A power management system and method suitable for photovoltaic storage and charging

Abstract

The present invention relates to an electric energy management system and method suitable for photovoltaic storage and charging, which obtains the power generation data of the photovoltaic power generation module in the historical time period and inputs it into the photovoltaic power generation prediction model. The prediction model predicts the photovoltaic power generation output power in the specified time period and draws a power generation power change curve. The charging data of the charging module in the historical time period is obtained and input into the charging prediction model, the charging power in the specified time period is predicted, and a charging power change curve of the charging power in the specified time period is drawn. Power pre-allocation is performed based on the power generation power change curve and the charging power change curve, the output power of the photovoltaic power generation module is collected in real time, and the power pre-allocation is adjusted. The real-time power adjusts the output power of the preset energy storage module and the mains module that changes with time; the input power of the charging device is collected in real time, and the photovoltaic charging module adjusts the output power of the charging device and the energy storage module, so that the output of the charging device is stable.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic storage and charging, and in particular relates to an electric energy management system and method suitable for photovoltaic storage and charging. Background Art

[0002] Photovoltaic charging and storage refers to a comprehensive energy system formed by combining photovoltaic power generation, energy storage systems and charging equipment for new energy vehicles. The system converts solar energy into electrical energy for use by charging equipment for new energy vehicles through photovoltaic power generation. Excess electrical energy is stored through energy storage equipment for use in power supply and charging when needed. By integrating photovoltaic power generation systems with energy storage systems and charging equipment, efficient use and storage of solar energy is achieved to meet electricity and charging needs. With the support of solar power generation and energy storage systems, electric vehicles can be charged, and the energy storage system is used to store excess electrical energy for use at night or in low light. The integrated photovoltaic charging and storage system not only improves energy utilization efficiency, but also promotes the use of renewable energy.

[0003] However, in the existing photovoltaic storage and charging systems, the power generation of the photovoltaic power generation module is unstable because the power generation power is affected by the time period and weather, which makes the output voltage unstable. When the output voltage is transmitted to the charging device, it may cause the charging device to fail to work under the normal working voltage, which may easily lead to abnormal operation or failure of the charging device, making the charging of new energy vehicles unstable and causing damage or abnormality to the charging components of new energy vehicles, which may reduce the service life of the charging equipment of new energy vehicles and increase maintenance costs. Summary of the invention

[0004] The purpose of the present invention is to provide an electric energy management system and method suitable for photovoltaic storage and charging, so as to solve the problem in the existing photovoltaic storage and charging system that the power generation power in the photovoltaic power generation module is unstable due to the influence of time period and weather, resulting in unstable output voltage. When the output voltage is transmitted to the charging device, it may cause the charging device to fail to work under the normal working voltage, which may easily lead to abnormal operation or failure of the charging device, making the charging of new energy vehicles unstable, causing damage or abnormality of the charging components of the new energy vehicles, and possibly reducing the service life of the charging equipment of the new energy vehicles and increasing the maintenance cost. Technical problems.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for managing electric energy suitable for photovoltaic storage and charging, comprising the following steps:

[0007] S1: Acquire power generation data of the photovoltaic power generation module in a historical time period, create a photovoltaic power generation prediction model, pre-process the power generation data and input it into the photovoltaic power generation prediction model for training, predict the photovoltaic power generation output power in a specified time period through the trained photovoltaic power generation prediction model, and draw a power generation power change curve of the photovoltaic power generation output power in the specified time period;

[0008] S2: Acquire charging data of the charging module in a historical time period, create a charging prediction model, pre-process the charging data and input it into the charging prediction model for training, predict the charging power in a specified time period through the trained charging prediction model, and draw a charging power change curve in the specified time period;

[0009] S3: Based on the power generation change curve and the charging power change curve, pre-allocate power according to the change over time, collect the output power of the photovoltaic power generation module in real time, and adjust the power pre-allocation based on the real-time power;

