Big data-based charging scheduling method for optical storage and charging intelligent micro-grid

Abstract

The invention belongs to the field of micro-grid charging scheduling, relates to a data analysis technology, and aims to solve the problem that self-adaptive multi-mode charging scheduling control cannot be carried out on a micro-grid in the prior art, in particular to a big-data-based charging scheduling method for an optical storage charging intelligent micro-grid, which comprises the following steps of: marking an energy management area of the micro-grid as an analysis area; generating an analysis period, obtaining illumination data GZ and wind power data FL of the analysis area in the analysis period, performing numerical calculation to obtain a prediction coefficient YC of the next analysis period relative to the current analysis period, and marking a productivity analysis value of the analysis period through the prediction coefficient YC; the method comprises the following steps: carrying out productivity prediction analysis on a microgrid, carrying out comparative analysis on weather data of an analysis region in an analysis period and weather prediction data of a next analysis period to obtain a prediction coefficient, and then marking a productivity analysis value of the analysis region through the prediction coefficient to provide data support for a balance analysis process.

Description

Technical Field

[0001] The present invention belongs to the field of microgrid charging dispatching and relates to data analysis technology, and specifically to a photovoltaic storage and charging intelligent microgrid charging dispatching method based on big data. Background Art

[0002] With the rapid development and application of renewable energy, microgrid systems have gradually become an important power supply mode. Microgrid systems are composed of multiple distributed energy resources, including solar panels, wind turbines, and energy storage systems. In microgrid systems, the effective implementation of energy management and scheduling methods is the key to ensuring the stable and efficient operation of the system.

[0003] The existing microgrid charging scheduling method can only use a single control mode to control the power scheduling, but it is unable to combine historical load data, production capacity data and weather forecast data to perform adaptive multi-mode charging scheduling control on the microgrid, resulting in low power scheduling efficiency and high power scheduling loss.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a photovoltaic storage and charging intelligent microgrid charging scheduling method based on big data, which is used to solve the problem that the prior art cannot perform adaptive multi-mode charging scheduling control on the microgrid;

[0006] The technical problem to be solved by the present invention is: how to provide a photovoltaic storage and charging intelligent microgrid charging scheduling method based on big data that can perform adaptive multi-mode charging scheduling control on the microgrid.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The charging dispatching method of a photovoltaic storage and charging intelligent microgrid based on big data includes the following steps:

[0009] Step 1: Perform capacity forecasting analysis on the microgrid: mark the energy management area of ​​the microgrid as the analysis area, generate an analysis period, obtain the light data GZ and wind data FL of the analysis area within the analysis period, and perform numerical calculations to obtain the prediction coefficient YC of the next analysis period relative to the current analysis period, and mark the capacity analysis value of the analysis period by the prediction coefficient YC;

[0010] Step 2: Perform a balance analysis on the capacity and demand of the microgrid and mark the microgrid charging scheduling mode as an independent scheduling mode or an on-demand scheduling mode based on the balance analysis results;

[0011] Step 3: Control and analyze the microgrid charging dispatch process: When the independent dispatch mode is used for microgrid charging dispatch, the electric energy generated in each analysis area is used for the power load and energy storage in the area first. When the production capacity and energy storage cannot meet the power demand, the charging dispatch platform sends a dispatch signal to the adjacent analysis area. When the on-demand dispatch mode is used for microgrid charging dispatch, the M1 analysis areas ranked first in the load sequence are marked as permanent storage areas, and the electric energy generated in the analysis area is used for the power load in the area and the energy storage in the adjacent permanent storage area first.

[0012] Step 4: Conduct charging scheduling management analysis on the microgrid.

[0013] As a preferred embodiment of the present invention, in step one, the process of obtaining the prediction coefficient YC includes: obtaining the illumination duration and wind force level of the analysis area within the analysis period at the end of the analysis period, obtaining the weather forecast data for the next analysis period, marking the absolute value of the difference between the total illumination duration in the weather forecast data and the illumination duration in the current analysis period as illumination data GZ, marking the difference between the wind force level of each natural day in the weather forecast data and the wind force level of the natural day corresponding to the current analysis period as the wind force difference of the natural day, summing up the wind force differences of all natural days in the analysis period and taking the absolute value to obtain the wind force data FL; performing numerical calculations on the illumination data GZ and the wind force data FL to obtain the prediction coefficient YC.

