perform corresponding operations, specifically: When receiving the power purchase and charging signal from the power grid, the power purchase operation is performed from the power grid according to the pre-set power purchase parameter set, including the power purchase power threshold, the charging curve characteristics of the energy storage module, and the power supply capacity constraint information of the power grid; When a stop electricity purchase order is received, the electricity purchase connection link with the power grid is immediately interrupted, and all ongoing electricity purchase operation processes are terminated.

[0031] In the present application, a method for optimizing the operation of a photovoltaic storage and charging integrated charging station comprises the following steps: S1, data collection step: collecting real-time operation data of energy storage modules, charging pile modules and photovoltaic modules and corresponding external environment data through the sensor network; S2, data processing step: respectively analyze the photovoltaic module, energy storage module and charging pile module to obtain the analysis results of each module, including power change curve related data, power generation efficiency related information, energy storage analysis results, and power supply information. At the same time, analyze the real-time power price information of the power grid to obtain the total required power price cost, and weight the total required power price cost, energy storage comprehensive evaluation value and model power value to obtain the supply value, set the supply signaling group and match the supply value with the signaling group value range to generate the corresponding signaling; S3, optimization control steps: execute corresponding operations according to the received signaling, start the collaborative power supply mechanism when receiving the photovoltaic storage charging signaling, so that the photovoltaic module and the energy storage module collaboratively supply power to the charging pile according to the predicted power generation power and the charging power provided by the energy storage; when receiving the energy storage charging signaling, use the energy storage module to supply power to the vehicle corresponding to the maximum charging rated power of the vehicle; when receiving the power purchase and power supply signaling, distribute the grid power to the charging pile for charging the vehicle.