[0010] S4: Preset the output power of the energy storage module and the mains module that changes with time based on the power generation change curve and the charging power change curve according to the change of time, and adjust the preset output power of the energy storage module and the mains module that changes with time based on the real-time power;

[0011] S5: collecting the input power of the charging device in real time, comparing the real-time input power of the charging device with the preset input power, and determining whether the real-time input power of the charging device is less than the preset input power and whether the power generation power of the photovoltaic power generation module is greater than the real-time input power of the charging device. If both are true, increasing the output power of the photovoltaic charging module to the charging device and reducing the output power to the energy storage module; otherwise, executing step S6;

[0012] S6: When the real-time input power of the charging device is less than the preset input power, and the power generation power of the photovoltaic power generation module is less than the real-time input power of the charging device, the voltage of the charging device is compensated through the energy storage module; when the real-time input power of the charging device is greater than the preset input power, the output power of the photovoltaic charging module to the charging device is reduced to the preset output power, and the remaining power of the photovoltaic charging module is output to the energy storage module;

[0013] S7: Iteratively execute steps S1-S6.

[0014] Preferably, the photovoltaic power generation prediction model in steps S1 and S2 is a SARIMA model, and the training process of the photovoltaic power generation prediction model is: initializing the parameters of the photovoltaic power generation prediction model, and inputting the power generation data of the photovoltaic power generation module in the preprocessed historical time period into the photovoltaic power generation prediction model, and traversing the parameter combinations of the photovoltaic power generation prediction model, and selecting the parameter combination with the best performance for each group of parameter combinations through a cross-validation evaluation method.

[0015] Preferably, the parameters of the photovoltaic power generation prediction model include seasonal parameters including the order of non-seasonal autoregression, the number of one-step differences, the non-seasonal moving average order, the moving average order, the seasonal autoregression order, and the number of seasonal differences.

[0016] Preferably, the specific process in step S3 is as follows:

[0017] S31: integrating the power generation change curve graph and the charging power change curve graph into one graph, and dividing the power generation change curve and the charging power change curve into different intervals using the intersection of the two lines as a dividing point;

[0018] S32: extracting the intervals in which the power generation curve in the divided intervals is higher than the charging power curve, allocating the power generation of the photovoltaic power generation module to the charging equipment based on the charging power in the corresponding intervals, and pre-allocating the remaining power generation to the energy storage module;

[0019] For the intervals where the charging power change curve is higher than the power generation power change curve in the divided intervals, all the power generation power of the photovoltaic power generation module is allocated to the charging equipment, and the insufficient charging power is compensated by the energy storage module;

[0020] S33: Collect the output power of the photovoltaic power generation module in real time, extract the points or intervals with differences in the power generation change curve, adjust the power generation change curve, and adjust the power pre-allocation according to the method in step S32 based on the adjusted power generation change curve and charging power change curve.

[0021] Preferably, based on the interval in which the charging power change curve in the extracted divided interval is higher than the power generation power change curve, a first difference change curve graph of the charging power and the power generation power in the corresponding interval is drawn, and a maximum output power change curve graph of the energy storage module is drawn on the first difference change curve graph, and the interval in which the first difference change curve is higher than the maximum output power change curve is extracted, and a second difference change curve is drawn, and based on the second difference change curve, power compensation is performed for the time period corresponding to the corresponding interval through the AC power module.

[0022] Preferably, the process of performing power compensation through the AC power module for the time period corresponding to the corresponding interval based on the second difference change curve is: processing the AC power through the transformer module, compensating the power in the form of voltage compensation, and calculating the compensated voltage value by collecting the real-time current value and the power value to be compensated, wherein the power value to be compensated is the difference between the corresponding intervals in the second difference change curve.