[0014] As a preferred embodiment of the present invention, in step one, the process of marking the capacity analysis value of the analysis area includes: obtaining the prediction threshold YCmax through the storage module, and comparing the prediction coefficient YC with the prediction threshold YCmax: if the prediction coefficient YC is less than or equal to the prediction threshold YCmax, then the capacity value of the analysis area in the current analysis period is marked as the capacity analysis value of the analysis area; if the prediction coefficient YC is greater than the prediction threshold YCmax, then the analysis period with the smallest prediction coefficient YC value relative to the next analysis period in the most recent L1 analysis periods is marked as the selected period, and the capacity value of the analysis area in the selected period is marked as the capacity analysis value of the analysis area; the capacity analysis value of the next analysis period is sent to the balancing analysis module through the charging scheduling platform.

[0015] As a preferred embodiment of the present invention, in step 2, the specific process of marking the microgrid charging scheduling mode includes: obtaining the electric energy consumption of the analysis area within the analysis period and marking it as a load analysis value, arranging the analysis areas in order from large to small according to the load analysis values ​​within the analysis period to obtain a load sequence, arranging the analysis areas in order from large to small according to the capacity analysis values ​​within the analysis period to obtain a capacity sequence, marking the absolute value of the difference between the sequence number of the analysis area in the load sequence and the sequence number in the capacity sequence as the balance value of the analysis area, summing and averaging the balance values ​​of all analysis areas to obtain a balance coefficient, obtaining a balance threshold through a storage module, and comparing the balance coefficient with the balance threshold: if the balance coefficient is less than the balance threshold, the microgrid charging scheduling mode is marked as an independent scheduling mode; if the balance coefficient is greater than or equal to the balance threshold, the microgrid charging scheduling mode is marked as an on-demand scheduling mode; and the microgrid charging scheduling mode is sent to the scheduling management module through the charging scheduling platform.

[0016] As a preferred implementation mode of the present invention, in step 4, the specific process of performing charging scheduling management analysis on the microgrid includes: obtaining scheduling data DD and power data DL at the end of the analysis period, the scheduling data DD is the number of times the charging scheduling platform receives the scheduling signal during the analysis period, and the power data DL is the total power value of the electric energy scheduling during the analysis period, and the management coefficient GL of the analysis period is obtained by numerically calculating the scheduling data DD and the power data DL, and judging whether the charging scheduling state of the microgrid during the analysis period meets the requirements by the management coefficient GL.

[0017] As a preferred implementation of the present invention, the specific process of determining whether the charging scheduling state of the microgrid within the analysis period meets the requirements includes: obtaining the management threshold GLmax through the storage module, and comparing the management coefficient GL of the analysis period with the management threshold GLmax: if the management coefficient GL is less than the management threshold GLmax, it is determined that the charging scheduling state of the microgrid within the analysis period meets the requirements; if the management coefficient GL is greater than or equal to the management threshold GLmax, it is determined that the charging scheduling state of the microgrid within the analysis period does not meet the requirements, generating a scheduling optimization signal and sending the scheduling optimization signal to the charging scheduling platform, and after receiving the scheduling optimization signal, the charging scheduling platform sends the scheduling optimization signal to the mobile phone terminal of the management personnel.

[0018] As a preferred embodiment of the present invention, it is applied to a photovoltaic storage and charging intelligent microgrid charging dispatching system based on big data, including a charging dispatching platform, wherein the charging dispatching platform is communicatively connected with a weather forecasting module, a balancing analysis module, a dispatching control module, a dispatching management module and a storage module;

[0019] The weather forecast module is used to perform capacity forecast analysis on the microgrid and mark the capacity analysis value of the analysis period;

[0020] The balance analysis module is used to perform a balance analysis on the capacity and demand of the microgrid and mark the microgrid charging scheduling mode according to the balance analysis result, and send the microgrid charging scheduling mode to the scheduling management module through the charging scheduling platform;

[0021] The dispatch control module is used to control and analyze the microgrid charging dispatch process in an independent dispatch mode or an on-demand dispatch mode;

[0022] The scheduling management module is used to perform charging scheduling management analysis on the microgrid and determine whether the charging scheduling state of the microgrid within the analysis period meets the requirements.