[0032] Application Example 1 The present invention is described in detail in combination with different application scenarios. For example, when the present invention is applied to a charging station in a highway service area, the present invention proposes a specific implementation process of an integrated photovoltaic storage and charging charging station operation optimization system: The charging station is set to be equipped with a storage module with a total capacity of 750 kWh, a photovoltaic module consisting of 150 photovoltaic panels and a charging pile module consisting of 25 120-kW fast charging piles. The total installed capacity of the photovoltaic module is 500 kW, and the storage module consists of 5 150-kWh storage battery packs. Setting Environment On a regular weekday afternoon, the data acquisition module comprehensively collects various types of data through the sensor network throughout each module; The data processing center analyzes the photovoltaic modules, energy storage modules and charging pile modules of the charging stations in the highway service areas respectively, specifically: The monitoring setting module uses 4 hours (12:00-16:00) before the current time (16:00) as the photovoltaic power generation monitoring time zone; The photovoltaic analysis module uses the collected data to calculate the real-time power generation of each photovoltaic panel group and summarizes the total power generation of the photovoltaic array; after constructing the power change curve, it is divided into daily cycle, weekly cycle and monthly cycle; In the daily cycle analysis, the total value of the rising slope is calculated to be 18 (reflecting the power increase around noon), the total value of the falling slope is -12 (reflecting the degree of power decrease in the afternoon), the oblique shadow value is 7, the similarity mean is 0.83 (indicating the similarity of the power change curve within the daily cycle), the similarity fluctuation value is 0.07 (characterizing the degree of fluctuation of the similarity value), the period shadow value is 3.5, and the comprehensive period shadow value is 3.2; in terms of power generation efficiency evaluation, the rated power of the photovoltaic panel is 340 watts, the actual light intensity is 850 watts / square meter on average, and the actual power generation ratio is calculated to be 0.93; the environmental data calculates the ring shadow value to be 0.5 (assumed parameters), the wind influence value is 0.2 (calculated according to wind direction, wind speed, etc.), and the wind shadow value is 0.3, and then the comprehensive power generation value is 0.82 (assumed weight), the group power value is 0.79, and the model power value is 0.76; Among them, the calculation formula for calculating the comprehensive power generation value DZ is expressed as: 1; The energy storage status analysis module calculates the percentage of remaining power of each energy storage battery pack, with an average value of 55%, and the average value of remaining power is 0.52 (weighted calculation); the average terminal voltage of the battery pack is 402 volts, the average charge and discharge power is 40 kilowatts, and the average battery temperature is 30.5°C, and the calculated battery pack evaluation value is 0.68 (weighted parameter); the average number of charge and discharge times is 400 times, and the factory time is 2.5 years. The comprehensive calculation results show that the energy storage evaluation value is 0.63, and the comprehensive energy storage evaluation value is 0.59; The charging demand analysis module detects 20 vehicles connected to the charging pile and obtains vehicle information. Taking a vehicle with a battery capacity of 80 kWh and a current power of 20 kWh as an example, the required charging capacity is 60 kWh, the maximum charging rated power is 120 kW, and the predicted charging time is 30 minutes. In the predicted charging time zone, the predicted power generation model is used to obtain a predicted power generation of 350 kW, and the charging power provided by the energy storage is -10 kW. During the charging process, the actual charging situation is monitored in real time. If there is a deviation, the charging power control coefficient is calculated to be 1.02 (assuming weighted calculation), and the predicted power generation is dynamically updated to 355 kW, and the charging power provided by the energy storage is 5 kW. The prediction model generation module collects a large amount of historical data, including the real-time operation data of the energy storage module, charging pile module and photovoltaic module in the past year, hourly external environment data and photovoltaic characteristic information; divides the historical data into 80% training set and 20% test set, selects and initializes the support vector regression model, uses the radial basis function as the kernel function to train the model, and calculates the mean square error; evaluates the generalization ability of the model through 10-fold cross validation to obtain the model evaluation value; if the model evaluation value is less than the set threshold (such as 0.06), the model is determined to be valid and the prediction power generation model is constructed; The power grid electricity price analysis module obtains the power grid electricity price in real time. For example, the current price is 1.3 yuan / kWh. For each charging vehicle, the electricity price cost is calculated. For example, the above vehicle needs to charge 60 kWh, and the electricity price cost is 78 yuan. All 20 vehicles are traversed and the total required electricity price cost is obtained by summing up. The comprehensive processing and judgment module weights the total electricity price cost, energy storage comprehensive evaluation value and analog electricity value according to the preset weights (assuming the total electricity price weight is 0.3, the energy storage comprehensive evaluation value weight is 0.35, and the analog electricity value weight is 0.35) to obtain the supply value; assuming the supply value is 0.7, after matching with the supply signaling group (the value range of the photovoltaic storage charging signaling is 0.65-0.85, the value range of the energy storage charging signaling is 0.45-0.65, and the value range of the power purchase and supply signaling is 0-0.45), the photovoltaic storage charging signaling is generated; After receiving the solar-storage-charging signal, the optimization control module immediately starts the collaborative power supply mechanism; it accurately controls the photovoltaic module to provide charging power with a predicted power generation power of 355 kilowatts and the energy storage module to provide charging power with 5 kilowatts of energy storage to collaboratively power the charging pile; during the entire charging process, the system re-collects and analyzes data every 3 minutes; if an emergency occurs, such as part of the photovoltaic panel being blocked by shadows, resulting in a decrease in power generation, the system will quickly recalculate various parameters and adjust the power supply strategy to ensure that the vehicle charging is not affected and always maintain an efficient and stable charging state, providing reliable and efficient charging services for electric vehicles in highway service areas.