[0023] An electric energy management system suitable for photovoltaic storage and charging, used to implement any one of the electric energy management methods suitable for photovoltaic storage and charging, comprising a data acquisition module, a model creation module, a photovoltaic power generation prediction model, a curve drawing module, a charging prediction model, a data acquisition module, a photovoltaic power generation module, an energy storage module, a charging module and a control module; the data acquisition module is connected to the photovoltaic power generation prediction model, the model creation module is respectively connected to the photovoltaic power generation prediction model and the charging prediction model, the photovoltaic power generation prediction model and the charging prediction model are respectively connected to the curve drawing module, and the photovoltaic power generation module, the energy storage module and the charging module are all connected to the control module;

[0024] The data acquisition module is used to acquire the power generation data of the photovoltaic power generation module within a historical time period;

[0025] The model creation module is used to create a photovoltaic power generation prediction model and a charging prediction model;

[0026] The photovoltaic power generation prediction model is used to perform training based on the preprocessed power generation data and predict the photovoltaic power generation output power within a specified time period;

[0027] The charging prediction model is used to perform training based on the preprocessed charging data and predict the charging power within a specified time period;

[0028] The curve drawing module is used to draw a curve diagram of photovoltaic power generation output power change in a specified time period and a curve diagram of charging power change in a specified time period;

[0029] The data acquisition module is used for the output power of the photovoltaic power generation module and the input power of the charging device;

[0030] The control module is used to control the photovoltaic power generation module, the energy storage module, and the charging module, and compare the real-time input power of the charging device with the preset input power, and determine whether the real-time input power of the charging device is less than the preset input power and whether the power generation power of the photovoltaic power generation module is greater than the real-time input power of the charging device.

[0031] The beneficial effects of the present invention include:

[0032] The power management system and method suitable for photovoltaic storage and charging provided by the present invention obtains the power generation data of the photovoltaic power generation module in the historical time period and inputs it into the photovoltaic power generation prediction model. The prediction model predicts the photovoltaic power generation output power in the specified time period and draws a power generation power change curve. The charging data of the charging module in the historical time period is obtained and input into the charging prediction model to predict the charging power in the specified time period, and draw a charging power change curve of the charging power in the specified time period. Power pre-allocation is performed based on the power generation power change curve and the charging power change curve, the output power of the photovoltaic power generation module is collected in real time, and the power pre-allocation is adjusted. The real-time power adjusts the output power of the preset energy storage module and the mains module that changes with time; the input power of the charging device is collected in real time, and the photovoltaic charging module adjusts the output power of the charging device and the energy storage module, so that the output of the charging device is stable.

[0033] First, by creating a photovoltaic power generation model and a charging prediction model, and training the photovoltaic power generation model and the charging prediction model based on the historical power generation data and charging data, respectively, the power generation and charging power of the specified time period are accurately predicted, and power pre-allocation is performed based on the predicted power generation and charging power. Since the power of photovoltaic power generation and the power of the charging module both show a certain regularity based on time, the predicted power generation and charging power can be used to know the change law of the power generation of the photovoltaic power generation module and the charging power of the charging module over time, thereby realizing the pre-allocation of power generation. If pre-allocation is not performed, it is necessary to measure the power generation and charging power at all times, and calculate the power allocation at all times, and the allocation efficiency is very low. Pre-allocation is performed by prediction according to the time period, and due to the accuracy of the two prediction models, only a small amount of allocation data is abnormal. Therefore, only part of the allocation data needs to be adjusted according to the real-time measured data in the future, which greatly improves the allocation efficiency.

[0034] Secondly, by initializing the parameters of the photovoltaic power generation prediction model, inputting the power generation data of the photovoltaic power generation module in the preprocessed historical time period into the photovoltaic power generation prediction model, and traversing the parameter combinations of the photovoltaic power generation prediction model, and selecting the best performance parameter combination for each group of parameter combinations through cross-validation evaluation, the model training optimization is carried out, so that the prediction accuracy of the model is high, which provides data support for the subsequent power pre-allocation based on the predicted data.

[0035] Thirdly, by integrating the power generation change curve and the charging power change curve into one graph, and taking the intersection of the two lines as the dividing point, the power generation change curve and the charging power change curve are divided into different intervals, and different intervals are processed differently, the power distribution accuracy is increased, and the photovoltaic storage and charging in the entire system are balanced and coordinated, thereby improving its stability.