[0023] The present invention has the following beneficial effects:

[0024] 1. The weather forecast module can be used to forecast and analyze the capacity of the microgrid. The forecast coefficient is obtained by comparing the weather data of the analysis area in the analysis period with the weather forecast data of the next analysis period. The capacity analysis value of the analysis area is then marked by the forecast coefficient to provide data support for the balance analysis process.

[0025] 2. The balance analysis module can be used to analyze the balance between the capacity and demand of the microgrid, determine whether the power demand in each analysis area matches the expected capacity, and then mark the charging scheduling mode according to the balance analysis results. According to different balance analysis results, targeted control modes are used to control charging scheduling and reduce the scheduling loss of electric energy;

[0026] 3. The dispatching control module can be used to control and analyze the microgrid charging dispatching process, and the dual mode can be used to adaptively control and manage the microgrid charging dispatching process, thereby reducing the frequency of power dispatching and avoiding the phenomenon that the power supply cannot meet the load demand;

[0027] 4. The dispatching management module can be used to analyze the charging dispatching management of the microgrid, and a comprehensive analysis and calculation of multiple power dispatching parameters within the analysis period can be performed to obtain the management coefficient. The management coefficient can be used to provide feedback on the charging dispatching status of the microgrid within the analysis period, and timely warnings can be issued when the charging dispatching status is abnormal. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0030] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0031] 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.

[0032] Embodiment 1

[0033] like Figure 1 As shown, the big data-based photovoltaic storage and charging intelligent microgrid charging dispatching system includes a charging dispatching platform, which is communicatively connected with a weather forecasting module, a balancing analysis module, a dispatching control module, a dispatching management module and a storage module.

[0034] The weather forecast module is used to perform capacity forecasting and analysis on the microgrid: mark the energy management area of ​​the microgrid as the analysis area, generate an analysis cycle, obtain the light duration and wind force level of the analysis area within the analysis cycle at the end of the analysis cycle, obtain the weather forecast data for the next analysis cycle, mark the absolute value of the difference between the total light duration in the weather forecast data and the light duration in the current analysis cycle as the light data GZ, mark the difference between the wind force level of each natural day in the weather forecast data and the wind force level of the natural day corresponding to the current analysis cycle as the wind force difference of the natural day, sum the wind force differences of all natural days in the analysis cycle and take the absolute value to obtain the wind force data FL; obtain the prediction coefficient YC of the next analysis cycle relative to the current analysis cycle through the formula YC=α1*GZ+α2*FL, where α1 and α2 are both proportional coefficients, and α1>α2>1; obtain the prediction threshold through the storage module YCmax, compare the prediction coefficient YC with the prediction threshold YCmax: if the prediction coefficient YC is less than or equal to the prediction threshold YCmax, the capacity value of the analysis area in the current analysis period is marked as the capacity analysis value of the analysis area; if the prediction coefficient YC is greater than the prediction threshold YCmax, the analysis period with the smallest prediction coefficient YC value relative to the next analysis period in the most recent L1 analysis periods is marked as the selected period, and the capacity value of the analysis area in the selected period is marked as the capacity analysis value of the analysis area; the capacity analysis value of the next analysis period is sent to the balance analysis module through the charging scheduling platform; the capacity forecast analysis of the microgrid is performed, and the prediction coefficient is obtained by comparing the weather data of the analysis area in the analysis period with the weather forecast data of the next analysis period, and then the capacity analysis value of the analysis area is marked by the prediction coefficient to provide data support for the balance analysis process.