[0033] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A photovoltaic storage and charging integrated charging station operation optimization system, characterized in that: It includes an energy storage module, a charging pile module, a photovoltaic module and a light storage and charging management platform; the energy storage module includes multiple energy storage battery groups, the charging pile module includes multiple charging piles, the photovoltaic module includes multiple photovoltaic panel groups, and the multiple photovoltaic panel groups form a photovoltaic array, and the light storage and charging management platform includes a data acquisition module, a data processing center and an optimization control module; A data acquisition module is used to collect real-time operation data of the energy storage module, charging pile module and photovoltaic module and corresponding external environment data through a sensor network; The data processing center is used to analyze the photovoltaic module, energy storage module and charging pile module respectively to obtain the analysis results of each module; it is also used to analyze the real-time electricity price information of the power grid to obtain the total required electricity price cost; the analysis results of each module include power change curve related data, power generation efficiency related information, energy storage analysis results, and power supply information; The total electricity price cost, energy storage comprehensive evaluation value and model electricity value are weighted to obtain the supply value; Set the supply signaling group, including the solar storage charging signaling, energy storage charging signaling, and power purchase and supply signaling, and match the supply value with the value range of the supply signaling group to generate the corresponding signaling; The optimization control module is used to receive corresponding signaling to perform corresponding operations, specifically: When the photovoltaic storage charging signal is received, the collaborative power supply mechanism is started; the collaborative power supply mechanism is used to control the photovoltaic module and the energy storage module to collaboratively provide the required electrical energy for the charging pile according to the predicted power generation power and the charging power provided by the energy storage; when the energy storage charging signal is received, the energy storage module is used to provide power for the vehicle with the maximum charging rated power corresponding to the charging pile; when the power purchase signal is received, the electricity purchased from the power grid is allocated to the charging pile to charge the vehicle.

2. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 1 is characterized in that: The data processing center includes a monitoring setting module, a photovoltaic energy storage analysis module, an energy storage status analysis module, a charging demand analysis module, a prediction model generation module and a comprehensive processing judgment module; A monitoring setting module, used to mark the time range of the preset photovoltaic power generation monitoring time before the current moment as the photovoltaic power generation monitoring time zone; Photovoltaic analysis module, used to analyze the real-time operation data of photovoltaic modules and obtain relevant data of power change curve; it also evaluates and analyzes the power generation efficiency of photovoltaic modules to obtain information related to power generation efficiency; the information related to power generation efficiency includes actual power generation ratio, environmental shadow value, wind shadow value, power generation comprehensive value, group power value, model power value, and wind impact value; The energy storage status analysis module is used to analyze the status of the energy storage module and obtain the energy storage analysis results; wherein the energy storage analysis results include the remaining power average value of the energy storage battery group, the battery group evaluation value, the number of charge and discharge times, the factory time, the energy storage evaluation value and the energy storage comprehensive evaluation value; The charging demand analysis module is used to analyze the demand corresponding to the charging pile and obtain the power supply information; wherein the power supply information provided includes the predicted power generation power of the photovoltaic module and the charging power provided by the energy storage module; The prediction model generation module is used to obtain the historical real-time operation data of the energy storage module, the charging pile module and the photovoltaic module, the historical external environment data and the photovoltaic characteristic information as historical data; the support vector regression model is trained using the historical data, the mean square error is calculated, and the model generalization ability is evaluated by cross-validation to obtain the model evaluation value; the model is generalized according to the model evaluation value and its corresponding threshold value, and finally the prediction power generation model is obtained; The power grid electricity price analysis module is used to analyze the real-time electricity price information of the power grid and obtain the required charging amount of the vehicle corresponding to any charging pile; obtain the real-time electricity price of the power grid and calculate the electricity price cost based on the real-time electricity price and the required charging amount; traverse all charging piles in the charging pile module and obtain the electricity price cost of the vehicle corresponding to each charging pile one by one; sum up the electricity price costs of all charging piles in the charging pile module to obtain the total required electricity price cost; The comprehensive processing and judgment module is used to perform weighted processing on the total required electricity price cost, the comprehensive evaluation value of energy storage and the analog electricity value to obtain the supply value; set the supply signaling group, including the photovoltaic storage charging signaling, the energy storage charging signaling, and the power purchase and supply signaling, and match the supply value with the value range of the supply signaling group to generate the corresponding signaling.

3. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 2 is characterized in that: Analyze the real-time operation data of photovoltaic modules, specifically: Obtain the current and voltage of each photovoltaic panel group of the photovoltaic module at any time in the photovoltaic power generation monitoring time zone, and calculate the real-time power generation power of each photovoltaic panel group at the corresponding time; Add up the power generation of all photovoltaic panel groups to obtain the total power generation of the photovoltaic array in the photovoltaic power generation monitoring time zone; construct a curve graph of the total power generation changing with time, with time as the horizontal axis and the total power generation as the vertical axis, and plot the total power generation calculated in each photovoltaic power generation monitoring time zone to obtain a power change curve graph; divide the power change curve graph into power change curve graphs corresponding to the cycle according to the set cycle group; the set cycle group includes daily cycle, weekly cycle, monthly cycle, seasonal cycle, and annual cycle group; Obtain a power change curve diagram of any cycle group in the set cycle group in chronological order, extract the power change curve in the power change curve diagram, and calculate the similarity values ​​of the power change curves of adjacent cycles by using the mean square error method; sort the similar values ​​in chronological order, construct a similar change line diagram, input the similar values ​​and the numbers of their corresponding cycles into the similar change line diagram, mark the positions of the similar values ​​in the similar change line diagram as similar points, and connect adjacent similar points to obtain adjacent phase lines; Calculate the slope of the adjacent phase line, mark the positive slope as the rising slope value, and mark the negative slope as the falling slope value; The total value of the rising slope and the total value of the falling slope are obtained by summing up all the rising slope values ​​and the falling slope values ​​in the same period group, and the total value of the rising slope and the total value of the falling slope are weighted to obtain the oblique shadow value; Calculate the mean and standard deviation of similar values ​​of adjacent periods in the same period group to obtain similar mean and similar fluctuation value; perform weighted processing on the oblique shadow value, similar mean and similar difference to obtain the period shadow value; The shadow values ​​of all cycle groups in the set cycle group are weighted to obtain the comprehensive shadow value; the rising slope value, falling slope value, shadow value, similar mean, similar fluctuation value, shadow value and comprehensive shadow value are marked as the power change curve related data of the corresponding cycle.

4. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 2 is characterized in that: The power generation efficiency of photovoltaic modules is evaluated and analyzed, specifically: The rated power and actual light intensity of any photovoltaic panel in each photovoltaic panel group in the photovoltaic module in the photovoltaic power generation monitoring time zone are obtained, and the theoretical power generation power at any time in the photovoltaic power generation monitoring time zone is calculated using the standard photovoltaic cell conversion efficiency formula; the theoretical power generation power at any time in the photovoltaic power generation monitoring time zone is divided by the actual power generation power to obtain the actual power generation ratio; Obtain external environmental data of photovoltaic panels, including environmental information and meteorological information. Environmental information includes temperature, humidity, and atmospheric pressure. Meteorological information includes air density, wind direction, and wind speed. Setting a reference value of any parameter in the environmental information, performing a difference calculation between the value of any parameter in the environmental information and the reference value corresponding to the parameter to obtain a reference difference value; Set the environmental monitoring time zone, and calculate the average value and standard deviation of the benchmark difference values ​​within the environmental monitoring time zone to obtain the benchmark difference mean value and benchmark difference fluctuation value; The base difference, base difference mean, and base difference fluctuation value of all parameters in the environmental information are weighted and calculated to obtain the ring shadow value; Obtain the tilt direction of the photovoltaic panel, analyze the tilt direction of the photovoltaic panel and meteorological information to obtain the wind impact value; calculate the average value and standard deviation of the wind impact value at any time in the environmental monitoring time zone to obtain the wind mean value and wind fluctuation value; perform weighted calculation on the wind impact value, wind mean value and wind fluctuation value to obtain the wind shadow value; The actual power generation ratio, the ambient shadow value, and the wind shadow value are weighted to obtain the total power generation value of the photovoltaic panel; the total power generation value of all photovoltaic panels in the photovoltaic panel group is weighted to obtain the group power value corresponding to the photovoltaic panel group; the group power values ​​of all photovoltaic panel groups in the photovoltaic module are weighted to obtain the module power value; The actual power generation ratio, environmental shadow value, wind shadow value, total power generation value, group power value, model power value, and wind influence value are marked as information related to power generation efficiency.

5. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 2 is characterized in that: The process of establishing the predictive power generation model is as follows: Acquire historical real-time operation data, historical external environment data and photovoltaic characteristic information of energy storage modules, charging pile modules and photovoltaic modules as historical data; divide the historical data into training sets and test sets according to a set ratio; A support vector regression model is selected and initialized, and the support vector regression model is trained using the training set data. The support vector regression model with radial basis function as the kernel function is used to calculate the mean square error between the power generation predicted by the photovoltaic module and the actual power generation; Cross-validation is then used to evaluate the generalization ability of the model. The training set is divided into k subsets, where k is a preset integer. For each round of validation, any subset is selected as the validation set, and the remaining k-1 subsets are selected as the training set. This process is repeated k times. In each round of training and validation, the mean square error on the validation set is calculated. The mean square error of the k-round validation set is calculated to obtain the model evaluation value, and the model evaluation threshold is set. If the model evaluation value is less than the model evaluation threshold, it means that the prediction error on the model validation set data is small, indicating that the model has good generalization ability; conversely, if the model evaluation value is greater than or equal to the model evaluation threshold, the data expansion method is used to increase the training data; Mark the model after training, verification and evaluation as a predictive power generation model; Among them, photovoltaic characteristic information includes data related to power change curves of different set periods and information related to power generation efficiency. Data related to power change curves of different set periods include rising slope value, falling slope value, oblique shadow value, similar mean value, similar fluctuation value, period shadow value and comprehensive period shadow value. Information related to power generation efficiency includes actual power generation ratio, ring shadow value, wind shadow value, comprehensive power generation value, group power value and model power value.

6. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 2 is characterized in that: Analyze the status of the energy storage module, specifically: Obtain the current power value of each energy storage battery group in the energy storage module, identify the rated power of the energy storage battery group, divide the current power value by the rated power to obtain the remaining power percentage value; average the remaining power percentage values ​​of all energy storage battery groups to obtain the remaining power average value; perform weighted calculation on the remaining power percentage value of the energy storage battery group and the remaining power average value of its energy storage module to obtain the remaining power average value corresponding to the energy storage battery group; Obtain the battery information of each energy storage battery group in the energy storage module, including terminal voltage, charge and discharge power, and battery temperature; count and calculate the statistical indicators of any information in the battery information, including the average value, maximum value, minimum value, average value and median, mode, and standard deviation; perform weighted calculation on the parameters in the statistical indicators to obtain the battery group evaluation value; Obtain the number of charge and discharge times of the energy storage battery pack; Identify the time when the energy storage battery pack was shipped from the factory, and calculate the time difference between the time when the battery was shipped from the factory and the current time to obtain the length of time when the battery was shipped from the factory; The remaining power average value, battery group evaluation value, charge and discharge times, and factory time are weighted to obtain the energy storage evaluation value; The energy storage evaluation values ​​of all energy storage battery groups in the energy storage module are weighted and calculated to obtain a comprehensive energy storage evaluation value; the remaining power average value of the energy storage battery group, the battery group evaluation value, the number of charge and discharge times, the factory time, the energy storage evaluation value and the comprehensive energy storage evaluation value are marked as the energy storage analysis results.

7. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 2 is characterized in that: Analyze the needs of charging piles, specifically: The charging pile determines the vehicle connection status by detecting the connection signal of the charging interface. The vehicle connection status includes the vehicle connected to the charging pile state and the vehicle not connected state; Obtain vehicle information of a vehicle connected to a charging station, including the vehicle type, battery capacity, and maximum charging rated power; Subtract the current charge from the vehicle's battery capacity to get the required charge; The connection time between the charging pile and the vehicle is recorded as the vehicle charging time; the predicted charging time is calculated based on the maximum charging rated power of the vehicle and the required charging amount; the time area of ​​the predicted charging time after the vehicle charging time is marked as the predicted charging time zone; the predicted power generation model is used to obtain the predicted power generation power at any time in the predicted charging time zone, and the maximum charging rated power of the vehicle is subtracted from the predicted power generation power to obtain the charging power provided by the energy storage; Then analyze the difference between the prediction and the actual charging process of the vehicle at the charging pile. During the vehicle charging process, obtain the charged amount of the vehicle at any time within the predicted charging time zone, and subtract the sum of the predicted power generation power and the charging power provided by the energy storage from the charged amount to obtain the insufficient charge amount at that moment; calculate the mean and standard deviation of the insufficient charge amount between the current moment and the vehicle charging moment to obtain the insufficient charge mean and insufficient charge fluctuation value; The current moment's undercharged amount, undercharged average value, and undercharged fluctuation value are weighted to obtain the charging control coefficient; the charging control coefficient is used to dynamically update the predicted power generation power and the charging power provided by the energy storage; the predicted power generation power of the photovoltaic module and the charging power provided by the energy storage module are marked as providing power supply information.

8. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 4 is characterized in that: The inclination direction and meteorological information of the photovoltaic panel are analyzed, specifically: the surface area and surface roughness of the photovoltaic panel are obtained, and the surface area and surface roughness are weighted to obtain the drag coefficient of the photovoltaic panel; the projection area of ​​the photovoltaic panel in the direction perpendicular to the wind direction is obtained, and the force of the wind on the photovoltaic panel is obtained, and the force of the wind on the photovoltaic panel in the parallel direction is calculated according to the drag coefficient, force, air density, wind speed, and projection area, and marked as the flat value; then the pressure coefficient of the wind direction is obtained, and the force of the wind on the photovoltaic panel in the vertical direction is calculated according to the pressure coefficient, force, air density, wind speed, and projection area, and marked as the vertical value; the flat value and the vertical value are weighted to obtain the wind impact value.

9. The photovoltaic storage and charging integrated charging station operation optimization system according to claim 1 is characterized in that: The real-time operating data of the energy storage module includes the power, voltage, current, charging and discharging power, and battery temperature of the energy storage battery group. The real-time operating data of the charging pile module includes the connection status, charging power, charging time, and charged amount of each charging pile. The photovoltaic module includes the light intensity, power generation power, voltage, and current of the photovoltaic panel group.

10. The method for optimizing the operation of a photovoltaic, storage and charging integrated charging station according to claim 1, using the system for optimizing the operation of a photovoltaic, storage and charging integrated charging station according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1, data collection step: collecting real-time operation data of energy storage modules, charging pile modules and photovoltaic modules and corresponding external environment data through the sensor network; S2, data processing step: respectively analyze the photovoltaic module, energy storage module and charging pile module to obtain the analysis results of each module, including power change curve related data, power generation efficiency related information, energy storage analysis results, and power supply information. At the same time, analyze the real-time power price information of the power grid to obtain the total required power price cost, and weight the total required power price cost, energy storage comprehensive evaluation value and model power value to obtain the supply value, set the supply signaling group and match the supply value with the signaling group value range to generate the corresponding signaling; S3, optimization control steps: execute corresponding operations according to the received signaling, start the collaborative power supply mechanism when receiving the photovoltaic storage charging signaling, so that the photovoltaic module and the energy storage module collaboratively supply power to the charging pile according to the predicted power generation power and the charging power provided by the energy storage; when receiving the energy storage charging signaling, use the energy storage module to supply power to the vehicle corresponding to the maximum charging rated power of the vehicle; when receiving the power purchase and power supply signaling, distribute the grid power to the charging pile for charging the vehicle.

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