[0036] Finally, the power of the AC power module is compensated for the time period corresponding to the corresponding interval through the second difference change curve, realizing the combination of the photovoltaic storage and charging system and the AC power, achieving multi-party collaboration, further improving the stability of the system, and ensuring that the charging module achieves stable output. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic flow chart of the electric energy management method applicable to photovoltaic storage and charging of the present invention.

[0038] Figure 2 This is a schematic diagram of integrating the power generation change curve diagram and the charging power change curve diagram of the present invention into one diagram.

[0039] Figure 3 It is a schematic diagram of the architecture of the power management system suitable for photovoltaic storage and charging of the present invention. DETAILED DESCRIPTION

[0040] The following is combined with Figure 1~Figure 3 The present invention is further described in detail:

[0041] Example 1

[0042] See attached Figure 1 As shown, a method for managing electric energy applicable to photovoltaic storage and charging comprises the following steps:

[0043] S1: Obtain the power generation data of the photovoltaic power generation module in the historical time period, create a photovoltaic power generation prediction model, pre-process the power generation data and input it into the photovoltaic power generation prediction model for training, predict the photovoltaic power generation output power in the specified time period through the trained photovoltaic power generation prediction model, and draw a power generation power change curve of the photovoltaic power generation output power in the specified time period. The process of pre-processing the power generation data includes removing noise data and invalid data in the power generation data, filling the missing values ​​with the mean or median, deleting or converting the abnormal values, and finally converting the data to obtain the data distribution and scale suitable for the model.

[0044] S2: Obtain charging data of the charging module in a historical time period, create a charging prediction model, pre-process the charging data and input it into the charging prediction model for training, predict the charging power in a specified time period through the trained charging prediction model, and draw a charging power change curve in the specified time period.

[0045] Since the power of photovoltaic power generation and the power of the charging module both show a certain regularity based on time, the predicted power generation and charging power can be used to know the change pattern of the power generation of the photovoltaic power generation module and the charging power of the charging module over time, thereby realizing the pre-allocation of power generation. Therefore, by creating a photovoltaic power generation model and a charging prediction model, and training the photovoltaic power generation model and the charging prediction model based on the acquired historical power generation data and charging data, the power generation and charging power of the specified time period are accurately predicted, and power pre-allocation is performed based on the predicted power generation and charging power.

[0046] S3: Based on the power generation change curve and the charging power change curve, power pre-allocation is performed according to the change of time, the output power of the photovoltaic power generation module is collected in real time, and the power pre-allocation is adjusted based on the real-time power. If pre-allocation is not performed, it is necessary to measure the power generation power and the charging power at all times, and calculate the power generation distribution amount at all times, and the distribution efficiency is very low. However, pre-allocation is performed by time period through prediction. Due to the accuracy of the two prediction models, only a small amount of distribution data has abnormalities. Therefore, only part of the distribution data needs to be adjusted according to the real-time measured data in the future, which greatly improves the distribution efficiency and the coordination of the entire system.

[0047] S4: Based on the power generation change curve and the charging power change curve, preset the output power of the energy storage module and the mains module that changes with time according to the change of time, and adjust the preset output power of the energy storage module and the mains module that changes with time based on the real-time power. When it can be known from the power generation change curve and the charging power change curve that the power generation power can meet the input power of the charging module, there is no need for the mains module to intervene, and the input power of the charging module is realized through the photovoltaic power generation module and the energy storage module. When the photovoltaic power generation module cannot meet the input power of the charging module, the mains module is used to make up for the shortfall, so that the charging module has sufficient and stable power input.

[0048] S5: Collect the input power of the charging device in real time, compare the real-time input power of the charging device with the preset input power, and determine whether the real-time input power of the charging device is less than the preset input power and whether the power generation power of the photovoltaic power generation module is greater than the real-time input power of the charging device. If both are yes, increase the output power of the photovoltaic charging module to the charging device and reduce the output power to the energy storage module; otherwise, execute step S6.