[0035] The balance analysis module is used to perform a balance analysis on the capacity and demand of the microgrid: obtain the power consumption of the analysis area within the analysis period and mark it as the load analysis value, arrange the analysis areas in descending order according to the load analysis values ​​within the analysis period to obtain a load sequence, arrange the analysis areas in descending order according to the capacity analysis values ​​within the analysis period to obtain a capacity sequence, mark the absolute value of the difference between the sequence number of the analysis area in the load sequence and the sequence number in the capacity sequence as the balance value of the analysis area, sum and average the balance values ​​of all analysis areas to obtain the balance coefficient, obtain the balance threshold through the storage module, and compare the balance coefficient with the average. The balance coefficient is compared with the balance threshold: if the balance coefficient is less than the balance threshold, the microgrid charging dispatch mode is marked as an independent dispatch mode; if the balance coefficient is greater than or equal to the balance threshold, the microgrid charging dispatch mode is marked as an on-demand dispatch mode; the microgrid charging dispatch mode is sent to the dispatch management module through the charging dispatch platform; the microgrid's production capacity and demand are balanced, and it is determined whether the power consumption demand in each analysis area can match the expected production capacity, and then the charging dispatch mode is marked according to the balance analysis results, and targeted control modes are used for charging dispatch control according to different balance analysis results to reduce the dispatch loss of electric energy.

[0036] The dispatching control module is used to control and analyze the microgrid charging dispatching process: when the independent dispatching mode is used for microgrid charging dispatching, the electric energy generated in each analysis area is used first for the power load and energy storage in the area. When the production capacity and energy storage cannot meet the power demand, the dispatching signal is sent to the adjacent analysis area through the charging dispatching platform; when the on-demand dispatching mode is used for microgrid charging dispatching, the M1 analysis areas ranked first in the load sequence are marked as permanent storage areas, and the electric energy generated in the analysis area is used first for the power load in the area and the energy storage in the adjacent permanent storage area; the microgrid charging dispatching process is controlled and analyzed, and the dual mode is used to adaptively control and manage the microgrid charging dispatching process, reducing the frequency of electric energy dispatching while avoiding the phenomenon that the electric energy supply cannot meet the load demand.

[0037] The dispatch management module is used to perform charging dispatch management analysis on the microgrid: at the end of the analysis period, the dispatch data DD and the power data DL are obtained. The dispatch data DD is the number of times the charging dispatch platform receives the dispatch signal during the analysis period, and the power data DL is the total power value of the electric energy dispatched during the analysis period. The management coefficient GL of the analysis period is obtained by the formula GL=β1*DD+β2*DL, where α1 and α2 are both proportional coefficients, and α1>α2>1; the management threshold GLmax is obtained through the storage module, and the management coefficient GL of the analysis period is compared with the management threshold GLmax: if the management coefficient GL is less than the management threshold GLmax, It is determined that the charging scheduling state of the microgrid within the analysis period meets the requirements; if the management coefficient GL is greater than or equal to the management threshold GLmax, it is determined that the charging scheduling state of the microgrid within the analysis period does not meet the requirements, and a scheduling optimization signal is generated and sent to the charging scheduling platform. After receiving the scheduling optimization signal, the charging scheduling platform sends the scheduling optimization signal to the mobile terminal of the manager; the charging scheduling management analysis of the microgrid is performed, and a comprehensive analysis and calculation of multiple electric energy scheduling parameters within the analysis period are performed to obtain the management coefficient. The charging scheduling state of the microgrid within the analysis period is fed back through the management coefficient, and timely warning is issued when the charging scheduling state is abnormal.

[0038] Embodiment 2

[0039] like Figure 2 As shown, the charging scheduling method of the photovoltaic storage intelligent microgrid based on big data includes the following steps:

[0040] Step 1: Perform capacity forecasting analysis on the microgrid: mark the energy management area of ​​the microgrid as the analysis area, generate an analysis period, obtain the light data GZ and wind data FL of the analysis area within the analysis period, and perform numerical calculations to obtain the prediction coefficient YC of the next analysis period relative to the current analysis period, and mark the capacity analysis value of the analysis period by the prediction coefficient YC;

[0041] Step 2: Perform a balance analysis on the capacity and demand of the microgrid and mark the microgrid charging scheduling mode as an independent scheduling mode or an on-demand scheduling mode based on the balance analysis results;

[0042] Step 3: Control and analyze the microgrid charging dispatch process: When the independent dispatch mode is used for microgrid charging dispatch, the electric energy generated in each analysis area is used for the power load and energy storage in the area first. When the production capacity and energy storage cannot meet the power demand, the charging dispatch platform sends a dispatch signal to the adjacent analysis area. When the on-demand dispatch mode is used for microgrid charging dispatch, the M1 analysis areas ranked first in the load sequence are marked as permanent storage areas, and the electric energy generated in the analysis area is used for the power load in the area and the energy storage in the adjacent permanent storage area first.