[0049] S6: When the real-time input power of the charging device is less than the preset input power, and the power generation power of the photovoltaic power generation module is less than the real-time input power of the charging device, the charging device is voltage compensated through the energy storage module; when the real-time input power of the charging device is greater than the preset input power, the output power of the photovoltaic charging module to the charging device is reduced to the preset output power, and the remaining power of the photovoltaic charging module is output to the energy storage module.

[0050] S7: Iterate and execute steps S1-S6. Through the above process, the problem that the power generation in the photovoltaic power generation module is unstable due to the influence of time period and weather in the existing photovoltaic storage and charging system is solved, which makes the output voltage unstable. When the output voltage is transmitted to the charging device, it may cause the charging device to fail to work under the normal working voltage, which may easily lead to abnormal operation or failure of the charging device, making the charging of new energy vehicles unstable, causing damage or abnormality of the charging components of new energy vehicles, and possibly reducing the service life of the charging equipment of new energy vehicles and increasing maintenance costs.

[0051] Example 2

[0052] On the basis of Example 1, the photovoltaic power generation prediction model and the charging prediction model in steps S1 and S2 are both SARIMA models, and the training process of the photovoltaic power generation prediction model is: initializing the parameters of the photovoltaic power generation prediction model, and inputting the power generation data of the photovoltaic power generation module in the preprocessed historical time period into the photovoltaic power generation prediction model, and traversing the parameter combinations of the photovoltaic power generation prediction model, and selecting the parameter combination with the best performance for each group of parameter combinations through a cross-validation evaluation method.

[0053] By initializing the parameters of the photovoltaic power generation prediction model, and inputting the power generation data of the photovoltaic power generation module in the preprocessed historical time period into the photovoltaic power generation prediction model, and traversing the parameter combinations of the photovoltaic power generation prediction model, and selecting the best performance parameter combination for each group of parameter combinations through a cross-validation evaluation method to perform model training optimization, the prediction accuracy of the model is high, and data support is provided for subsequent power pre-allocation based on the predicted data. The parameters of the photovoltaic power generation prediction model include seasonal parameters, including the order of non-seasonal autoregression, the number of one-step differences, the order of non-seasonal moving average, the order of moving average, the order of seasonal autoregression, and the number of seasonal differences. The selection of these parameters determines the stability of the prediction results of the photovoltaic power generation prediction model and the charging prediction model.

[0054] Example 3

[0055] Based on Example 1 or Example 2, the specific process in step S3 is as follows:

[0056] S31: Integrate the power generation change curve diagram and the charging power change curve diagram into one diagram, see Figure 2 As shown, the horizontal axis is time, and the vertical axis is power. Curve 1 is the charging power change curve, and curve 2 is the power generation change curve. The intersection of the two lines is used as the dividing point. The power generation change curve and the charging power change curve can be divided into different intervals. In some intervals, the charging power is greater than the power generation power, and in some intervals, the power generation power is greater than the charging power. Therefore, the intervals can be divided into two categories, one is that the charging power is greater than the power generation power, and the other is that the power generation power is greater than the charging power. Therefore, the two types of intervals are processed separately. That is, by integrating the power generation change curve and the charging power change curve into one figure, and using the intersection of the two lines as the dividing point, the power generation change curve and the charging power change curve are divided into different intervals, and different intervals are processed differently. The specific process is steps S32-S33, which increases the power distribution accuracy, makes the photovoltaic storage and charging in the whole system balanced and coordinated, and improves its stability.

[0057] S32: extracting the interval in which the generation power variation curve is higher than the charging power variation curve in the divided interval, that is, Figure 2 [0-t1], [t2-t3], [t2-t3], [t4-t5] in the interval, the power generation power of the photovoltaic power generation module is allocated to the charging device based on the charging power in the corresponding interval, and the remaining power generation power is pre-allocated to the energy storage module;

[0058] For the interval where the charging power change curve in the divided interval is higher than the generation power change curve, that is, Figure 2 In [t1-t2] and [t3-t4], all the power generated by the photovoltaic power generation module is allocated to the charging equipment, and the insufficient charging power is compensated by the energy storage module;

[0059] S33: Collect the output power of the photovoltaic power generation module in real time, extract the points or intervals with differences in the power generation change curve, adjust the power generation change curve, and adjust the power pre-allocation according to the method in step S32 based on the adjusted power generation change curve and charging power change curve.

[0060] In this embodiment, based on the interval in which the charging power change curve in the extracted divided interval is higher than the power generation power change curve, a first difference change curve graph of the charging power and the power generation power in the corresponding interval is drawn, and a maximum output power change curve graph of the energy storage module is drawn on the first difference change curve graph, and the interval in which the first difference change curve is higher than the maximum output power change curve is extracted, and a second difference change curve is drawn, and power compensation is performed for the time period corresponding to the corresponding interval through the AC power module based on the second difference change curve.

[0061] The process of performing power compensation for the time period corresponding to the corresponding interval through the mains module based on the second difference change curve is as follows: the mains is processed through the transformer module, the power is compensated in the form of voltage compensation, and the compensated voltage value is calculated by collecting the real-time current value and the power value to be compensated, and the power value to be compensated is the difference of the corresponding interval in the second difference change curve. Finally, the power is compensated for the time period corresponding to the corresponding interval through the mains module through the second difference change curve, realizing the combination of the photovoltaic storage and charging system and the mains, achieving multi-party collaboration, further improving the stability of the system, and ensuring that the charging module achieves stable output.

[0062] See also Figure 3 As shown, an electric energy management system suitable for photovoltaic storage and charging is used to implement any one of the electric energy management methods suitable for photovoltaic storage and charging, including a data acquisition module, a model creation module, a photovoltaic power generation prediction model, a curve drawing module, a charging prediction model, a data acquisition module, a photovoltaic power generation module, an energy storage module, a charging module and a control module; the data acquisition module is connected to the photovoltaic power generation prediction model, the model creation module is respectively connected to the photovoltaic power generation prediction model and the charging prediction model, the photovoltaic power generation prediction model and the charging prediction model are respectively connected to the curve drawing module, and the photovoltaic power generation module, the energy storage module and the charging module are all connected to the control module.

[0063] The data acquisition module is used to acquire the power generation data of the photovoltaic power generation module within the historical time period; the model creation module is used to create a photovoltaic power generation prediction model and a charging prediction model; the photovoltaic power generation prediction model is used to train based on the preprocessed power generation data and predict the photovoltaic power generation output power within a specified time period; the charging prediction model is used to train based on the preprocessed charging data and predict the charging power within a specified time period; the curve drawing module is used to draw a power generation power change curve diagram of the photovoltaic power generation output power in a specified time period and a charging power change curve diagram of the charging power in a specified time period; the data acquisition module is used for the output power of the photovoltaic power generation module and the input power of the charging device; the control module is used to control the photovoltaic power generation module, the energy storage module, and the charging module, and compare the real-time input power of the charging device with the preset input power, and determine whether the real-time input power of the charging device is less than the preset input power and whether the power generation power of the photovoltaic power generation module is greater than the real-time input power of the charging device.

[0064] In summary, the power management system and method for photovoltaic storage and charging provided by the present invention creates a photovoltaic power generation model and a charging prediction model, and trains the photovoltaic power generation model and the charging prediction model based on the historical power generation data and charging data, respectively, and then accurately predicts the power generation and charging power of the specified time period, and pre-allocates power based on the predicted power generation and charging power. Since the power of photovoltaic power generation and the power of the charging module both show a certain regularity based on time, the predicted power generation and charging power can be used to know the change law of the power generation of the photovoltaic power generation module and the charging power of the charging module over time, thereby realizing the pre-allocation of power generation. If pre-allocation is not performed, it is necessary to measure the power generation and charging power at all times, and calculate the power generation allocation at all times, and the allocation efficiency is very low. Pre-allocation is performed in a time period by prediction, and due to the accuracy of the two prediction models, only a small amount of allocation data is abnormal, so only a part of the allocation data needs to be adjusted according to the real-time measured data in the future, which greatly improves the allocation efficiency.