[0043] Step 4: Manage and analyze the charging scheduling status of the microgrid during the analysis period.

[0044] The method for charging scheduling of a smart microgrid with photovoltaic storage and charging based on big data, when working, marks the energy management area of ​​the microgrid as the analysis area, generates an analysis period, obtains the light data GZ and wind data FL of the analysis area in the analysis period, and performs numerical calculation to obtain the prediction coefficient YC of the next analysis period relative to the current analysis period, and marks the capacity analysis value of the analysis period by the prediction coefficient YC; performs a balance analysis on the capacity and demand of the microgrid, and marks the charging scheduling mode of the microgrid as an independent scheduling mode or an on-demand scheduling mode by the balance analysis result; when the independent scheduling mode is used for charging scheduling of the microgrid, the electric energy generated in each analysis area is preferentially used for the power load and energy storage in the area, and when the capacity and energy storage cannot meet the power demand, a scheduling signal is sent to the adjacent analysis area through the charging scheduling platform; when the on-demand scheduling mode is used for charging scheduling of the microgrid, the M1 analysis areas ranked first in the load sequence are marked as permanent storage areas, and the electric energy generated in the analysis area is preferentially used for the power load in the area and the energy storage in the adjacent permanent storage area; and the charging scheduling status of the microgrid in the analysis period is managed and analyzed.

[0045] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0046] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula YC = α1*GZ+α2*FL; technicians in this field collect multiple groups of sample data and set corresponding prediction coefficients for each group of sample data; substitute the set prediction coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values ​​of α1 and α2 as 3.62 and 2.75 respectively;

[0047] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding prediction coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the prediction coefficient is proportional to the value of the illumination data.

[0048] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0049] 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 charging dispatching method for a photovoltaic storage and charging intelligent microgrid based on big data, characterized in that: The following steps are involved: Step 1: Perform capacity forecasting analysis on the microgrid: mark the energy management area of ​​the microgrid as the analysis area, generate an analysis period, obtain the light data GZ and wind data FL of the analysis area within the analysis period, and perform numerical calculations to obtain the prediction coefficient YC of the next analysis period relative to the current analysis period, and mark the capacity analysis value of the analysis period by the prediction coefficient YC; Step 2: Perform a balance analysis on the capacity and demand of the microgrid and mark the microgrid charging scheduling mode as an independent scheduling mode or an on-demand scheduling mode based on the balance analysis results; Step 3: Control and analyze the microgrid charging dispatch process: When the independent dispatch mode is used for microgrid charging dispatch, the electric energy generated in each analysis area is used first for the power load and energy storage in the area. When the production capacity and energy storage cannot meet the power demand, the charging dispatch platform sends a dispatch signal to the adjacent analysis area. When the on-demand dispatch mode is used for microgrid charging dispatch, the M1 analysis areas ranked first in the load sequence are marked as permanent storage areas, and the electric energy generated in the analysis area is used first for the power load in the area and the energy storage in the adjacent permanent storage area. Step 4: Conduct charging scheduling management analysis on the microgrid.

2. The charging scheduling method for a photovoltaic storage and charging intelligent microgrid based on big data according to claim 1 is characterized in that: In step one, the process of obtaining the prediction coefficient YC includes: obtaining the illumination duration and wind force level of the analysis area within the analysis period at the end of the analysis period, obtaining the weather forecast data for the next analysis period, marking the absolute value of the difference between the total illumination duration in the weather forecast data and the illumination duration in the current analysis period as illumination data GZ, marking the difference between the wind force level of each natural day in the weather forecast data and the wind force level of the natural day corresponding to the current analysis period as the wind force difference of the natural day, summing up the wind force differences of all natural days in the analysis period and taking the absolute value to obtain the wind force data FL; performing numerical calculations on the illumination data GZ and the wind force data FL to obtain the prediction coefficient YC.