[0065] By initializing the parameters of the photovoltaic power generation prediction model, and inputting the power generation data of the photovoltaic power generation module in the preprocessed historical time period into the photovoltaic power generation prediction model, and traversing the parameter combinations of the photovoltaic power generation prediction model, and selecting the best performance parameter combination for each group of parameter combinations through cross-validation evaluation, the model training optimization is performed, so that the prediction accuracy of the model is high, and data support is provided for the subsequent power pre-allocation based on the predicted data. By integrating the power generation change curve and the charging power change curve into one graph, and taking the intersection of the two lines as the dividing point, the power generation change curve and the charging power change curve are divided into different intervals, and different intervals are processed differently, the power allocation accuracy is increased, and the photovoltaic storage and charging in the whole system are balanced and coordinated, and its stability is improved. Through the second difference change curve, the corresponding time period of the corresponding interval is compensated by the mains module, realizing the combination between the photovoltaic storage and charging system and the mains, realizing multi-party collaboration, further improving the stability of the system, and ensuring the stable output of the charging module.

Claims

1. A method for managing electric energy suitable for photovoltaic storage and charging, characterized in that: The following steps are involved: S1: Acquire power generation data of the photovoltaic power generation module in a historical time period, create a photovoltaic power generation prediction model, pre-process the power generation data and input it into the photovoltaic power generation prediction model for training, predict the photovoltaic power generation output power in a specified time period through the trained photovoltaic power generation prediction model, and draw a power generation power change curve of the photovoltaic power generation output power in the specified time period; S2: Acquire charging data of the charging module in a historical time period, create a charging prediction model, pre-process the charging data and input it into the charging prediction model for training, predict the charging power in a specified time period through the trained charging prediction model, and draw a charging power change curve in the specified time period; S3: Based on the power generation change curve and the charging power change curve, pre-allocate power according to the change over time, collect the output power of the photovoltaic power generation module in real time, and adjust the power pre-allocation based on the real-time power; S4: Preset the output power of the energy storage module and the mains module that changes with time based on the power generation change curve and the charging power change curve according to the change of time, and adjust the preset output power of the energy storage module and the mains module that changes with time based on the real-time power; S5: collecting the input power of the charging device in real time, comparing the real-time input power of the charging device with the preset input power, and determining whether the real-time input power of the charging device is less than the preset input power and whether the power generation power of the photovoltaic power generation module is greater than the real-time input power of the charging device. If both are true, increasing the output power of the photovoltaic charging module to the charging device and reducing the output power to the energy storage module; otherwise, executing step S6; S6: When the real-time input power of the charging device is less than the preset input power, and the power generation power of the photovoltaic power generation module is less than the real-time input power of the charging device, the voltage of the charging device is compensated through the energy storage module; when the real-time input power of the charging device is greater than the preset input power, the output power of the photovoltaic charging module to the charging device is reduced to the preset output power, and the remaining power of the photovoltaic charging module is output to the energy storage module; S7: Iteratively execute steps S1-S6.

2. The method for managing electric energy suitable for photovoltaic storage and charging according to claim 1, characterized in that: The photovoltaic power generation prediction model in steps S1 and S2 is a SARIMA model. The training process of the photovoltaic power generation prediction model is as follows: initializing the parameters of the photovoltaic power generation prediction model, inputting the power generation data of the photovoltaic power generation module in the preprocessed historical time period into the photovoltaic power generation prediction model, traversing the parameter combinations of the photovoltaic power generation prediction model, and selecting the parameter combination with the best performance for each group of parameter combinations through a cross-validation evaluation method.

3. The method for managing electric energy suitable for photovoltaic storage and charging according to claim 2, characterized in that: The parameters of the photovoltaic power generation prediction model include seasonal parameters including the order of non-seasonal autoregression, the number of one-step differences, the order of non-seasonal moving average, the moving average order, the seasonal autoregression order, and the number of seasonal differences.

4. The method for managing electric energy applicable to photovoltaic storage and charging according to claim 1, characterized in that: The specific process in step S3 is as follows: S31: integrating the power generation change curve graph and the charging power change curve graph into one graph, and dividing the power generation change curve and the charging power change curve into different intervals using the intersection of the two lines as a dividing point; S32: extracting the intervals in which the power generation curve in the divided intervals is higher than the charging power curve, allocating the power generation of the photovoltaic power generation module to the charging equipment based on the charging power in the corresponding intervals, and pre-allocating the remaining power generation to the energy storage module; For the intervals where the charging power change curve is higher than the power generation power change curve in the divided intervals, all the power generation power of the photovoltaic power generation module is allocated to the charging equipment, and the insufficient charging power is compensated by the energy storage module; S33: Collect the output power of the photovoltaic power generation module in real time, extract the points or intervals with differences in the power generation change curve, adjust the power generation change curve, and adjust the power pre-allocation according to the method in step S32 based on the adjusted power generation change curve and charging power change curve.

5. The method for managing electric energy applicable to photovoltaic storage and charging according to claim 4, characterized in that: Based on the interval in which the charging power change curve in the extracted divided interval is higher than the power generation power change curve, a first difference change curve diagram of the charging power and the power generation power in the corresponding interval is drawn, and a maximum output power change curve diagram of the energy storage module is drawn on the first difference change curve diagram, and the interval in which the first difference change curve is higher than the maximum output power change curve is extracted, and a second difference change curve is drawn, and based on the second difference change curve, power compensation is performed for the time period corresponding to the corresponding interval through the AC power module.

6. The method for managing electric energy applicable to photovoltaic storage and charging according to claim 5, characterized in that: The process of performing power compensation for the time period corresponding to the corresponding interval through the AC power module based on the second difference change curve is: processing the AC power through the transformer module, compensating the power in the form of voltage compensation, and calculating the compensated voltage value by collecting the real-time current value and the power value to be compensated, wherein the power value to be compensated is the difference between the corresponding intervals in the second difference change curve.

7. An electric energy management system suitable for photovoltaic storage and charging, used to implement an electric energy management method suitable for photovoltaic storage and charging as described in any one of claims 1 to 6, characterized in that: It includes a data acquisition module, a model creation module, a photovoltaic power generation prediction model, a curve drawing module, a charging prediction model, a data acquisition module, a photovoltaic power generation module, an energy storage module, a charging module and a control module; the data acquisition module is connected to the photovoltaic power generation prediction model, the model creation module is respectively connected to the photovoltaic power generation prediction model and the charging prediction model, the photovoltaic power generation prediction model and the charging prediction model are respectively connected to the curve drawing module, and the photovoltaic power generation module, the energy storage module and the charging module are all connected to the control module; The data acquisition module is used to acquire the power generation data of the photovoltaic power generation module within a historical time period; The model creation module is used to create a photovoltaic power generation prediction model and a charging prediction model; The photovoltaic power generation prediction model is used to perform training based on the preprocessed power generation data and predict the photovoltaic power generation output power within a specified time period; The charging prediction model is used to perform training based on the preprocessed charging data and predict the charging power within a specified time period; The curve drawing module is used to draw a curve diagram of photovoltaic power generation output power change in a specified time period and a curve diagram of charging power change in a specified time period; The data acquisition module is used for the output power of the photovoltaic power generation module and the input power of the charging device; The control module is used to control the photovoltaic power generation module, the energy storage module, and the charging module, and compare the real-time input power of the charging device with the preset input power, and determine whether the real-time input power of the charging device is less than the preset input power and whether the power generation power of the photovoltaic power generation module is greater than the real-time input power of the charging device.

Images