3. The charging scheduling method for a smart microgrid with photovoltaic storage and charging based on big data according to claim 2 is characterized in that: In step one, the process of marking the capacity analysis value of the analysis area includes: obtaining the prediction threshold YCmax through the storage module, and comparing the prediction coefficient YC with the prediction threshold YCmax: if the prediction coefficient YC is less than or equal to the prediction threshold YCmax, then the capacity value of the analysis area in the current analysis period is marked as the capacity analysis value of the analysis area; if the prediction coefficient YC is greater than the prediction threshold YCmax, then the analysis period with the smallest prediction coefficient YC value relative to the next analysis period in the most recent L1 analysis periods is marked as the selected period, and the capacity value of the analysis area in the selected period is marked as the capacity analysis value of the analysis area; the capacity analysis value of the next analysis period is sent to the balancing analysis module through the charging scheduling platform.

4. The charging scheduling method for a photovoltaic storage and charging intelligent microgrid based on big data according to claim 3 is characterized in that: In step 2, the specific process of marking the microgrid charging scheduling mode includes: obtaining the electric energy consumption of the analysis area within the analysis period and marking it as a load analysis value, arranging the analysis areas in order from large to small according to the load analysis values ​​within the analysis period to obtain a load sequence, arranging the analysis areas in order from large to small according to the capacity analysis values ​​within the analysis period to obtain a capacity sequence, marking the absolute value of the difference between the sequence number of the analysis area in the load sequence and the sequence number in the capacity sequence as the balance value of the analysis area, summing and averaging the balance values ​​of all analysis areas to obtain a balance coefficient, obtaining a balance threshold through a storage module, and comparing the balance coefficient with the balance threshold: if the balance coefficient is less than the balance threshold, the microgrid charging scheduling mode is marked as an independent scheduling mode; if the balance coefficient is greater than or equal to the balance threshold, the microgrid charging scheduling mode is marked as an on-demand scheduling mode; and the microgrid charging scheduling mode is sent to the scheduling management module through the charging scheduling platform.

5. The charging scheduling method for a photovoltaic storage and charging intelligent microgrid based on big data according to claim 4 is characterized in that: In step 4, the specific process of charging dispatch management analysis for the microgrid includes: obtaining dispatch data DD and power data DL at the end of the analysis period, the dispatch data DD is the number of times the charging dispatch platform receives the dispatch signal during the analysis period, and the power data DL is the total power value of the electric energy dispatched during the analysis period. The management coefficient GL of the analysis period is obtained by numerically calculating the dispatch data DD and the power data DL, and the management coefficient GL is used to determine whether the charging dispatch state of the microgrid during the analysis period meets the requirements.

6. The method for charging and dispatching a photovoltaic storage and charging intelligent microgrid based on big data according to claim 5 is characterized in that: The specific process of determining whether the charging scheduling state of the microgrid during the analysis period meets the requirements includes: obtaining the management threshold GLmax through the storage module, and comparing the management coefficient GL of the analysis period with the management threshold GLmax: if the management coefficient GL is less than the management threshold GLmax, it is determined that the charging scheduling state of the microgrid during the analysis period meets the requirements; if the management coefficient GL is greater than or equal to the management threshold GLmax, it is determined that the charging scheduling state of the microgrid during the analysis period does not meet the requirements, generating a scheduling optimization signal and sending the scheduling optimization signal to the charging scheduling platform. After receiving the scheduling optimization signal, the charging scheduling platform sends the scheduling optimization signal to the mobile terminal of the management personnel.

7. The method for charging and dispatching a photovoltaic storage and charging intelligent microgrid based on big data according to any one of claims 1 to 6, characterized in that: Applied to the big data-based photovoltaic storage and charging intelligent microgrid charging dispatching system, including a charging dispatching platform, which is communicatively connected with a weather forecasting module, a balancing analysis module, a dispatching control module, a dispatching management module and a storage module; The weather forecast module is used to perform capacity forecast analysis on the microgrid and mark the capacity analysis value of the analysis period; The balance analysis module is used to perform a balance analysis on the capacity and demand of the microgrid and mark the microgrid charging scheduling mode according to the balance analysis result, and send the microgrid charging scheduling mode to the scheduling management module through the charging scheduling platform; The dispatch control module is used to control and analyze the microgrid charging dispatch process in an independent dispatch mode or an on-demand dispatch mode; The scheduling management module is used to perform charging scheduling management analysis on the microgrid and determine whether the charging scheduling state of the microgrid within the analysis period meets the requirements.

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